Homologous viewing data analysis method and device, medium and electronic equipment

Abstract

The invention relates to the technical field of big data processing, in particular to a homologous viewing data analysis method and device, a medium and electronic equipment. The method comprises the following steps: acquiring viewing data of each home gateway reported by a viewing data acquisition system, wherein the viewing data is obtained by acquiring playing data of each terminal device associated with the home gateway by the viewing data acquisition system; and determining audience rating statistical data of each home gateway for a target audience rating program according to each piece of audience rating data, wherein the audience rating statistical data is obtained according to audience rating statistical sub-data of each home gateway. According to the invention, the audience rating, market share and other analysis data of the target audience program in the network platform can be accurately analyzed; high commercial value is provided for commercial means such as accurate pushing of program preferences for each user in each home gateway by a subsequent big data platform, audience rating analysis of each audience program provided for a streaming media platform, and the like.

Description

Background Art

[0002] The audience data of streaming media has extremely high commercial value in the field of video consumption. The analysis of streaming media ratings can stimulate the use value of massive user data and promote the deep integration of the digital economy and the real economy. However, in the existing analysis of audience data, only the audience behavior of some specific devices (such as set-top boxes, Internet TV IPTV) can be analyzed. The analysis value of the audience data collected in this way is low, and it is impossible to accurately conduct a comprehensive audience statistics and analysis of the viewing programs in the market. Therefore, the problem existing in the prior art is that the analysis of user viewing behavior is one-sided, and it is impossible to accurately and effectively count the audience data of each user in the market. Summary of the invention

[0003] The present application provides a method, device, medium and electronic device for analyzing homologous audience data to solve one or more technical problems existing in the prior art and at least provide a beneficial choice or create conditions.

[0004] Other features and advantages of the present application will become apparent from the following detailed description, or may be learned in part by the practice of the present application.

[0005] According to one aspect of an embodiment of the present application, a method for analyzing homologous audience ratings data is provided. The method is executed on the big data platform, and the method includes: Acquiring viewing data of each home gateway reported by a viewing data collection system, wherein the viewing data is obtained by collecting playback data of each terminal device associated with the home gateway by the viewing data collection system; Determine the viewing statistics of each home gateway for the target viewing program according to each viewing data, wherein the viewing statistics are obtained according to the viewing statistics sub-data of each home gateway; The single viewing statistics sub-data is obtained through the following steps: Acquire target viewing data of a target home gateway corresponding to the viewing statistics sub-data; According to the target viewing data, determining that there is target same-source viewing data of a cross-terminal session in a plurality of the terminal devices associated with the target home gateway, the target same-source viewing data comprising a plurality of same-source viewing sub-data, each of the same-source viewing sub-data corresponding to the same target viewing program; The plurality of homologous viewing sub-data in the target homologous viewing data are integrated to obtain viewing statistical sub-data of the target home gateway viewing the target viewing program.

[0006] In one embodiment of the present application, based on the above scheme, the target viewing data includes multiple viewing sub-data, each of which corresponds to each terminal device one by one; and determining that there is target homologous viewing data of a cross-terminal session in multiple target terminal devices according to the target viewing data includes: Searching out each of the viewing sub-data for each homologous viewing sub-data associated with the target viewing program; The target homologous audience rating data is determined according to each of the homologous audience rating sub-data.

[0007] In one embodiment of the present application, based on the above solution, the cross-terminal session is identified by the following steps: Performing data cleaning on the target viewing data to obtain terminal devices corresponding to each of the viewing sub-data in the target viewing data and target viewing programs corresponding to each of the terminal devices; If a plurality of target terminal devices all play the target viewing program, it is determined that the cross-terminal session exists between the plurality of target terminal devices.

