CN116436904B - Streaming media data push method, device, server and storage medium - Google Patents
Streaming media data push method, device, server and storage medium Download PDFInfo
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- 238000000034 method Methods 0.000 title claims abstract description 35
- 238000003860 storage Methods 0.000 title claims abstract description 9
- 230000011664 signaling Effects 0.000 claims abstract description 36
- 238000005304 joining Methods 0.000 claims abstract description 22
- 230000006399 behavior Effects 0.000 claims description 336
- 238000013507 mapping Methods 0.000 claims description 150
- 238000012512 characterization method Methods 0.000 claims description 24
- 238000004458 analytical method Methods 0.000 claims description 18
- 238000009826 distribution Methods 0.000 claims description 12
- 238000012546 transfer Methods 0.000 claims description 12
- 230000004931 aggregating effect Effects 0.000 claims description 10
- 238000013135 deep learning Methods 0.000 claims description 10
- 238000005457 optimization Methods 0.000 claims description 4
- 238000012216 screening Methods 0.000 claims description 4
- 238000004590 computer program Methods 0.000 claims description 3
- 230000006978 adaptation Effects 0.000 description 2
- 230000005540 biological transmission Effects 0.000 description 2
- 238000004891 communication Methods 0.000 description 2
- 230000006870 function Effects 0.000 description 2
- 230000000977 initiatory effect Effects 0.000 description 2
- 238000011835 investigation Methods 0.000 description 2
- 230000009286 beneficial effect Effects 0.000 description 1
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L65/00—Network arrangements, protocols or services for supporting real-time applications in data packet communication
- H04L65/1066—Session management
- H04L65/1083—In-session procedures
- H04L65/1093—In-session procedures by adding participants; by removing participants
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L65/00—Network arrangements, protocols or services for supporting real-time applications in data packet communication
- H04L65/1066—Session management
- H04L65/1083—In-session procedures
- H04L65/1089—In-session procedures by adding media; by removing media
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L65/00—Network arrangements, protocols or services for supporting real-time applications in data packet communication
- H04L65/1066—Session management
- H04L65/1101—Session protocols
- H04L65/1108—Web based protocols, e.g. webRTC
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L65/00—Network arrangements, protocols or services for supporting real-time applications in data packet communication
- H04L65/60—Network streaming of media packets
- H04L65/61—Network streaming of media packets for supporting one-way streaming services, e.g. Internet radio
- H04L65/613—Network streaming of media packets for supporting one-way streaming services, e.g. Internet radio for the control of the source by the destination
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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
- H04L67/01—Protocols
- H04L67/02—Protocols based on web technology, e.g. hypertext transfer protocol [HTTP]
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
- H04N21/00—Selective content distribution, e.g. interactive television or video on demand [VOD]
- H04N21/40—Client devices specifically adapted for the reception of or interaction with content, e.g. set-top-box [STB]; Operations thereof
- H04N21/47—End-user applications
- H04N21/472—End-user interface for requesting content, additional data or services; End-user interface for interacting with content, e.g. for content reservation or setting reminders, for requesting event notification, for manipulating displayed content
- H04N21/47202—End-user interface for requesting content, additional data or services; End-user interface for interacting with content, e.g. for content reservation or setting reminders, for requesting event notification, for manipulating displayed content for requesting content on demand, e.g. video on demand
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- Computer Networks & Wireless Communication (AREA)
- Business, Economics & Management (AREA)
- General Business, Economics & Management (AREA)
- Databases & Information Systems (AREA)
- Human Computer Interaction (AREA)
- Information Transfer Between Computers (AREA)
Abstract
The present disclosure relates to a streaming media data push method, a device, a server and a storage medium, including: according to the call request corresponding to the mobile terminal, the token information, the IP address of the signaling server and the link port address are returned to the mobile terminal; responding to the login request, logging in the mobile terminal, and if the login of the mobile terminal is successful, returning login success feedback to the mobile terminal; receiving a room login request initiated by the mobile terminal under the condition of receiving a successful login feedback, and adding the mobile terminal into a room queue according to the room login request; if the mobile terminal is successful in joining the room queue, returning room queue joining success information to the mobile terminal, so that the mobile terminal initiates a push flow request under the condition that the mobile terminal receives the room queue joining success information; and if the push request sent by the mobile terminal is received, returning a push server address to the mobile terminal, and calling streaming media service to complete streaming media data push of the mobile terminal according to the push server address.
Description
Technical Field
The present disclosure relates to the field of streaming media technologies, and in particular, to a streaming media data push method, a device, a server, and a storage medium.
Background
With the gradual popularization of mobile office, various audio and video application scenes appear. Video conferences, remote data auditing and the like, such as audio and video acquisition, audio and video encoding and decoding, network transmission and the like, are carried out by means of Web instant messaging WebRTC (Web Real-Time Communication), and digitally processed audio and video user data exchange is carried out based on SIP (session initiation protocal, session initiation protocol). The H.264 is used for higher coding efficiency and higher quality video pictures. The terminal application program is enabled to initiate audio and video dialing, and video seat personnel can answer audio and video calls through the PC client side, the PAD side and the like. However, the video functions of the mode are not complete, only one-to-one conversation of calling and video is supported by the audio and video basic functions, so that data transmission and check cannot be performed in real time in the video process, and the remote data auditing convenience is low.
Disclosure of Invention
In order to overcome the technical problem of low convenience of streaming media data pushing in the related art, the present disclosure provides a streaming media data pushing method, a streaming media data pushing device, a server and a storage medium.
In a first aspect of an embodiment of the present disclosure, a streaming media data push method is provided, applied to a server, where the streaming media data push method includes:
According to a call request corresponding to a mobile terminal, returning token information, a signaling server IP address and a link port address to the mobile terminal, so that the mobile terminal initiates a login request according to the token information, the signaling server IP address and the link port address;
Responding to the login request, logging in the mobile terminal, and returning login success feedback to the mobile terminal under the condition that the mobile terminal is successfully logged in;
receiving a room login request initiated by the mobile terminal under the condition that the login success feedback is received, and adding the mobile terminal into a room queue according to the room login request;
returning room queue joining success information to the mobile terminal under the condition that the mobile terminal joins the room queue successfully, so that the mobile terminal initiates a push flow request under the condition that the mobile terminal receives the room queue joining success information;
And under the condition that the push request sent by the mobile terminal is received, returning a push server address to the mobile terminal, and calling streaming media service to complete streaming media data push of the mobile terminal according to the push server address.
