WO2020244283A1 - 一种视频业务处理方法、系统及装置 - Google Patents

一种视频业务处理方法、系统及装置 Download PDF

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Publication number
WO2020244283A1
WO2020244283A1 PCT/CN2020/080899 CN2020080899W WO2020244283A1 WO 2020244283 A1 WO2020244283 A1 WO 2020244283A1 CN 2020080899 W CN2020080899 W CN 2020080899W WO 2020244283 A1 WO2020244283 A1 WO 2020244283A1
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Prior art keywords
video
video service
collection
data
unit
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PCT/CN2020/080899
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English (en)
French (fr)
Inventor
何国圆
智亚丹
魏超
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中兴通讯股份有限公司
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Publication of WO2020244283A1 publication Critical patent/WO2020244283A1/zh

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    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L41/00—Arrangements for maintenance, administration or management of data switching networks, e.g. of packet switching networks
    • H04L41/08—Configuration management of networks or network elements
    • H04L41/0803—Configuration setting
    • H04L41/0823—Configuration setting characterised by the purposes of a change of settings, e.g. optimising configuration for enhancing reliability
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L41/00—Arrangements for maintenance, administration or management of data switching networks, e.g. of packet switching networks
    • H04L41/50—Network service management, e.g. ensuring proper service fulfilment according to agreements
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
    • H04N17/00—Diagnosis, testing or measuring for television systems or their details
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
    • H04N17/00—Diagnosis, testing or measuring for television systems or their details
    • H04N17/004—Diagnosis, testing or measuring for television systems or their details for digital television systems
    • 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
    • H04N21/43—Processing of content or additional data, e.g. demultiplexing additional data from a digital video stream; Elementary client operations, e.g. monitoring of home network or synchronising decoder's clock; Client middleware
    • H04N21/442—Monitoring of processes or resources, e.g. detecting the failure of a recording device, monitoring the downstream bandwidth, the number of times a movie has been viewed, the storage space available from the internal hard disk

Definitions

  • This application relates to, but is not limited to, wireless network technology, in particular to a video service processing method, system and device.
  • Video services will be more and more used in people’s daily lives, and will become a major part of mobile communication systems. Main business form.
  • the purpose of the big video strategy is to establish an end-to-end standard for video experience, to formulate current network video traffic forecasts based on video performance evaluation, and to reasonably schedule and allocate wireless resources to provide high-quality video services and reduce mobile terminals.
  • the user's network delay improves the user experience.
  • This application provides a video service processing method, system, and device, which can provide a basis for network optimization, thereby effectively improving the user's experience of the video service.
  • This application provides a video service processing system, including: a management unit, an identification unit, an acquisition unit, and an analysis unit; wherein the management unit is used to deliver video collection to one or more acquisition units that need to perform video service quality evaluation Task; identification unit, used to detect data packets in the network using deep packet inspection DPI technology, when the real video service data is detected in the data packets, notify the collection unit associated with the identification unit; the collection unit uses After receiving the video service collection notification from the identification unit, it detects that it has a video service collection task, collects according to the parameters of the video service collection task, and reports the collection result to the analysis unit; the analysis unit is used to analyze the reported data And statistics, and according to the configured model, periodically output the video service quality analysis results.
  • the management unit is used to deliver video collection to one or more acquisition units that need to perform video service quality evaluation Task
  • identification unit used to detect data packets in the network using deep packet inspection DPI technology, when the real video service data is detected in the data packets, notify the collection unit associated with the identification unit; the
  • the identification unit includes: a distribution service module and a DPI module, wherein the distribution service module is configured to copy the data message and send it to the DPI module; receive the recognition result fed back by the DPI module, The recognition result that meets the preset rules and the corresponding user port are carried in the video service collection notification and sent to the collection unit; the DPI module is used to perform real-time detection of the received data message according to the pre-configured The detection strategy identifies whether the data message contains real video service data. If a video feature code is detected in the data message, it is determined that the data message contains real video service data, and the identification result is fed back Give the triage service module.
  • the collection unit is specifically configured to: receive a video service collection notification from the identification unit, perform matching according to the video provider ID, and find videos with the same APPID; and then identify according to the video ID, Videos with the same APPID and video ID are considered the same video; a linked list node is established for each terminal identified as having video services, and the IP (Internet Protocol, Internet Protocol) of each terminal is different; The parameters carried by the video collection task, collect the data reported eventually and periodically during the video service process of the terminal identified as having the video service, and when the video of the terminal identified as having the video service ends or when the terminal is released, The data of the terminal is reported to the analysis unit configured in the video collection task.
  • IP Internet Protocol, Internet Protocol
  • the analysis unit is specifically configured to: receive the data reported by the acquisition unit, and, according to the data reporting period and the task time carried in the video acquisition task, report to the acquisition unit within a specified time Analysis of the video service data to obtain the result of the video service quality analysis.
  • the analysis of the video service data of the collecting unit within a specified time in the analysis unit to obtain the video service quality analysis result includes: according to the video source of the video service data A quality score, an interactive experience quality score, and a viewing experience quality score are obtained, and the total video service quality score is obtained as the video service quality analysis result.
  • the management unit is further configured to: stop the issued video capture task.
  • the management unit is further configured to: change the parameters of the video task.
  • one identification unit corresponds to one collection unit; or, one identification unit serves as a convergence node in the network and corresponds to one or more collection units.
  • the management unit is a network element management system EMS
  • the identification unit is a mobile edge computing MEC
  • the collection unit is a base station
  • the analysis unit is a big data server.
  • This application also provides a video service processing method, including: a base station receives a video service collection notification and detects whether there is a video service collection task; the base station detects that a video service collection task exists, collects according to the parameters of the video service collection task, and reports The result is collected for analysis to obtain a video service quality analysis result.
  • the collection according to the parameters of the video service collection task includes: matching according to the video APPID to find videos with the same APPID; identifying according to the video ID, and videos with the same APPID and video ID are considered It is the same video; a linked list node is established for each terminal IP identified as having video services; according to the parameters carried by the video collection task, the data and periodicity reported during the video service process of the terminals identified as having video services are collected For the reported data, when the video of the terminal identified as having the video service ends or when the terminal is released, the data of the terminal is reported for analysis to obtain the video service quality analysis result.
  • the collection object included in the parameters of the video collection task includes any combination of the following:
  • the reporting method is the initial buffering stage data of the video reported by the event
  • the reporting method is periodic video data information collection for periodic reporting
  • the reporting method is the start and end time of the video freeze of the incident
  • the reporting method is the end time of the video of the incident reporting
  • the reporting method is the video collection period of periodic reporting.
  • This application further provides a computer-readable storage medium that stores computer-executable instructions, and the computer-executable instructions are used to execute any one of the video service processing methods described above.
  • This application further provides a device for realizing video service processing, including a processor and a memory; wherein a computer program that can be run on the processor is stored in the memory: The steps of the business processing method.
  • This application also provides another video service processing method, including: MEC uses DPI technology to detect data messages in the network; when real video service data is detected in the data message, notifying the base station associated with the MEC Video services are collected.
  • the use of DPI technology to detect data packets in the network includes: copying the data packets and performing real-time detection, and identifying the data packets in the data packets according to a pre-configured detection strategy Whether it contains real video service data, if a video feature code is detected in the data message, it is determined that the data message contains real video service data, and the recognition result is obtained.
  • the notifying the base station associated with the MEC to collect video services includes: carrying the identification result that meets preset rules and corresponding user port information in the video service collection notification Sent to the base station associated with the MEC.
