CN113542158A - Broadcast television network-oriented data processing method - Google Patents
Broadcast television network-oriented data processing method Download PDFInfo
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- H—ELECTRICITY
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- H04L49/00—Packet switching elements
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- G06F9/542—Event management; Broadcasting; Multicasting; Notifications
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- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L47/00—Traffic control in data switching networks
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Abstract
The invention discloses a data processing method for a broadcast television network, which comprises the following steps: step one, collecting user data; after data acquisition is finished, embedding points in the acquired data by using front-end equipment; step three, performing distributed communication on the data after the points are buried; analyzing the distributed information; and step five, providing a model by utilizing online analysis processing, and importing the analysis result of the distributed information into the corresponding model. The invention provides a data processing method special for a broadcast television network, which can improve the outgoing processing efficiency of the broadcast television network, and the data structure corresponding to the method is more optimized and is more matched with the data characteristics of the broadcast television network.
Description
Technical Field
The invention relates to a data processing method, in particular to a data processing method for a broadcast television network.
Background
Broadcast television is a news delivery tool that delivers sound, images, video over radio waves or wires. Broadcasting only sound, called sound broadcasting; broadcasting images and sounds is called television broadcasting. A broadcast television network composed of a signal transmitting terminal and a plurality of receiving terminals becomes a broadcast television network.
Broadcast television networks are a transmission medium for broadcasting audio and video programs to a wide area via radio waves or via wires, and are collectively called broadcasting. Broadcasting only sound, called sound broadcasting; broadcasting images and sounds is called television broadcasting. In a narrow sense, broadcasting uses radio waves and wires to spread contents only by sound. In a broad sense, broadcasting includes what we commonly consider as sound-only broadcasting and television with both sound and images.
However, in the prior art, under the condition of massive data based on the broadcast and television convergence media, a dynamic and extensible user interest model supporting the convergence service is urgently needed to be researched according to the technical requirements of user behavior data analysis and decision assistance in the development of new media services of a new generation of broadcast and television network. The prior art does not have sufficient technologies such as personalized recommendation based on deep learning technology, content analysis and behavior collaborative analysis, and also lacks the development of a real-time data acquisition, cache and analysis system oriented to a broadcast and television multi-network multi-terminal fusion environment, so that the functions of real-time management and analysis of massive user data cannot be well supported, and nationwide broadcast and television user data management without regional and business differences is realized. Therefore, the traditional broadcast television network faces the difficulty of transforming value-added services, information push and business operation strategies under the background of transforming to the internet new media enterprises.
Disclosure of Invention
In view of the above problems in the prior art, an object of the present invention is to provide a data processing method for a broadcast television network.
In order to achieve the purpose, the invention adopts the following technical scheme:
a data processing method for a broadcast television network comprises the following steps: step one, collecting user data; after data acquisition is finished, embedding points in the acquired data by using front-end equipment; step three, performing distributed communication on the data after the points are buried; analyzing the distributed information; and step five, providing a model by utilizing online analysis processing, and importing the analysis result of the distributed information into the corresponding model.
Further, the first step comprises: the method defines the format of the collected data uniformly, defines log information according to the main body of the event occurrence, the event category, the attribute related to the event, the time of the event occurrence, the position of the event occurrence and the result of the event, and comprises the following steps: an application identification field, a device information field, a user identification field, an action event field, an action object field, an action time field, an action geographic field, and an action result field.
Further, the second step comprises: data reporting is carried out by clicking events at the bottom layer of the hook, and data sorting is carried out in a unified mode at the reported place; and setting whether to report the click event of the element according to the attribute value of the UI element, wherein the setting is used for acquiring the information of other elements related to the element.
Further, the third step comprises: exchanging information between distributed applications by using the message queue; message queues reside in memory or on disk, and the queues store messages until they are read by the application.
Further, the fourth step includes: the method comprises the steps of receiving data of a real-time stream by utilizing a Spark Streaming technology, splitting the data into batch data according to a certain time interval, processing the batch data by utilizing a Spark Engine technology, and finally obtaining processed batch result data.
