Video quality difference analysis method and device based on user perception and electronic equipment

Abstract

The invention provides a video poor quality analysis method and device based on user perception and electronic equipment, and belongs to the technical field of data processing, and the method comprises the steps: analyzing software development kit data based on the slice granularity of video transmission, and obtaining perception abnormal event data of a poor quality video; determining a perceived degraded user based on the perceived abnormal event data; delimiting a target poor-quality object of the poor-quality video based on the sensing abnormal event data of the sensing degradation user and the network topology data of the sensing degradation user; the network topology data for perceiving degraded users is determined from pre-associated home-wide network resource data. According to the method, the target poor-quality object of the poor-quality video is jointly delimited by comprehensively considering the sensing abnormal event data of the upper layer and the network topology data of the bottom layer, the reduction capability of poor-quality analysis on user perception is improved, and more comprehensive and more accurate video poor-quality analysis highly matched with the user perception is realized.

Description

Technical Field

[0001] The present invention relates to the field of data processing technology, and in particular to a method, device and electronic equipment for analyzing video quality differences based on user perception. Background Art

[0002] In order to ensure the user experience of real-time video streaming services, operators providing services need to have quality analysis capabilities such as monitoring video services and product quality, and demarcating and locating poor-quality videos.

[0003] At present, the method for analyzing video quality is mainly based on the quality analysis method of Deep Packet Inspection (DPI) deployed at the provincial network exit. By obtaining and analyzing the underlying DPI data of the network exit deployed in a certain geographical range, the network topology information and measurement information involved in the video service can be obtained to realize the analysis of the service quality of video services and products.

[0004] However, the DPI-based quality difference analysis method is unable to directly collect the characteristics of the video service traffic of video users, resulting in the detection mechanism that uses the underlying interface data packets to restore the video stream download process. It is prone to the problem of mismatch between user perception and DPI quality difference analysis results, and the ability to restore user perception is insufficient. Summary of the invention

[0005] The present invention provides a video quality difference analysis method, device and electronic device based on user perception, so as to solve the defect of the quality difference analysis method based on DPI in the prior art that the user perception does not match the DPI quality difference analysis result, and realize a video quality difference analysis that is highly matched with the user perception, more comprehensive and more accurate.

[0006] The present invention provides a video quality analysis method based on user perception, comprising: Parsing SDK data based on slice granularity of video transmission to obtain perceived abnormal event data of poor quality video; Determining a user with degraded perception based on the perception abnormal event data; Based on the perception abnormal event data of the perception-degraded user and the network topology data of the perception-degraded user, the target poor-quality object of the poor-quality video is delimited; the network topology data of the perception-degraded user is determined from pre-associated home broadband network resource data.

[0007] According to a method for analyzing video quality difference based on user perception provided by the present invention, the perceived abnormal event data includes event timestamp, public network protocol address data and transmission control protocol port data, and the method of determining a perceived degraded user based on the perceived abnormal event data includes: Determining a private network protocol address of a video user from an associated network address translation log based on the event timestamp, the public network protocol address data, and the transmission control protocol port data; Based on the private network protocol address, determining the user broadband account of the video user from the associated remote identity authentication dial-up user service log; Summarize the perceived abnormal event data according to the user broadband account to obtain the cumulative number of abnormal events of the video user; Based on the accumulated number of abnormal events, users with perception degradation are determined from all video users.

[0008] According to a method for analyzing poor video quality based on user perception provided by the present invention, based on the perceived abnormal event data of the perceived degraded user and the network topology data of the perceived degraded user, the target poor quality object of the poor quality video is delimited, including: Classifying the perception abnormal event data of the perception-degraded user and the network topology data of the perception-degraded user to obtain user-level data, playback-level data, and slice-level data of the perception-degraded user; generating an end-to-end service path for the perception-degraded user based on the user-level data, the playback-level data, and the slice-level data; Based on the end-to-end service path, a target poor quality object of the poor quality video is delimited.

[0009] According to a method for analyzing poor video quality based on user perception provided by the present invention, the method of defining a target poor quality object of the poor quality video based on the end-to-end service path includes: Matching the path data of the end-to-end service path with the list data of the first quality difference list to obtain a first quality difference matching object; the first quality difference list is determined based on the perceived abnormality index of the software development kit data of all video users; Matching the path data of the end-to-end service path with the list data of the second quality difference list to obtain a second quality difference matching object; the second quality difference list is determined based on the operation and maintenance performance indicators of the home broadband network devices of all video users; A target quality difference object of the quality difference video is defined based on the first quality difference matching object and the second quality difference matching object.

[0010] According to a method for analyzing poor video quality based on user perception provided by the present invention, after defining the target poor quality object of the poor quality video based on the abnormal event data of the perceived degradation user and the network topology information, the method further includes: Based on the target quality-poor objects, determine the quality-poor problems and their responsible departments; Based on the target poor quality object and the poor quality problem, generating a poor quality problem work order; The poor quality problem work order is pushed to the work order receiving interface of the responsible department so that the responsible department can handle the poor quality problem.