  • the present application further provides a computer-readable storage medium that stores computer-executable instructions, and the computer-executable instructions are used to execute another video service processing method described in any one of the above.
  • the present application further provides a device for realizing video service processing, including a processor and a memory; wherein a computer program that can be run on the processor is stored in the memory: The steps of the frequency service processing method.
  • Figure 1 is a schematic diagram of the structure of the application video service processing system
  • FIG. 2 is a schematic flowchart of an embodiment of a video service processing method of this application
  • FIG. 3 is a schematic flowchart of another embodiment of a video service processing method of this application.
  • the computing device includes one or more processors (CPUs), input/output interfaces, network interfaces, and memory.
  • processors CPUs
  • input/output interfaces network interfaces
  • memory volatile and non-volatile memory
  • the memory may include non-permanent memory in computer readable media, random access memory (RAM) and/or non-volatile memory, such as read-only memory (ROM) or flash memory (flash RAM). Memory is an example of computer readable media.
  • RAM random access memory
  • ROM read-only memory
  • flash RAM flash memory
  • Computer-readable media includes permanent and non-permanent, removable and non-removable media, and information storage can be realized by any method or technology.
  • the information can be computer-readable instructions, data structures, program modules, or other data.
  • Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology, CD-ROM, digital versatile disc (DVD) or other optical storage, Magnetic cassettes, magnetic tape storage or other magnetic storage devices or any other non-transmission media can be used to store information that can be accessed by computing devices.
  • computer-readable media does not include non-transitory computer-readable media (transitory media), such as modulated data signals and carrier waves.
  • FIG 1 is a schematic diagram of the composition architecture of the video service processing system of this application, as shown in Figure 1, including: a management unit, an identification unit, a collection unit, and an analysis unit.
  • the management unit is used to issue video collection tasks to one or more collection units that need to perform video service quality evaluation.
  • the parameters carried by the video capture task include any combination of the following:
  • Task time including, for example, the start time of video capture and the stop time of video capture, and further, it can also include: support for setting the time period of busy and idle time, etc.;
  • Task parameters including, for example, the data reporting period, the number of UE (User Equipment) samples in the cell, etc.;
  • Analysis information such as the name of the analysis unit, data receiving IP (Internet Protocol) address, and file transfer protocol (FTP, File Transfer Protocol) information for reporting data;
  • IP Internet Protocol
  • FTP File Transfer Protocol
  • the collection object that is, which types of data are collected by the video collection task, can be found in Table 1 for any combination of collection items.
  • the management unit is also used to: stop the issued video capture task.
  • the sent video capture task will automatically stop. Further, before the video capture stop time is reached, it can also be stopped manually, for example, the user issues a stop command through the interface provided by the management unit.
  • the management unit is further used to: change the parameters of the video capture task.
  • the management unit may be a network element management system (EMS, Network Element Management System).
  • EMS Network Element Management System
  • the identification unit is used to detect data packets in the network using Deep Packet Inspection (DPI) technology.
  • DPI Deep Packet Inspection
  • the identification unit is deployed at the edge of the mobile network, which is a network architecture that utilizes functions such as providing services required by users and cloud computing nearby the edge of the wireless network.
  • the identification unit integrates the service provider and the wireless network.
  • one identification unit corresponds to one collection unit; or, one identification unit can also serve as a sink node in the network, corresponding to one or more collection units.
  • DPI message identification is based on DPI rules
  • DPI rules include two major categories: first-level rules and second-level rules.
  • the first-level rule is L3/L4 message identification, and the incoming parameters are quintuples of the message;
  • the second-level rule is L7 message identification, and the incoming parameters are the seven-layer load information of the message.
  • This application adopts DPI technology, which not only realizes fast matching of data at the network layer (such as IP address/protocol type, etc.) and transport layer (such as TCP/UDP port number, etc.), but also realizes the deep layer of the TCP/UDP payload. Analysis, thereby achieving accurate protocol identification and application protocol event identification, and supporting single-packet feature identification and flow-based multi-packet stateful feature identification, which greatly improves the accuracy of protocol identification.
  • the identification unit includes: a distribution service module and a DPI module.
  • the offloading service module is used to copy the data message, that is, the user's downlink service flow message, and send it to the DPI module; receive the recognition result fed back by the DPI module, the recognition result that will meet the preset rules, and the corresponding user Port and other information is carried in the video service collection notice and sent to the collection unit;
  • Table 2 shows an embodiment of the preset rule. It should be noted that the content of the preset rule includes but is not limited to APPID and protocol name.
  • the preset rule may also include, but is not limited to, information such as the initial buffer duration and the secondary buffer duration as shown in Table 3:
  • the DPI module is used to perform real-time detection of the received data message, that is, user data, and identify whether the data message contains real video service data according to a pre-configured detection strategy.
  • the video feature code is detected in the message, the data message is determined to contain real video service data, and the recognition result is fed back to the offloading service module.
  • the identification result may include, but is not limited to, such as: video provider ID (APPID), video ID, bandwidth, resolution, etc.
  • the identification unit may notify the collection unit of the identification result through a special message.
  • the special message may be a private message between the identification unit and the collection unit, such as a user plane GPRS (General Packet Radio Service, General Packet Radio Service) tunneling protocol (GTP-U) message Wait.
  • GPRS General Packet Radio Service, General Packet Radio Service
  • GTP-U tunneling protocol
  • the identification unit may be a Mobile Edge Computing (MEC, Mobile Edge Computing) node.
  • MEC Mobile Edge Computing
  • This application also provides an MEC, which includes any of the functions described in the identification unit.
  • the collection unit is used to receive the video service collection notification from the identification unit, detect that it has a video service collection task, collect according to the parameters of the video service collection task, and report the collection result to the analysis unit.
  • all the message statistics of the collection unit are collected when the uplink status report of the downlink message is returned, and the collection unit performs video recording after receiving the video service collection notification from the identification unit.
  • APPID video provider ID
  • the video collection task collect the eventually reported data and periodically reported data during the video service process of the terminal identified as having the video service.
  • Report the terminal's data to the analysis unit configured in the video capture task.
  • the collection unit mainly builds index information based on the IP address of the terminal, that is, establishes a linked list node for each terminal IP that is identified as having video services, and the IP address node information includes APPID linked list information. Contains video ID and related statistical linked list information. When the start of the video is identified, the linked list node is established.
  • the content contained in each linked list node includes but is not limited to:
  • Video start time and video end time
  • the data of the terminal may be compressed and reported to the analysis unit corresponding to the video collection task in a manner such as the file transfer protocol SFTP (Secure File Transfer Protocol, Secure File Transfer Protocol).
  • SFTP Secure File Transfer Protocol, Secure File Transfer Protocol
  • This application is based on the use of DPI technology to identify video services. Compared with the traditional Deep Flow Inspection (DFI, Deep Flow Inspection) technology, this application can identify video services and applications in the network more finely and accurately. The configured rules forward and process the identified services. Moreover, through the identification unit of this application, the wireless network and Internet technology are effectively integrated, and functions such as calculation, storage, and processing are added on the wireless network side. The information interaction between the open wireless network and the business server is effectively Reduced network delay, improved wireless link utilization, and upgraded traditional wireless base stations to intelligent base stations.
  • DFI Deep Flow Inspection
  • the collection unit may be a base station.
  • This application also provides a base station, including any of the functions described in the collection unit.
  • the analysis unit is used to analyze and count the reported data, and periodically output the video service quality analysis results according to the parameters carried in the video collection task issued by the management unit.
