CN113672486A - Caton analysis method and CDN server - Google Patents
Caton analysis method and CDN server Download PDFInfo
- Publication number
- CN113672486A CN113672486A CN202110949326.9A CN202110949326A CN113672486A CN 113672486 A CN113672486 A CN 113672486A CN 202110949326 A CN202110949326 A CN 202110949326A CN 113672486 A CN113672486 A CN 113672486A
- Authority
- CN
- China
- Prior art keywords
- data
- analysis
- heartbeat
- mobile terminal
- pause
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
- 238000004458 analytical method Methods 0.000 title claims abstract description 174
- 238000000034 method Methods 0.000 claims abstract description 32
- 230000000903 blocking effect Effects 0.000 claims description 21
- 238000004590 computer program Methods 0.000 claims description 9
- 235000011464 Pachycereus pringlei Nutrition 0.000 claims description 7
- 240000006939 Pachycereus weberi Species 0.000 claims description 7
- 235000011466 Pachycereus weberi Nutrition 0.000 claims description 7
- 238000010586 diagram Methods 0.000 description 8
- 238000012545 processing Methods 0.000 description 8
- 230000008569 process Effects 0.000 description 7
- 238000007405 data analysis Methods 0.000 description 5
- 238000004891 communication Methods 0.000 description 3
- 238000013480 data collection Methods 0.000 description 3
- 238000005516 engineering process Methods 0.000 description 3
- 230000031068 symbiosis, encompassing mutualism through parasitism Effects 0.000 description 3
- 230000005540 biological transmission Effects 0.000 description 2
- 230000006870 function Effects 0.000 description 2
- 230000010354 integration Effects 0.000 description 2
- 230000003287 optical effect Effects 0.000 description 2
- 230000004044 response Effects 0.000 description 2
- 230000003068 static effect Effects 0.000 description 2
- 238000004364 calculation method Methods 0.000 description 1
- 230000008094 contradictory effect Effects 0.000 description 1
- 238000011161 development Methods 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 230000010365 information processing Effects 0.000 description 1
- 230000003993 interaction Effects 0.000 description 1
- 238000010295 mobile communication Methods 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 238000005457 optimization Methods 0.000 description 1
- 230000001960 triggered effect Effects 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F11/00—Error detection; Error correction; Monitoring
- G06F11/30—Monitoring
- G06F11/34—Recording or statistical evaluation of computer activity, e.g. of down time, of input/output operation ; Recording or statistical evaluation of user activity, e.g. usability assessment
- G06F11/3409—Recording or statistical evaluation of computer activity, e.g. of down time, of input/output operation ; Recording or statistical evaluation of user activity, e.g. usability assessment for performance assessment
- G06F11/3419—Recording or statistical evaluation of computer activity, e.g. of down time, of input/output operation ; Recording or statistical evaluation of user activity, e.g. usability assessment for performance assessment by assessing time
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F11/00—Error detection; Error correction; Monitoring
- G06F11/30—Monitoring
- G06F11/34—Recording or statistical evaluation of computer activity, e.g. of down time, of input/output operation ; Recording or statistical evaluation of user activity, e.g. usability assessment
- G06F11/3466—Performance evaluation by tracing or monitoring
Abstract
The application discloses a Canton analysis method, which is used in a CDN server, wherein the CDN server is configured with a plurality of associated service programs, the associated service programs are bound and symbiotic with each other, and the plurality of associated service programs comprise a heartbeat collection service program and a Canton collection service program; the method comprises the following steps: acquiring heartbeat data reported by each mobile terminal through the heartbeat collecting service program; acquiring the pause data reported by each mobile terminal through the pause collection service program; analyzing the heartbeat data and the pause data reported by each mobile terminal through the pause collecting service program to obtain analysis data; and sending the analysis data to a central server so that the central server can carry out card analysis according to the analysis data. The card pause analysis method can collect more comprehensive and real-time data and analyze more detailed card pause information.
Description
Technical Field
The present application relates to the field of computer technologies, and in particular, to a checkpoint analysis method, a checkpoint analysis system, a checkpoint analysis computer device, a checkpoint analysis computer readable storage medium, and a CDN server.
Background
With the development of internet and multimedia technology, people begin to play online media by means of network technology. For example, webcasting is one of the popular items of the current internet. In the current market, people develop various live APP based on operating systems such as Android and IOS. The anchor can conduct live broadcast operation through the live broadcast APPs.
However, during the playing process of the online video, the pause is difficult to avoid in fact, and the playing experience is seriously influenced. To improve the viewing experience, it is often necessary to collect morton information from the user side and to analyze the cause of the morton from the morton information. However, the conventional server-side katton analysis is inefficient in data collection and analysis.
Disclosure of Invention
An object of an embodiment of the present application is to provide a stuck analysis method, a system, a computer device, a computer-readable storage medium, and a CDN server, which are used to solve the following problems: the existing server-side stuck analysis has low efficiency of data collection and analysis.
One aspect of the embodiments of the present application provides a morton analysis method, which is used in a CDN server, where the CDN server is configured with a plurality of companion service programs, and the companion service programs are bound and symbiotic with each other, where the plurality of companion service programs include a heartbeat collection service program and a morton collection service program; the method comprises the following steps:
acquiring heartbeat data reported by each mobile terminal through the heartbeat collecting service program;
acquiring the pause data reported by each mobile terminal through the pause collection service program;
analyzing the heartbeat data and the pause data reported by each mobile terminal through the pause collecting service program to obtain analysis data; and
and sending the analysis data to a central server so that the central server can carry out card analysis according to the analysis data.