[0011] According to a method for analyzing video quality difference based on user perception provided by the present invention, the network topology data includes an optical network unit identifier, a passive optical network port identifier, an optical line terminal identifier and a broadband access server identifier.

[0012] The present invention also provides a video quality analysis device based on user perception, comprising: A perception anomaly data acquisition module is used to parse the software development kit data based on the slice granularity of video transmission to obtain the perception anomaly event data of poor quality video; A perception-degraded user determination module, configured to determine a perception-degraded user based on the perception abnormal event data; The target poor quality object determination module is used to define the target poor quality object of the poor quality video based on the perceived abnormal event data of the perceptually degraded user and the network topology data of the perceptually degraded user; the network topology data of the perceptually degraded user is determined from pre-associated home broadband network resource data.

[0013] The present invention also provides an electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, the video quality difference analysis method based on user perception as described above is implemented.

[0014] The present invention also provides a non-transitory computer-readable storage medium having a computer program stored thereon, and when the computer program is executed by a processor, the video quality difference analysis method based on user perception as described above is implemented.

[0015] The present invention also provides a computer program product, including a computer program, wherein when the computer program is executed by a processor, the computer program implements any of the above-mentioned methods for analyzing video quality differences based on user perception.

[0016] The video quality difference analysis method, device and electronic device based on user perception provided by the present invention parse the SDK data of the upper application layer with the sliced ​​video file as the granularity to obtain the perception abnormal event data that is highly consistent with the user perception, thereby overcoming the problem that the user perception and the DPI quality difference analysis result do not match when the quality difference analysis is performed based on the underlying data such as DPI data, realizing a quality difference analysis method that matches the user perception, and improving the restoration ability of the quality difference analysis for the user perception; further determining the network topology data of the perceived degraded user from the pre-associated home broadband network resource data, and comprehensively considering the upper-layer perception abnormal event data and the underlying network topology data to jointly define the target quality difference object of the quality difference video, accurately locating the perceived degradation caused by network reasons, avoiding the situation that the quality difference analysis based on the upper-layer SDK data alone cannot define the abnormality of the underlying network device, and starting from the upper-layer user perception abnormal event, and then defining the underlying cross-domain network problem in an associated analysis method, realizing a video quality difference analysis that is highly matched with the user perception, more comprehensive and more accurate. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] In order to more clearly illustrate the technical solutions in the present invention or the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0018] Figure 1 It is a flow chart of the video quality difference analysis method based on user perception provided by the present invention.

[0019] Figure 2 It is a structural schematic diagram of a video quality difference analysis device based on user perception provided by the present invention.

[0020] Figure 3 It is a structural schematic diagram of the electronic device provided by the present invention. DETAILED DESCRIPTION

[0021] In order to make the purpose, technical solution and advantages of the present invention clearer, the technical solution of the present invention will be clearly and completely described below in conjunction with the drawings of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0022] It should be noted that, in the description of the present invention, the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also includes other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. For those of ordinary skill in the art, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0023] The terms "first", "second", etc. in the present invention are used to distinguish similar objects, and are not used to describe a specific order or sequence. It should be understood that the terms used in this way can be interchangeable under appropriate circumstances, so that the embodiments of the present invention can be implemented in an order other than those illustrated or described herein, and the objects distinguished by "first", "second", etc. are generally of the same type, and the number of objects is not limited. For example, the first object can be one or more.

[0024] Combine the following Figure 1-Figure 3 The present invention describes a method, device and electronic device for analyzing video quality difference based on user perception.

[0025] Video live streaming services are usually implemented by slicing and encoding videos in chronological order, and continuously downloading, buffering, and playing the sliced ​​video files. Video file resources are usually distributed to service nodes close to users of video live streaming services (hereinafter referred to as video users) through content delivery networks (CDNs) to save backbone transmission resources and reduce the transmission delay of video user file downloads.

[0026] During the downloading, buffering and playback of video files, problems such as program source freezes, HTTP (Hypertext Transfer Protocol) server performance degradation, transmission damage (such as network delay, jitter, packet loss, etc.), terminal device performance, client software functions, etc. may cause the video player to under-buffer. From the perspective of video users, this is reflected in slow playback startup, freezes during playback, etc., resulting in poor video user experience.

[0027] In order to ensure the user experience of video users regarding video services and products, operators providing services and products need to have quality analysis capabilities such as quality monitoring of video services and products, identification of user-perceived degradation, and location and demarcation of quality differences.

[0028] At present, the method for operators to analyze video quality is mainly based on the quality analysis method of Deep Packet Inspection (DPI) deployed at the provincial network exit. The extended data record (xDR) of the HTTP protocol and the xDR data synthesized by the video service are obtained for analysis, thereby obtaining network topology information such as users, terminals, cells, core network elements, servers, domain names, video content uniform resource locators (URL) involved in the video service, as well as measurement information such as success and failure, transmission delay, and transmission rate, thereby realizing the analysis of the service quality of video services and products.