In a preferred embodiment, the step of returning token information, a signaling server IP address and a link port address to the mobile terminal according to a call request corresponding to the mobile terminal, so that the mobile terminal initiates a login request according to the token information, the signaling server IP address and the link port address includes:
Responding to a call request corresponding to the mobile terminal, and accessing a token acquisition service corresponding to the mobile terminal, wherein the token acquisition service is used for the mobile terminal to acquire token information of a server;
Under the condition that the access to the token is successful in obtaining the service, transmitting the token information to the mobile terminal;
And receiving a hypertext transfer protocol request initiated by the mobile terminal under the condition of receiving the token information, and returning a signaling server IP address and a link port address to the mobile terminal aiming at the hypertext transfer protocol request, so that the mobile terminal initiates the login request under the condition of receiving the signaling server IP address and the link port address.
In a preferred embodiment, the calling the streaming media service to complete streaming media data push by the mobile terminal according to the push server address includes:
Invoking streaming media service, and receiving a webtc streaming media data packet pushed by the mobile terminal based on a webtc protocol according to the address of the push server;
Analyzing the webrtc streaming media data packet to obtain an identifiable streaming media data packet;
and pushing the identifiable streaming media data packet to vrs services to finish streaming media data pushing by the mobile terminal.
In a preferred embodiment, the method further comprises:
receiving streaming media nodes and streaming media information reported by an agent of vrs services;
Scheduling each streaming media node according to the streaming media data calling instruction so as to schedule the streaming media information corresponding to the streaming media node.
In a preferred embodiment, the adding the mobile terminal to a room queue according to the login room request includes:
And adding the mobile terminal into a room queue according to the application program identifier app_id and the room identifier room_id carried in the login room request, wherein the server manages the room queue in a mode of combining the application program identifier app_id with the room identifier room_id.
In a preferred embodiment, the token information includes at least one of an application identification app_id, a user identification user_id, and an absolute timeout time expired_time;
and the step of calling the streaming media service to complete streaming media data streaming by the mobile terminal according to the address of the streaming server comprises the following steps:
Invoking streaming media service, extracting streaming media data mapped by the address of the push server accessed by the mobile terminal, and determining behavior interest point distribution corresponding to the mobile terminal based on past streaming media behavior data of the mobile terminal;
Screening the streaming media data based on the behavior interest point distribution corresponding to the mobile terminal so as to push the screened streaming media data to the mobile terminal;
The step of determining the behavior interest point distribution corresponding to the mobile terminal based on the past streaming media behavior data of the mobile terminal comprises the following steps:
performing behavior sub-barreling on the past streaming media behavior data, and outputting a plurality of sub-barreled data of the past streaming media behavior data;
respectively converting behavior trigger nodes in the plurality of sub-bucket data into behavior directed graph features;
Based on a clustering strategy, classifying all behavior directed graph features of the past streaming media behavior data into feature mapping areas with target quantity;
Aggregating all behavior directed graph features contained in any feature mapping region in the past streaming media behavior data, and outputting feature mapping region characterization information of any feature mapping region;
Inputting the feature mapping region characterization information of any feature mapping region into a preset behavior interest point analysis network, and outputting behavior directed graph features of priori interest points of any feature mapping region; the behavior interest point analysis network is generated by taking characteristic mapping region characterization information of each characteristic mapping region of each streaming media behavior sample in a first streaming media behavior sample library as input and taking behavior directed graph characteristics of prior interest points of the corresponding characteristic mapping region as output to optimize a deep learning network;
Calculating a matching value between each behavior directed graph feature of any feature mapping region and a behavior directed graph feature of a priori interest point of the any feature mapping region respectively, and determining a behavior trigger node corresponding to the behavior directed graph feature of N before descending order of the matching value in all behavior directed graph features of the any feature mapping region as the interest point of the any feature mapping region;
Extracting interest points of the past streaming media behavior data based on the interest points of each feature mapping region of the past streaming media behavior data;
The calculating, respectively, a matching value between each behavior directed graph feature of the any feature mapping region and a behavior directed graph feature of a priori interest point of the any feature mapping region, and determining a behavior trigger node corresponding to a behavior directed graph feature of N before descending order of the matching value in all behavior directed graph features of the any feature mapping region as the interest point of the any feature mapping region includes:
calculating the feature deviation cost of each behavior directed graph feature of any feature mapping region and the behavior directed graph feature of the prior interest point of the any feature mapping region respectively, and outputting a behavior trigger node corresponding to the behavior directed graph feature with the minimum feature deviation cost between all behavior directed graph features of the any feature mapping region and the behavior directed graph feature of the prior interest point of the any feature mapping region as the interest point of the any feature mapping region;
the method further comprises the steps of:
Responding to the optimization indication of the behavior interest point analysis network, performing behavior barreling on each stream media behavior sample in a first stream media behavior sample library, and outputting a plurality of barrel-dividing data of each stream media behavior sample;
Respectively converting behavior trigger nodes in a plurality of sub-bucket data of the streaming media behavior sample into behavior directed graph characteristics;
based on a clustering strategy, classifying all behavior directed graph features of the streaming media behavior sample into a priori number of feature mapping areas; wherein the prior number is the number of prior interest points in the streaming media behavior sample;
for each prior interest point of the streaming media behavior sample, determining a feature mapping region to which a behavior directed graph feature most similar to the behavior directed graph feature of the corresponding prior interest point in all behavior directed graph features of the streaming media behavior sample belongs, and outputting a feature mapping region to which the behavior directed graph feature of the corresponding prior interest point belongs;
Aggregating all behavior directed graph features contained in any feature mapping region in the streaming media behavior sample, and outputting feature mapping region characterization information of any feature mapping region in the streaming media behavior sample;
Optimizing the deep learning network according to characteristic mapping region characterization information of each characteristic mapping region of each stream media behavior sample in the first stream media behavior sample library as input and behavior directed graph characteristics of prior interest points of the corresponding characteristic mapping region as output so as to generate the behavior interest point analysis network;
the step of respectively converting the behavior trigger nodes in the plurality of sub-bucket data into behavior directed graph features comprises the following steps:
Converting behavior trigger nodes in a plurality of sub-bucket data of the past streaming media behavior data into behavior directed graph characteristics with target modes; the step of converting the behavior trigger nodes in the sub-bucket data of the streaming media behavior sample into behavior directed graph features respectively includes: converting behavior trigger nodes in a plurality of sub-bucket data of the streaming media behavior sample into behavior directed graph features with the target mode;
the method further comprises the steps of:
performing behavior sub-barreling on each stream media behavior sample in a first stream media behavior sample library, and outputting a plurality of barrel-dividing data of each stream media behavior sample;
respectively converting behavior trigger nodes in a plurality of sub-bucket data of the streaming media behavior sample into behavior directed graph characteristics; based on a clustering strategy, classifying all behavior directed graph features of the streaming media behavior sample into a priori number of feature mapping areas; wherein the prior number is the number of prior interest points in the streaming media behavior sample;
for each prior interest point of the streaming media behavior sample, determining a feature mapping region to which a behavior directed graph feature most similar to the behavior directed graph feature of the corresponding prior interest point in all behavior directed graph features of the streaming media behavior sample belongs, and outputting a feature mapping region to which the behavior directed graph feature of the corresponding prior interest point belongs;
Aggregating all behavior directed graph features contained in any feature mapping region in the streaming media behavior sample, and outputting feature mapping region characterization information of any feature mapping region;
And optimizing the deep learning network according to the characteristic mapping region characterization information of each characteristic mapping region of each stream media behavior sample in the first stream media behavior sample library as input and the behavior directed graph characteristics of the prior interest points of the corresponding characteristic mapping region as output so as to generate a behavior interest point analysis network.