Optionally, the analyzing, by the morton collection service program, the heartbeat data and the morton data reported by each mobile terminal to obtain analysis data includes:
analyzing the heartbeat data reported by each mobile terminal to obtain heartbeat analysis data in the analysis data;
wherein the heartbeat analysis data includes at least any one of: the number of online devices in the live broadcast room, the number of live broadcast definitions, the number of player versions, the number of various mobile terminals and the number of streaming servers.
Optionally, the analyzing, by the morton collection service program, the heartbeat data and the morton data reported by each mobile terminal to obtain analysis data includes:
analyzing the stuck data reported by each mobile terminal to obtain stuck analysis data in the analysis data;
wherein the Cartton analysis data comprises at least any one of: the method comprises the steps of Catton live broadcast room ID, live broadcast definition, player version, mobile terminal equipment classification and stream server IP.
Optionally, the analyzing, by the morton collection service program, the heartbeat data and the morton data reported by each mobile terminal to obtain analysis data includes:
analyzing according to the heartbeat analysis data and the katton analysis data to obtain first analysis data taking a live broadcast room as an analysis object;
wherein the first analytical data comprises at least any one of: the method comprises the steps of directly broadcasting live broadcast room blocking rate, each live broadcast definition blocking rate, each player version blocking rate and blocking rates of various mobile terminals of the live broadcast room.
Optionally, the analyzing, by the morton collection service program, the heartbeat data and the morton data reported by each mobile terminal to obtain analysis data includes:
analyzing according to the heartbeat analysis data and the katton analysis data to obtain second analysis data taking equipment as an analysis object;
wherein the second analysis data comprises a pause rate of the pull flow server IP;
the pause rate of the pull stream server IP is the ratio of the pause number of all live broadcast rooms on the pull stream server to the heartbeat report number of all live broadcast rooms on the pull stream server.
Optionally, the method further includes:
when any one of the plurality of companion service programs is detected to stop running, stopping running of other programs;
and when any one of the plurality of companion service programs is detected to start running, starting running of other programs.
An aspect of an embodiment of the present application further provides a stuck analysis system,
the method is used for a CDN server, the CDN server is configured with a plurality of companion service programs, the companion service programs are bound and symbiotic with each other, and the plurality of companion service programs comprise a heartbeat collection service program and a cardon collection service program; the system comprises:
the first acquisition module is used for acquiring heartbeat data reported by each mobile terminal through the heartbeat collection service program;
a second obtaining module, configured to obtain the morton data reported by each mobile terminal through the morton collection service program;
the analysis module is used for analyzing the heartbeat data and the pause data reported by each mobile terminal through the pause collection service program to obtain analysis data; and
and the sending module is used for sending the analysis data to a central server so that the central server can carry out card analysis according to the analysis data.
An aspect of embodiments of the present application further provides a computer device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, the processor when executing the computer program implementing the steps of the above-mentioned katon analysis method.
An aspect of embodiments of the present application further provides a computer-readable storage medium having stored therein a computer program, the computer program being executable by at least one processor to cause the at least one processor to perform the steps of the above-mentioned katton analysis method.
One aspect of the present embodiment of the application further provides a CDN server, including a plurality of companion service programs, where each companion service program is bound and symbiotic with another companion service program, and the plurality of companion service programs include a heartbeat collection service program and a morton collection service program; wherein:
the heartbeat collecting service program is used for acquiring heartbeat data reported by each mobile terminal through the heartbeat collecting service program;
the pause collecting service program is used for acquiring pause data reported by each mobile terminal and analyzing heartbeat data and pause data reported by each mobile terminal to obtain analysis data for pause analysis.
The morton analysis method, the system, the equipment and the computer readable storage medium provided by the embodiment of the application and the CDN server have the following advantages that:
firstly, the method comprises the following steps: the CDN server is internally provided with a heartbeat collecting service program and a stuck collecting service program, the heartbeat collecting service program is used for collecting heartbeat data of each mobile terminal, and the stuck collecting service program is used for collecting stuck data of the mobile terminal which is stuck. The heartbeat collection service program and the calorie inching collection service program are bound with each other and are located in the same CDN server, so that the heartbeat collection service program and the calorie inching collection service program can communicate with each other in real time, more effective and accurate information can be obtained, and the performance of the CDN server can be analyzed.
Secondly, the method comprises the following steps: unlike the central server collecting data of a full platform, the heartbeat collection service program is built in the CDN servers at the edge of the network in the present embodiment, and heartbeat data within the range can be collected in real time by the CDN servers, so that it can collect more detailed heartbeat data including more field data for the morton analysis.
Thirdly, the method comprises the following steps: unlike data processing delay caused by the fact that a large data platform needs to collect data of a whole platform, in the embodiment, the morton collection service program is embedded in the CDN server at the edge of the network, and the morton data within the range can be collected in real time by the CDN server, so that the morton data can be received and processed in real time, for example, heartbeat data and the morton data of each mobile terminal can be analyzed in real time.
Fourthly: the heartbeat collection service program and the katton collection service program are bound and symbiotic with each other. The so-called symbiosis can complete the collection of heartbeat data and cardon data on one CDN server, and can bear larger data volume and QPS.
Drawings
FIG. 1 schematically illustrates an application environment diagram of a Kanton analysis method according to an embodiment of the present application;
FIG. 2 schematically shows a flow chart of a stuck analysis method according to a first embodiment of the present application;
3-6 are sub-step diagrams of step S204 of FIG. 2;
FIG. 7 is a flow chart schematically illustrating additional steps of a stuck analysis method according to a first embodiment of the present application;
FIG. 8 schematically illustrates a block diagram of a Cartesian analysis system according to a second embodiment of the present application;
fig. 9 schematically shows a hardware architecture diagram of a computer device suitable for implementing the katton analysis method according to a third embodiment of the present application; and
fig. 10 schematically shows an architecture diagram of a live architecture according to an embodiment of the present application.