Further, the fifth step comprises: performing data analysis on a large data volume by using an online analysis processing engine, and aiming at immobilized query in a Kylin-based MOLAP mode; the roap approach based on SparkSQL is directed to processing interactive queries.
Further, the Message Queue is in a Message Queue mode; the message is a byte array, and the supported data formats comprise String, JSON and Avro; binding a key value to each message; each message is sent to only one consumer in the group, and all messages with the same key value are sent to the consumers.
In the technical scheme, the invention provides the data processing method special for the broadcast television network, the outgoing processing efficiency of the broadcast television network can be improved, and the data structure corresponding to the method is more optimized and is more matched with the data characteristics of the broadcast television network.
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FIG. 1 is a flow chart of the method of the present invention.
Detailed Description
The technical scheme of the invention is further explained by combining the drawings and the embodiment.
The invention discloses a data processing method specially matched with broadcast television network data, which mainly comprises the following steps.
Firstly, a data acquisition end is used for acquiring user data.
The invention defines the format of the collected data in a collecting client side uniformly, defines log information according to the main body of event occurrence, the event type, the attribute related to the event, the time of event occurrence, the position of event occurrence and the result of the event, and mainly comprises a plurality of fields: an application identification field, a device information field, a user identification field, an action event field, an action object field, an action time field, an action geographical field, and an action result field, as shown in table 1 below.
Table 1: log record information field definition
The invention mainly collects EPG portal log, DCMS log and VS log. Wherein the EPG portal log is user real
For media service systems, the user's operations on the front-end UI mostly appear in three categories:
the first type is that a certain media page is opened, metadata information in the media page is browsed, and then interesting contents are clicked for further playing;
and in the second category, a certain page is opened, relevant information is searched according to the prompt of the UI, and then the page is clicked to submit, search results and play the results.
And the third category is operations in the playing process, such as fast-backward fast-forward pause playing and the like.
The behavior of the user can be summarized into three types: browse, click, input and control operations are important events that cause page changes and logic processing, and input is always associated with a click event.
Therefore, the above three categories are the objects to be acquired by the present invention.
In addition to the data information in these pages, the present invention also focuses on two important dimensional information: user and time. The user dimension is used for associating the behavior of the same user on a certain client, the adopted scheme is that a random UUID is generated by the back end, the front end caches the UUID after the UUID is taken, and if the UUID is a login user, the UUID can be associated through the user id in the metadata. The time dimension is mainly used for data statistics, and considering that the front end may delay reporting, the occurrence time of an event is added when the front end reports (at present, most of the mobile terminals normally used, time information should be automatically synchronized).
And step two, after the data acquisition is finished, point burying is carried out on the acquired data by using front-end equipment.
After the data format and the reporting mode are organized, how to bury the points is the key work of the front end. The traditional embedded point method organizes data at a position needing to be reported, calls an API (application programming interface) and transmits the data to a back end, such as Baidu statistics and Googlean analysis, which are the most commonly used method, but has the defect that the call needs to be embedded in a code and is coupled with business logic.
The invention adopts the click event at the bottom layer of hook to report the data, and the data is arranged in the reported place in a unified way. Whether the click event of the element is reported or not is set through the attribute value of the UI element, that is, the association relationship of the element is set through the attribute value of the UI element, so as to obtain the information of other elements associated with the element.
And step three, carrying out distributed communication on the data after the points are buried.
The invention adopts the message queue technology to exchange information among distributed applications. The message queue may reside on memory or disk, the queue storing messages until they are read by the application. With the message queue, applications can execute independently-they need not know each other's location, or wait for the receiving program to receive the message before continuing execution. In a distributed computing environment, in order to integrate distributed applications, developers need to provide an effective communication means for distributed applications in a heterogeneous network environment. In order to manage the information that needs to be shared, it is important to provide a common information exchange mechanism for the applications. A common Message Queue technology is Message Queue.
Communication mode of Message Queue:
point-to-point communication: the peer-to-peer mode is the most traditional and common communication mode, and supports various configuration modes such as one-to-one, one-to-many, many-to-one and various topologies such as tree and mesh.