In a second aspect of the embodiments of the present disclosure, a streaming media data pushing device is provided and applied to a server, where the streaming media data pushing device includes:
The first return module is configured to return token information, a signaling server IP address and a link port address to the mobile terminal according to a call request corresponding to the mobile terminal, so that the mobile terminal initiates a login request according to the token information, the signaling server IP address and the link port address;
The second return module is configured to respond to the login request, login the mobile terminal and return login success feedback to the mobile terminal under the condition that the login to the mobile terminal is successful;
The receiving module is configured to receive a login room request initiated by the mobile terminal under the condition that the login success feedback is received, and add the mobile terminal into a room queue according to the login room request;
the third return module is configured to return room queue joining success information to the mobile terminal under the condition that the mobile terminal joins the room queue successfully, so that the mobile terminal initiates a push flow request under the condition that the mobile terminal receives the room queue joining success information;
the calling module is configured to return a push server address to the mobile terminal and call streaming media service under the condition that the push request sent by the mobile terminal is received, so that streaming media data push of the mobile terminal according to the push server address is completed
In a preferred embodiment, the first return module is configured to:
Responding to a call request corresponding to the mobile terminal, and accessing a token acquisition service corresponding to the mobile terminal, wherein the token acquisition service is used for the mobile terminal to acquire token information of a server;
Under the condition that the access to the token is successful in obtaining the service, transmitting the token information to the mobile terminal;
And receiving a hypertext transfer protocol request initiated by the mobile terminal under the condition of receiving the token information, and returning a signaling server IP address and a link port address to the mobile terminal aiming at the hypertext transfer protocol request, so that the mobile terminal initiates the login request under the condition of receiving the signaling server IP address and the link port address.
In a preferred embodiment, the calling module is configured to:
Invoking streaming media service, and receiving a webtc streaming media data packet pushed by the mobile terminal based on a webtc protocol according to the address of the push server;
Analyzing the webrtc streaming media data packet to obtain an identifiable streaming media data packet;
and pushing the identifiable streaming media data packet to vrs services to finish streaming media data pushing by the mobile terminal.
In a preferred embodiment, the streaming media data push device further includes: a scheduling module configured to:
receiving streaming media nodes and streaming media information reported by an agent of vrs services;
Scheduling each streaming media node according to the streaming media data calling instruction so as to schedule the streaming media information corresponding to the streaming media node.
In a preferred embodiment, the receiving module is configured to:
And adding the mobile terminal into a room queue according to the application program identifier app_id and the room identifier room_id carried in the login room request, wherein the server manages the room queue in a mode of combining the application program identifier app_id with the room identifier room_id.
In a preferred embodiment, the token information includes at least one of an application identification app_id, a user identification user_id, and an absolute timeout time.
In a third aspect of embodiments of the present disclosure, there is provided a server comprising:
A processor;
a memory for storing processor-executable instructions;
wherein the processor is configured to execute executable instructions in the memory to implement the method of any of the first aspects.
In a fourth aspect of embodiments of the present disclosure, there is provided a computer readable storage medium having stored thereon computer program instructions which, when executed by a processor, implement the steps of the method of any of the first aspects.
The technical scheme provided by the embodiment of the disclosure can comprise the following beneficial effects:
according to the call request corresponding to the mobile terminal, the token information, the IP address of the signaling server and the link port address are returned to the mobile terminal, so that the mobile terminal initiates a login request according to the token information, the IP address of the signaling server and the link port address; responding to the login request, logging in the mobile terminal, and returning login success feedback to the mobile terminal under the condition that the mobile terminal is successfully logged in; receiving a room login request initiated by the mobile terminal under the condition of receiving a successful login feedback, and adding the mobile terminal into a room queue according to the room login request; under the condition that the mobile terminal is successful in joining the room queue, room queue joining success information is returned to the mobile terminal, so that the mobile terminal initiates a push flow request under the condition that the mobile terminal receives the room queue joining success information; and under the condition of receiving a push request sent by the mobile terminal, returning a push server address to the mobile terminal, and calling streaming media service to complete streaming media data push of the mobile terminal according to the push server address. The method provides a convenient and quick real-time investigation mode, and can conveniently upload streaming media data through the mobile terminal, thereby rapidly and remotely transacting business. The convenience of remote auditing and the convenience of business handling are improved.
It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the disclosure.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the disclosure and together with the description, serve to explain the principles of the disclosure.