Detailed Description
In order to make the objects, technical solutions and advantages of the present application more apparent, the present application is described in further detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the present application and are not intended to limit the present application. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.
It should be noted that the descriptions relating to "first", "second", etc. in the embodiments of the present application are only for descriptive purposes and are not to be construed as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one such feature. In addition, technical solutions between various embodiments may be combined with each other, but must be realized by a person skilled in the art, and when the technical solutions are contradictory or cannot be realized, such a combination should not be considered to exist, and is not within the protection scope of the present application.
In the live broadcast system, the number of viewers in the live broadcast is often required to be counted for business needs, and for improving the viewing quality, the morton information of the mobile terminal is often required to be used for data analysis, but the inventor knows that:
the anchor side pushes the stream to the CDN server.
Secondly, the audience end pulls up the stream from different CDN server nodes to watch, and the CDN nodes can mutually transmit data.
And thirdly, the audience regularly reports the heartbeat data.
I.e. to center the heartbeat data on a server. And summarizing the heartbeat data by the central server, and counting the watching number of people in each live broadcast room.
And fourthly, after the jamming occurs, the spectator end can automatically report the jamming information to the big data platform. And the big data platform analyzes the data. It should be noted that the central server and the big data platform belong to different server clusters. The large data platform is responsible for processing all data of the whole platform, and the data volume is too large, so that the delay is too high.
The inventors found that, based on the above-described procedure, the following problems were involved:
the data are independent, and the available value is low: the central server collects heartbeat data and counts the number of on-line people, the mobile terminal reports the card pause data to the big data platform for recording the card pause information, but the central server and the big data platform are not communicated with each other, more effective information cannot be obtained, and the performance of the CDN server cannot be analyzed.
Secondly, the amount of the reported heartbeat data collected by the central server is small: the central server is generally deployed in a central machine room, and has high cost and small quantity, and cannot bear higher QPS and larger data volume. And for a large live broadcast platform, the scale of users is huge. In order to reduce the pressure of the regular reporting on the central server, the data size is generally reduced, and only the fields such as whether the fields are watched or not are reported, so that more detailed data analysis and pause analysis cannot be performed by using the reported heartbeat.
The big data platform has high delay: if the information reported to the big data platform and the heartbeat data are analyzed so as to judge the card pause rate, the information of the big data platform and the heartbeat data need to be integrated, but the big data platform needs to provide full platform service, so that the data collection amount is very large, the whole data delay is large, and the delay is generally several hours.
In view of this, the present application is directed to a morton analysis method, which places a heartbeat collection service and a morton collection service in the same server by binding a companion service. Namely, the heartbeat service and the pause service are placed in the CDN server, so that more comprehensive and real-time data volume is collected, and more detailed pause information is analyzed.
It should be noted that, by analyzing the data collected by the heartbeat collection service and the morton collection service in the CDN server in the form of the companion service, a larger data volume and a higher QPS can be borne, and the real-time performance and the accuracy are ensured.
The present application provides various embodiments for introducing the katton analysis scheme, with particular reference to the following.
In the description of the present application, it should be understood that the numerical references before the steps do not identify the order of performing the steps, but merely serve to facilitate the description of the present application and to distinguish each step, and therefore should not be construed as limiting the present application.
The following are the term explanations of the present application:
live streaming: live audiovisual data transmission that can be transmitted as a steady and continuous stream over a network for viewing by an audience.
Live broadcasting and stream pushing: and the anchor pushes the acquired streaming media to a live broadcast cloud receiving end in real time through the streaming address acquired by the service server from the live broadcast cloud platform.
Live broadcasting and stream pulling: the live streaming refers to a process of pulling the live streaming from a source station specified by a user through a live streaming cloud platform.
And (5) live broadcast blocking: the phenomenon of unsmooth watching or playing a black screen or still pictures occurs.
A CDN (Content Delivery Network) server is an edge server deployed in various places. The CDN server can enable a user to obtain required content nearby through functional modules of load balancing, content distribution, scheduling and the like of the central platform, network congestion is reduced, and the access response speed and hit rate of the user are improved.
A pull stream server IP (Internet Protocol Address), when a certain mobile terminal pulls a live stream from a server, the IP Address of the server is the pull stream server IP of the mobile terminal.
QPS (Queries Per Second is the query rate Per Second), is the number of Queries that a server can respond to Per Second, and is a measure of how much traffic a server processes within a specified time, i.e. the number of response requests Per Second.
Id (identity), id number.
Fig. 1 schematically shows an environment application diagram of a katton analysis method according to a first embodiment of the present application. In a live scenario, the anchor 2 may push live data to the audience 6 through the CDN server 4 in real time.
And the anchor terminal 2 is used for generating live broadcast data in real time and carrying out stream pushing operation on the live broadcast data. The live data may include audio data or video data. The anchor terminal 2 may be a smartphone, a tablet computer, etc. based on the IOS system. In other embodiments, the anchor 2 may be a live device based on a system such as Android.
And the audience terminal 6 can be configured to receive live broadcast data of the anchor terminal 2 in real time through the CDN server. The viewer side 6 may be any type of computing device such as a smart phone, tablet device, laptop computer, set-top box, smart television, etc. The viewer side 6 may have a browser or a dedicated program built therein, and receives the live data through the browser or the dedicated program to output the content to the user. The content may include video, audio, commentary, textual data, and/or the like.
Example one
The embodiment a provides a stuck analysis method, which may be executed in the CDN server 4.
The CDN server 4 is configured with a plurality of companion service programs, and each companion service program is bound and symbiotic with another companion service program, where the plurality of companion service programs include a heartbeat collection service program and a morton collection service program.