[0029] Furthermore, when operators widely use DPI technology to split and mirror the service traffic on the network interface, collect original bit streams, parse protocols, generate xDR data, record and restore the detailed interaction process of various services, due to the huge Internet traffic, operators can usually only collect Internet traffic within a certain geographical range.

[0030] On this basis, the quality difference analysis method based on DPI has the problem of not supporting the perception analysis of video users accessing home broadband. On the one hand, based on the characteristics that video users will directly access CDN nodes within a certain geographical range, the DPI deployed at the network exit of the certain geographical range cannot directly collect the video service traffic of video users, and naturally cannot support the perception analysis of video users. On the other hand, DPI restores the HTTP interaction process from the data packets of the underlying interface, and further restores the detection mechanism of the video stream download process, which is easy to cause the problem that the user perception and the DPI quality difference analysis results do not match, that is, the ability to restore user perception is insufficient. For example, transmission jitter will cause the transmission delay of individual video slices to be too large. If the data packet arrives later than the expected time, it will be discarded by the video player, which is the packet loss caused by jitter. Under the same jitter situation, the number of packet losses will change when the buffer size is adjusted. Since DPI cannot obtain the video service traffic directly collected from video users, it is impossible to know the dynamic adjustment changes of the client buffer, and it is difficult to accurately determine the occurrence of jamming. The quality difference analysis results obtained will be different from the jamming perceived by users.

[0031] In view of this, the present invention provides a method, device and electronic device for analyzing video quality difference based on user perception to solve at least one of the above technical problems.

[0032] Figure 1 FIG. 1 is a flow chart of a method for analyzing video quality difference based on user perception provided by the present invention. Figure 1 As shown, the video quality analysis method based on user perception includes but is not limited to steps 101 to 103.

[0033] It should be noted that the executor of the user perception-based video quality analysis method provided by the present invention is the corresponding user perception-based video quality analysis device, which can specifically be a server, computer equipment, such as a mobile phone, tablet computer, laptop computer, PDA, vehicle-mounted electronic equipment, wearable device, Ultra-Mobile Personal Computer (UMPC), netbook or Personal Digital Assistant (PDA), etc.

[0034] Step 101: parse the Software Development Kit (SDK) data based on the slice granularity of the video transmission to obtain the perceived abnormal event data of the poor quality video.

[0035] SDK data is obtained through the built-in SDK software in the video application client. The video application client can collect and report various events through the built-in SDK software. For example, it can collect and report user behavior events such as play / stop, drag and drop, and switch bitrate, as well as report business quality events such as freeze and other abnormal events.

[0036] Poor-quality videos refer to video files that are below expectations or standards in terms of performance indicators, such as video files with quality issues such as delays and packet loss. Poor-quality videos are often perceived by users in the form of video playback failures, excessive first broadcast delays, and video slicing freezes.

[0037] Specifically, the SDK software built into the video application client is used to obtain the SDK data of the video service accessed by the home broadband network when transmitting video files, and the SDK data is parsed according to the slice granularity of the video transmission to obtain the perceived abnormal event data of poor quality video after slicing, such as playback failure, excessive first broadcast delay, and slice freeze.

[0038] The perceived abnormal event data includes but is not limited to at least one of event timestamp, abnormal event data, video user data, user terminal data, content network data, content resource data, associated data, and the like.

[0039] Abnormal event data includes but is not limited to abnormal types (such as freeze, playback failure, first broadcast delay, etc.), abnormal details (such as freeze duration, first broadcast time, etc.), and other data.

[0040] Video user data includes but is not limited to public network protocol address (public network IP) data, Transmission Control Protocol (TCP) port data and other data.

[0041] User terminal data includes but is not limited to terminal model, terminal operating system version, video playback software version and other data.

[0042] Content network data includes but is not limited to domain names, CDN node IP addresses and other data.

[0043] Content resource data includes but is not limited to program type (such as live broadcast, on-demand, etc.), program name and other data.

[0044] Related data includes but is not limited to data such as playback account.

[0045] It is understandable that any video service information that can be obtained through the SDK software built into the video application client and form SDK data can be obtained by parsing the SDK data based on the slice granularity of the video transmission.

[0046] Step 102: Determine users with degraded perception based on the perception abnormality event data.

[0047] Specifically, when SDK data is collected and reported using the SDK software built into the video application client, the user terminal data, video user data, and associated data in the perception abnormal event data all include certain video user identity information. By performing certain processing steps on these related perception abnormal event data containing video user identity information, each video user who has experienced a perception abnormal event can be obtained.

[0048] According to the granularity of video users, various abnormal events in the perceived abnormal event data are summarized to determine the perceived abnormal event data related to each video user, thereby determining and identifying the users with perceived degradation among the video users.