Multicast broadcasting: MQs are suitable for different types of applications. Of importance, and also under development, are "multicast" applications, i.e., applications that are capable of sending messages to multiple Destination sites (Destination List). A single MQ instruction may be used to send a single message to multiple destination sites and ensure that each site is reliably provided with information. Not only does MQ provide multicast functionality, but also possesses intelligent message distribution functionality, where, when a message is sent to multiple users on the same system, the MQ sends a replicated version of the message and a list of recipients on the system to the target MQ system. The target MQ system locally replicates these messages and sends them to the queues on the list, thereby minimizing the traffic on the network.
Publish/Subscribe (Publish/Subscribe) mode: the publish/subscribe function enables the distribution of messages to break the limitation of the geographic orientation of the destination queue, enables the messages to be distributed according to specific topics and even contents, and enables users or application programs to receive required messages according to the topics or the contents. The publish/subscribe function loosens the coupling between the sender and the receiver, and the sender does not need to be concerned about the destination address of the receiver, and the receiver does not need to be concerned about the sending address of the message, but only sends and receives the message according to the subject of the message.
Cluster (Cluster): to simplify system configuration in a point-to-point communication mode, MQ provides a Cluster solution. The Cluster is similar to a Domain (Domain), when the queue managers in the Cluster communicate with each other, a message channel does not need to be established between every two Cluster managers, and the Cluster (Cluster) channel is adopted to communicate with other members, so that the system configuration is greatly simplified. In addition, load balancing can be automatically carried out among the queue managers in the cluster, and when a certain queue manager breaks down, other queue managers can take over the work of the queue manager, so that the high reliability of the system is greatly improved.
In the present invention, the information is a byte array in which the programmer can store any object, and the supported data formats include String, JSON, Avro. Kafka guarantees that the producer can send all messages to a given location by binding a key to each message. The consumers belonging to a certain consumer group subscribe to a topic, all messages related to the topic can be received by the subscribing consumers in a node-crossing manner, each message is only sent to one consumer in the group, and all messages with the same key value are guaranteed to be sent to the one consumer.
And step four, analyzing the distributed information.
The invention adopts Spark Streaming to receive data of real-time stream, and divides the data into batch data according to a certain time interval, and then processes the batch data through Spark Engine to finally obtain processed batch result data.
The Spark Streaming of the present invention splits a real-time data stream into discrete data streams (dsstream) in units of time slices. Discrete data stream (DStream) is used as a basic abstraction in Spark streaming, a set of discrete time-axis-keyed elastic Distributed data set (RDD) sequences is maintained inside the DStream, the RDD sequences respectively represent data sets in different time periods, and various operations on the DStream are finally mapped onto the internal RDD, so that seamless connection with Spark is realized.
The invention adopts Spark Streaming to analyze the broadcast television network data, has the advantages of supporting more complex processing logic, converting the original unstructured event data into structured data and storing the structured data into formatted data such as queue when the data is output to distributed file systems such as HDFS, Alluxio and the like, and is convenient for users to process the data by Spark SQL subsequently.
Aiming at the collected log data, the invention supplements and perfects each domain according to the application identification domain, the equipment information domain, the user identification domain, the action event domain, the action object domain, the action time domain, the action geographic domain, the action result domain and other event domains, filters and eliminates abnormal data based on the basic IP data information and the geographic domain information, and then loads the abnormal data into a distributed column storage by a uniform data object for further analysis and processing.
And step five, providing a model by utilizing online analysis processing, and importing the analysis result of the distributed information into the corresponding model.
The core of the online analytical processing (OLAP) engine is utilized by the method, the data analysis is carried out aiming at the large data volume, and support is provided for intelligent decision. A common classification of OLAP engines is as follows:
relational olap (rolap): ROLAP is based on a relational model, and aggregation operation is carried out according to original data during calculation. Common implementations, for example, small data volumes are performed using traditional relational databases such as MySQL, Oracle, etc., while large data volumes are performed using items such as distributed computing engines Spark SQL, Presto, etc.