Fig. 2 schematically shows a flowchart of a katton analysis method according to a first embodiment of the present application.
As shown in fig. 2, the katon analysis method may include steps S200 to S204, in which:
and step S200, acquiring heartbeat data reported by each mobile terminal through the heartbeat collecting service program.
Since the CDN server 4 can better collect and process data within its scope relative to a central server, the heartbeat data can be changed from a single field data composition to a multiple field data composition. As an example, the heartbeat data reported by each mobile terminal at regular time may include the following field data: live room ID, live definition, login status, mobile/web end, player version, mobile device type. Specifically, the method comprises the following steps:
live broadcast room ID: the method is used for analyzing the total watching number of people in the live broadcast room and providing basic information for judging whether the live broadcast room is a single live broadcast room subsequently.
Live broadcast definition: for analyzing the number of people live with different live broadcast definitions. Live definition can be used to judge whether the user is stuck due to the difference of definition or not, or all definitions have problems.
Version of the player: the player version at the viewer end may cause the user to jam because the version, for example, grays a new version, and it is found that the jam of the new version is more than that of the old version, which indicates the jam caused by the player version.
Classifying mobile terminal equipment: the viewer side is classified as mobile/web side, and side device configuration. Due to the diversity of the user plug-flow information, different devices may cause different types of stuck information for flows. Thus, the types of mobile terminals may be analyzed for stuck to determine if the stuck is due to a device type.
And pulling a streaming server IP: a CDN server providing the pull streaming service is determined to determine whether the seizure is due to the CDN server itself.
Step S202, acquiring the pause data reported by each mobile terminal through the pause collection service program.
Because the big data platform provides services for the whole platform, the collection and processing of the morton data through the big data platform have serious delay. In contrast, the CDN server 4 is an edge server located closer to the audience, and is configured to provide a pull service to the audience in its scope. By installing the stuck collection service program in the CDN server 4 and collecting the stuck data reported by the viewer who has stuck through the stuck collection service program, the situation of the viewer who has stuck can be quickly known. As an example, when a player or a playing page in a viewer ends a pause, the viewer ends automatically report pause data including the following field data to a pause collection service program bound to the same pull streaming server (i.e. CDN server 4):
ID of a live broadcast room entered when a player or a playing page is jammed;
live broadcast definition used when a player or a playing page is paused:
whether the player or the playing page is logged in when the player or the playing page is jammed;
the version of the player when the player or the playing page is jammed;
the type of the player or the mobile terminal equipment where the playing page is located, where the jamming occurs;
and when the card pause occurs, the player or the pull streaming server IP for playing the page pull stream.
It should be noted that the field data are only exemplary and are not used to limit the scope of the present application.
Step S204, the heartbeat data and the pause data reported by each mobile terminal are analyzed through the pause collection service program to obtain analysis data.
And performing early-stage analysis and integration of data in the CDN server 4 to improve the analysis efficiency of the checkpoint.
As an example, as shown in fig. 3, the step S204 may include the step S300: analyzing the heartbeat data reported by each mobile terminal to obtain heartbeat analysis data in the analysis data; wherein the heartbeat analysis data includes at least any one of: the number of online devices in the live broadcast room, the number of live broadcast definitions, the number of player versions, the number of various mobile terminals and the number of streaming servers. In this embodiment, the current heartbeat overall data may be analyzed in real time according to the heartbeat data of each mobile terminal pulled by the CDN server 4 and the heartbeat data of each mobile terminal, so as to be used for subsequent stuck analysis.
As an example, as shown in fig. 4, the step S204 may include the step S400: analyzing the stuck data reported by each mobile terminal to obtain stuck analysis data in the analysis data; wherein the Cartton analysis data comprises at least any one of: the number of IDs of all Canton live broadcast rooms, the number of definition of all live broadcasts, the number of versions of all players, the number of various mobile terminal devices and the number of IP of all pull stream servers. In this embodiment, the current jam condition may be analyzed in real time according to the jam data of each mobile terminal pulled by the CDN server 4 and according to the jam data of each mobile terminal, so as to be used for subsequent jam analysis.
As an example, as shown in fig. 5, the step S204 may include the step S500: analyzing according to the heartbeat analysis data and the katton analysis data to obtain first analysis data taking a live broadcast room as an analysis object; wherein the first analytical data comprises at least any one of: the method comprises the steps of directly broadcasting live broadcast room blocking rate, each live broadcast definition blocking rate, each player version blocking rate and blocking rates of various mobile terminals of the live broadcast room. In this embodiment, the CDN server 4 may calculate, in real time, first analysis data of a live broadcast room dimension according to the heartbeat analysis data and the katton analysis data, so as to obtain a katton condition of each live broadcast room. Specifically, the method comprises the following steps:
the live broadcast room seizure rate is the total seizure number of the live broadcast room/the heartbeat report number of the live broadcast room;
the pause rate of each live broadcast definition is the live broadcast definition pause number of the live broadcast room/the live broadcast definition heartbeat report number of the live broadcast room;
the pause rate of each player version is the pause number of the player version in the live broadcast room/the heartbeat report number of the player in the live broadcast room;
and the card pause rate of each type of mobile terminal in the live broadcast room is equal to the number of card pauses generated in the live broadcast watching process when the type of mobile terminal enters the live broadcast room/the heartbeat report number entering the live broadcast room through the type of mobile terminal.