[0049] For example, after summarizing various abnormal events in the perceived abnormal event data, the cumulative number of abnormal events for each video user is determined, and the video users whose cumulative number of abnormal events is greater than a preset threshold are determined as perceptually degraded users; or, the video users are sorted in descending order according to the cumulative number of abnormal events, and a preset number of video users ranked first are determined as perceptually degraded users.

[0050] For another example, after summarizing various abnormal events in the perceived abnormal event data, determine the abnormal type and the corresponding cumulative number of times in the perceived abnormal event data related to each video user, assign different weights to different abnormal types, and determine the abnormal comprehensive index value of each video user based on the weighted sum of the abnormal type weight and the corresponding cumulative number. Determine the video user whose abnormal comprehensive index value is greater than a preset threshold as a user with perceived degradation; or, sort the video users in descending order of the abnormal comprehensive index value, and determine the preset number of video users ranked first as users with perceived degradation.

[0051] Step 103: Delimiting a target poor-quality object of the poor-quality video based on the perceptual abnormal event data of the perceptually degraded user and the network topology data of the perceptually degraded user.

[0052] The network topology data of the perceived degradation user is determined from pre-associated home broadband network resource data.

[0053] Specifically, after the perception-degraded user is determined, the network topology data of the perception-degraded user is acquired from pre-associated home broadband network resource data according to user information (such as broadband account information, etc.) of the perception-degraded user.

[0054] Further, based on the perceptual abnormal event data of perceptually degraded users obtained through SDK data and combined with the network topology data of perceptually degraded users, the perceptually degraded users and poor quality videos are analyzed and processed according to certain steps to delimit several target poor quality objects of poor quality videos.

[0055] It is understandable that the target poor quality object can be an object related to the perceived abnormal event data (such as a terminal of a certain model, a terminal operating system of a certain version, a video playback software of a certain version, etc.), or an object related to the network topology data.

[0056] It is understandable that the pre-associated home broadband network resource data pre-stores the network topology data of all video users, and thus the network topology data of some of the users with perception degradation among all video users can be obtained therefrom.

[0057] As an optional embodiment, the network topology data includes an optical network unit (Optical Network Unit, ONU) identifier, a passive optical network (Passive Optical Network, PON) port identifier, an optical line terminal (Optical Line Terminal, OLT) identifier and a broadband access server (Broadband Remote Access Server, BRAS) identifier.

[0058] Therefore, when the delimited target quality difference object is related to the network topology data, the target quality difference object may be a certain ONU device, PON port, OTL device or BRAS device, etc. corresponding to each network device identifier.

[0059] The video quality difference analysis method based on user perception provided by the present invention obtains perception abnormal event data that is highly consistent with user perception by parsing SDK data at the upper application layer with sliced ​​video files as the granularity, thereby overcoming the problem of mismatch between user perception and DPI quality difference analysis results when relying on underlying data such as DPI data for quality difference analysis, realizing a quality difference analysis method that matches user perception, and improving the ability of quality difference analysis to restore user perception; further determining network topology data of users with perception degradation from pre-associated home broadband network resource data, and comprehensively considering the upper-layer perception abnormal event data and the underlying network topology data to jointly define the target quality difference object of the quality difference video, accurately locating the perception degradation caused by network reasons, avoiding the situation where the quality difference analysis based on the upper-layer SDK data alone cannot define the abnormality of the underlying network device, and starting from the upper-layer user perception abnormal event, and then defining the underlying cross-domain network problem in an associated analysis method, realizing a video quality difference analysis that is highly matched with user perception, more comprehensive, and more accurate.

[0060] Based on the above embodiment, as an optional embodiment, the perception abnormal event data includes an event timestamp, public network protocol address data, and transmission control protocol port data, and determining the perception-degraded user based on the perception abnormal event data includes: Based on the event timestamp, the public IP data and the TCP port data, determining the private network protocol address (private IP) of the video user from the associated Network Address Translation (NAT) log; Based on the private network IP, determining the user broadband account of the video user from an associated Remote Authentication Dial-In User Service (RADIUS) log; Summarize the perceived abnormal event data according to the user broadband account to obtain the cumulative number of abnormal events of the video user; Based on the accumulated number of abnormal events, users with perception degradation are determined from all video users.

[0061] NAT logs record the data traffic and address translation activities between private network devices and external networks. Multiple private network devices can share a public IP.

[0062] RADIUS logs record the authentication, authorization, and accounting activities related to the RADIUS server. They can be used to centrally manage user authentication and authorization. The user broadband account of the video user can be obtained through the RADIUS log.

[0063] Specifically, the abnormal event data obtained by parsing the SDK data includes the event timestamp of the abnormal event, the public IP data and TCP port data of the video user. According to the event timestamp, public IP data and TCP port data, the private IP of the video user can be matched and determined from the associated NAT log. According to the private IP of the video user, the user broadband account of the video user is obtained from the associated home broadband access RADIUS log.

