WO2022228390A1 - Procédé, appareil et dispositif de traitement de contenu multimédia, et support de stockage - Google Patents

Procédé, appareil et dispositif de traitement de contenu multimédia, et support de stockage Download PDF

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Publication number
WO2022228390A1
WO2022228390A1 PCT/CN2022/088995 CN2022088995W WO2022228390A1 WO 2022228390 A1 WO2022228390 A1 WO 2022228390A1 CN 2022088995 W CN2022088995 W CN 2022088995W WO 2022228390 A1 WO2022228390 A1 WO 2022228390A1
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media content
target
cdn node
tree model
target media
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PCT/CN2022/088995
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English (en)
Chinese (zh)
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朱亚光
李孟杰
严冰
黄胜兰
李小成
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北京字跳网络技术有限公司
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Publication of WO2022228390A1 publication Critical patent/WO2022228390A1/fr

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    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L67/00—Network arrangements or protocols for supporting network services or applications
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L67/00—Network arrangements or protocols for supporting network services or applications
    • H04L67/50—Network services
    • H04L67/60—Scheduling or organising the servicing of application requests, e.g. requests for application data transmissions using the analysis and optimisation of the required network resources
    • H04L67/63—Routing a service request depending on the request content or context

Definitions

  • the embodiments of the present disclosure relate to the technical field of streaming media processing, for example, to a media content processing method, apparatus, device, and storage medium.
  • streaming media applications In order to transmit media content data to clients as quickly as possible, streaming media applications generally use Content Delivery Network (CDN) technology.
  • CDN Content Delivery Network
  • Embodiments of the present disclosure provide a media content processing method, apparatus, device, and storage medium, which improve the hit rate of media content in a CDN node and reduce back-to-source bandwidth.
  • an embodiment of the present disclosure provides a media content processing method, including:
  • the target media content is obtained from the node.
  • an embodiment of the present disclosure further provides a media content processing apparatus, including:
  • the heat level determination module is set to determine the heat level of the target media content
  • a node type determination module configured to determine the target content distribution network CDN node type corresponding to the target media content based on the popularity level of the target media content, wherein the popularity level corresponds to the CDN node type one-to-one;
  • a target node determination module configured to determine the target CDN node corresponding to the target media content based on the target CDN node type, and send the node information of the target CDN node to the client, so that the client is based on the target CDN node.
  • the node information acquires the target media content from the target CDN node.
  • an embodiment of the present disclosure further provides a media content processing device, including:
  • processors one or more processors
  • memory arranged to store one or more programs
  • the one or more processors When the one or more programs are executed by the one or more processors, the one or more processors implement the media content processing method according to any one of the embodiments of the present disclosure.
  • an embodiment of the present disclosure further provides a computer-readable storage medium, where the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, any one of the embodiments of the present disclosure is implemented The media content processing method.
  • Fig. 1 is a CDN typical topology diagram provided by an embodiment of the present disclosure
  • FIG. 2 is a flowchart of a media content processing method provided by an embodiment of the present disclosure
  • FIG. 3 is a flowchart of another media content processing method provided by an embodiment of the present disclosure.
  • 4a is a schematic diagram of a CDN node in the prior art
  • FIG. 4b is a schematic diagram of a CDN node classification provided by an embodiment of the present disclosure.
  • FIG. 5 is a structural diagram of a media content processing device provided by an embodiment of the present disclosure.
  • FIG. 6 is a structural diagram of a media content processing device provided by an embodiment of the present disclosure.
  • the term “including” and variations thereof are open-ended inclusions, ie, "including but not limited to”.
  • the term “based on” is “based at least in part on.”
  • the term “one embodiment” means “at least one embodiment”; the term “another embodiment” means “at least one additional embodiment”; the term “some embodiments” means “at least some embodiments”. Relevant definitions of other terms will be given in the description below.
  • the CDN technology in the related art caches the recently accessed media content on the CDN node, so that when the client obtains the media content, it can directly obtain the media content from the CDN node, without the need to return data to the source, thereby reducing the impact on the source site. number of visits.
  • the CDN technology in the related art caches all media content in the CDN node indiscriminately.
  • the media content with low heat level may be squeezed out of the CDN node, resulting in low heat level.