As an example, as shown in fig. 6, the step S204 may include the step S600: analyzing according to the heartbeat analysis data and the katton analysis data to obtain second analysis data taking equipment as an analysis object; wherein the second analysis data comprises a pause rate of the pull flow server IP; the pause rate of the pull stream server IP is the ratio of the pause number of all live broadcast rooms on the pull stream server to the heartbeat report number of all live broadcast rooms on the pull stream server. In this embodiment, the CDN server 4 may calculate second analysis data of the device dimension in real time according to the heartbeat analysis data and the stuck analysis data, so as to obtain a stuck condition of the CDN server. In this embodiment, by the associated heartbeat collection service program and the morton collection service program, more effective information cannot be obtained, and the second analysis data of the CDN server 4 itself is obtained.
Step S206, the analysis data is sent to a central server, so that the central server can carry out card analysis according to the analysis data.
After the analysis data is obtained, the analysis data can be reported to a central server, and the central server performs further data analysis and integration to judge the blockage rate of the whole platform with the live broadcast room and the equipment as dimensions.
And in the central server, acquiring the pause rate of the whole platform by taking the live broadcast room as a dimension, and obtaining first analysis data by referring to the CDN server.
In the central server, the blocking rates of the whole platform with the device as the dimension are obtained, the total blocking rate of each CDN server is arranged in a descending order, and when the total blocking rate of a certain CDN server exceeds a preset threshold value, the blocking of the mobile terminal pulling the stream from the CDN server is caused by the CDN server, so that the problem of the CDN server needs to be checked.
The stuck analysis method provided by the embodiment at least includes the following advantages:
firstly, the method comprises the following steps: the CDN server is internally provided with a heartbeat collecting service program and a stuck collecting service program, the heartbeat collecting service program is used for collecting heartbeat data of each mobile terminal, and the stuck collecting service program is used for collecting stuck data of the mobile terminal which is stuck. The heartbeat collection service program and the calorie inching collection service program are bound with each other and are located in the same CDN server, so that the heartbeat collection service program and the calorie inching collection service program can communicate with each other in real time, more effective and accurate information can be obtained, and the performance of the CDN server can be analyzed.
Secondly, the method comprises the following steps: unlike the central server collecting data of a full platform, the heartbeat collection service program is built in the CDN servers at the edge of the network in the present embodiment, and heartbeat data within the range can be collected in real time by the CDN servers, so that it can collect more detailed heartbeat data including more field data for the morton analysis.
Thirdly, the method comprises the following steps: unlike data processing delay caused by the fact that a large data platform needs to collect data of a whole platform, in the embodiment, the morton collection service program is embedded in the CDN server at the edge of the network, and the morton data within the range can be collected in real time by the CDN server, so that the morton data can be received and processed in real time, for example, heartbeat data and the morton data of each mobile terminal can be analyzed in real time.
Fourthly: the heartbeat collection service program and the katton collection service program are bound and symbiotic with each other. The symbiosis means that one service program is bound with one service program and the symbiosis is performed between the two service programs bound with each other. Specifically, if the heartbeat collection service is started, the morton collection service must also be started. If the heartbeat collection service is not able to provide service for some reason, the morton collection service is also not able to provide service alone. The associated setting specifically allows the CDN server to provide services such as pull streaming, heartbeat collection, and hiton collection at the same time. The heartbeat collection service program and the morton collection service program cannot exist independently, otherwise, the collection of heartbeat data and the morton data cannot be completed in an edge server (CDN server), so that larger data volume and QPS can be borne, otherwise, the method is equal to the method of the central server and does not play a role in shunting. It should be noted that, instead of setting heartbeat collection and cardon collection in one program, separate and concomitant heartbeat collection service programs and cardon collection service programs are set, because each program is processed separately, which speeds up processing and data volume. If placed in a merged program, they will interact.
As an example, as illustrated in fig. 7, the katon analysis method may further include the steps of: step S700, when detecting that any one of the plurality of companion service programs stops running, stopping running of other programs; step S702, when detecting that any one of the plurality of companion service programs starts to run, starting running of another program. In this embodiment, symbiotic coexistence and extinguishment of the respective companion service programs can be realized.
In summary, in the morton analysis method provided in this embodiment, the heartbeat collection service and the morton collection service are placed in the same server by binding the companion service. That is, the CDN server provides a heartbeat collecting service and a stuck collecting service, thereby collecting more comprehensive and real-time data and analyzing more detailed stuck information.
In addition, data collected by the heartbeat collection service and the cardon collection service are analyzed in the CDN server in the form of the associated service, so that larger data volume and higher QPS can be borne, and the real-time performance and the accuracy are ensured.
Example two
Fig. 8 schematically illustrates a block diagram of a stuck analysis system according to a second embodiment of the present application, which may be partitioned into one or more program modules, stored in a storage medium, and executed by one or more processors to implement the second embodiment of the present application. The program modules referred to in the embodiments of the present application refer to a series of computer program instruction segments that can perform specific functions, and the following description will specifically describe the functions of the program modules in the embodiments.
The morton analysis system is used in a CDN server, the CDN server is configured with a plurality of companion service programs, the companion service programs are bound and symbiotic with each other, and the plurality of companion service programs comprise a heartbeat collection service program and a morton collection service program.
As shown in fig. 8, the katton analysis system 800 may include a first obtaining module 810, a second obtaining module 820, an analysis module 830, and a sending module 840, wherein:
the first acquisition module is used for acquiring heartbeat data reported by each mobile terminal through the heartbeat collection service program;
a second obtaining module, configured to obtain the morton data reported by each mobile terminal through the morton collection service program;
the analysis module is used for analyzing the heartbeat data and the pause data reported by each mobile terminal through the pause collection service program to obtain analysis data; and
and the sending module is used for sending the analysis data to a central server so that the central server can carry out card analysis according to the analysis data.