[0064] Generally speaking, the data of abnormal event perception includes more than two abnormal events, and the video user of each abnormal event is uncertain, while the video user and the user broadband account correspond one to one. Repeat the steps of determining the user broadband account of the video user from the associated network logs (i.e., NAT logs and RADIUS logs) based on the event timestamp, the public network IP data and TCP port data of the video user, and then determine the user broadband account of the video user of each abnormal event.

[0065] The perception abnormal event data is further summarized according to different user broadband accounts to obtain the cumulative number of abnormal events for each video user, and the perception-degraded users are determined from all video users based on the cumulative number of abnormal events.

[0066] For example, the video users are sorted in descending order according to the cumulative number of abnormal events, and a preset number of video users ranked first are determined as users with perception degradation.

[0067] For another example, a video user whose cumulative number of abnormal events is greater than a preset threshold is determined as a user with degraded perception.

[0068] The video quality difference analysis method based on user perception provided by the present invention determines the perception-degraded users by utilizing the upper-layer SDK data and combining network logs such as NAT logs and RADIUS logs. It can accurately identify the perception-degraded users who perceive abnormal video playback, which helps to improve the ability of quality difference analysis to restore user perception.

[0069] Based on the above embodiment, as an optional embodiment, the step of defining the target poor-quality object of the poor-quality video based on the perceptual abnormal event data of the perceptually degraded user and the network topology data of the perceptually degraded user includes: Classifying the perception abnormal event data of the perception-degraded user and the network topology data of the perception-degraded user to obtain user-level data, playback-level data, and slice-level data of the perception-degraded user; generating an end-to-end service path for the perception-degraded user based on the user-level data, the playback-level data, and the slice-level data; Based on the end-to-end service path, a target poor quality object of the poor quality video is delimited.

[0070] Specifically, all or part of the perception abnormal event data of the perception-degraded users and the network topology data of the perception-degraded users are integrated, and then the integrated data are classified from three dimensions: user level, playback level, and slice level to obtain user-level data, playback-level data, and slice-level data of the perception-degraded users.

[0071] For example, abnormal event data, user terminal data, content network data and content resource data are obtained from the perceived abnormal event data of the perceived degraded user, and the network topology data of the perceived degraded user is integrated and classified to obtain the path data of the perceived degraded user, and the path data includes user-level data, playback-level data and slice-level data. Among them, user-level data includes abnormal event data, user terminal data and network topology data, which can be used to characterize the access network type, broadband account, number of perceived abnormal playbacks, abnormal type, etc. of the perceived degraded user; playback-level data includes content resource data and abnormal event data; slice-level data includes content network data and abnormal event data.

[0072] By including abnormal event data in user-level data, playback-level data, and slice-level data, it is possible to ensure consistency of data on the end-to-end service path.

[0073] Further, based on the classified user-level data, playback-level data, and slice-level data, an end-to-end service path of the perceptually degraded user is generated, and then the target poor-quality object of the poor-quality video is defined.

[0074] The video quality difference analysis method based on user perception provided by the present invention generates an end-to-end service path for each perceived degraded user by integrating the perceived abnormal event data and the underlying network topology data obtained based on the upper-layer SDK data, and classifying them in three dimensions: user level, playback level and slice level, so as to ultimately define the target quality difference object, and to start from the upper-layer user perceived abnormal events, and then define the associated path analysis of the underlying cross-domain network problems, thereby achieving a more comprehensive quality difference video analysis that is highly matched with user perception.

[0075] In addition, product interaction and user perception assurance of video services often involve many entities. By dividing the integrated data into user-level data, playback-level data, and slice-level data, for the long chain of end-to-end service paths that include multiple domains such as users, terminals, home broadband networks, content networks, and program resources, the processing entities of target quality-poor objects can be accurately defined based on the end-to-end service paths.

[0076] If the target quality-poor object belongs to the path node corresponding to user-level data, the sub-operator that directly provides home broadband network services can process the target quality-poor object; if the target quality-poor object belongs to the path node corresponding to playback-level data, the third-party provider that provides video content can process the target quality-poor object; if the target quality-poor object belongs to the path node corresponding to slice-level data, it can be processed by the general operator that builds the CDN network.

[0077] Based on the above embodiment, as an optional embodiment, the step of defining the target quality difference object of the quality difference video based on the end-to-end service path includes: Matching the path data of the end-to-end service path with the list data of the first quality difference list to obtain a first quality difference matching object; the first quality difference list is determined based on the perceived abnormality index of the software development kit data of all video users; Matching the path data of the end-to-end service path with the list data of the second quality difference list to obtain a second quality difference matching object; the second quality difference list is determined based on the operation and maintenance performance indicators of the home broadband network devices of all video users; A target quality difference object of the quality difference video is defined based on the first quality difference matching object and the second quality difference matching object.

[0078] Among them, perception abnormality indicators include but are not limited to the proportion of freezing duration, playback success rate, average first broadcast delay and other indicators; operation and maintenance performance indicators are also called OMC performance indicators, including but not limited to bandwidth utilization rate of home broadband network equipment, low light rate and other indicators.