  • the media content is not on the CDN node.
  • the client accesses media content with a low popularity level, the media content cannot be found on the CDN node, resulting in a low hit rate of the media content with a low popularity level. Increase back-to-source bandwidth.
  • the embodiments of the present disclosure can be applied to any scenario of accessing a content distribution network to obtain media content, for example, it can be applied to a feed streaming scenario, can also be applied to a scenario of obtaining media content through a media playback platform or a website, and can also be applied to Click on the media content scene from the home page of the media content publisher.
  • the “feed” in the embodiment of the present disclosure may be a content aggregator that combines several news sources actively subscribed by the user to help the user continuously obtain the latest feed content.
  • the feed is a Really Simple Syndication (RSS) The interface used to receive the source of this information.
  • RSS Really Simple Syndication
  • the feed stream in the embodiment of the present disclosure is a continuously updated information stream, which can push the information in the RSS to the user.
  • the server's recommendation system pushes a series of media content (such as videos) to the client.
  • a series of media content such as videos
  • the client responds to the user's operation and sends a series of media content to the server.
  • the server determines the popularity level of the media content corresponding to the media content request in response to the media content request, obtains the media content from a corresponding type of CDN node according to the popularity level, and distributes it to the client.
  • FIG. 1 is a typical topology structure diagram of a CDN provided by an embodiment of the present disclosure.
  • the CDN technology principle is: when a user requests a certain Uniform Resource Locator (URL), a media content request is initiated to the CDN node, and the CDN node will detect the corresponding media content request sent by the user. Whether the media content is expired, if not, it directly responds to the media content request, and returns the media content corresponding to the media content request to the client. At this point, a complete http request ends.
  • URL Uniform Resource Locator
  • the CDN node needs to send a back to the source request to the origin site to pull the media content corresponding to the latest media content request, update the locally cached media content, and send The media content corresponding to the latest media content request is returned to the client.
  • the expiration of the requested content means that the media content corresponding to the media content request cannot be queried at the CDN node.
  • Embodiments of the present disclosure provide a media content processing method, apparatus, device, and storage medium.
  • the embodiments of the present disclosure differentiate media content by degree of popularity, based on the corresponding relationship between the degree of popularity and node type, in different types of CDNs. Searching for the media content corresponding to the popularity in the node can improve the hit rate of the media content in the CDN node and reduce the back-to-source bandwidth.
  • FIG. 2 is a flowchart of a method for processing media content provided by an embodiment of the present disclosure. This embodiment is applicable to a situation in which a client requests media content from a CDN node.
  • the method may be executed by a media content processing apparatus.
  • the media content processing apparatus may be implemented by means of software and/or hardware.
  • the media content processing method is applied in a server.
  • the media content processing method provided by this embodiment mainly includes steps S11 , S12 , S13 and S14 .
  • the media content refers to the content displayed on the client, which may be one or a combination of video, audio, text, and cards. It should be noted that the media content in this embodiment may be the media content in the feed stream, or may be the media content of a certain video website or the homepage of a video publisher.
  • the target media content refers to the media content requested by the client from the server.
  • a method of determining target media content receives the media content request sent by the client, parses the media content request, and determines the media content corresponding to the media content request as the target media content.
  • a method of determining target media content is provided.
  • the feed stream request sent by the client is received, the feed stream request is parsed, each media content in the feed stream is determined as the target media content, and steps S12, S13 and S14 are performed in sequence.
  • the popularity level is used to represent the attention degree of each media content.
  • the heat level can be divided into multiple levels according to the needs, for example: it can be divided into four levels: S, A, B, and F.
  • S the highest
  • a level is lower than the S level.
  • B grade is lower than the A grade
  • F grade the lowest grade.
  • It can also be divided into three levels: high, medium and low. This embodiment only describes the level division, but is not limited.
  • the heat level is divided into two levels, hot and cold.
  • Hot media content refers to media content with a higher degree of attention in a short period of time.
  • hot media content can be media content with tens of thousands of playbacks within an hour, and hot media content can also be commented and clicked within an hour.
  • Cold media content refers to media content that has received less attention in a short period of time.
  • cold media content can be media content with very low playback volume within an hour, and cold media content can also be media content with comment volume within an hour, and the growth rate of likes is very slow or does not increase.