Fig. 1 is a flowchart illustrating a streaming media data push method according to an exemplary embodiment.
Fig. 2 is a flow chart illustrating one implementation of S11 in fig. 1 according to an exemplary embodiment.
Fig. 3 is a flow chart illustrating one implementation of S12 of fig. 1, according to an exemplary embodiment.
Fig. 4 is a block diagram illustrating a streaming media data push device 400 according to an exemplary embodiment.
Description of the embodiments
Reference will now be made in detail to exemplary embodiments, examples of which are illustrated in the accompanying drawings. When the following description refers to the accompanying drawings, the same numbers in different drawings refer to the same or similar elements, unless otherwise indicated. The implementations described in the following exemplary examples are not representative of all implementations consistent with the present disclosure. Rather, they are merely examples of apparatus and methods consistent with some aspects of the present disclosure as detailed in the accompanying claims.
Fig. 1 is a flowchart of a streaming media data push method according to an exemplary embodiment, which is applied to a server, where the server may be a cloud server, for example, a bank, for storing streaming media data information. As shown in fig. 1, the method includes the following steps.
S11, according to the call request corresponding to the mobile terminal, the token information, the IP address of the signaling server and the link port address are returned to the mobile terminal, so that the mobile terminal initiates a login request according to the token information, the IP address of the signaling server and the link port address.
In the embodiment of the disclosure, the mobile terminal may be, for example, a smart phone carried by a salesman, a tablet computer, a law enforcement recorder or the like, which is configured with a camera and a microphone. The mobile terminal and the server can complete communication connection based on the Nginx forward and reverse proxy of the application program. And, one or more servers of a component server, a recording server, an audio and video application server and an audio and video bottom server can be configured in the server for completing audio and video recording and storage based on streaming media.
The call request corresponding to the mobile terminal may be initiated to the mobile terminal by the terminal device corresponding to the video teller through the nginnx forward and reverse proxy, and then sent to the server when the mobile terminal receives the call request.
And S12, responding to the login request, logging in the mobile terminal, and returning login success feedback to the mobile terminal under the condition that the mobile terminal is successfully logged in.
In the embodiments of the present disclosure, the mobile terminal may establish a long connection with the server, for example, based on TCP:15001/WSS:13001 a long connection is established and a login request is initiated to the server.
S13, receiving a room login request initiated by the mobile terminal under the condition that the login success feedback is received, and adding the mobile terminal into a room queue according to the room login request;
Among other things, it is equally possible to base on TCP:15001/WSS:13001 initiate a request to create a room, and in case of successful creation of a room, return user information, stream data information, IM (ISTANT MESSAGING, instant messaging, real-time messaging) information, etc. to the mobile terminal.
Further, the mobile terminal may initiate a login room request based on the user information, the stream data information, the IM information.
And S14, returning room queue joining success information to the mobile terminal under the condition that the mobile terminal joins the room queue successfully, so that the mobile terminal initiates a push flow request under the condition that the mobile terminal receives the room queue joining success information.
In the embodiment of the disclosure, the user information, the stream data information, the IM information and the like can be updated under the condition that the mobile terminal is successfully added to the room queue. And then under the condition that the mobile terminal receives the room queue joining success information, based on TCP:15001/WSS:13001 initiate a push request to be sent,
And S15, returning a push server address to the mobile terminal and calling streaming media service under the condition that the push request sent by the mobile terminal is received, so that streaming media data push of the mobile terminal according to the push server address is completed.
The streaming media data push flow can be an H5 end push flow, and further the streaming media data push flow is performed based on a webrtc protocol, wherein the streaming media data of the webrtc protocol can comprise two information of signaling and streaming media data packets. The H5 push may be, for example, access wss, streaming media data: udp 3478, native pull stream data: udp 8123. And then transcoding, recoding and writing files are carried out on the streaming media data, so that streaming media data pushing by the mobile terminal according to the pushing server address is completed.
According to the technical scheme, the token information, the IP address of the signaling server and the link port address are returned to the mobile terminal according to the call request corresponding to the mobile terminal, so that the mobile terminal initiates a login request according to the token information, the IP address of the signaling server and the link port address; responding to the login request, logging in the mobile terminal, and returning login success feedback to the mobile terminal under the condition that the mobile terminal is successfully logged in; receiving a room login request initiated by the mobile terminal under the condition of receiving a successful login feedback, and adding the mobile terminal into a room queue according to the room login request; under the condition that the mobile terminal is successful in joining the room queue, room queue joining success information is returned to the mobile terminal, so that the mobile terminal initiates a push flow request under the condition that the mobile terminal receives the room queue joining success information; and under the condition of receiving a push request sent by the mobile terminal, returning a push server address to the mobile terminal, and calling streaming media service to complete streaming media data push of the mobile terminal according to the push server address. The method provides a convenient and quick real-time investigation mode, and can conveniently upload streaming media data through the mobile terminal, thereby rapidly and remotely transacting business. The convenience of remote auditing and the convenience of business handling are improved.
In a preferred embodiment, referring to fig. 2, S11, the step of returning token information, a signaling server IP address and a link port address to the mobile terminal according to a call request corresponding to the mobile terminal, so that the mobile terminal initiates a login request according to the token information, the signaling server IP address and the link port address includes:
s111, responding to a call request corresponding to the mobile terminal, and accessing a token acquisition service corresponding to the mobile terminal, wherein the token acquisition service is used for the mobile terminal to acquire token information of a server;
The token acquisition service can frequently request data from the server at the mobile terminal, the server side frequently goes to the database to inquire the user name and the password, compares the user name and the password, judges whether the user name and the password are correct or not, and gives a corresponding prompt.
And S112, sending the token information to the mobile terminal under the condition that the access to the token is successful in obtaining the service.
S113, receiving a hypertext transfer protocol request initiated by the mobile terminal under the condition that the token information is received, and returning a signaling server IP address and a link port address to the mobile terminal aiming at the hypertext transfer protocol request, so that the mobile terminal initiates the login request under the condition that the signaling server IP address and the link port address are received.