[0079] Specifically, multi-dimensional statistics are performed on the SDK data of all video users in advance based on the perceived abnormal indicators and their corresponding indicator thresholds, the proportion of abnormal events, and the indicator impact, and list data such as poor quality terminal models, poor quality operating system versions, poor quality playback software versions, poor quality domain names, poor quality CDN node IP addresses, and poor quality programs are obtained, thereby constructing the first poor quality list.

[0080] Similarly, the network topology data of the home broadband network devices of all video users are counted in advance according to the operation and maintenance performance indicators, and the list data of poor quality ONU devices, poor quality PON devices, poor quality OLT interfaces, poor quality BRAS devices, etc. are obtained to construct a second poor quality list.

[0081] When defining the target quality difference object of the quality difference video, the path data of the end-to-end service path of the perceived degradation user is matched with the list data of the first quality difference list and the list data of the second quality difference list, respectively, to obtain the first quality difference matching object and the second quality difference matching object. It can be understood that the first quality difference matching object is a certain model of quality difference terminal, a certain version of quality difference operating system, etc. in the first quality difference list, and the second quality difference matching object is a certain identified quality difference ONU device, quality difference PON device, etc. in the second quality difference list.

[0082] Finally, a target quality difference object of the quality difference video is jointly defined according to the first quality difference matching object and the second quality difference matching object. The target quality difference object may be one or more.

[0083] The video quality analysis method based on user perception provided by the present invention can identify the specific quality objects that cause user perception degradation in the complete chain of terminal, home broadband network, content network or content resource domain by associating the end-to-end service path of the perceived abnormal event to the first quality difference list and the second quality difference list, which is helpful to comprehensively analyze the quality difference video.

[0084] Based on the above embodiment, as an optional embodiment, after defining the target poor quality object of the poor quality video based on the perception abnormal event data of the perception-degraded user and the network topology information, generating and pushing a poor quality problem work order based on the target poor quality object includes: Based on the target quality-poor objects, determine the quality-poor problems and their responsible departments; Based on the target poor quality object and the poor quality problem, generating the poor quality problem work order; The poor quality problem work order is pushed to the work order receiving interface of the responsible department so that the responsible department can handle the poor quality problem.

[0085] Specifically, after the target poor quality object is defined, the target poor quality object is analyzed to determine the poor quality problem of the target poor quality object, and the responsible department responsible for handling the target poor quality object and the poor quality problem is retrieved from the database. A poor quality problem work order including the target poor quality object and the poor quality problem is further generated, and the poor quality problem work order is pushed to the work order receiving interface of the responsible department, and the responsible department handles the poor quality problem on the poor quality problem work order.

[0086] For example, when the target poor quality object is an OLT port identified in the network topology data, the poor quality problem of the OLT port is found to be excessive bandwidth utilization based on the poor quality performance indicator analysis, and the responsible department for the poor quality problem is determined from the database to be the relevant business department of the sub-operator. Therefore, a poor quality problem work order is generated and pushed to the work order receiving interface of the relevant business department.

[0087] For another example, when the target poor quality object is a certain version of video playback software, analysis shows that the video playback software has a playback vulnerability, and the department responsible for the poor quality problem is determined from the database to be the third-party provider of the video playback software. Therefore, a poor quality problem work order is generated and pushed to the work order receiving interface of the third-party provider.

[0088] In one embodiment, after defining the target quality difference object of the quality difference video based on the end-to-end service path, the target quality difference object is always one of the user-level data, playback-level data and slice-level data in the path data of the end-to-end service path. At this time, when determining the responsible department of the target quality difference object, it is only necessary to determine which data of the three dimensional data of user-level data, playback-level data and slice-level data the target quality difference object is related to, and it can be determined that the target quality difference object is the sub-operator, third-party provider and main operator corresponding to the three dimensional data.

[0089] The video quality analysis method based on user perception provided by the present invention can notify the responsible department of the quality problem and object in time by automatically generating a quality problem work order after defining the target quality problem object and pushing it to the work order receiving interface of the responsible department, so as to facilitate the responsible department to optimize in time.

[0090] Compared with the analysis and demarcation scheme based on DPI data, due to the limitation that it cannot collect home broadband access traffic and the characteristics of video services distributed through CDN, it can only support quality analysis and problem demarcation of mobile network access services, but cannot support quality analysis and problem demarcation of home broadband access video services; or, compared with the analysis scheme based only on SDK data, it can only identify the results of poor quality of upper-layer applications, but cannot demarcate the specific underlying network devices and locate the root cause of the problem. The video quality analysis method based on user perception provided by the present invention as a whole comprehensively considers the upper-layer perception abnormal event data and the underlying network topology data for correlation analysis, thereby realizing the discovery of user-perceived abnormal events based on the upper layer. It also comprehensively identifies the root causes of poor video quality problems from top to bottom, achieving a more comprehensive and accurate video quality analysis that is highly matched with user perception; further, the demarcation analysis results based on the end-to-end service path can support cross-subject collaborative user perception optimization of sub-operators, third-party providers and main operators; and the first quality difference list and the second quality difference list are quality difference lists about terminals, content networks and content resources, which are not only suitable for demarcation analysis of home broadband access, but also for demarcation analysis of mobile network access with common problems, and can be used to demarcate problems of mobile network wireless cells, transmission networks and core network elements, to achieve perception analysis of fixed-mobile convergence, and ensure the overall perception of users under different network access conditions.