Optionally, the analysis module is further configured to:
analyzing the heartbeat data reported by each mobile terminal to obtain heartbeat analysis data in the analysis data;
wherein the heartbeat analysis data includes at least any one of: the number of online devices in the live broadcast room, the number of live broadcast definitions, the number of player versions, the number of various mobile terminals and the number of streaming servers.
Optionally, the analysis module is further configured to:
analyzing the stuck data reported by each mobile terminal to obtain stuck analysis data in the analysis data;
wherein the Cartton analysis data comprises at least any one of: the method comprises the steps of Catton live broadcast room ID, live broadcast definition, player version, mobile terminal equipment classification and stream server IP.
Optionally, the analysis module is further configured to:
analyzing according to the heartbeat analysis data and the katton analysis data to obtain first analysis data taking a live broadcast room as an analysis object;
wherein the first analytical data comprises at least any one of: the method comprises the steps of directly broadcasting live broadcast room blocking rate, each live broadcast definition blocking rate, each player version blocking rate and blocking rates of various mobile terminals of the live broadcast room.
Optionally, the analysis module is further configured to:
analyzing according to the heartbeat analysis data and the katton analysis data to obtain second analysis data taking equipment as an analysis object;
wherein the second analysis data comprises a pause rate of the pull flow server IP;
the pause rate of the pull stream server IP is the ratio of the pause number of all live broadcast rooms on the pull stream server to the heartbeat report number of all live broadcast rooms on the pull stream server.
Optionally, the system further comprises a companion module (not shown) configured to:
when any one of the plurality of companion service programs is detected to stop running, stopping running of other programs;
and when any one of the plurality of companion service programs is detected to start running, starting running of other programs.
EXAMPLE III
Fig. 9 schematically shows a hardware architecture diagram of a computer device 10000 suitable for implementing the katton analysis method according to the third embodiment of the present application. In this embodiment, the computer device 10000 may be the CDN server 4 or a part of the CDN server 4. In this embodiment, the computer device 10000 is a device capable of automatically performing numerical calculation and/or information processing according to a preset or stored instruction. For example, it may be a smartphone, tablet computer, etc. As shown in fig. 9, computer device 10000 includes at least, but is not limited to: the memory 10010, processor 10020, and network interface 10030 may be communicatively linked to each other via a system bus. Wherein:
the memory 10010 includes at least one type of computer-readable storage medium including a flash memory, a hard disk, a multimedia card, a card-type memory (e.g., SD or DX memory, etc.), a Random Access Memory (RAM), a Static Random Access Memory (SRAM), a read-only memory (ROM), an electrically erasable programmable read-only memory (EEPROM), a programmable read-only memory (PROM), a magnetic memory, a magnetic disk, an optical disk, and the like. In some embodiments, the storage 10010 may be an internal storage module of the computer device 10000, such as a hard disk or a memory of the computer device 10000. In other embodiments, the memory 10010 may also be an external storage device of the computer device 10000, such as a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card), or the like, provided on the computer device 10000. Of course, the memory 10010 may also include both internal and external memory modules of the computer device 10000. In this embodiment, the memory 10010 is generally used for storing an operating system and various application software installed in the computer device 10000, such as program codes of the katon analysis method. In addition, the memory 10010 may also be used to temporarily store various types of data that have been output or are to be output.
Processor 10020, in some embodiments, can be a Central Processing Unit (CPU), controller, microcontroller, microprocessor, or other data Processing chip. The processor 10020 is generally configured to control overall operations of the computer device 10000, such as performing control and processing related to data interaction or communication with the computer device 10000. In this embodiment, the processor 10020 is configured to execute program codes stored in the memory 10010 or process data.
Network interface 10030 may comprise a wireless network interface or a wired network interface, and network interface 10030 is generally used to establish a communication link between computer device 10000 and other computer devices. For example, the network interface 10030 is used to connect the computer device 10000 to an external terminal through a network, establish a data transmission channel and a communication link between the computer device 10000 and the external terminal, and the like. The network may be a wireless or wired network such as an Intranet (Intranet), the Internet (Internet), a Global System of Mobile communication (GSM), Wideband Code Division Multiple Access (WCDMA), a 4G network, a 5G network, Bluetooth (Bluetooth), or Wi-Fi.
It should be noted that fig. 9 only illustrates a computer device having the components 10010-10030, but it should be understood that not all illustrated components are required and that more or fewer components may be implemented instead.
In this embodiment, the stuck analysis method stored in the memory 10010 can be further divided into one or more program modules and executed by one or more processors (in this embodiment, the processor 10020) to complete the embodiment of the present application.
Example four
The present application also provides a computer readable storage medium having stored thereon a computer program which, when executed by a processor, implements the steps of the stuck analysis method in an embodiment.
In this embodiment, the computer-readable storage medium includes a flash memory, a hard disk, a multimedia card, a card type memory (e.g., SD or DX memory, etc.), a Random Access Memory (RAM), a Static Random Access Memory (SRAM), a Read Only Memory (ROM), an Electrically Erasable Programmable Read Only Memory (EEPROM), a Programmable Read Only Memory (PROM), a magnetic memory, a magnetic disk, an optical disk, and the like. In some embodiments, the computer readable storage medium may be an internal storage unit of the computer device, such as a hard disk or a memory of the computer device. In other embodiments, the computer readable storage medium may be an external storage device of the computer device, such as a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card), and the like provided on the computer device. Of course, the computer-readable storage medium may also include both internal and external storage devices of the computer device. In this embodiment, the computer-readable storage medium is generally used for storing an operating system and various types of application software installed in the computer device, for example, the program code of the katon analysis method in the embodiment, and the like. Further, the computer-readable storage medium may also be used to temporarily store various types of data that have been output or are to be output.