  • a gradient boosting decision tree (Gradient Boosting Decision Tree, GBDT) tree model is used to predict the growth amount of the media content playback in the next hour, based on the correspondence between the growth amount and the popularity level relationship to determine the popularity level corresponding to the target media content.
  • GDT gradient boosting decision tree
  • the media content has a heat level of hot media content
  • the media content has a heat level of cold. media content.
  • the way of judging the popularity level according to the increase in comments and the increase in likes is similar to the above playback increment, and will not be repeated here.
  • multiple growth amount ranges can also be set to divide the popularity level of the media content in more detail. For example, four growth ranges are set, and the media content can be divided into four levels: S, A, B, and F.
  • the target media content by normalizing the relevant features of the target media content, and assigning different weights to each relevant feature, and performing weighting processing based on the normalized relevant features and their corresponding weights, Obtain the heat index corresponding to the target media content. Based on the correspondence between the popularity index and the popularity level, the popularity level corresponding to the target media content is determined.
  • CDN node group includes at least one CDN node.
  • one popularity level corresponds to one CDN node type.
  • the heat levels S, A, B, and F correspond to the first type of CDN node, the second type of CDN node, the third type of CDN node, and the fourth type of CDN node of the CDN node type, respectively.
  • hot media content corresponds to a hot type CDN node
  • cold media content corresponds to a cold type CDN node.
  • the corresponding relationship between the popularity level and the CDN node type is preselected and stored in the server.
  • the server After determining the popularity level of the target media content, the server searches for the target CDN node type corresponding to the target media content in the correspondence between the popularity level and the CDN node type.
  • the target CDN node may be understood as the CDN node included in the target CDN node type, that is, the type to which the target CDN node belongs is the target CDN node type.
  • the hot type CDN node includes the first CDN node, the second CDN node, the third CDN node, and the fourth CDN node
  • the cold type CDN node includes the fifth CDN node and the sixth CDN node.
  • a CDN node type includes one or more CDN nodes.
  • Determining the target CDN node corresponding to the target media content based on the target CDN node type may be to determine all CDN nodes included in the target CDN node type as target CDN nodes corresponding to the target media content.
  • node information (eg, node identification information) of the target CDN node may be sent to the client. Therefore, the client can request the corresponding target CDN node to acquire the target media content according to the node information.
  • the server when the server determines that the popularity level of the target media content is the hot media content, it determines the hot type CDN node corresponding to the target media content, then determines the CDN node included in the hot type CDN node as the target CDN node, and sets The node information of the target CDN node is sent to the client. Therefore, the client can send a request for acquiring the target media content to the target CDN node based on the node information.
  • the target CDN node when it receives the request from the client to obtain the target media content, it can search for the target media content, and if the target media content is found, transmit the target media content to the client; if the target media content is not found, The target CDN node sends a back to the source request to the origin site to pull the latest target media content, cache the latest target media content, and at the same time, return the latest target media content to the client.
  • the server determines that the target media content is cold media content, it determines that the target media content is cached in the cold type CDN node, determines the CDN node included in the cold type CDN node as the target CDN node, and sends the node information of the target CDN node. to the client. Therefore, the client can send a request for acquiring the target media content to the target CDN node based on the node information.
  • the target CDN node when it receives the request from the client to obtain the target media content, it can search for the target media content, and if the target media content is found, transmit the target media content to the client; if the target media content is not found, The target CDN node sends a back-to-origin request to the origin site to pull the latest target media content, cache the latest target media content, and at the same time, return the latest target media content to the client.
  • the server determines that the target media content is hot media content, it determines that the target media content is cached in the CDN nodes included in the hot type CDN node, then determines the CDN nodes included in the hot type CDN node as the target CDN node, and the target CDN node node information is sent to the client. Therefore, the client can send a request for acquiring the target media content to the CDN nodes included in the hot type CDN node based on the node information.
  • the CDN node included in the hot type CDN node receives the request from the client to obtain the target media content, it searches for the target media content.
  • the target media content is transmitted to the client; if the target media content is not found.
  • a back-to-origin request is sent to the origin site to pull the latest target media content, cache the latest target media content, and at the same time, return the latest target media content to the client.