[0091] Figure 2 is a schematic diagram of the structure of the video quality analysis device based on user perception provided by the present invention. Figure 2 As shown, the video quality difference analysis device based on user perception includes but is not limited to a perception abnormality data acquisition module 201 , a perception degradation user determination module 202 and a target quality difference object determination module 203 .

[0092] The perception anomaly data acquisition module 201 is used to parse the software development kit data based on the slice granularity of the video transmission to obtain the perception anomaly event data of the poor quality video.

[0093] The perception-degraded user determination module 202 is used to determine the perception-degraded users based on the perception abnormality event data.

[0094] The target poor quality object determination module 203 is used to define the target poor quality object of the poor quality video based on the perceptual abnormal event data of the perceptually degraded user and the network topology data of the perceptually degraded user; the network topology data of the perceptually degraded user is determined from pre-associated home broadband network resource data.

[0095] It should be noted that the video quality difference analysis device based on user perception provided by the present invention can execute the video quality difference analysis method based on user perception described in any of the above embodiments during specific operation, which will not be elaborated in this embodiment.

[0096] The present invention provides a structural schematic diagram of a video quality difference analysis device based on user perception. By parsing the SDK data of the upper application layer with the sliced ​​video file as the granularity, the perception abnormal event data that is highly consistent with the user perception is obtained, and the problem of mismatch between user perception and DPI quality difference analysis results that occurs when the quality difference analysis relies on the underlying data such as DPI data is overcome, and a quality difference analysis method that matches the user perception is realized, thereby improving the ability of the quality difference analysis to restore the user perception; further, the network topology data of the perceived degraded user is determined from the pre-associated home broadband network resource data, and the upper-layer perception abnormal event data and the underlying network topology data are comprehensively considered to jointly define the target quality difference object of the quality difference video, and the perception degradation caused by network reasons is accurately located. The situation that the quality difference analysis based on the upper-layer SDK data alone cannot be delimited as the abnormality of the underlying network device is avoided, and the association analysis method that starts from the upper-layer user perception abnormal event and then delimits the underlying cross-domain network problem is realized, which is highly matched with the user perception, more comprehensive and more accurate video quality difference analysis.

[0097] Figure 3 is a schematic diagram of the structure of the electronic device provided by the present invention, such as Figure 3As shown, the electronic device may include: a processor (Processor) 310, a communication interface (Communications Interface) 320, a memory (Memory) 330 and a communication bus 340, wherein the processor 310, the communication interface 320, and the memory 330 communicate with each other through the communication bus 340. The processor 310 may call the logic instructions in the memory 330 to execute the video quality difference analysis method based on user perception provided in any of the above embodiments, and the video quality difference analysis method based on user perception includes but is not limited to the following steps: parsing the software development kit data based on the slice granularity of the video transmission to obtain the perceived abnormal event data of the poor quality video; determining the perceived degraded user based on the perceived abnormal event data; delimiting the target poor quality object of the poor quality video based on the perceived abnormal event data of the perceived degraded user and the network topology data of the perceived degraded user; the network topology data of the perceived degraded user is determined from the pre-associated home broadband network resource data.

[0098] In addition, the logic instructions in the above-mentioned memory 330 can be implemented in the form of a software functional unit and can be stored in a computer-readable storage medium when it is sold or used as an independent product. Based on such an understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art or the part of the technical solution, can be embodied in the form of a software product, and the computer software product is stored in a storage medium, including a number of instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM), random access memory (RAM), disk or optical disk, etc., various media that can store program codes.

[0099] On the other hand, the present invention also provides a computer program product, which includes a computer program. The computer program can be stored on a non-transitory computer-readable storage medium. When the computer program is executed by a processor, the computer can execute the video quality difference analysis method based on user perception provided in any of the above embodiments. The video quality difference analysis method based on user perception includes but is not limited to the following steps: parsing software development toolkit data based on the slice granularity of video transmission to obtain perceptual abnormal event data of the poor quality video; determining perceptually degraded users based on the perceptual abnormal event data; delimiting the target poor quality object of the poor quality video based on the perceptual abnormal event data of the perceptually degraded users and the network topology data of the perceptually degraded users; the network topology data of the perceptually degraded users is determined from pre-associated home broadband network resource data.