  • the analysis unit receives the data reported by the collection unit, and analyzes the video service data of the collection unit within a specified time according to the data reporting period and task time carried in the video collection task to obtain the video Business quality analysis results.
  • the video service data can be evaluated from different dimensions such as the video source quality score, the interactive experience quality score, and the viewing experience quality score, so as to obtain the total video service quality score as the video service quality analysis result.
  • the quality of the video source mainly depends on the definition, fluency, fidelity and other factors of the video source. For example, it can cover the resolution, frame rate, bit rate, content, encoding and terminal of the video source.
  • the average bitrate and resolution score of the video source is used as the scoring result of the video service quality to indicate the quality of the video source.
  • the scoring standard setting please refer to Table 2 shows.
  • the average code rate can be as shown in formula (1):
  • L k is the total traffic at the current bit rate, in bits; T k is the playable duration of data at the corresponding bit rate, in seconds; N is the number of bit rate changes during video playback.
  • the quality of interactive experience mainly depends on the system's response speed to user interactive operations, which refers to the convenience and efficiency of the user's business operations during the use of video services.
  • the scoring of the initial buffering duration is taken as an example to indicate the interactive experience quality, that is, the scoring result of the interactive experience quality is the scoring result of the initial buffering duration, and the example of the scoring standard setting can be seen in Table 3 below.
  • the calculation method of the initial buffer duration T1 may include:
  • the network side (such as the base station side) detects that the user terminal successfully receives the total data traffic at the corresponding bit rate at the time L, and the video playable time is T;
  • T L/default bit rate
  • T L1/R1+L2/R2+...Ln/Rn
  • Ln represents the total data flow when the bit rate is Rn
  • N is an integer greater than 1, indicating different code rates and different total data flows.
  • the quality of the viewing experience mainly depends on the quality of the program signal during the video playback, that is, whether there is quality degradation such as discontinuity of the video image and abnormal image.
  • two indicators, the proportion of the recovery duration of the freeze and the frequency of the freeze may be evaluated to indicate the viewing experience quality. For an example of setting the scoring standard, see Table 3.
  • the proportion of the freeze recovery duration may be as shown in formula (2):
  • Ts k represents the length of each time Caton recovery seconds; N is the number of times during video playback Caton; the length in seconds when the video.
  • the freezing frequency can be as shown in formula (3):
  • Formula (3) represents the number of video freezes every 10 minutes, where N is the number of freezes during video playback, and the unit of video playback duration is converted to minutes.
  • Table 4 is an example of setting the quality score of a video source in this application.
  • Table 5 is an example of setting scores for interactive experience quality and viewing experience quality of this application.
  • scoring criteria can also be set according to the specific requirements of the video service, and the specific setting method is not used to limit the scope of protection of this application.
  • the total score of video service quality can be calculated.
  • the analysis result can be notified to the wireless operation and maintenance personnel in the form of emails or short messages on a regular basis.
  • wireless operation and maintenance personnel can conveniently optimize the network of the base station based on the analysis results, for example, perform troubleshooting, locate faults, and optimize the network for base stations with poor video service quality. Improve the user video experience.
  • the analysis unit may be a big data server.
  • the video service processing system provided by this application identifies video services based on DPI technology, and more precisely and accurately identifies video services and applications in the network; moreover, through the identification unit of this application, wireless network and Internet technologies are effectively
  • the integration of locations and locations provides a basis for network optimization, thereby effectively improving users’ experience of video services.
  • This application collects the video service data of users under the designated base station, including information such as video start time/end time, video bit rate/resolution, initial buffering time/stop start and end time, and report to the data analysis center, and data analysis Through intelligent analysis of the data reported by the base station, the center conducts real-time and effective evaluation of the video service quality of the base station within a specified period of time, and regularly informs the operation and maintenance personnel of the evaluation results. The operation and maintenance personnel have achieved targeted
  • the network is optimized in a manner that effectively improves the user experience quality of video services.
  • This application realizes the automated operation and maintenance of video services, greatly reduces the workload of wireless operation and maintenance personnel, and does not need to perform network optimization every time a user complains, but takes network optimization as a continuous periodic work. All in all, the video service processing system of this application has the advantages of high degree of automation, less intervention by operation and maintenance personnel, and good long-term user experience, and has played a huge role in the intelligent operation and maintenance of wireless networks.
  • FIG. 2 is a schematic flowchart of an embodiment of a video service processing method of this application, as shown in FIG. 2, including:
  • Step 200 The base station receives the video service collection notification and detects whether there is a video service collection task.
  • the video service collection task comes from a management unit such as an EMS.
  • the parameters carried by the video capture task include any combination of the following:
  • Task time including, for example, the start time of video capture and the stop time of video capture, and further, it can also include: support for setting the time period of busy and idle time, etc.;
  • Task parameters including data reporting period, number of UE samples in the cell, etc.
  • Analysis information such as the name of the analysis unit, the data receiving IP address, and the file transfer protocol (FTP, File Transfer Protocol) information of the reported data;
  • FTP File Transfer Protocol
  • the collection objects that is, what types of data are collected by the video collection task, see Table 1 for details.
  • the video service collection notification comes from an identification unit such as an MEC node.
  • the video service collection notification carries information such as: a recognition result that will meet a preset rule, and a corresponding user port.
  • Step 201 The base station detects that there is a video service collection task, performs collection according to the parameters of the video service collection task, and reports the collection result for analysis to obtain the video service quality analysis result.
  • this step may include:
  • Match according to the video APPID find the video with the same APPID; identify according to the video ID, the video with the same APPID and video ID is considered to be the same video;
  • the parameters carried by the video collection task collect the eventually reported data and periodically reported data during the video service process of the terminal identified as having the video service.
  • the data of the terminal is reported for analysis to obtain the video service quality analysis result.
  • establishing a linked list node for each terminal IP identified as having video services includes:
  • IP address to create index information, that is, create a linked list node for each terminal IP identified as having video services.
  • the IP address node information includes APPID linked list information, and APPID includes video ID and related statistical linked list information.
  • a linked list node is established at the beginning of the video. The content contained in each linked list node includes but is not limited to:
  • Video start time and video end time
  • the reporting of the collection result may include:
  • An embodiment of the present application also provides a computer-readable storage medium that stores computer-executable instructions, and the computer-executable instructions are used to execute any one of the video service processing methods shown in FIG. 2.
  • An embodiment of the present application also provides a device for realizing video service processing, including a processor and a memory; wherein a computer program that can run on the processor is stored in the memory: The steps of the video service processing method.
  • This application realizes the automated operation and maintenance of video services, greatly reduces the workload of wireless operation and maintenance personnel, and does not need to perform network optimization every time a user complains, but takes network optimization as a continuous periodic work. All in all, the video service processing system of this application has the advantages of high degree of automation, less intervention by operation and maintenance personnel, and good long-term user experience, and has played a huge role in the intelligent operation and maintenance of wireless networks.
  • Fig. 3 is a schematic flowchart of another embodiment of a video service processing method of this application, as shown in Fig. 2, including:
  • Step 300 The MEC uses DPI technology to detect data packets in the network.
  • DPI message identification is based on DPI rules
  • DPI rules include two major categories: first-level rules and second-level rules.
  • the first-level rule is L3/L4 message identification, and the incoming parameters are quintuples of the message;
  • the second-level rule is L7 message identification, and the incoming parameters are the seven-layer load information of the message.
  • This application adopts DPI technology, which not only realizes fast matching of data at the network layer (such as IP address/protocol type, etc.) and transport layer (such as TCP/UDP port number, etc.), but also realizes the deep layer of the TCP/UDP payload. Analysis, thereby achieving accurate protocol identification and application protocol event identification, and supporting single-packet feature identification and flow-based multi-packet stateful feature identification, which greatly improves the accuracy of protocol identification.