In a preferred embodiment, referring to fig. 3, S15, the invoking the streaming service to complete streaming data push by the mobile terminal according to the push server address includes:
s151, calling a streaming media service, and receiving a webrtc streaming media data packet which is pushed by the mobile terminal based on a webrtc protocol according to the address of the push server;
S152, analyzing the webrtc streaming media data packet to obtain an identifiable streaming media data packet;
and S153, pushing the identifiable streaming media data packet to vrs services to finish streaming media data pushing by the mobile terminal.
In a preferred embodiment, the method further comprises:
receiving streaming media nodes and streaming media information reported by an agent of vrs services;
Scheduling each streaming media node according to the streaming media data calling instruction so as to schedule the streaming media information corresponding to the streaming media node.
In a preferred embodiment, the adding the mobile terminal to a room queue according to the login room request includes:
And adding the mobile terminal into a room queue according to the application program identifier app_id and the room identifier room_id carried in the login room request, wherein the server manages the room queue in a mode of combining the application program identifier app_id with the room identifier room_id.
In a preferred embodiment, the token information includes at least one of an application identification app_id, a user identification user_id, and an absolute timeout time.
In a preferred embodiment, the step of calling a streaming service to complete streaming of the streaming data by the mobile terminal according to the address of the streaming server includes:
Invoking streaming media service, extracting streaming media data mapped by the address of the push server accessed by the mobile terminal, and determining behavior interest point distribution corresponding to the mobile terminal based on past streaming media behavior data of the mobile terminal;
Screening the streaming media data based on the behavior interest point distribution corresponding to the mobile terminal so as to push the screened streaming media data to the mobile terminal;
The step of determining the behavior interest point distribution corresponding to the mobile terminal based on the past streaming media behavior data of the mobile terminal comprises the following steps:
performing behavior sub-barreling on the past streaming media behavior data, and outputting a plurality of sub-barreled data of the past streaming media behavior data;
respectively converting behavior trigger nodes in the plurality of sub-bucket data into behavior directed graph features;
Based on a clustering strategy, classifying all behavior directed graph features of the past streaming media behavior data into feature mapping areas with target quantity;
Aggregating all behavior directed graph features contained in any feature mapping region in the past streaming media behavior data, and outputting feature mapping region characterization information of any feature mapping region;
Inputting the feature mapping region characterization information of any feature mapping region into a preset behavior interest point analysis network, and outputting behavior directed graph features of priori interest points of any feature mapping region; the behavior interest point analysis network is generated by taking characteristic mapping region characterization information of each characteristic mapping region of each streaming media behavior sample in a first streaming media behavior sample library as input and taking behavior directed graph characteristics of prior interest points of the corresponding characteristic mapping region as output to optimize a deep learning network;
Calculating a matching value between each behavior directed graph feature of any feature mapping region and a behavior directed graph feature of a priori interest point of the any feature mapping region respectively, and determining a behavior trigger node corresponding to the behavior directed graph feature of N before descending order of the matching value in all behavior directed graph features of the any feature mapping region as the interest point of the any feature mapping region;
Extracting interest points of the past streaming media behavior data based on the interest points of each feature mapping region of the past streaming media behavior data;
The calculating, respectively, a matching value between each behavior directed graph feature of the any feature mapping region and a behavior directed graph feature of a priori interest point of the any feature mapping region, and determining a behavior trigger node corresponding to a behavior directed graph feature of N before descending order of the matching value in all behavior directed graph features of the any feature mapping region as the interest point of the any feature mapping region includes:
calculating the feature deviation cost of each behavior directed graph feature of any feature mapping region and the behavior directed graph feature of the prior interest point of the any feature mapping region respectively, and outputting a behavior trigger node corresponding to the behavior directed graph feature with the minimum feature deviation cost between all behavior directed graph features of the any feature mapping region and the behavior directed graph feature of the prior interest point of the any feature mapping region as the interest point of the any feature mapping region;
the method further comprises the steps of:
Responding to the optimization indication of the behavior interest point analysis network, performing behavior barreling on each stream media behavior sample in a first stream media behavior sample library, and outputting a plurality of barrel-dividing data of each stream media behavior sample;
Respectively converting behavior trigger nodes in a plurality of sub-bucket data of the streaming media behavior sample into behavior directed graph characteristics;
based on a clustering strategy, classifying all behavior directed graph features of the streaming media behavior sample into a priori number of feature mapping areas; wherein the prior number is the number of prior interest points in the streaming media behavior sample;
for each prior interest point of the streaming media behavior sample, determining a feature mapping region to which a behavior directed graph feature most similar to the behavior directed graph feature of the corresponding prior interest point in all behavior directed graph features of the streaming media behavior sample belongs, and outputting a feature mapping region to which the behavior directed graph feature of the corresponding prior interest point belongs;
Aggregating all behavior directed graph features contained in any feature mapping region in the streaming media behavior sample, and outputting feature mapping region characterization information of any feature mapping region in the streaming media behavior sample;
Optimizing the deep learning network according to characteristic mapping region characterization information of each characteristic mapping region of each stream media behavior sample in the first stream media behavior sample library as input and behavior directed graph characteristics of prior interest points of the corresponding characteristic mapping region as output so as to generate the behavior interest point analysis network;
the step of respectively converting the behavior trigger nodes in the plurality of sub-bucket data into behavior directed graph features comprises the following steps:
Converting behavior trigger nodes in a plurality of sub-bucket data of the past streaming media behavior data into behavior directed graph characteristics with target modes; the step of converting the behavior trigger nodes in the sub-bucket data of the streaming media behavior sample into behavior directed graph features respectively includes: converting behavior trigger nodes in a plurality of sub-bucket data of the streaming media behavior sample into behavior directed graph features with the target mode;
the method further comprises the steps of:
performing behavior sub-barreling on each stream media behavior sample in a first stream media behavior sample library, and outputting a plurality of barrel-dividing data of each stream media behavior sample;
respectively converting behavior trigger nodes in a plurality of sub-bucket data of the streaming media behavior sample into behavior directed graph characteristics; based on a clustering strategy, classifying all behavior directed graph features of the streaming media behavior sample into a priori number of feature mapping areas; wherein the prior number is the number of prior interest points in the streaming media behavior sample;
for each prior interest point of the streaming media behavior sample, determining a feature mapping region to which a behavior directed graph feature most similar to the behavior directed graph feature of the corresponding prior interest point in all behavior directed graph features of the streaming media behavior sample belongs, and outputting a feature mapping region to which the behavior directed graph feature of the corresponding prior interest point belongs;
Aggregating all behavior directed graph features contained in any feature mapping region in the streaming media behavior sample, and outputting feature mapping region characterization information of any feature mapping region;
And optimizing the deep learning network according to the characteristic mapping region characterization information of each characteristic mapping region of each stream media behavior sample in the first stream media behavior sample library as input and the behavior directed graph characteristics of the prior interest points of the corresponding characteristic mapping region as output so as to generate a behavior interest point analysis network.