EXAMPLE five
The application also provides a CDN server which comprises a plurality of associated service programs, wherein the associated service programs are bound and symbiotic with each other, and comprise a heartbeat collection service program and a cardon collection service program; wherein:
the heartbeat collecting service program is used for acquiring heartbeat data reported by each mobile terminal through the heartbeat collecting service program;
the pause collecting service program is used for acquiring pause data reported by each mobile terminal and analyzing heartbeat data and pause data reported by each mobile terminal to obtain analysis data for pause analysis.
As shown in fig. 10, the anchor 2 is streaming to CDN server 4A, CDN server 4B, ….
The CDN server 4A sends live broadcast data to the audience 6A, and the audience 6 reports heartbeat data and pause data of the audience to the CDN server 4A. It should be noted that the mortgage data is generated by the viewer side 6A before the report is triggered.
The CDN server 4B sends live broadcast data to the audience 6B, and the audience 6B reports heartbeat data and pause data of the audience to the CDN server 4B.
That is, the spectator side 6A, 6B, … pulls the stream from which CDN server, and reports its heartbeat data and hiton data to which CDN server.
It should be noted that, for further techniques or effects and the like of the present embodiment, reference may be made to other embodiments, which are not described herein in detail.
It will be apparent to those skilled in the art that the modules or steps of the embodiments of the present application described above may be implemented by a general purpose computing device, they may be centralized on a single computing device or distributed across a network of multiple computing devices, and alternatively, they may be implemented by program code executable by a computing device, such that they may be stored in a storage device and executed by a computing device, and in some cases, the steps shown or described may be performed in an order different from that described herein, or they may be separately fabricated into individual integrated circuit modules, or multiple ones of them may be fabricated into a single integrated circuit module. Thus, embodiments of the present application are not limited to any specific combination of hardware and software.
It should be noted that the technical solution of the present application is mainly/specifically directed to optimization of a Web player based on DASH streaming media. The above description is only a preferred embodiment of the present application, and not intended to limit the scope of the present application, and all modifications of equivalent structures and equivalent processes, which are made by the contents of the specification and the drawings, or applied to other related technical fields, are intended to be covered by the present application.
Claims (10)
1. The Canton analysis method is used in a CDN server, wherein the CDN server is configured with a plurality of companion service programs, the companion service programs are bound and symbiotic with each other, and the plurality of companion service programs comprise a heartbeat collection service program and a Canton collection service program; the method comprises the following steps:
acquiring heartbeat data reported by each mobile terminal through the heartbeat collecting service program;
acquiring the pause data reported by each mobile terminal through the pause collection service program;
analyzing the heartbeat data and the pause data reported by each mobile terminal through the pause collecting service program to obtain analysis data; and
and sending the analysis data to a central server so that the central server can carry out card analysis according to the analysis data.
2. The katon analysis method of claim 1, wherein the analyzing the heartbeat data and the katon data reported by each mobile terminal by the katon collection service to obtain the analysis data comprises:
analyzing the heartbeat data reported by each mobile terminal to obtain heartbeat analysis data in the analysis data;
wherein the heartbeat analysis data includes at least any one of: the number of online devices in the live broadcast room, the number of live broadcast definitions, the number of player versions, the number of various mobile terminals and the number of streaming servers.
3. The katon analysis method of claim 2, wherein the analyzing the heartbeat data and the katon data reported by each mobile terminal by the katon collection service to obtain the analysis data comprises:
analyzing the stuck data reported by each mobile terminal to obtain stuck analysis data in the analysis data;
wherein the Cartton analysis data comprises at least any one of: the method comprises the steps of Catton live broadcast room ID, live broadcast definition, player version, mobile terminal equipment classification and stream server IP.
4. The katon analysis method of claim 3, wherein the analyzing the heartbeat data and the katon data reported by each mobile terminal by the katon collection service to obtain the analysis data comprises:
analyzing according to the heartbeat analysis data and the katton analysis data to obtain first analysis data taking a live broadcast room as an analysis object;
wherein the first analytical data comprises at least any one of: the method comprises the steps of directly broadcasting live broadcast room blocking rate, each live broadcast definition blocking rate, each player version blocking rate and blocking rates of various mobile terminals of the live broadcast room.
5. The katon analysis method of claim 3, wherein the analyzing the heartbeat data and the katon data reported by each mobile terminal by the katon collection service to obtain the analysis data comprises:
analyzing according to the heartbeat analysis data and the katton analysis data to obtain second analysis data taking equipment as an analysis object;
wherein the second analysis data comprises a pause rate of the pull flow server IP; the pause rate of the pull stream server IP is the ratio of the pause number of all live broadcast rooms on the pull stream server to the heartbeat report number of all live broadcast rooms on the pull stream server.
6. The katon analysis method of any one of claims 1 to 5, further comprising:
when any one of the plurality of companion service programs is detected to stop running, stopping running of other programs;
and when any one of the plurality of companion service programs is detected to start running, starting running of other programs.
7. The Canton analysis system is used in a CDN server, wherein the CDN server is configured with a plurality of companion service programs, the companion service programs are bound and symbiotic with each other, and the plurality of companion service programs comprise a heartbeat collection service program and a Canton collection service program; the system comprises:
the first acquisition module is used for acquiring heartbeat data reported by each mobile terminal through the heartbeat collection service program;
a second obtaining module, configured to obtain the morton data reported by each mobile terminal through the morton collection service program;
the analysis module is used for analyzing the heartbeat data and the pause data reported by each mobile terminal through the pause collection service program to obtain analysis data; and
and the sending module is used for sending the analysis data to a central server so that the central server can carry out card analysis according to the analysis data.
8. A computer device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, characterized in that the processor, when executing the computer program, is adapted to carry out the steps of the katton analysis method of any one of claims 1 to 6.