  • the MEC adopts DPI technology to detect data packets in the network, which may include:
  • the identification result may include, but is not limited to, such as: video provider ID (APPID), video ID, bandwidth, resolution, etc.
  • Step 301 When the real video service data is detected in the data message, the base station associated with the MEC is notified to collect the video service.
  • this step may include:
  • the identification result that meets the preset rules, the corresponding user port and other information are carried in the video service collection notification and sent to the base station associated with the MEC.
  • the recognition result can be notified through a special message.
  • the video service processing method provided by this application identifies video services based on DPI technology, and more precisely and accurately identifies video services and applications in the network; moreover, through this application, wireless network and Internet technologies are effectively integrated into Together, it provides a basis for network optimization, thereby effectively improving the user experience of video services.
  • An embodiment of the present application also provides a computer-readable storage medium that stores computer-executable instructions, and the computer-executable instructions are used to execute any one of the video service processing methods shown in FIG. 3.
  • An embodiment of the present application also provides an apparatus for implementing video service processing, including a processor and a memory; wherein a computer program that can run on the processor is stored in the memory: The steps of the video service processing method.
  • the video service processing method provided in this application can be applied to the technical field of accurate delivery capabilities for recommending personalized content to users. For example, first build a user video habit model for personalized customized user groups (such as VIP users of video service providers or users with customized functions such as video subscription/hot video recommendation), and the video service task management part is issued to the base station for collection User viewing time, viewing behavior, content preference, viewing market, location and other related indicators tasks, base station sensors identify user habit models, and collect video data for a period of time, that is, the user is issued and collected through the management unit of this application The task, the content of the collection and the above will change accordingly according to the application scenario.
  • a user video habit model for personalized customized user groups such as VIP users of video service providers or users with customized functions such as video subscription/hot video recommendation
  • the video service task management part is issued to the base station for collection
  • User viewing time, viewing behavior, content preference, viewing market, location and other related indicators tasks, base station sensors identify user habit models, and collect video data for a period of time, that is, the user is
  • the collection process is still identified through the interaction between the identification unit and the collection unit of this application; then, the base station reports data signaling to the data analysis center, and the big data analysis center
  • the user data is analyzed through the analysis unit of this application, and the network platform with the highest video quality is identified, and then the statistical data is fed back to the video service provider to establish content stickiness and explore video content Category is a differentiated, community-based operation model. Through group management of segmented users, all video resources related to this type of video are identified and the network platform with the highest video quality is comprehensively analyzed.