In an embodiment of the present disclosure, a streaming media data pushing device 400 is further provided and applied to a server, where the streaming media data pushing device 400 includes:
A first return module 410, configured to return, according to a call request corresponding to a mobile terminal, token information, a signaling server IP address, and a link port address to the mobile terminal, so that the mobile terminal initiates a login request according to the token information, the signaling server IP address, and the link port address;
A second return module 420, configured to respond to the login request, log in the mobile terminal, and return login success feedback to the mobile terminal if the login to the mobile terminal is successful;
A receiving module 430, configured to receive a login room request initiated by the mobile terminal when the login success feedback is received, and add the mobile terminal to a room queue according to the login room request;
A third returning module 440, configured to return room queue joining success information to the mobile terminal if the mobile terminal joins into the room queue successfully, so that the mobile terminal initiates a push flow request if the room queue joining success information is received;
a calling module 450 configured to return a push server address to the mobile terminal and call a streaming media service to complete streaming media data push of the mobile terminal according to the push server address when the push request sent by the mobile terminal is received
In a preferred embodiment, the first return module 410 is configured to:
Responding to a call request corresponding to the mobile terminal, and accessing a token acquisition service corresponding to the mobile terminal, wherein the token acquisition service is used for the mobile terminal to acquire token information of a server;
Under the condition that the access to the token is successful in obtaining the service, transmitting the token information to the mobile terminal;
And receiving a hypertext transfer protocol request initiated by the mobile terminal under the condition of receiving the token information, and returning a signaling server IP address and a link port address to the mobile terminal aiming at the hypertext transfer protocol request, so that the mobile terminal initiates the login request under the condition of receiving the signaling server IP address and the link port address.
In a preferred embodiment, the calling module 450 is configured to:
Invoking streaming media service, and receiving a webtc streaming media data packet pushed by the mobile terminal based on a webtc protocol according to the address of the push server;
Analyzing the webrtc streaming media data packet to obtain an identifiable streaming media data packet;
and pushing the identifiable streaming media data packet to vrs services to finish streaming media data pushing by the mobile terminal.
In a preferred embodiment, the streaming media data push device 400 further includes: a scheduling module configured to:
receiving streaming media nodes and streaming media information reported by an agent of vrs services;
Scheduling each streaming media node according to the streaming media data calling instruction so as to schedule the streaming media information corresponding to the streaming media node.
In a preferred embodiment, the receiving module 430 is configured to:
And adding the mobile terminal into a room queue according to the application program identifier app_id and the room identifier room_id carried in the login room request, wherein the server manages the room queue in a mode of combining the application program identifier app_id with the room identifier room_id.
In a preferred embodiment, the token information includes at least one of an application identification app_id, a user identification user_id, and an absolute timeout time.
The specific manner in which the various modules perform the operations in the apparatus of the above embodiments have been described in detail in connection with the embodiments of the method, and will not be described in detail herein.
There is also provided in an embodiment of the present disclosure an electronic device including:
A processor;
a memory for storing processor-executable instructions;
wherein the processor is configured to execute the executable instructions in the memory to implement the streaming media data push method of any of the preceding embodiments.
Embodiments of the present disclosure also provide a computer-readable storage medium having stored thereon computer program instructions which, when executed by a processor, implement the steps of the streaming media data push method of any of the previous embodiments.
Other embodiments of the disclosure will be apparent to those skilled in the art from consideration of the specification and practice of the disclosure. This disclosure is intended to cover any adaptations, uses, or adaptations of the disclosure following the general principles of the disclosure and including such departures from the present disclosure as come within known or customary practice within the art to which the disclosure pertains. It is intended that the specification and examples be considered as exemplary only, with a true scope and spirit of the disclosure being indicated by the following claims.
It is to be understood that the present disclosure is not limited to the precise arrangements and instrumentalities shown in the drawings, and that various modifications and changes may be effected without departing from the scope thereof. The scope of the present disclosure is limited only by the appended claims.