  • the server determines that the target media content is cold media content, and determines that the target media content is cached in the CDN nodes included in the cold-type CDN node
  • the CDN nodes included in the cold-type CDN node are determined as the target CDN node, and the target CDN node's Node information is sent to the client. Therefore, the client can send a request for acquiring the target media content to the CDN nodes included in the cold-type CDN node based on the node information.
  • the CDN node included in the cold type CDN node searches for the target media content when receiving the request from the client to obtain the target media content, and if the target media content is found, transmits the target media content to the client; For the target media content, a back-to-origin request is sent to the origin site to pull the latest target media content, cache the latest target media content, and at the same time, return the latest target media content to the client.
  • An embodiment of the present disclosure provides a media content processing method, including: determining a popularity level of a target media content; determining a target CDN node type corresponding to the target media content based on the popularity level of the target media content, wherein the popularity level and the CDN node type are one-to-one Corresponding; determine the target CDN node corresponding to the target media content based on the target CDN node type, and send the node information of the target CDN node to the client, so that the client obtains the target media content from the target CDN node based on the node information.
  • the embodiment of the present disclosure can improve the hit rate of the media content in CDN nodes by distinguishing the media content by the popularity level, and searching for the media content corresponding to the popularity in different types of CDN nodes based on the corresponding relationship between the popularity level and the node type. , reducing the back-to-source bandwidth.
  • the embodiment of the present disclosure further refines the media content processing method. , using a pre-trained tree model to predict the popularity level of the target media content, wherein the media content features include one or more of the following: media content visits, media content viewing, media content likes, media content comments volume, media content downloads, media content sharing”.
  • the media content feature and the real media content popularity level identifier can be used as input to obtain the media content predicted popularity level output, based on the loss function, the real media content popularity level and the predicted media content popularity level Adjust the model parameters until the model output is as expected, and get the trained tree model.
  • the model can be used to predict the popularity level of the media content, and the media content feature is used as an input to obtain the popularity level of the model. For example, during training and use, the tree model predicts the hotness or coldness of the media content by predicting whether the playback volume of the media content in a preset time period in the future exceeds a playback volume threshold.
  • the media content access volume may be the number of times the client accesses the media content, and the access volume may be determined by counting the number of times the client accesses the CDN address corresponding to the media content, and the specific statistical method is not performed in this embodiment. limited.
  • the media content viewing volume refers to the number of times the client plays the media content.
  • the tree model is also called the decision tree model, and the decision tree model is a simple and easy-to-use non-parametric classifier.
  • any decision tree model can be selected for training to obtain a specific training method of the pre-trained tree model, which is not limited in this embodiment.
  • the amount of media content visits, the amount of media content viewing, the amount of media content likes, the amount of media content comments, the amount of media content downloads, and the slope of the amount of media content sharing are used as media content features, and are input into the pre-trained tree.
  • the pre-trained tree model directly outputs the popularity level of the target media content.
  • the amount of media content visits, the amount of media content viewing, the amount of media content likes, the amount of media content comments, the amount of media content downloads, and the slope of the amount of media content sharing are used as media content features, and are input into the pre-trained tree.
  • the pre-trained tree model outputs the target media content playback growth amount, and determines the popularity level of the target media content based on the playback growth amount.
  • the pre-trained tree model is used to predict the popularity level of the target media content, which can improve the accuracy of the prediction required for the hot and cold prediction of the target media content.
  • FIG. 3 is a flowchart of another media content processing method provided by an embodiment of the present disclosure. As shown in FIG. 3 , another media content processing method provided by an embodiment of the present disclosure mainly includes the following steps:
  • the preset time period in the future may refer to one hour in the future, or two hours in the future. In this embodiment, it can be within an hour in the future.
  • the play increment may refer to the play increment of the media content within a preset time period in the future, for example, the play increment of the target media content in the next hour.
  • the relationship between the playback increase and the playback increase threshold includes: the playback increase exceeds the playback increase threshold, or the playback increase is lower than the playback increase threshold.
  • the amount of media content visits, the amount of media content viewing, the amount of media content likes, the amount of media content comments, the amount of media content downloads, and the slope of the amount of media content sharing are used as media content features, and are input into the pre-trained tree.