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Abstract

本申请公开了一种视频业务处理方法、系统及装置。本申请提供的视频业务处理方法，包括：基站接收到视频业务采集通知，检测是否存在视频业务采集任务；基站检测出存在视频业务采集任务，根据视频业务采集任务的参数进行采集，上报采集结果以用于分析得到视频业务质量分析结果。

Description

一种视频业务处理方法、系统及装置
相关申请的交叉引用
本申请基于申请号为201910476215.3、申请日为2019年6月3日的中国专利申请提出，并要求该中国专利申请的优先权，该中国专利申请的全部内容在此以引入方式并入本申请。
技术领域
本申请涉及但不限于无线网络技术，尤指一种视频业务处理方法、系统及装置。
背景技术
随着移动通信技术的飞速发展和移动终端产品，特别是智能手机的不断升级，视频用户逐年增多，视频业务将越来越多的应用在人们的日常生活中，并将成为移动通信系统中的主要业务形式。
大视频背景下，视频业务将成为视频服务商、运营商、设备商的基础业务，成为引领未来的坚实保证。高清化、多屏化、交互化、社交化、实时化将成为消费者视频消费的主要需求，然而，无线资源的稀缺性和无线链路的不稳定性，与用户对于视频业务质量日益提高的需求之间的矛盾却给无线视频业务的发展带来了挑战。
对于运营商而言，大视频战略的目的是建立视频体验的端到端标准，基于视频性能评估制定现网的视频流量预测，合理调度分配无线资源以为提供高质量的视频业务服务，减少移动端用户的网络延迟从而提升用户体验。
发明内容
本申请提供一种视频业务处理方法、系统及装置，能够为网络优化提供依据，从而有效提升用户对视频业务的体验。
本申请提供了一种视频业务处理系统，包括：管理单元、识别单元、采集单元和分析单元；其中，管理单元，用于向需要进行视频业务质量评估的一个或一个以上采集单元下发视频采集任务；识别单元，用于采用深度包检测DPI技术对网络中的数据报文进行检测，当在 数据报文中检测到真正的视频业务数据，通知与识别单元关联的采集单元；采集单元，用于接收到来自识别单元的视频业务采集通知，检测出自身存在视频业务采集任务，根据视频业务采集任务的参数进行采集，将采集结果上报给分析单元；分析单元，用于对上报的数据进行分析和统计，并根据配置的模型，定期输出视频业务质量分析结果。
在一种示例性实例中，所述识别单元包括：分流服务模块、DPI模块，其中，分流服务模块，用于将所述数据报文复制后发送到DPI模块；接收DPI模块反馈的识别结果，将符合预设规则的识别结果，以及对应的用户端口携带在视频业务采集通知中发送给所述采集单元；DPI模块，用于对接收到的所述数据报文进行实时检测，根据预先配置的检测策略识别所述数据报文中是否包含有真正的视频业务数据，如果在所述数据报文中检测出视频特征码，确定所述数据报文包含有真正的视频业务数据，将识别结果反馈给分流服务模块。
在一种示例性实例中，所述采集单元具体用于：接收到来自所述识别单元的视频业务采集通知，根据视频提供商ID进行匹配，找到APPID相同的视频；再根据视频ID进行识别，APPID和视频ID均相同的视频被认为是同一个视频；针对每一个识别为有视频业务的终端建立一个链表节点，每个终端的IP(Internet Protocol，网际互联协议)是不一样的；按照所述视频采集任务携带的参数，采集识别为有视频业务的终端的视频业务过程中事件性上报的数据和周期性上报的数据，在识别为有视频业务的终端的视频结束时或者终端释放时，将该终端的数据上报到视频采集任务中配置的所述分析单元。
在一种示例性实例中，所述分析单元具体用于：收到所述采集单元上报的数据，根据所述视频采集任务中携带的数据上报周期和任务时间，对指定时间内所述采集单元的视频业务数据进行分析，以获取所述视频业务质量分析结果。
在一种示例性实例中，所述分析单元中的对指定时间内所述采集单元的视频业务数据进行分析，以获取所述视频业务质量分析结果，包括：根据所述视频业务数据的视频源质量评分、交互体验质量评分和观看体验质量评分，获取所述视频业务质量总评分作为所述视频业务质量分析结果。
在一种示例性实例中，所述管理单元还用于：停止已下发的所述视频采集任务。
在一种示例性实例中，所述管理单元还用于：变更所述视频任务的参数。
在一种示例性实例中，一个所述识别单元与一个所述采集单元对应；或者，一个所述识别单元作为网络中的汇聚节点，对应一个或一个以上所述采集单元。
在一种示例性实例中，所述管理单元为网元管理系统EMS，所述识别单元为移动边缘计算MEC，所述采集单元为基站，所述分析单元为大数据服务器。
本申请还提供了一种视频业务处理方法，包括：基站接收到视频业务采集通知，检测是否存在视频业务采集任务；基站检测出存在视频业务采集任务，根据视频业务采集任务的参数进行采集，上报采集结果以用于分析得到视频业务质量分析结果。
在一种示例性实例中，所述根据视频业务采集任务的参数进行采集，包括：根据视频APPID进行匹配，找到APPID相同的视频；根据视频ID进行识别，APPID和视频ID均相同的视频被认为是同一个视频；针对每一个识别为有视频业务的终端IP建立一个链表节点；按照视频采集任务携带的参数，采集识别为有视频业务的终端的视频业务过程中事件性上报的数据和周期性上报的数据，在识别为有视频业务的终端的视频结束时或者终端释放时，将该终端的数据上报，以用于分析得到视频业务质量分析结果。
在一种示例性实例中，所述视频采集任务的参数中包括的采集对象；所述采集对象包括以下任意组合：
上报方式为事件上报的视频初始缓冲阶段数据；
上报方式为周期上报的周期型视频数据信息采集；
上报方式为事件上报的视频卡顿起止时间；
上报方式为事件上报的视频结束时间；
上报方式为周期上报的视频采集周期。
本申请又提供了一种计算机可读存储介质，存储有计算机可执行指令，所述计算机可执行指令用于执行上述任一项所述的一种视频业务处理方法。
本申请再提供了一种实现视频业务处理的装置，包括处理器、存储器；其中，存储器上存储有可在处理器上运行的计算机程序：用于执行上述任一项所述的视一种频业务处理方法的步骤。
本申请还提供了另一种视频业务处理方法，包括：MEC采用DPI技术对网络中的数据报文进行检测；当在数据报文中检测到真正的视频业务数据，通知与MEC关联的基站对视频业务进行采集。
在一种示例性实例中，所述采用DPI技术对网络中的数据报文进行检测，包括：将所述 数据报文复制后进行实时检测，根据预先配置的检测策略识别所述数据报文中是否包含有真正的视频业务数据，如果在所述数据报文中检测出视频特征码，确定所述数据报文包含有真正的视频业务数据，获取识别结果。
在一种示例性实例中，所述通知与MEC关联的基站对视频业务进行采集，包括：将符合预设规则的所述识别结果，以及对应的用户端口信息携带在所述视频业务采集通知中发送给与所述MEC关联的基站。
本申请又提供了一种计算机可读存储介质，存储有计算机可执行指令，所述计算机可执行指令用于执行上述任一项所述的另一种视频业务处理方法。
本申请再提供了一种实现视频业务处理的装置，包括处理器、存储器；其中，存储器上存储有可在处理器上运行的计算机程序：用于执行上述任一项所述的视另一种频业务处理方法的步骤。
本发明的其它特征和优点将在随后的说明书中阐述，并且，部分地从说明书中变得显而易见，或者通过实施本发明而了解。本发明的目的和其他优点可通过在说明书、权利要求书以及附图中所特别指出的结构来实现和获得。
附图说明
附图用来提供对本申请技术方案的进一步理解，并且构成说明书的一部分，与本申请的实施例一起用于解释本申请的技术方案，并不构成对本申请技术方案的限制。
图1为本申请视频业务处理系统的组成架构示意图；
图2为本申请视频业务处理方法的一种实施例的流程示意图；
图3为本申请视频业务处理方法的另一种实施例的流程示意图。
具体实施方式
在本申请一个典型的配置中，计算设备包括一个或多个处理器(CPU)、输入/输出接口、网络接口和内存。