Claims (9)
1. The streaming media data push method is characterized by being applied to a server, and comprises the following steps:
According to a call request corresponding to a mobile terminal, returning token information, a signaling server IP address and a link port address to the mobile terminal, so that the mobile terminal initiates a login request according to the token information, the signaling server IP address and the link port address;
Responding to the login request, logging in the mobile terminal, and returning login success feedback to the mobile terminal under the condition that the mobile terminal is successfully logged in;
Receiving a room login request initiated by the mobile terminal under the condition that the login success feedback is received, and adding the mobile terminal into a room queue according to the room login request;
returning room queue joining success information to the mobile terminal under the condition that the mobile terminal joins the room queue successfully, so that the mobile terminal initiates a push flow request under the condition that the mobile terminal receives the room queue joining success information;
Under the condition that the push request sent by the mobile terminal is received, returning a push server address to the mobile terminal, and calling streaming media service to complete streaming media data push of the mobile terminal according to the push server address;
the token information comprises at least one of an application program identifier app_id, a user identifier user_id and an absolute timeout time expire;
and the step of calling the streaming media service to complete streaming media data streaming by the mobile terminal according to the address of the streaming server comprises the following steps:
Invoking streaming media service, extracting streaming media data mapped by the address of the push server accessed by the mobile terminal, and determining behavior interest point distribution corresponding to the mobile terminal based on past streaming media behavior data of the mobile terminal;
Screening the streaming media data based on the behavior interest point distribution corresponding to the mobile terminal so as to push the screened streaming media data to the mobile terminal;
The step of determining the behavior interest point distribution corresponding to the mobile terminal based on the past streaming media behavior data of the mobile terminal comprises the following steps:
performing behavior sub-barreling on the past streaming media behavior data, and outputting a plurality of sub-barreled data of the past streaming media behavior data;
respectively converting behavior trigger nodes in the plurality of sub-bucket data into behavior directed graph features;
Based on a clustering strategy, classifying all behavior directed graph features of the past streaming media behavior data into feature mapping areas with target quantity;
Aggregating all behavior directed graph features contained in any feature mapping region in the past streaming media behavior data, and outputting feature mapping region characterization information of any feature mapping region;
Inputting the feature mapping region characterization information of any feature mapping region into a preset behavior interest point analysis network, and outputting behavior directed graph features of priori interest points of any feature mapping region; the behavior interest point analysis network is generated by taking characteristic mapping region characterization information of each characteristic mapping region of each streaming media behavior sample in a first streaming media behavior sample library as input and taking behavior directed graph characteristics of prior interest points of the corresponding characteristic mapping region as output to optimize a deep learning network;
Calculating a matching value between each behavior directed graph feature of any feature mapping region and a behavior directed graph feature of a priori interest point of the any feature mapping region respectively, and determining a behavior trigger node corresponding to the behavior directed graph feature of N before descending order of the matching value in all behavior directed graph features of the any feature mapping region as the interest point of the any feature mapping region;
Extracting interest points of the past streaming media behavior data based on the interest points of each feature mapping region of the past streaming media behavior data;
The calculating, respectively, a matching value between each behavior directed graph feature of the any feature mapping region and a behavior directed graph feature of a priori interest point of the any feature mapping region, and determining a behavior trigger node corresponding to a behavior directed graph feature of N before descending order of the matching value in all behavior directed graph features of the any feature mapping region as the interest point of the any feature mapping region includes:
calculating the feature deviation cost of each behavior directed graph feature of any feature mapping region and the behavior directed graph feature of the prior interest point of the any feature mapping region respectively, and outputting a behavior trigger node corresponding to the behavior directed graph feature with the minimum feature deviation cost between all behavior directed graph features of the any feature mapping region and the behavior directed graph feature of the prior interest point of the any feature mapping region as the interest point of the any feature mapping region;
the method further comprises the steps of:
Responding to the optimization indication of the behavior interest point analysis network, performing behavior barreling on each stream media behavior sample in a first stream media behavior sample library, and outputting a plurality of barrel-dividing data of each stream media behavior sample;
Respectively converting behavior trigger nodes in a plurality of sub-bucket data of the streaming media behavior sample into behavior directed graph characteristics;
based on a clustering strategy, classifying all behavior directed graph features of the streaming media behavior sample into a priori number of feature mapping areas; wherein the prior number is the number of prior interest points in the streaming media behavior sample;
for each prior interest point of the streaming media behavior sample, determining a feature mapping region to which a behavior directed graph feature most similar to the behavior directed graph feature of the corresponding prior interest point in all behavior directed graph features of the streaming media behavior sample belongs, and outputting a feature mapping region to which the behavior directed graph feature of the corresponding prior interest point belongs;
Aggregating all behavior directed graph features contained in any feature mapping region in the streaming media behavior sample, and outputting feature mapping region characterization information of any feature mapping region in the streaming media behavior sample;
Optimizing the deep learning network according to characteristic mapping region characterization information of each characteristic mapping region of each stream media behavior sample in the first stream media behavior sample library as input and behavior directed graph characteristics of prior interest points of the corresponding characteristic mapping region as output so as to generate the behavior interest point analysis network;
the step of respectively converting the behavior trigger nodes in the plurality of sub-bucket data into behavior directed graph features comprises the following steps:
Converting behavior trigger nodes in a plurality of sub-bucket data of the past streaming media behavior data into behavior directed graph characteristics with target modes; the step of converting the behavior trigger nodes in the sub-bucket data of the streaming media behavior sample into behavior directed graph features respectively includes: and converting behavior trigger nodes in a plurality of sub-bucket data of the streaming media behavior sample into behavior directed graph features with the target mode.
2. The streaming media data pushing method according to claim 1, wherein the step of returning token information, a signaling server IP address, and a link port address to the mobile terminal according to a call request corresponding to the mobile terminal, so that the mobile terminal initiates a login request according to the token information, the signaling server IP address, and the link port address includes:
Responding to a call request corresponding to the mobile terminal, and accessing a token acquisition service corresponding to the mobile terminal, wherein the token acquisition service is used for the mobile terminal to acquire token information of a server;
Under the condition that the access to the token is successful in obtaining the service, transmitting the token information to the mobile terminal;
And receiving a hypertext transfer protocol request initiated by the mobile terminal under the condition of receiving the token information, and returning a signaling server IP address and a link port address to the mobile terminal aiming at the hypertext transfer protocol request, so that the mobile terminal initiates the login request under the condition of receiving the signaling server IP address and the link port address.
3. The method for pushing streaming media data according to claim 1, wherein the calling streaming media service to complete the streaming media data pushing by the mobile terminal according to the pushing server address comprises:
Invoking streaming media service, and receiving a webtc streaming media data packet pushed by the mobile terminal based on a webtc protocol according to the address of the push server;
Analyzing the webrtc streaming media data packet to obtain an identifiable streaming media data packet;
and pushing the identifiable streaming media data packet to vrs services to finish streaming media data pushing by the mobile terminal.
4. A streaming media data push method according to claim 3, the method further comprising:
receiving streaming media nodes and streaming media information reported by an agent of vrs services;
Scheduling each streaming media node according to the streaming media data calling instruction so as to schedule the streaming media information corresponding to the streaming media node.
5. The streaming media data push method of claim 1, wherein the adding the mobile terminal to a room queue according to the login room request includes:
And adding the mobile terminal into a room queue according to the application program identifier app_id and the room identifier room_id carried in the login room request, wherein the server manages the room queue in a mode of combining the application program identifier app_id with the room identifier room_id.