  • the pre-trained tree model outputs the relationship between the playback growth of the target media content and the playback growth threshold.
  • determining the popularity level of the target media content based on the relationship between the play increase amount of the target media content and the play increase amount threshold includes: in response to determining that the play increase amount of the target media content is greater than or equal to An increase amount threshold, it is determined that the popularity level of the target media content is the first popularity; in response to determining that the playback increase amount of the target media content is less than the increase amount threshold, it is determined that the popularity level of the target media content is the second popularity.
  • the popularity level of the media content is the first popularity level, that is, the media content is hot media content, in response to determining that the increase amount is lower than the increase amount threshold.
  • the increase amount threshold, the popularity level of the media content is the second popularity, that is, the media content is cold media content.
  • Fig. 4a is a schematic diagram of a CDN node in the prior art
  • Fig. 4b is a schematic diagram of a CDN node classification provided by an embodiment of the present disclosure
  • each original CDN node in the CDN system contains hot media content and cold media content.
  • the CDN nodes are divided into hot-type CDN nodes that cache hot media content and cold-type CDN nodes that cache cold media content.
  • the target media content is predicted to be hot media content, it is obtained from the hot type CDN node, and the target media content is predicted to be cold media content and obtained from the cold type CDN node, so that the resources are re-divided.
  • Bringing the cold media content back to one node prevents the mixed cold and hot nodes from squeezing the cold resources out of the CDN's cache, resulting in a decrease in the hit rate of the cold resource CDN.
  • the method further includes: a training process of the tree model.
  • the tree model training method provided by the embodiments of the present disclosure mainly includes: using a binary cross-entropy loss function to train the tree model.
  • the tree model can be trained by using the tree model training samples constructed from multiple media contents and the binary cross-entropy loss function determined based on the weight of each media content.
  • using a binary cross-entropy loss function to train the tree model to obtain a pre-trained tree model including: constructing a tree model training sample by using multiple media contents; The feature determines the weight of each media content, and determines the binary cross-entropy loss function according to the weight of each media content; the tree model is trained based on the tree model training samples and the binary-class cross entropy loss function to obtain Pretrained tree model.
  • the binary cross-entropy loss function is:
  • N is the total amount of media content in the tree model training sample
  • yi represents the label of the ith media content in the tree model training sample
  • pi represents the probability that the ith media content in the tree model training sample is predicted to be a positive example
  • ⁇ i represents the weight of the i-th media content in the tree model training sample
  • vv represents the viewing volume of the ith media content sample in the tree model training sample in a preset time period in the future
  • VV represents the total viewing volume of the ith media content sample in the tree model training sample
  • T represents the ith media content sample in the tree model training sample.
  • t represents the viewing duration of the i-th media content sample in the tree model training sample from its creation to the current time point.
  • this embodiment can also use the existing GBDT tree model, which will not be repeated here.
  • more weights are set for the loss of media content with a higher proportion of the viewing volume vv in the future preset time period, thereby improving the prediction effect of the later media content with a relatively high viewing volume.
  • FIG. 5 is a structural diagram of a media content processing apparatus provided by an embodiment of the present disclosure. This embodiment is applicable to a situation where a client requests media content from a CDN node, and the media content processing apparatus can be processed through software and/or implemented in hardware.
  • the media content processing device is integrated in the server.
  • the media content processing apparatus mainly includes a heat level determination module 51 , a node type determination module 52 and a target node determination module 53 .
  • the heat level determination module 51 is set to determine the heat level of the target media content
  • the node type determination module 52 is configured to determine the target content distribution network CDN node type corresponding to the target media content based on the popularity level of the target media content, wherein the popularity level corresponds to the CDN node type one-to-one;
  • the target node determination module 53 is configured to determine the target CDN node corresponding to the target media content based on the target CDN node type, and send the node information of the target CDN node to the client, so that the client is based on the target CDN node.
  • the node information acquires the target media content from the target CDN node.
  • An embodiment of the present disclosure provides a media content processing apparatus, which is mainly configured to perform the following operations: determine the popularity level of the target media content; determine the target CDN node type corresponding to the target media content based on the popularity level of the target media content, wherein the popularity level is the same as One-to-one correspondence between the types of CDN nodes; determine the target CDN node corresponding to the target media content based on the target CDN node type, and send the node information of the target CDN node to the client, so that the client can select the target CDN node based on the node information. obtain the target media content from the target CDN node.