内存可能包括计算机可读介质中的非永久性存储器，随机存取存储器(RAM)和/或非易失性内存等形式，如只读存储器(ROM)或闪存(flash RAM)。内存是计算机可读介质的示例。
计算机可读介质包括永久性和非永久性、可移动和非可移动媒体可以由任何方法或技术 来实现信息存储。信息可以是计算机可读指令、数据结构、程序的模块或其他数据。计算机的存储介质的例子包括，但不限于相变内存(PRAM)、静态随机存取存储器(SRAM)、动态随机存取存储器(DRAM)、其他类型的随机存取存储器(RAM)、只读存储器(ROM)、电可擦除可编程只读存储器(EEPROM)、快闪记忆体或其他内存技术、只读光盘只读存储器(CD-ROM)、数字多功能光盘(DVD)或其他光学存储、磁盒式磁带，磁带磁盘存储或其他磁性存储设备或任何其他非传输介质，可用于存储可以被计算设备访问的信息。按照本文中的界定，计算机可读介质不包括非暂存电脑可读媒体(transitory media)，如调制的数据信号和载波。
为使本申请的目的、技术方案和优点更加清楚明白，下文中将结合附图对本申请的实施例进行详细说明。需要说明的是，在不冲突的情况下，本申请中的实施例及实施例中的特征可以相互任意组合。
图1为本申请视频业务处理系统的组成架构示意图，如图1所示，包括：管理单元、识别单元、采集单元和分析单元。其中，管理单元，用于向需要进行视频业务质量评估的一个或一个以上采集单元下发视频采集任务。
在一种示例性实例中，视频采集任务携带的参数包括以下任意组合：
任务时间，包括如视频采集开始时间和视频采集停止时间，进一步地，还可以包括：支持忙时和闲时的时间段设置等；
任务参数，包括如数据上报周期，小区内UE(User Equipment，用户设备)抽样数等；
分析信息，比如分析单元的名称、数据接收IP(网际互联协议，Internet Protocol)地址、上报数据的文件传输协议(FTP，File Transfer Protocol)信息；
采集对象，也就是视频采集任务采集哪些类型的数据，具体可参见表1中的任意采集项的组合。
Figure PCTCN2020080899-appb-000001
Figure PCTCN2020080899-appb-000002
表1
在一种示例性实例中，管理单元还用于：停止已下发的视频采集任务。
当到达视频采集停止时间时，已发下的视频采集任务会自动停止。进一步地，未到视频采集停止时间，也可以通过手动停止，比如：用户通过管理单元提供的界面下发停止命令等。
在一种示例性实例中，管理单元还用于：变更视频采集任务的参数。
需要说明的是，在进行变更之前需要先停止视频采集任务，然后修改视频采集任务后再重新下发，比如修改任务时间、任务参数等信息。
在一种示例性实例中，管理单元可以是网元管理系统(EMS，Network Element Management System)。
识别单元，用于采用深度包检测(DPI，Deep Packet Inspect)技术对网络中的数据报文进行检测，当在数据报文中检测到真正的视频业务数据，通知与识别单元关联的采集单元。
在一种示例性实例中，识别单元部署在移动网络边缘，是一种网络架构，利用在无线网络边缘就近提供用户所需服务和云端计算等功能。识别单元将业务提供商和无线网络融合在一起。
在一种示例性实例中，一个识别单元与一个采集单元对应；或者，一个识别单元也可以作为网络中的汇聚节点，对应一个或一个以上采集单元。
在一种示例性实例中，DPI的报文识别基于DPI规则，DPI规则包括一级规则和二级规则两大类。一级规则是L3/L4报文识别，入参为报文五元组；二级规则是L7报文识别，入参为报文的七层载荷信息。本申请采用DPI技术，不仅实现了对网络层(如IP地址/协议类型等)、传输层(如TCP/UDP端口号等)数据进行快速匹配，而且实现了对TCP/UDP的载荷部分进行深层分析，从而实现了精确协议识别和应用协议事件识别，并且支持了单包特征识别和基于流的多包有状态特征识别，这样大大提升了协议识别的准确度。
在一种示例性实例中，识别单元包括：分流服务模块、DPI模块。其中，分流服务模块， 用于将所述数据报文即用户的下行业务流报文复制后发送到DPI模块；接收DPI模块反馈的识别结果，将符合预设规则的识别结果，以及对应的用户端口等信息携带在视频业务采集通知中发送给采集单元；
表2所示为预设规则的一个实施例，需要说明的是，预设规则的内容包括但不限于APPID和协议名称。
Figure PCTCN2020080899-appb-000003
表2
在一种示例性实例中，预设规则还可以包含但不限于如表3所示的初始缓冲时长、二次缓冲时长等信息：
Figure PCTCN2020080899-appb-000004
表3
DPI模块，用于对接收到的所述数据报文即用户的数据进行实时检测，根据预先配置的检测策略，识别所述数据报文中是否包含有真正的视频业务数据，如果在所述数据报文中检测出视频特征码，确定所述数据报文包含有真正的视频业务数据，将识别结果反馈给分流服务模块。在一种示例性实例中，识别结果可以包括但不限于如：视频提供商ID(APPID)、视频ID、带宽、分辨率等。
在一种示例性实例中，识别单元可以通过特殊的报文将识别结果通知给采集单元。在一种示例性实例中，特殊的报文可以是识别单元和采集单元之间的私有报文，比如用户面GPRS(General Packet Radio Service，通用分组无线业务)隧道协议(GTP-U)报文等。
在一种示例性实例中，识别单元可以是移动边缘计算(MEC，Mobile Edge Computing)节点。
本申请还提供一种MEC，包括任一项识别单元所述的功能。
采集单元，用于接收到来自识别单元的视频业务采集通知，检测出自身存在视频业务采集任务，根据视频业务采集任务的参数进行采集，将采集结果上报给分析单元。
在一种示例性实例中，采集单元所有的报文统计，都是在下行报文的上行状态报告返回的时候进行统计的，采集单元在接收到来自识别单元的视频业务采集通知，进行视频的合并计算：先根据视频提供商ID(APPID)进行匹配，找到APPID相同的视频，然后再根据视频ID进行识别，APPID和视频ID均相同的视频被认为是同一个视频；
针对每一个识别为有视频业务的终端IP建立一个链表节点；
按照视频采集任务携带的参数，采集识别为有视频业务的终端的视频业务过程中事件性上报的数据和周期性上报的数据，在识别为有视频业务的终端的视频结束时或者终端释放时，将该终端的数据上报到视频采集任务中配置的分析单元。
在一种示例性实例中，采集单元主要是以终端的IP地址建立索引信息，即针对每一个识别为有视频业务的终端IP建立一个链表节点，IP地址节点信息中包括APPID链表信息，APPID中包含视频ID和相关统计链表信息，当识别出视频开始时建立链表节点，每个链表节点包含的内容包括但不限于：
APPID和视频ID；
总数据量和端口号列表；
视频开始时间和视频结束时间；
当前视频播放时刻；
视频可播放时长和视频已播放时长；
卡顿开始时间和卡顿结束时间；
当前视频码率和当前视频分辨率等。
在一种示例性实例中，可以将终端的数据压缩以文件传输协议SFTP(安全文件传输协议，Secure File Transfer Protocol)等方式上报到视频采集任务对应的分析单元。
本申请基于采用DPI技术对视频业务进行识别，相对于传统的深度流量检测(DFI，Deep Flow Inspection)技术，更加精细和准确地识别出了网络中的视频业务和应用，同时还实现了根据预先配置的规则对识别出的业务进行转发和处理。而且，通过本申请的识别单元，将无线网络和互联网技术有效地融合在了一起，并在无线网络侧增加计算、存储、处理等功能，通过开放无线网络与业务服务器之间的信息交互，有效降低了网络延迟，提高了无线链路利用率，将传统的无线基站升级为了智能化基站。
在一种示例性实例中，采集单元可以是基站。
本申请还提供一种基站，包括任一项采集单元所述的功能。
分析单元，用于对上报的数据进行分析和统计，并根据管理单元下发的视频采集任务中携带的参数，定期输出视频业务质量分析结果。
在一种示例性实例中，分析单元收到采集单元上报的数据，根据视频采集任务中携带的数据上报周期和任务时间，对指定时间内采集单元的视频业务数据进行分析，以获取所述视频业务质量分析结果。比如，可以从视频源质量评分、交互体验质量评分和观看体验质量评分等不同的维度对视频业务数据进行评估，以获取视频业务质量总评分作为所述视频业务质量分析结果。
在一种示例性实例中，视频源质量主要取决于视频源的清晰度、流畅度、保真度等因素，比如可以涵盖视频源的分辨率、帧率、码率、内容、编码和终端六个维度的指标，在本申请一种实施例中，使用对视频源的平均码率和分辨率的评分作为对视频业务质量的评分结果来表示视频源质量，评分标准设定的实施例可参见表2所示。