Multidimensional OLAP (MOLAP): the MOLAP calculates in advance which metrics are based on a predefined model, e.g., which metrics are to be calculated from a certain dimension, and stores the results on the engine. When inquiring, simply summarizing according to the result.
Mixed olap (holap): the HOLAP comprises the two OLAP engines, and the HOLAP is routed to different engines for calculation according to the service scene.
OLAP adopted by the invention comprises the support of interactive query requirements and the fixed data query with higher response time requirements. For the fixed query, the query dimension is determined, and the query can be completed by the Kylin-based MOLAP technology, while for the interactive query, the requirements are variable in form and complex in logic, and the SparkSQL-based ROLAP technology can be completed.
Kylin-based MOLAP technology
Apache Kylin is an open-source distributed analysis engine, provides SQL query interface over Hadoop and multidimensional analysis capability to support very large scale data, was originally developed by eBay and contributed to the open-source community, and formally became the Apache top-level project in 2015 in 11 months. Kylin is a standard MOLAP scheme, the dimension and the index need to be predefined, calculation is carried out in advance, the result is stored in HBase, SQL is analyzed during query, the SQL is routed to the HBase to a corresponding table, and return can be carried out within a sub-second time range.
The core idea of Kylin is to pre-calculate, namely pre-calculate the measurement possibly used by multidimensional analysis by changing space time, store the calculated result into Cube and store the Cube into Hbase, thereby converting the operations of high-complexity aggregation operation, multi-table connection and the like into the query of the pre-calculated result for direct access during query.
In summary, the present invention provides a data processing method with a new architecture, which aims at the data characteristics of the broadcast television network, and therefore has better efficiency and a more reasonable architecture for data analysis of the broadcast television network.
It should be understood by those skilled in the art that the above embodiments are only for illustrating the present invention and are not to be used as a limitation of the present invention, and that changes and modifications to the above described embodiments are within the scope of the claims of the present invention as long as they are within the spirit and scope of the present invention.
Claims (7)
1. A data processing method for a broadcast television network is characterized by comprising the following steps:
step one, collecting user data;
after data acquisition is finished, embedding points in the acquired data by using front-end equipment;
step three, performing distributed communication on the data after the points are buried;
analyzing the distributed information;
and step five, providing a model by utilizing online analysis processing, and importing the analysis result of the distributed information into the corresponding model.
2. The data processing method for the broadcast television network according to claim 1, wherein the step one comprises:
the method defines the format of the collected data uniformly, defines log information according to the main body of the event occurrence, the event category, the attribute related to the event, the time of the event occurrence, the position of the event occurrence and the result of the event, and comprises the following steps: an application identification field, a device information field, a user identification field, an action event field, an action object field, an action time field, an action geographic field, and an action result field.
3. The data processing method for the broadcast television network according to claim 2, wherein the second step comprises:
data reporting is carried out by clicking events at the bottom layer of the hook, and data sorting is carried out in a unified mode at the reported place;
and setting whether to report the click event of the element according to the attribute value of the UI element, wherein the setting is used for acquiring the information of other elements related to the element.
4. The data processing method for the broadcast television network according to claim 3, wherein the third step comprises:
exchanging information between distributed applications by using the message queue;
message queues reside in memory or on disk, and the queues store messages until they are read by the application.
5. The data processing method for the broadcast television network according to claim 4, wherein the fourth step comprises:
the method comprises the steps of receiving data of a real-time stream by utilizing a Spark Streaming technology, splitting the data into a batch of data according to a certain time interval, processing the batch of data by utilizing a Spark Engine technology, and finally obtaining processed batch of result data.
6. The data processing method for the broadcast television network according to claim 5, wherein the step five comprises:
data analysis is performed for large data volumes using an online analytical processing engine,
query for immobilization by way of Kylin-based mol ap;
the roap approach based on SparkSQL is directed to processing interactive queries.
7. The broadcast television network-oriented data processing method of claim 4, wherein:
the Message Queue is in a Message Queue mode;
the message is a byte array, and the supported data formats comprise String, JSON and Avro;
binding a key value to each message;
each message is sent to only one consumer in the group, and all messages with the same key value are sent to the consumers.
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