6. A streaming media data push device, which is applied to a server, the streaming media data push device comprising:
The first return module is configured to return token information, a signaling server IP address and a link port address to the mobile terminal according to a call request corresponding to the mobile terminal, so that the mobile terminal initiates a login request according to the token information, the signaling server IP address and the link port address;
The second return module is configured to respond to the login request, login the mobile terminal and return login success feedback to the mobile terminal under the condition that the login to the mobile terminal is successful;
The receiving module is configured to receive a login room request initiated by the mobile terminal under the condition that the login success feedback is received, and add the mobile terminal into a room queue according to the login room request;
the third return module is configured to return room queue joining success information to the mobile terminal under the condition that the mobile terminal joins the room queue successfully, so that the mobile terminal initiates a push flow request under the condition that the mobile terminal receives the room queue joining success information;
The calling module is configured to return a push server address to the mobile terminal and call streaming media service under the condition that the push request sent by the mobile terminal is received, so that streaming media data push of the mobile terminal according to the push server address is completed;
the token information comprises at least one of an application program identifier app_id, a user identifier user_id and an absolute timeout time expire;
And calling the streaming media service to complete streaming media data push of the mobile terminal according to the push server address, wherein the method comprises the following steps:
Invoking streaming media service, extracting streaming media data mapped by the address of the push server accessed by the mobile terminal, and determining behavior interest point distribution corresponding to the mobile terminal based on past streaming media behavior data of the mobile terminal;
Screening the streaming media data based on the behavior interest point distribution corresponding to the mobile terminal so as to push the screened streaming media data to the mobile terminal;
The determining the behavior interest point distribution corresponding to the mobile terminal based on the past streaming media behavior data of the mobile terminal comprises the following steps:
performing behavior sub-barreling on the past streaming media behavior data, and outputting a plurality of sub-barreled data of the past streaming media behavior data;
respectively converting behavior trigger nodes in the plurality of sub-bucket data into behavior directed graph features;
Based on a clustering strategy, classifying all behavior directed graph features of the past streaming media behavior data into feature mapping areas with target quantity;
Aggregating all behavior directed graph features contained in any feature mapping region in the past streaming media behavior data, and outputting feature mapping region characterization information of any feature mapping region;
Inputting the feature mapping region characterization information of any feature mapping region into a preset behavior interest point analysis network, and outputting behavior directed graph features of priori interest points of any feature mapping region; the behavior interest point analysis network is generated by taking characteristic mapping region characterization information of each characteristic mapping region of each streaming media behavior sample in a first streaming media behavior sample library as input and taking behavior directed graph characteristics of prior interest points of the corresponding characteristic mapping region as output to optimize a deep learning network;
Calculating a matching value between each behavior directed graph feature of any feature mapping region and a behavior directed graph feature of a priori interest point of the any feature mapping region respectively, and determining a behavior trigger node corresponding to the behavior directed graph feature of N before descending order of the matching value in all behavior directed graph features of the any feature mapping region as the interest point of the any feature mapping region;
Extracting interest points of the past streaming media behavior data based on the interest points of each feature mapping region of the past streaming media behavior data;
The calculating, respectively, a matching value between each behavior directed graph feature of the any feature mapping region and a behavior directed graph feature of a priori interest point of the any feature mapping region, and determining a behavior trigger node corresponding to a behavior directed graph feature of N before descending order of the matching value in all behavior directed graph features of the any feature mapping region as the interest point of the any feature mapping region includes:
calculating the feature deviation cost of each behavior directed graph feature of any feature mapping region and the behavior directed graph feature of the prior interest point of the any feature mapping region respectively, and outputting a behavior trigger node corresponding to the behavior directed graph feature with the minimum feature deviation cost between all behavior directed graph features of the any feature mapping region and the behavior directed graph feature of the prior interest point of the any feature mapping region as the interest point of the any feature mapping region;
responding to the optimization indication of the behavior interest point analysis network, performing behavior barreling on each stream media behavior sample in a first stream media behavior sample library, and outputting a plurality of barrel-dividing data of each stream media behavior sample;
Respectively converting behavior trigger nodes in a plurality of sub-bucket data of the streaming media behavior sample into behavior directed graph characteristics;
based on a clustering strategy, classifying all behavior directed graph features of the streaming media behavior sample into a priori number of feature mapping areas; wherein the prior number is the number of prior interest points in the streaming media behavior sample;
for each prior interest point of the streaming media behavior sample, determining a feature mapping region to which a behavior directed graph feature most similar to the behavior directed graph feature of the corresponding prior interest point in all behavior directed graph features of the streaming media behavior sample belongs, and outputting a feature mapping region to which the behavior directed graph feature of the corresponding prior interest point belongs;
Aggregating all behavior directed graph features contained in any feature mapping region in the streaming media behavior sample, and outputting feature mapping region characterization information of any feature mapping region in the streaming media behavior sample;
Optimizing the deep learning network according to characteristic mapping region characterization information of each characteristic mapping region of each stream media behavior sample in the first stream media behavior sample library as input and behavior directed graph characteristics of prior interest points of the corresponding characteristic mapping region as output so as to generate the behavior interest point analysis network;
the step of respectively converting the behavior trigger nodes in the plurality of sub-bucket data into behavior directed graph features comprises the following steps:
Converting behavior trigger nodes in a plurality of sub-bucket data of the past streaming media behavior data into behavior directed graph characteristics with target modes; the step of converting the behavior trigger nodes in the sub-bucket data of the streaming media behavior sample into behavior directed graph features respectively includes: and converting behavior trigger nodes in a plurality of sub-bucket data of the streaming media behavior sample into behavior directed graph features with the target mode.
7. The streaming media data push device of claim 6, wherein the first return module is configured to:
Responding to a call request corresponding to the mobile terminal, and accessing a token acquisition service corresponding to the mobile terminal, wherein the token acquisition service is used for the mobile terminal to acquire token information of a server;
Under the condition that the access to the token is successful in obtaining the service, transmitting the token information to the mobile terminal;
And receiving a hypertext transfer protocol request initiated by the mobile terminal under the condition of receiving the token information, and returning a signaling server IP address and a link port address to the mobile terminal aiming at the hypertext transfer protocol request, so that the mobile terminal initiates the login request under the condition of receiving the signaling server IP address and the link port address.
8. A server, comprising:
A processor;
a memory for storing processor-executable instructions;
Wherein the processor is configured to execute executable instructions in the memory to implement the method of any of claims 1-5.
9. A computer readable storage medium having stored thereon computer program instructions, which when executed by a processor, implement the steps of the method of any of claims 1-5.
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