在一种示例性实例中，平均码率可以按照如公式(1)所示：
Figure PCTCN2020080899-appb-000005
公式(1)中，L k为当前码率下总的流量，单位为比特；T k为对应码率下数据的可播放时长，单位为秒；N为视频播放期间码率变化次数。
在一种示例性实例中，交互体验质量主要取决于系统对用户交互操作的响应速度，是指用户在视频业务使用过程中业务操作的便捷性和效率，在本申请一种实施例中，使用初始缓冲时长的评分为例来表示交互体验质量，即交互体验质量的评分结果就是初始缓冲时长的评分结果，评分标准设定的实施例可参见下文表3所示。
在一种示例性实例中，初始缓冲时长T1的计算方法可以包括：
假设用户视频播放请求时刻记录为T0，网络侧(如基站侧)检测用户终端成功接收时刻对应码率下总数据流量为L，视频可播放时长为T；
当期间无码率上报时，T＝L/默认码率；当期间有码率上报时，T＝L1/R1+L2/R2+…Ln/Rn，其中，Ln表示码率为Rn时总数据流量，n为大于1的整数，表示不同的码率、不同的总数据流量。
当视频可播放时长T>＝T min时，则记该时刻为播放时刻Tp；
则：初始缓冲时长T1＝Tp-T0。其中，如何对T1进行评分可以参考下文中的表5。
在一种示例性实例中，观看体验质量主要取决于视频在播放过程中的节目信号质量，即是否有出现视频图像不连续、图像出现异常等质量劣化的情况。在本申请一种实施例中，可 以以对卡顿恢复时长占比和卡顿频次两个指标进行评估来表示观看体验质量，评分标准设定的实施例可参见表3所示。
在一种示例性实例中，卡顿恢复时长占比可以按照如公式(2)所示：
Figure PCTCN2020080899-appb-000006
公式(2)中，Ts k表示每次卡顿恢复时长，单位为秒；N为视频播放期间卡顿次数；视频播放时长的单位秒。
在一种示例性实例中，卡顿频次可以按照如公式(3)所示：
Figure PCTCN2020080899-appb-000007
公式(3)表示每10分钟视频卡顿次数，其中，N为视频播放期间卡顿次数，视频播放时长单位换算为分钟。
对于上述涉及的评分的标准，可以参考视频业务的业界评分机制，设定实施例可以如下表4和表5所示。
Figure PCTCN2020080899-appb-000008
表4
表4为本申请一种视频源质量评分设置的实施例。
Figure PCTCN2020080899-appb-000009
表5
表5为本申请一种交互体验质量和观看体验质量评分设置的实施例。
需要说明的是，上述涉及的评分的标准，也可以根据视频业务的具体要求进行设置，具体设置方式并不用于限定本申请的保护范围。
这样，根据如视频源质量、交互体验质量和观看体验的评分结果，便可以统计出视频业务质量的总评分。
在一种示例性实例中，可以通过定期以邮件、或短信等形式将分析结果通知给无线运维人员。这样，无线运维人员便可以方便地根据分析结果，有针对性地对该基站的网络进行优化，比如，对视频业务质量差的基站进行问题指标逐个排查、定位故障、网络优化等，从而提升了用户视频体验。
在一种示例性实例中，分析单元可以是大数据服务器。
本申请提供的视频业务处理系统，基于DPI技术对视频业务进行识别，更加精细和准确地识别出了网络中的视频业务和应用；而且，通过本申请的识别单元，将无线网络和互联网技术有效地融合在了一起，为网络优化提供了依据，从而有效提升了用户对视频业务的体验。
本申请通过采集指定基站下用户的视频业务数据，包含如视频开始时间/结束时间、视频码率/分辨率、初始缓冲时长/卡顿起止时间等信息，并上报到数据分析中心，而数据分析中心通过对基站上报的数据进行智能分析，对指定时间段内该基站的视频业务质量进行实时有效评估，并定期将评估结果通知到运维人员，运维人员根据分析报告内容，实现了有针对性地对网络进行优化，从而有效提升了用户视频业务体验质量。
本申请实现了对视频业务的自动化运维，大大减少了无线运维人员的工作量，不用每次出现用户投诉的时候进行网络优化，而是将网络优化作为了一个持续的周期性的工作。总而言之，本申请视频业务处理系统具有高程度的自动化处理、运维人员干预少、用户长期体验好等的优势，在无线网络智能运维中发挥了巨大的作用。
图2为本申请视频业务处理方法的一种实施例的流程示意图，如图2所示，包括：
步骤200：基站接收到视频业务采集通知，检测是否存在视频业务采集任务。
在一种示例性实例中，视频业务采集任务来自于管理单元如EMS。
在一种示例性实例中，视频采集任务携带的参数包括以下任意组合：
任务时间，包括如视频采集开始时间和视频采集停止时间，进一步地，还可以包括：支持忙时和闲时的时间段设置等；
任务参数，包括如数据上报周期，小区内UE抽样数等；
分析信息，比如分析单元的名称、数据接收IP地址、上报数据的文件传输协议(FTP，File Transfer Protocol)信息；
采集对象，也就是视频采集任务采集哪些类型的数据，具体可参见表1。
在一种示例性实例中，视频业务采集通知来自于识别单元如MEC节点。
在一种示例性实例中，视频业务采集通知中携带有如：将符合预设规则的识别结果，以及对应的用户端口等信息。
步骤201：基站检测出存在视频业务采集任务，根据视频业务采集任务的参数进行采集，上报采集结果以用于分析得到视频业务质量分析结果。
在一种示例性实例中，本步骤可以包括：
根据视频APPID进行匹配，找到APPID相同的视频；根据视频ID进行识别，APPID和视频ID均相同的视频被认为是同一个视频；
针对每一个识别为有视频业务的终端IP建立一个链表节点；
按照视频采集任务携带的参数，采集识别为有视频业务的终端的视频业务过程中事件性上报的数据和周期性上报的数据，在识别为有视频业务的终端的视频结束时或者终端释放时，将该终端的数据上报，以用于分析得到视频业务质量分析结果。
在一种示例性实例中，针对每一个识别为有视频业务的终端IP建立一个链表节点，包括：
以终端的IP地址建立索引信息，即针对每一个识别为有视频业务的终端IP建立一个链表节点，IP地址节点信息中包括APPID链表信息，APPID中包含视频ID和相关统计链表信息，当识别出视频开始时建立链表节点，每个链表节点包含的内容包括但不限于：
APPID和视频ID；
总数据量和端口号列表；
视频开始时间和视频结束时间；
当前视频播放时刻；
视频可播放时长和视频已播放时长；
卡顿开始时间和卡顿结束时间；
当前视频码率和当前视频分辨率；等等。
在一种示例性实例中，所述上报采集结果，可以包括：
将终端的数据压缩以文件传输协议如SFTP等安全文件传输协议的方式上报。
本申请实施例还提供一种计算机可读存储介质，存储有计算机可执行指令，所述计算机可执行指令用于执行图2所示任一项所述的视频业务处理方法。
本申请实施例还提供一种实现视频业务处理的装置，包括处理器、存储器；其中，存储器上存储有可在处理器上运行的计算机程序：用于执行图2所示任一项所述的视频业务处理方法的步骤。
本申请实现了对视频业务的自动化运维，大大减少了无线运维人员的工作量，不用每次出现用户投诉的时候进行网络优化，而是将网络优化作为了一个持续的周期性的工作。总而言之，本申请视频业务处理系统具有高程度的自动化处理、运维人员干预少、用户长期体验好等的优势，在无线网络智能运维中发挥了巨大的作用。
图3为本申请视频业务处理方法的另一种实施例的流程示意图，如图2所示，包括：
步骤300：MEC采用DPI技术对网络中的数据报文进行检测。
在一种示例性实例中，DPI的报文识别基于DPI规则，DPI规则包括一级规则和二级规 则两大类。一级规则是L3/L4报文识别，入参为报文五元组；二级规则是L7报文识别，入参为报文的七层载荷信息。本申请采用DPI技术，不仅实现了对网络层(如IP地址/协议类型等)、传输层(如TCP/UDP端口号等)数据进行快速匹配，而且实现了对TCP/UDP的载荷部分进行深层分析，从而实现了精确协议识别和应用协议事件识别，并且支持了单包特征识别和基于流的多包有状态特征识别，这样大大提升了协议识别的准确度。
在一种示例性实例中，所述MEC采用DPI技术对网络中的数据报文进行检测，可以包括：
将所述数据报文即用户的下行业务流报文复制后进行实时检测，根据预先配置的检测策略，识别所述数据报文即用户的数据中是否包含有真正的视频业务数据，如果在所述数据报文中检测出视频特征码，确定所述数据报文包含有真正的视频业务数据，获取识别结果。在一种示例性实例中，识别结果可以包括但不限于如：视频提供商ID(APPID)、视频ID、带宽、分辨率等。
步骤301：当在数据报文中检测到真正的视频业务数据，通知与MEC关联的基站对视频业务进行采集。
在一种示例性实例中，本步骤可以包括：