  • the embodiment of the present disclosure can improve the hit rate of the media content in CDN nodes by distinguishing the media content by the popularity level, and searching for the media content corresponding to the popularity in different types of CDN nodes based on the corresponding relationship between the popularity level and the node type. , reducing the back-to-source bandwidth.
  • the popularity level determination module 51 is configured to use a pre-trained tree model to predict the popularity level of the target media content based on the media content characteristics of the target media content, wherein the media content characteristics include the following One or more of: media content visits, media content viewings, media content likes, media content comments, media content downloads, and media content sharing.
  • the heat level determination module 51 includes:
  • an increase amount prediction unit configured to predict the relationship between the play increase amount and the play increase amount threshold of the target media content in a preset time period in the future by using the pre-trained tree model
  • the popularity level determination unit is configured to determine the popularity level of the target media content based on the relationship between the play increase amount of the target media content and the play increase amount threshold.
  • the popularity level determination unit is configured to determine that the popularity level of the target media content is the first popularity in response to determining that the play increase of the target media content is greater than or equal to an increase threshold; The play growth amount is less than the growth amount threshold, and it is determined that the popularity level of the target media content is the second popularity.
  • the apparatus further includes: a model training module, configured as:
  • the tree model is trained using a binary cross-entropy loss function to obtain a pre-trained tree model.
  • the model training module is configured to: construct a tree model training sample by using a plurality of media contents; determine the weight of each media content according to the media content characteristics of the plurality of media contents, and The weight of the content determines a binary cross-entropy loss function; the tree model is trained based on the tree model training sample and the binary cross-entropy loss function to obtain a pre-trained tree model.
  • the binary cross-entropy loss function is:
  • N is the total amount of media content in the tree model training sample
  • yi represents the label of the ith media content in the tree model training sample
  • pi represents the probability that the ith media content in the tree model training sample is predicted to be a positive example
  • ⁇ i represents the weight of the i-th media content in the tree model training sample
  • vv represents the viewing volume of the ith media content sample in the tree model training sample in a preset time period in the future
  • VV represents the total viewing volume of the ith media content sample in the tree model training sample
  • T represents the ith media content sample in the tree model training sample.
  • t represents the viewing duration of the i-th media content sample in the tree model training sample from its creation to the current time point.
  • the media content processing apparatus provided in this embodiment can execute the media content processing method provided by any embodiment of the present disclosure, and has corresponding functional modules and beneficial effects for executing the media content processing method.
  • FIG. 6 it shows a schematic structural diagram of an electronic device (eg, a terminal device or a server in FIG. 6 ) 600 suitable for implementing an embodiment of the present disclosure.
  • Terminal devices in the embodiments of the present disclosure may include, but are not limited to, such as mobile phones, notebook computers, digital broadcast receivers, PDAs (personal digital assistants), PADs (tablets), PMPs (portable multimedia players), vehicle-mounted terminals (eg, mobile terminals such as in-vehicle navigation terminals), etc., and stationary terminals such as digital TVs, desktop computers, and the like.
  • the electronic device shown in FIG. 6 is only an example, and should not impose any limitation on the function and scope of use of the embodiments of the present disclosure.
  • an electronic device 600 may include a processing device (eg, a central processing unit, a graphics processor, etc.) 601 that may be loaded into random access according to a program stored in a read only memory (ROM) 602 or from a storage device 608
  • a program in a memory (RAM) 603 executes various appropriate actions and processes.
  • the processing device 601, the ROM 602, and the RAM 603 are connected to each other through a bus 604.
  • An input/output (I/O) interface 605 is also connected to bus 604 .
  • I/O interface 605 input devices 606 including, for example, a touch screen, touchpad, keyboard, mouse, camera, microphone, accelerometer, gyroscope, etc.; including, for example, a liquid crystal display (LCD), speakers, vibration An output device 607 of a computer, etc.; a storage device 608 including, for example, a magnetic tape, a hard disk, etc.; and a communication device 609.