9. A computer-readable storage medium, in which a computer program is stored which is executable by at least one processor to cause the at least one processor to perform the steps of the katon analysis method of any one of claims 1 to 6.
10. A CDN server, comprising a plurality of companion service programs, each of which is bound and symbiotic with each other, the plurality of companion service programs including a heartbeat collection service program and a cardon collection service program; wherein:
the heartbeat collecting service program is used for acquiring heartbeat data reported by each mobile terminal through the heartbeat collecting service program;
the pause collecting service program is used for acquiring pause data reported by each mobile terminal and analyzing heartbeat data and pause data reported by each mobile terminal to obtain analysis data for pause analysis.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110949326.9A CN113672486A (en) | 2021-08-18 | 2021-08-18 | Caton analysis method and CDN server |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110949326.9A CN113672486A (en) | 2021-08-18 | 2021-08-18 | Caton analysis method and CDN server |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN113672486A true CN113672486A (en) | 2021-11-19 |
Family
ID=78543585
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202110949326.9A Pending CN113672486A (en) | 2021-08-18 | 2021-08-18 | Caton analysis method and CDN server |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN113672486A (en) |
Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104991855A (en) * | 2015-06-16 | 2015-10-21 | 广州华多网络科技有限公司 | Processing method and device for interface lag |
| WO2017113868A1 (en) * | 2015-12-29 | 2017-07-06 | 网宿科技股份有限公司 | Method and system for self-adaptive bandwidth control for cdn platform |
| CN107105309A (en) * | 2017-04-25 | 2017-08-29 | 北京潘达互娱科技有限公司 | Live dispatching method and device |
| CN107911722A (en) * | 2017-10-31 | 2018-04-13 | 链家网（北京）科技有限公司 | A kind of content distributing network dispatching method and device |
| CN111601102A (en) * | 2020-04-30 | 2020-08-28 | 微梦创科网络科技（中国）有限公司 | Live broadcast stuck detection method and system |
| CN112261429A (en) * | 2020-10-21 | 2021-01-22 | 北华大学 | Live broadcast application system, method, equipment and storage medium of cardless intelligent terminal |
| CN113094239A (en) * | 2021-04-27 | 2021-07-09 | 上海哔哩哔哩科技有限公司 | Method for determining reason of live broadcast abnormity and server |
-
2021
- 2021-08-18 CN CN202110949326.9A patent/CN113672486A/en active Pending
Patent Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104991855A (en) * | 2015-06-16 | 2015-10-21 | 广州华多网络科技有限公司 | Processing method and device for interface lag |
| WO2017113868A1 (en) * | 2015-12-29 | 2017-07-06 | 网宿科技股份有限公司 | Method and system for self-adaptive bandwidth control for cdn platform |
| CN107105309A (en) * | 2017-04-25 | 2017-08-29 | 北京潘达互娱科技有限公司 | Live dispatching method and device |
| CN107911722A (en) * | 2017-10-31 | 2018-04-13 | 链家网（北京）科技有限公司 | A kind of content distributing network dispatching method and device |
| CN111601102A (en) * | 2020-04-30 | 2020-08-28 | 微梦创科网络科技（中国）有限公司 | Live broadcast stuck detection method and system |
| CN112261429A (en) * | 2020-10-21 | 2021-01-22 | 北华大学 | Live broadcast application system, method, equipment and storage medium of cardless intelligent terminal |
| CN113094239A (en) * | 2021-04-27 | 2021-07-09 | 上海哔哩哔哩科技有限公司 | Method for determining reason of live broadcast abnormity and server |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| US11863654B2 (en) | Detecting user interest in presented media items by observing volume change events | |
| US11917270B2 (en) | Methods and apparatus to monitor media | |
| US20230115724A1 (en) | Methods and apparatus for determining audience metrics across different media platforms | |
| AU2016216690A1 (en) | Methods and apparatus to measure exposure to streaming media | |
| EP2559237A1 (en) | Platform-independent interactivity with media broadcasts | |
| WO2019057034A1 (en) | Method, device, storage medium and electronic device for determining video segment | |
| US11736750B2 (en) | Methods and apparatus to generate reference signatures from streaming media | |
| US20160119672A1 (en) | Methods and apparatus to identify media using image recognition | |
| CN104135678A (en) | Method for realizing video advertisement putting in video on demand based on HLS protocol | |
| CN109982068B (en) | Method, apparatus, device and medium for evaluating quality of synthesized video | |
| CN110944217A (en) | Live broadcast information processing method and device, server and storage medium | |
| CN106028070A (en) | Live broadcast or carousel channel recommending method and device and video server | |
| US11838584B2 (en) | Methods and apparatus to monitor digital media | |
| CN111586432B (en) | Method and device for determining air-broadcast live broadcast room, server and storage medium | |
| CN113672486A (en) | Caton analysis method and CDN server | |
| CN109151498B (en) | Hotspot event processing method and device, server and storage medium | |
| CN104506892B (en) | Data adjustment method and device | |
| CN113438555A (en) | Bandwidth detection method and device, computer equipment and storage medium | |
| CN109587522A (en) | Switching at runtime advertisement video clarity processing method, playback terminal and storage medium | |
| CN109474486A (en) | Transmission of flow media data owes fast determination method, system and device | |
| US20240163531A1 (en) | Methods and apparatus to monitor media | |
| CN113747189A (en) | Display control method and device of live broadcast information, electronic equipment and computer medium | |
| KR20160130884A (en) | Method and System for User Behavior Analysis Based Video Streaming Data Transmission Architecture Determination | |
| CN114598897A (en) | Live scheduling method, device and readable medium | |
| CN117579867A (en) | Live broadcasting room line scheduling method and device and electronic equipment |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination |