CN109889577B - Streaming media data flow analysis method and system - Google Patents
Streaming media data flow analysis method and system Download PDFInfo
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Abstract
The technical scheme of the invention comprises a method and a system for analyzing the flow of streaming media data, which are used for realizing the following steps: reading a log file of a website to obtain browsing records of each user in the website streaming media, and acquiring information of the website streaming media; calculating the stream media expected value aiming at each user according to the acquired log file and the website stream media information; when a user browses a website, according to the expected value of streaming media calculated by the user in the background, the recommended content is launched in the designated area of the website according to the expected value. The invention has the beneficial effects that: the method has the advantages that the streaming media close to the user interest are pushed to the user, the implementation mode is simple, the labor cost is low, a huge user scale is not needed to be used as an extraction sample, the data to be evaluated only need browsing records of the user and streaming media information stored in a target website, and massive content data are not needed.
Description
Technical Field
The invention relates to a method and a system for analyzing streaming media data flow, and belongs to the technical field of internet.
Background
With the rapid development of the internet industry in recent years, the rapid expansion of massive streaming media makes the selection of users more difficult. Therefore, the target client is analyzed by utilizing the flow direction of the streaming media data flow, and the corresponding streaming media content is put in aiming at the target client in a purposeful mode, so that the efficient streaming media data push is realized.
There are two main methods currently used to generate network traffic:
firstly, network flow playback, namely, a network sniffer is utilized to sniff a network and record acquired data in a log file, and then network flow is generated according to the content recorded in the log file;
and secondly, generating model flow, namely establishing a mathematical model for the network flow after the network characteristics are known, and sending a data packet according to the mathematical model, so as to generate the network flow which accords with the overall network characteristics.
The flow generated by the method I is constrained by a log file, and the flow generated each time is the same, so that the flow is too mechanical; and starting from a probability model of network traffic overall obedience, the generated network traffic is closer to the real traffic on the whole, but the behavior of a single user cannot be reflected, so that the method is insufficient in many environments. For example, in a service-oriented network, when pushing a service, it is often necessary to count the number of requests and preference degrees of a single user for a certain service content to determine whether to push the service, which cannot be effectively supported by the conventional method.
Disclosure of Invention
In order to solve the above problems, an object of the present invention is to provide a method and a system for analyzing streaming media data traffic, which reads a log file of a website, obtains browsing records of each user on the streaming media of the website, and obtains information of the streaming media of the website; calculating the stream media expected value aiming at each user according to the acquired log file and the website stream media information; when a user browses a website, according to the expected value of streaming media calculated by the user in the background, the recommended content is launched in the designated area of the website according to the expected value.
The technical scheme adopted by the invention for solving the problems is as follows: a method for analyzing streaming media data flow is characterized by comprising the following steps: s100, reading a log file of a website to obtain browsing records of each user on the streaming media of the website, wherein the browsing records comprise playing times of each streaming media, total duration of each streaming media, duration of each streaming media watched by the user, streaming media browsed by the user on the website first every day and pause times of the user on the streaming media watched; s200, acquiring information of the website streaming media, including playing amount, uploading time, total streaming media duration and streaming media label types; s300, calculating a streaming media expected value for each user according to the acquired log file and the website streaming media information; s400, when the user browses the website, according to the expected value of the streaming media calculated by the user in the background, the recommended content is launched in the designated area of the website according to the expected value.
Further, the S100 includes: s101, acquiring a website log file of a streaming media user by adopting a network sniffer, wherein the website log file comprises browsing records of each user, and the browsing records comprise playing times of each streaming media, total time of each streaming media, time of each streaming media watched by the user, streaming media first browsed by the user by opening a website every day and pause times of the user in watching the streaming media;
further, the S300 includes: s301, marking each streaming media label browsed by a user, and inducing according to categories; s302, dividing the streaming media corresponding to each label with all the streaming media browsed by the user to obtain the proportion of each type of streaming media in the interest of the user label; s303, taking the total time length of the streaming media browsed by the user and the time length of each streaming media watched by the user as input sources, and calculating expected values of each streaming media in each user by using a mathematical model, wherein the mathematical model comprises but is not limited to a discrete random model and a continuous random model; s304, combining the proportion of the streaming media in the user tag interest and the expected value of the streaming media in each user, wherein the proportion of the streaming media and the expected value of the streaming media can be customized to obtain the expected value of the streaming media.
Further, the S300 further includes: s305, combining the playing times of each streaming media, whether the streaming media are browsed by the user for the first time on the day and the pause times of the streaming media watched by the user as a weight ratio with the streaming media expected value obtained in the step S304; s306, obtaining the expected value of the processed streaming media.
Further, the S400 includes: s401, detecting user information of a currently browsed website, and putting a specified amount of streaming media contents into a library to be pushed according to a streaming media expected value of a user; s402, when the user clicks the pause button is detected, a certain amount of streaming media are extracted from the library to be pushed according to the extraction rule in the content appointed by the webpage for pushing, wherein the certain amount can be customized.
Further, the extraction rule is to perform random pushing according to the expected value of the streaming media, wherein the probability of random pushing is proportional to the expected value of the streaming media.
The other aspect of the technical scheme adopted by the invention for solving the problems is as follows: a streaming media data traffic analysis system, comprising: the reading module is used for reading the log file of the website to obtain browsing records of each user on the streaming media of the website, wherein the browsing records comprise the playing times of each streaming media, the total time length of each streaming media, the time length of each streaming media watched by the user, the streaming media first browsed by the user when the user opens the website every day and the pause times of the user on viewing the streaming media; the capturing module is used for acquiring information of the website streaming media, wherein the information comprises playing amount, uploading time, total streaming media duration and streaming media label types; the analysis module is used for calculating the stream media expected value aiming at each user according to the acquired log file and the website stream media information; and the pushing module is used for launching the recommended content in the specified area of the website according to the expected value of the streaming media calculated by the user in the background when the user browses the website.
Further, the reading module includes a network sniffer for acquiring website log files of streaming media users, where the website log files include browsing records of each user, and the browsing records include playing times of each streaming media, total duration of each streaming media, duration of each streaming media watched by the user, streaming media first browsed by the user by opening a website every day, and pause times of the user in viewing streaming media;
further, the analysis module comprises: the marking induction module is used for marking each streaming media label browsed by the user and inducing according to the category; the calculation module is used for dividing the streaming media corresponding to each tag with all the streaming media browsed by the user to obtain the proportion of each type of streaming media in the interest of the user tag, taking the total time length of the streaming media browsed by the user and the time length of each streaming media watched by the user as input sources, and calculating the expected value of each streaming media in each user by using a mathematical model, wherein the mathematical model comprises but is not limited to a discrete random model and a continuous random model; and the output module is used for combining the results obtained by the calculation module and the reading module according to a certain proportion to obtain the stream media expected value.
Further, the pushing module comprises: the detection module is used for detecting the user information of the currently browsed website and putting the specified amount of streaming media contents into a library to be pushed according to the streaming media expected value of the user; the to-be-pushed library module is used for temporarily storing the streaming media content to be pushed; and the extraction module is used for randomly pushing the streaming media from the library to be pushed according to the extraction rule and the expected value of the streaming media, wherein the probability of random pushing is in direct proportion to the expected value of the streaming media.
The invention has the beneficial effects that: the method has the advantages that the streaming media close to the user interest are pushed to the user, the implementation mode is simple, the labor cost is low, a huge user scale is not needed to be used as an extraction sample, the data to be evaluated only need browsing records of the user and streaming media information stored in a target website, and massive content data are not needed.
Drawings
FIG. 1 is a schematic flow diagram of the process of the present invention;
FIG. 2 is a schematic diagram of the system architecture of the present invention;
fig. 3 is a schematic diagram of probability density.
Detailed Description
The conception, the specific structure and the technical effects of the present invention will be clearly and completely described in conjunction with the embodiments and the accompanying drawings to fully understand the objects, the schemes and the effects of the present invention.
It should be noted that, unless otherwise specified, when a feature is referred to as being "fixed" or "connected" to another feature, it may be directly fixed or connected to the other feature or indirectly fixed or connected to the other feature. Furthermore, the descriptions of upper, lower, left, right, etc. used in the present disclosure are only relative to the mutual positional relationship of the constituent parts of the present disclosure in the drawings. As used in this disclosure, the singular forms "a", "an", and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise. Furthermore, unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art. The terminology used in the description herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention. As used herein, the term "and/or" includes any combination of one or more of the associated listed items.
It will be understood that, although the terms first, second, third, etc. may be used herein to describe various elements, these elements should not be limited by these terms. These terms are only used to distinguish one element of the same type from another. For example, a first element could be termed a second element, and, similarly, a second element could be termed a first element, without departing from the scope of the present disclosure. The use of any and all examples, or exemplary language ("e.g.," such as "or the like") provided herein, is intended merely to better illuminate embodiments of the invention and does not pose a limitation on the scope of the invention unless otherwise claimed.
Referring to figure 1 a schematic flow diagram of the method of the present invention,
s100, reading a log file of a website to obtain browsing records of each user on the streaming media of the website, wherein the browsing records comprise playing times of each streaming media, total duration of each streaming media, duration of each streaming media watched by the user, streaming media browsed by the user on the website first every day and pause times of the user on the streaming media watched; s200, acquiring information of the website streaming media, including playing amount, uploading time, total streaming media duration and streaming media label types; s300, calculating a streaming media expected value for each user according to the acquired log file and the website streaming media information; s400, when the user browses the website, according to the expected value of the streaming media calculated by the user in the background, the recommended content is launched in the designated area of the website according to the expected value.
S100 includes: s101, acquiring a website log file of a streaming media user by adopting a network sniffer, wherein the website log file comprises browsing records of each user, and the browsing records comprise playing times of each streaming media, total time of each streaming media, time of each streaming media watched by the user, streaming media first browsed by the user by opening a website every day and pause times of the user in watching the streaming media;
s300 comprises the following steps: s301, marking each streaming media label browsed by a user, and inducing according to categories; s302, dividing the streaming media corresponding to each label with all the streaming media browsed by the user to obtain the proportion of each type of streaming media in the interest of the user label; s303, taking the total time length of the streaming media browsed by the user and the time length of each streaming media watched by the user as input sources, and calculating expected values of each streaming media in each user by using a mathematical model, wherein the mathematical model comprises but is not limited to a discrete random model and a continuous random model; s304, combining the proportion of the streaming media in the user tag interest and the expected value of the streaming media in each user, wherein the proportion of the streaming media and the expected value of the streaming media can be customized to obtain the expected value of the streaming media.
Random variables can be divided into two basic types according to their possible values:
discrete type
Discrete random variables are finite or several in a certain interval. Such as the number of births, deaths of a person in a certain area, the number of effective patients, the number of ineffective patients for a certain drug treatment, etc. Discrete random variables are generally classified according to a probability mass function, and are mainly classified into: bernoulli random variables, binomial random variables, geometric random variables, and poisson random variables.
Continuous type
There are infinite continuous (continuous) random variables, that is, there are infinite variables in a certain interval, or the values cannot be listed one by one. Such as the length and weight of healthy male adults in a certain area, the serum transaminase measurement of a batch of infectious hepatitis patients, etc. There are several important continuous random variables that often appear in probability theory, such as: uniform random variables, exponential random variables, gamma random variables, and normal random variables.
S300 further comprises: s305, combining the playing times of each streaming media, whether the streaming media are browsed by the user for the first time on the day and the pause times of the streaming media watched by the user as a weight ratio with the streaming media expected value obtained in the step S304; s306, obtaining the expected value of the processed streaming media.
S400 includes: s401, detecting user information of a currently browsed website, and putting a specified amount of streaming media contents into a library to be pushed according to a streaming media expected value of a user; s402, when the user clicks the pause button is detected, a certain amount of streaming media are extracted from the library to be pushed according to the extraction rule in the content appointed by the webpage for pushing, wherein the certain amount can be customized.
The extraction rule is that random pushing is carried out according to the size of the stream media expected value, wherein the probability of random pushing is in direct proportion to the size of the stream media expected value.
Referring to figure 2 is a schematic diagram of the system architecture of the present invention,
the method comprises the following steps: the reading module is used for reading the log file of the website to obtain browsing records of each user on the streaming media of the website, wherein the browsing records comprise the playing times of each streaming media, the total time length of each streaming media, the time length of each streaming media watched by the user, the streaming media first browsed by the user when the user opens the website every day and the pause times of the user on viewing the streaming media; the capturing module is used for acquiring information of the website streaming media, wherein the information comprises playing amount, uploading time, total streaming media duration and streaming media label types; the analysis module is used for calculating the stream media expected value aiming at each user according to the acquired log file and the website stream media information; and the pushing module is used for launching the recommended content in the specified area of the website according to the expected value of the streaming media calculated by the user in the background when the user browses the website.
The reading module comprises a network sniffer and is used for acquiring website log files of streaming media users, wherein the website log files comprise browsing records of each user, and the browsing records comprise playing times of each streaming media, total time of each streaming media, time of each streaming media watched by the user, streaming media first browsed by the user by opening a website every day and pause times of the user in watching the streaming media;
the analysis module comprises: the marking induction module is used for marking each streaming media label browsed by the user and inducing according to the category; the calculation module is used for dividing the streaming media corresponding to each tag with all the streaming media browsed by the user to obtain the proportion of each type of streaming media in the interest of the user tag, taking the total time length of the streaming media browsed by the user and the time length of each streaming media watched by the user as input sources, and calculating the expected value of each streaming media in each user by using a mathematical model, wherein the mathematical model comprises but is not limited to a discrete random model and a continuous random model; and the output module is used for combining the results obtained by the calculation module and the reading module according to a certain proportion to obtain the stream media expected value.
The push module includes: the detection module is used for detecting the user information of the currently browsed website and putting the specified amount of streaming media contents into a library to be pushed according to the streaming media expected value of the user; the to-be-pushed library module is used for temporarily storing the streaming media content to be pushed; and the extraction module is used for randomly pushing the streaming media from the library to be pushed according to the extraction rule and the expected value of the streaming media, wherein the probability of random pushing is in direct proportion to the expected value of the streaming media.
Referring to figure 3 a schematic diagram of probability density is shown,
the probability density of the distribution is shown in fig. 3, where the X axis represents the value and the Y axis represents the probability f (X) of taking the value, where 0 ═ f (X) <1. The method for generating the random number according to the obedient distribution and the parameters comprises the following steps:
generating a random number x between 1 and N (N is the maximum value of the value), and obtaining the probability f (x) of the value according to the probability density and the parameters;
and (2) randomly generating a number i between 0 and 1, wherein if i < ═ f (x) is exceeded, the random number x is needed, otherwise, turning to (i) until a corresponding random number is generated. Thus, when the probability of taking the number x according to the probability density is large, the probability of being selected is large, and a large number of random numbers generated in this way are consistent with the designated distribution as a whole.
Simply put, a random variable refers to a representation of the number of random events. For example, a batch of animals injected with a poison, the number of animals that die within a certain time; the measured value of the hemoglobin amount of each of a plurality of healthy male adults in a certain area; and so on. There are other phenomena that are not directly expressed as numbers, such as gender of the population, positive or negative test results, etc., but we can specify that men are 1 and women are 0, and the non-number flag can also be expressed as numbers. The quantities mentioned in these examples, although they may be of various particulars, represent the same thing from a mathematical point of view, that is to say that each variable can take different values at random, and that it is impossible to predict that this variable will take a certain value before carrying out the test or measurement.
It should be recognized that embodiments of the present invention can be realized and implemented by computer hardware, a combination of hardware and software, or by computer instructions stored in a non-transitory computer readable memory. The methods may be implemented in a computer program using standard programming techniques, including a non-transitory computer-readable storage medium configured with the computer program, where the storage medium so configured causes a computer to operate in a specific and predefined manner, according to the methods and figures described in the detailed description. Each program may be implemented in a high level procedural or object oriented programming language to communicate with a computer system. However, the program(s) can be implemented in assembly or machine language, if desired. In any case, the language may be a compiled or interpreted language. Furthermore, the program can be run on a programmed application specific integrated circuit for this purpose.
Further, the operations of processes described herein can be performed in any suitable order unless otherwise indicated herein or otherwise clearly contradicted by context. The processes described herein (or variations and/or combinations thereof) may be performed under the control of one or more computer systems configured with executable instructions, and may be implemented as code (e.g., executable instructions, one or more computer programs, or one or more applications) collectively executed on one or more processors, by hardware, or combinations thereof. The computer program includes a plurality of instructions executable by one or more processors.
Further, the method may be implemented in any type of computing platform operatively connected to a suitable interface, including but not limited to a personal computer, mini computer, mainframe, workstation, networked or distributed computing environment, separate or integrated computer platform, or in communication with a charged particle tool or other imaging device, and the like. Aspects of the invention may be embodied in machine-readable code stored on a non-transitory storage medium or device, whether removable or integrated into a computing platform, such as a hard disk, optically read and/or write storage medium, RAM, ROM, or the like, such that it may be read by a programmable computer, which when read by the storage medium or device, is operative to configure and operate the computer to perform the procedures described herein. Further, the machine-readable code, or portions thereof, may be transmitted over a wired or wireless network. The invention described herein includes these and other different types of non-transitory computer-readable storage media when such media include instructions or programs that implement the steps described above in conjunction with a microprocessor or other data processor. The invention also includes the computer itself when programmed according to the methods and techniques described herein.
A computer program can be applied to input data to perform the functions described herein to transform the input data to generate output data that is stored to non-volatile memory. The output information may also be applied to one or more output devices, such as a display. In a preferred embodiment of the invention, the transformed data represents physical and tangible objects, including particular visual depictions of physical and tangible objects produced on a display.
The above description is only a preferred embodiment of the present invention, and the present invention is not limited to the above embodiment, and any modifications, equivalent substitutions, improvements, etc. within the spirit and principle of the present invention should be included in the protection scope of the present invention as long as the technical effects of the present invention are achieved by the same means. The invention is capable of other modifications and variations in its technical solution and/or its implementation, within the scope of protection of the invention.
Claims (8)
1. A method for analyzing streaming media data flow is characterized by comprising the following steps:
s100, reading a log file of a website to obtain browsing records of each user on the streaming media of the website, wherein the browsing records comprise playing times of each streaming media, total duration of each streaming media, duration of each streaming media watched by the user, streaming media browsed by the user on the website first every day and pause times of the user on the streaming media watched;
s200, acquiring information of the website streaming media, including playing amount, uploading time, total streaming media duration and streaming media label types;
s300, calculating a streaming media expected value for each user according to the acquired log file and the website streaming media information;
s400, when a user browses a website, according to an expected value of streaming media calculated by the user in the background, delivering recommended content in a designated area of the website according to the expected value;
wherein the S300 includes:
s301, marking each streaming media label browsed by a user, and inducing according to categories;
s302, dividing the streaming media corresponding to each label with all the streaming media browsed by the user to obtain the proportion of each type of streaming media in the interest of the user label;
s303, taking the total time length of the streaming media browsed by the user and the time length of each streaming media watched by the user as input sources, and calculating expected values of each streaming media in each user by using a mathematical model, wherein the mathematical model comprises but is not limited to a discrete random model and a continuous random model;
s304, combining the proportion of the streaming media in the user tag interest and the expected value of the streaming media in each user, wherein the proportion of the streaming media and the expected value of the streaming media can be customized to obtain the expected value of the streaming media.