  • Communication means 609 may allow electronic device 600 to communicate wirelessly or by wire with other devices to exchange data.
  • FIG. 6 shows an electronic device 600 having various means, it should be understood that not all of the illustrated means are required to be implemented or provided. More or fewer devices may alternatively be implemented or provided.
  • embodiments of the present disclosure include a computer program product comprising a computer program carried on a non-transitory computer readable medium, the computer program comprising program code arranged to perform the method illustrated in the flowchart.
  • the computer program may be downloaded and installed from the network via the communication device 609, or from the storage device 608, or from the ROM 602.
  • the processing apparatus 601 the above-mentioned functions defined in the methods of the embodiments of the present disclosure are executed.
  • the computer-readable medium mentioned above in the present disclosure may be a computer-readable signal medium or a computer-readable storage medium, or any combination of the above two.
  • the computer-readable storage medium can be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus or device, or a combination of any of the above. More specific examples of computer readable storage media may include, but are not limited to, electrical connections with one or more wires, portable computer disks, hard disks, random access memory (RAM), read only memory (ROM), erasable Programmable read only memory (EPROM or flash memory), fiber optics, portable compact disk read only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the foregoing.
  • a computer-readable storage medium may be any tangible medium that contains or stores a program that can be used by or in conjunction with an instruction execution system, apparatus, or device.
  • a computer-readable signal medium may include a data signal propagated in baseband or as part of a carrier wave with computer-readable program code embodied thereon. Such propagated data signals may take a variety of forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the foregoing.
  • a computer-readable signal medium can also be any computer-readable medium other than a computer-readable storage medium that can transmit, propagate, or transport the program for use by or in connection with the instruction execution system, apparatus, or device .
  • Program code embodied on a computer readable medium may be transmitted using any suitable medium including, but not limited to, electrical wire, optical fiber cable, RF (radio frequency), etc., or any suitable combination of the foregoing.
  • the client and server can use any currently known or future developed network protocol such as HTTP (HyperText Transfer Protocol) to communicate, and can communicate with digital data in any form or medium Communication (eg, a communication network) interconnects.
  • HTTP HyperText Transfer Protocol
  • Examples of communication networks include local area networks (“LAN”), wide area networks (“WAN”), the Internet (eg, the Internet), and peer-to-peer networks (eg, ad hoc peer-to-peer networks), as well as any currently known or future development network of.
  • the above-mentioned computer-readable medium may be included in the above-mentioned electronic device; or may exist alone without being assembled into the electronic device.
  • the above-mentioned computer-readable medium carries one or more programs, and when the above-mentioned one or more programs are executed by the electronic device, the electronic device:
  • the target media content is obtained from the node.
  • Computer program code for performing operations of the present disclosure may be written in one or more programming languages, including but not limited to object-oriented programming languages—such as Java, Smalltalk, C++, and This includes conventional procedural programming languages - such as the "C" language or similar programming languages.
  • the program code may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer, or entirely on the remote computer or server.
  • the remote computer may be connected to the user's computer through any kind of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (eg, using an Internet service provider through Internet connection).
  • LAN local area network
  • WAN wide area network
  • each block in the flowchart or block diagrams may represent a module, segment, or portion of code that contains one or more logical functions for implementing the specified functions executable instructions.
  • the functions noted in the blocks may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved.
  • each block of the block diagrams and/or flowchart illustrations, and combinations of blocks in the block diagrams and/or flowchart illustrations can be implemented in dedicated hardware-based systems that perform the specified functions or operations , or can be implemented in a combination of dedicated hardware and computer instructions.
  • the units involved in the embodiments of the present disclosure may be implemented in a software manner, and may also be implemented in a hardware manner. Among them, the name of the unit does not constitute a limitation of the unit itself under certain circumstances.
  • exemplary types of hardware logic components include: Field Programmable Gate Arrays (FPGAs), Application Specific Integrated Circuits (ASICs), Application Specific Standard Products (ASSPs), Systems on Chips (SOCs), Complex Programmable Logical Devices (CPLDs) and more.
  • FPGAs Field Programmable Gate Arrays
  • ASICs Application Specific Integrated Circuits
  • ASSPs Application Specific Standard Products
  • SOCs Systems on Chips
  • CPLDs Complex Programmable Logical Devices
  • a machine-readable medium may be a tangible medium that may contain or store a program for use by or in connection with the instruction execution system, apparatus or device.