将符合预设规则的识别结果，以及对应的用户端口等信息携带在视频业务采集通知中发送给与MEC关联的基站。
在一种示例性实例中，可以通过特殊的报文通知识别结果。
本申请提供的视频业务处理方法，基于DPI技术对视频业务进行识别，更加精细和准确地识别出了网络中的视频业务和应用；而且，通过本申请将无线网络和互联网技术有效地融合在了一起，为网络优化提供了依据，从而有效提升了用户对视频业务的体验。
本申请实施例还提供一种计算机可读存储介质，存储有计算机可执行指令，所述计算机可执行指令用于执行图3所示任一项所述的视频业务处理方法。
本申请实施例还提供一种实现视频业务处理的装置，包括处理器、存储器；其中，存储器上存储有可在处理器上运行的计算机程序：用于执行图3所示任一项所述的视频业务处理方法的步骤。
本申请提供的视频业务处理方法可以拓展应用在为用户推荐个性化内容的精准投放能 力的技术领域。比如，先对个性化定制的用户群体(如视频服务商的VIP用户或开启了视频订阅/热门视频推荐等定制功能的用户)，构建用户视频习惯模型，视频业务任务管理部分向基站下发采集用户收看时段、收看行为、内容偏好、收看市场、所在区域等相关指标的任务，基站传感器识别用户习惯模型，采集一段时间的视频数据，也就是说，通过本申请的管理单元对用户下发采集任务，采集的内容与上文会根据应用场景相应变化，采集的过程还是通过本申请的识别单元和采集单元的交互进行识别的；然后，基站向数据分析中心上报数据信令，大数据分析中心通过对这些视频数据进行智能分析后，即通过本申请的分析单元分析用户数据，识别出视频质量最高的网络平台，然后将统计数据反馈给视频服务商，以建立内容黏性，探索以视频内容品类为区分的社群化运营模式，通过对细分用户的群组化管理，识别出所有和此类视频相关的视频资源并综合分析出视频质量最高的网络平台。后期，当用户打开浏览器搜索相关内容时优先推荐高质量的网络平台，在使用任意网络平台时优先推荐相关视频资源，从而满足了用户的个性订阅，并根据用户习惯模型提升广告分发等商业模式价值。
以上所述，仅为本发明的较佳实例而已，并非用于限定本发明的保护范围。凡在本发明的精神和原则之内，所做的任何修改、等同替换、改进等，均应包含在本发明的保护范围之内。

Claims (19)

  1. 一种视频业务处理系统，包括：管理单元、识别单元、采集单元和分析单元；其中，
    管理单元，用于向需要进行视频业务质量评估的一个或一个以上采集单元下发视频采集任务；
    识别单元，用于采用深度包检测DPI技术对网络中的数据报文进行检测，当在数据报文中检测到真正的视频业务数据，通知与识别单元关联的采集单元；
    采集单元，用于接收到来自识别单元的视频业务采集通知，检测出自身存在视频业务采集任务，根据视频业务采集任务的参数进行采集，将采集结果上报给分析单元；
    分析单元，用于对上报的数据进行分析和统计，并根据配置的模型，定期输出视频业务质量分析结果。
  2. 根据权利要求1所述的视频业务处理系统，其中，所述识别单元包括：分流服务模块、DPI模块，其中，
    分流服务模块，用于将所述数据报文复制后发送到DPI模块；接收DPI模块反馈的识别结果，将符合预设规则的识别结果，以及对应的用户端口携带在视频业务采集通知中发送给所述采集单元；
    DPI模块，用于对接收到的所述数据报文进行实时检测，根据预先配置的检测策略识别所述数据报文中是否包含有真正的视频业务数据，如果在所述数据报文中检测出视频特征码，确定所述数据报文包含有真正的视频业务数据，将识别结果反馈给分流服务模块。
  3. 根据权利要求1所述的视频业务处理系统，其中，所述采集单元具体用于：
    接收到来自所述识别单元的视频业务采集通知，根据视频提供商ID进行匹配，找到APPID相同的视频；再根据视频ID进行识别，APPID和视频ID均相同的视频被认为是同一个视频；
    针对每一个识别为有视频业务的终端IP建立一个链表节点；
    按照所述视频采集任务携带的参数，采集识别为有视频业务的终端的视频业务过程中事件性上报的数据和周期性上报的数据，在识别为有视频业务的终端的视频结束时或者终端释放时，将该终端的数据上报到视频采集任务中配置的所述分析单元。
  4. 根据权利要求1所述的视频业务处理系统，其中，所述分析单元具体用于：
    收到所述采集单元上报的数据，根据所述视频采集任务中携带的数据上报周期和任务时间，对指定时间内所述采集单元的视频业务数据进行分析，以获取所述视频业务质量分析结果。
  5. 根据权利要求4所述的视频业务处理系统，其中，所述分析单元中的对指定时间内所述采集单元的视频业务数据进行分析，以获取所述视频业务质量分析结果，包括：
    根据所述视频业务数据的视频源质量评分、交互体验质量评分和观看体验质量评分，获取所述视频业务质量总评分作为所述视频业务质量分析结果。
  6. 根据权利要求1～4任一项所述的视频业务处理系统，其中，所述管理单元还用于：停止已下发的所述视频采集任务。
  7. 根据权利要求1～4任一项所述的视频业务处理系统，其中，所述管理单元还用于：变更所述视频任务的参数。
  8. 根据权利要求1～4任一项所述的视频业务处理系统，其中，一个所述识别单元与一个所述采集单元对应；或者，一个所述识别单元作为网络中的汇聚节点，对应一个或一个以上所述采集单元。
  9. 根据权利要求1～4任一项所述的视频业务处理系统，其中，所述管理单元为网元管理系统EMS，所述识别单元为移动边缘计算MEC，所述采集单元为基站，所述分析单元为大数据服务器。
  10. 一种视频业务处理方法，包括：
    基站接收到视频业务采集通知，检测是否存在视频业务采集任务；
    基站检测出存在视频业务采集任务，根据视频业务采集任务的参数进行采集，上报采集结果以用于分析得到视频业务质量分析结果。
  11. 根据权利要求10所述的视频业务处理方法，其中，所述根据视频业务采集任务的参数进行采集，包括：
    根据视频APPID进行匹配，找到APPID相同的视频；根据视频ID进行识别，APPID和视频ID均相同的视频被认为是同一个视频；
    针对每一个识别为有视频业务的终端IP建立一个链表节点；
    按照视频采集任务携带的参数，采集识别为有视频业务的终端的视频业务过程中事件性上报的数据和周期性上报的数据，在识别为有视频业务的终端的视频结束时或者终端释放时，将该终端的数据上报，以用于分析得到视频业务质量分析结果。
  12. 根据权利要求10所述的视频业务处理方法，其中，所述视频采集任务的参数中包括的采集对象；所述采集对象包括以下任意组合：
    上报方式为事件上报的视频初始缓冲阶段数据；
    上报方式为周期上报的周期型视频数据信息采集；
    上报方式为事件上报的视频卡顿起止时间；
    上报方式为事件上报的视频结束时间；
    上报方式为周期上报的视频采集周期。
  13. 一种计算机可读存储介质，存储有计算机可执行指令，所述计算机可执行指令用于执行权利要求10～权利要求12任一项所述的视频业务处理方法。
  14. 一种实现视频业务处理的装置，包括处理器、存储器；其中，存储器上存储有可在处理器上运行的计算机程序：用于执行权利要求10～权利要求12任一项所述的视频业务处理方法的步骤。
  15. 一种视频业务处理方法，包括：
    MEC采用DPI技术对网络中的数据报文进行检测；
    当在数据报文中检测到真正的视频业务数据，通知与MEC关联的基站对视频业务进行采集。
  16. 根据权利要求15所述的视频业务处理方法，其中，所述采用DPI技术对网络中的数据报文进行检测，包括：
    将所述数据报文复制后进行实时检测，根据预先配置的检测策略识别所述数据报文中是否包含有真正的视频业务数据，如果在所述数据报文中检测出视频特征码，确定所述数据报文包含有真正的视频业务数据，获取识别结果。
  17. 根据权利要求15所述的视频业务处理方法，其中，所述通知与MEC关联的基站对视频业务进行采集，包括：
    将符合预设规则的所述识别结果，以及对应的用户端口信息携带在所述视频业务采集通知中发送给与所述MEC关联的基站。
  18. 一种计算机可读存储介质，存储有计算机可执行指令，所述计算机可执行指令用于执行权利要求15～权利要求17任一项所述的视频业务处理方法。
  19. 一种实现视频业务处理的装置，包括处理器、存储器；其中，存储器上存储有可在处理器上运行的计算机程序：用于执行权利要求15～权利要求17任一项所述的视频业务处理方法的步骤。
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