2. The streaming media data traffic analysis method according to claim 1, wherein the S100 includes:
s101, a website log file of a streaming media user is obtained by adopting a network sniffer, wherein the website log file comprises browsing records of each user, and the browsing records comprise playing times of each streaming media, total time of each streaming media, time of each streaming media watched by the user, streaming media first browsed by the user by opening a website every day and pause times of the user in watching the streaming media.
3. The streaming media data traffic analyzing method according to claim 1, wherein the S300 further comprises:
s305, combining the playing times of each streaming media, whether the streaming media are browsed by the user for the first time on the day and the pause times of the streaming media watched by the user as a weight ratio with the streaming media expected value obtained in the step S304;
s306, obtaining the expected value of the processed streaming media.
4. The streaming media data traffic analyzing method according to claim 1, wherein the S400 includes:
s401, detecting user information of a currently browsed website, and putting a specified amount of streaming media contents into a library to be pushed according to a streaming media expected value of a user;
s402, when the user clicks the pause button is detected, a certain amount of streaming media are extracted from the library to be pushed according to the extraction rule in the content appointed by the webpage for pushing, wherein the certain amount can be customized.
5. The streaming media data traffic analyzing method of claim 4, wherein the extraction rule is to perform random pushing according to the expected value of the streaming media, and a probability of the random pushing is proportional to the expected value of the streaming media.
6. A streaming media data traffic analysis system, comprising:
the reading module is used for reading the log file of the website to obtain browsing records of each user on the streaming media of the website, wherein the browsing records comprise the playing times of each streaming media, the total time length of each streaming media, the time length of each streaming media watched by the user, the streaming media first browsed by the user when the user opens the website every day and the pause times of the user on viewing the streaming media;
the capturing module is used for acquiring information of the website streaming media, wherein the information comprises playing amount, uploading time, total streaming media duration and streaming media label types;
the analysis module is used for calculating the stream media expected value aiming at each user according to the acquired log file and the website stream media information;
the pushing module is used for delivering recommended contents in a specified area of the website according to expected values of streaming media calculated by the user in the background when the user browses the website;
the analysis module includes:
the marking induction module is used for marking each streaming media label browsed by the user and inducing according to the category;
the calculation module is used for dividing the streaming media corresponding to each tag with all the streaming media browsed by the user to obtain the proportion of each type of streaming media in the interest of the user tag, taking the total time length of the streaming media browsed by the user and the time length of each streaming media watched by the user as input sources, and calculating the expected value of each streaming media in each user by using a mathematical model, wherein the mathematical model comprises but is not limited to a discrete random model and a continuous random model;
and the output module is used for combining the results obtained by the calculation module and the reading module according to a certain proportion to obtain the stream media expected value.
7. The streaming media data traffic analysis system according to claim 6, wherein the reading module includes a web sniffer for obtaining a website log file of the streaming media user, wherein the website log file includes browsing records of each user, and the browsing records include playing times of each streaming media, total duration of each streaming media, duration of each streaming media watched by the user, streaming media first browsed by a user opening a website every day, and pause times of the user in viewing streaming media.
8. The streaming media data traffic analysis system of claim 6, wherein the push module comprises:
the detection module is used for detecting the user information of the currently browsed website and putting the specified amount of streaming media contents into a library to be pushed according to the streaming media expected value of the user;
the to-be-pushed library module is used for temporarily storing the streaming media content to be pushed;
and the extraction module is used for randomly pushing the streaming media from the library to be pushed according to the extraction rule and the expected value of the streaming media, wherein the probability of random pushing is in direct proportion to the expected value of the streaming media.
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| CN107562848A (en) * | 2017-08-28 | 2018-01-09 | 广州优视网络科技有限公司 | A kind of video recommendation method and device |
| CN109165367A (en) * | 2018-07-02 | 2019-01-08 | 昆明理工大学 | A kind of news recommended method subscribed to based on RSS |
| CN109189951A (en) * | 2018-07-03 | 2019-01-11 | 上海掌门科技有限公司 | A kind of multimedia resource recommended method, equipment and storage medium |
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