  • the machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium.
  • Machine-readable media may include, but are not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices, or devices, or any suitable combination of the foregoing.
  • machine-readable storage media would include one or more wire-based electrical connections, portable computer disks, hard disks, random access memory (RAM), read only memory (ROM), erasable programmable read only memory (EPROM or flash memory), fiber optics, compact disk read only memory (CD-ROM), optical storage, magnetic storage, or any suitable combination of the foregoing.
  • RAM random access memory
  • ROM read only memory
  • EPROM or flash memory erasable programmable read only memory
  • CD-ROM compact disk read only memory
  • magnetic storage or any suitable combination of the foregoing.
  • a media content processing method, apparatus, device, and storage medium including:
  • the target media content is obtained from the node.
  • a media content processing method, apparatus, device, and storage medium which determine the popularity level of target media content, including:
  • a pre-trained tree model is used to predict the popularity level of the target media content, wherein the media content features include one or more of the following: media content visits, media content viewings , media content likes, media content comments, media content downloads, media content sharing.
  • a media content processing method, apparatus, device, and storage medium wherein a pre-trained tree model is used to predict the popularity level of the target media content, including:
  • the popularity level of the target media content is determined based on the relationship between the play increase amount of the target media content and the play increase amount threshold.
  • a media content processing method, apparatus, device, and storage medium wherein the target media content is determined based on the relationship between a play increase amount of the target media content and a play increase amount threshold heat levels, including:
  • determining that the play increase of the target media content is greater than or equal to the increase threshold determining that the popularity level of the target media content is the first popularity
  • determining that the play increase of the target media content is less than the increase threshold In response to determining that the play increase of the target media content is less than the increase threshold, determining that the popularity level of the target media content is the second popularity.
  • a media content processing method, apparatus, device, and storage medium further comprising:
  • the tree model is trained using a binary cross-entropy loss function to obtain a pre-trained tree model.
  • a media content processing method, apparatus, device, and storage medium are provided.
  • the tree model is trained by using a binary cross-entropy loss function to obtain a pre-trained tree model, including:
  • the tree model is trained based on the tree model training samples and the binary cross-entropy loss function to obtain a pre-trained tree model.
  • a media content processing method, apparatus, device, and storage medium wherein the binary cross-entropy loss function is:
  • N is the total amount of media content in the tree model training sample
  • yi represents the label of the ith media content in the tree model training sample
  • pi represents the probability that the ith media content in the tree model training sample is predicted to be a positive example
  • ⁇ i represents the weight of the i-th media content in the tree model training sample
  • vv represents the viewing volume of the ith media content sample in the tree model training sample in a preset time period in the future
  • VV represents the total viewing volume of the ith media content sample in the tree model training sample
  • T represents the ith media content sample in the tree model training sample.
  • t represents the viewing duration of the i-th media content sample in the tree model training sample from its creation to the current time point.

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  • Engineering & Computer Science (AREA)
  • Computer Networks & Wireless Communication (AREA)
  • Signal Processing (AREA)
  • Information Transfer Between Computers (AREA)

Abstract

Des modes de réalisation de la présente divulgation concernent un procédé, un appareil et un dispositif de traitement de contenu multimédia, et un support de stockage. Le procédé de traitement de contenu multimédia comprend les étapes suivantes : détermination d'un niveau de popularité de contenu multimédia cible ; sur la base du niveau de popularité du contenu multimédia cible, détermination d'un type de nœud CDN cible correspondant au contenu multimédia cible, les niveaux de popularité présentant une correspondance biunivoque avec des types de nœuds CDN ; sur la base du type de nœud CDN cible, détermination d'un nœud CDN cible correspondant au contenu multimédia cible, et envoi d'informations de nœud du nœud CDN cible à un client, de sorte que le client obtienne le contenu multimédia cible à partir du nœud CDN cible sur la base des informations de nœud.
PCT/CN2022/088995 2021-04-26 2022-04-25 Procédé, appareil et dispositif de traitement de contenu multimédia, et support de stockage WO2022228390A1 (fr)

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