[0100] On the other hand, the present invention also provides a non-transitory computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the user-perception-based video quality analysis method provided in any of the above-mentioned embodiments, wherein the user-perception-based video quality analysis method includes but is not limited to the following steps: obtaining perceptual abnormal event data of the poor-quality video based on the slice granularity analysis software development toolkit data of the video transmission; determining perceptually degraded users based on the perceptual abnormal event data; delimiting the target poor-quality object of the poor-quality video based on the perceptual abnormal event data of the perceptually degraded users and the network topology data of the perceptually degraded users; the network topology data of the perceptually degraded users is determined from pre-associated home broadband network resource data.

[0101] The device embodiments described above are merely illustrative, wherein the units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the scheme of this embodiment. Ordinary technicians in this field can understand and implement it without paying creative labor.

[0102] Through the description of the above implementation methods, those skilled in the art can clearly understand that each implementation method can be implemented by means of software plus a necessary general hardware platform, and of course, can also be implemented by hardware. Based on this understanding, the above technical solution is essentially or the part that contributes to the prior art can be embodied in the form of a software product, and the computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, a disk, an optical disk, etc., including a number of instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods described in each embodiment or some parts of the embodiments.

[0103] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A video quality analysis method based on user perception, characterized in that: include: Parsing SDK data based on slice granularity of video transmission to obtain perceived abnormal event data of poor quality video; Determining a user with degraded perception based on the perception abnormal event data; Based on the perception abnormal event data of the perception-degraded user and the network topology data of the perception-degraded user, the target poor-quality object of the poor-quality video is delimited; the network topology data of the perception-degraded user is determined from pre-associated home broadband network resource data.

2. The video quality analysis method based on user perception according to claim 1, characterized in that: The perception abnormal event data includes an event timestamp, public network protocol address data, and transmission control protocol port data. The determining of the perception-degraded user based on the perception abnormal event data includes: Determining a private network protocol address of a video user from an associated network address translation log based on the event timestamp, the public network protocol address data, and the transmission control protocol port data; Based on the private network protocol address, determining the user broadband account of the video user from the associated remote identity authentication dial-up user service log; Summarize the perceived abnormal event data according to the user broadband account to obtain the cumulative number of abnormal events of the video user; Based on the accumulated number of abnormal events, users with perception degradation are determined from all video users.

3. The video quality analysis method based on user perception according to claim 1, characterized in that: The step of defining a target poor-quality object of the poor-quality video based on the abnormal perception event data of the perceptually degraded user and the network topology data of the perceptually degraded user includes: Classifying the perception abnormal event data of the perception-degraded user and the network topology data of the perception-degraded user to obtain user-level data, playback-level data, and slice-level data of the perception-degraded user; generating an end-to-end service path for the perception-degraded user based on the user-level data, the playback-level data, and the slice-level data; Based on the end-to-end service path, a target poor quality object of the poor quality video is delimited.

4. The method for analyzing video quality difference based on user perception according to claim 3, characterized in that: Defining the target quality difference object of the quality difference video based on the end-to-end service path includes: Matching the path data of the end-to-end service path with the list data of the first quality difference list to obtain a first quality difference matching object; the first quality difference list is determined based on the perceived abnormality index of the software development kit data of all video users; Matching the path data of the end-to-end service path with the list data of the second quality difference list to obtain a second quality difference matching object; the second quality difference list is determined based on the operation and maintenance performance indicators of the home broadband network devices of all video users; A target quality difference object of the quality difference video is defined based on the first quality difference matching object and the second quality difference matching object.

5. The method for analyzing video quality difference based on user perception according to claim 1, characterized in that: After defining the target poor quality object of the poor quality video based on the perception abnormal event data of the perception-degraded user and the network topology information, generating and pushing a poor quality problem work order based on the target poor quality object includes: Based on the target quality-poor objects, determine the quality-poor issues and their responsible departments; Based on the target poor quality object and the poor quality problem, generating the poor quality problem work order; The poor quality problem work order is pushed to the work order receiving interface of the responsible department so that the responsible department can handle the poor quality problem.

6. The method for analyzing video quality difference based on user perception according to claim 1, characterized in that: The network topology data includes an optical network unit identifier, a passive optical network port identifier, an optical line terminal identifier and a broadband access server identifier.

7. A video quality analysis device based on user perception, characterized in that: include: A perception anomaly data acquisition module is used to parse the software development kit data based on the slice granularity of video transmission to obtain the perception anomaly event data of poor quality video; A perception-degraded user determination module, configured to determine a perception-degraded user based on the perception abnormal event data; The target poor quality object determination module is used to define the target poor quality object of the poor quality video based on the perceived abnormal event data of the perceptually degraded user and the network topology data of the perceptually degraded user; the network topology data of the perceptually degraded user is determined from pre-associated home broadband network resource data.

8. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the computer program, the method for analyzing video quality difference based on user perception as claimed in any one of claims 1 to 6 is implemented.

9. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the method for analyzing video quality difference based on user perception as claimed in any one of claims 1 to 6 is implemented.

10. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the method for analyzing video quality difference based on user perception as claimed in any one of claims 1 to 6 is implemented.

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