[0008] In one embodiment of the present application, based on the above scheme, the homologous viewing sub-data has duration information and playback platform information, the duration information is used to characterize the playback duration of the target viewing program by the terminal device corresponding to the homologous viewing sub-data, and the playback platform information is used to characterize the playback platform used by the terminal device corresponding to the homologous viewing sub-data when playing the target viewing program; the integration of multiple homologous viewing sub-data in the target homologous viewing data to obtain the viewing statistical sub-data of the target home gateway viewing the target viewing program includes: The duration information of each of the homologous viewing sub-data and the broadcast platform information are analyzed and counted to obtain the viewing statistical sub-data of the target home gateway viewing the target viewing program.

[0009] In one embodiment of the present application, based on the above scheme, determining the viewing statistics of each home gateway for the target viewing program according to each viewing data includes: Determine the ratings information of the target viewing program on each of the playback platforms according to the duration information of each of the viewing statistical sub-data and the playback platform information; Determine the market share information of each of the broadcasting platforms for the target viewing program according to the viewing rate information; The audience rating statistics are generated based on the audience rating information and the market share information.

[0010] In one embodiment of the present application, based on the above solution, the method further includes: Determine the definition information of the target viewing program when it is played on each of the playback platforms and the identity level information of the user logged in on each of the playback platforms according to the viewing statistics sub-data and the playback platform information of the viewing statistics sub-data; A media information relationship table corresponding to each of the playback platforms is generated based on each of the definition rate information and each of the user identity level information.

[0011] In one embodiment of the present application, based on the above-mentioned solution, the viewing data collection system and each terminal device transmit the viewing sub-data through a preset message protocol, and the association relationship between the target home gateway and each terminal device is determined by the following steps: Obtaining the protocol type of the preset message protocol; According to the protocol type and the message characteristics of the viewing sub-data, each terminal device associated with the target home gateway is determined.

[0012] According to one aspect of an embodiment of the present application, a homologous viewing data analysis device is provided, which is applied to a data collection and analysis system, wherein the data collection and analysis system includes a big data platform and a viewing data collection system, wherein the viewing data collection system is used to collect viewing data of each terminal device associated with a home gateway, and the device includes: An acquisition unit, used to acquire viewing data of each home gateway reported by a viewing data acquisition system, wherein the viewing data is acquired by the viewing data acquisition system by acquiring playback data of each terminal device associated with the home gateway; A viewing statistics unit, used for determining viewing statistics of each home gateway for a target viewing program according to each viewing data, wherein the viewing statistics are obtained according to the viewing statistics sub-data of each home gateway; The single viewing statistics sub-data is obtained through the following steps: Acquire target viewing data of a target home gateway corresponding to the viewing statistics sub-data; According to the target viewing data, determining that there is target same-source viewing data of a cross-terminal session in a plurality of the terminal devices associated with the target home gateway, the target same-source viewing data comprising a plurality of same-source viewing sub-data, each of the same-source viewing sub-data corresponding to the same target viewing program; The plurality of homologous viewing sub-data in the target homologous viewing data are integrated to obtain viewing statistical sub-data of the target home gateway viewing the target viewing program.

[0013] According to one aspect of an embodiment of the present application, a computer-readable storage medium is provided, on which a computer program is stored. The computer program includes executable instructions. When the executable instructions are executed by a processor, the homologous audience data analysis method described in the above embodiment is implemented.

[0014] According to one aspect of an embodiment of the present application, an electronic device is provided, comprising: one or more processors; and a memory for storing executable instructions of the processors, wherein when the executable instructions are executed by the one or more processors, the one or more processors implement the homologous audience data analysis method as described in the above embodiments.

[0015] The beneficial effect of the present application is that the viewing data collection system proposed in the present application can collect the viewing data of each terminal device associated with the home gateway, that is, it can not only collect the viewing data of large-screen terminal devices such as set-top boxes, Internet TV IPTV, etc., but also collect the viewing data of small-screen terminal devices such as mobile phones, computers, tablets, etc. connected to the network in the home gateway.

[0016] Based on this, the present application can collect the viewing data of each terminal device associated with each home gateway, and then conduct a comprehensive viewing behavior statistics and analysis based on the collected viewing data. Taking a single home gateway as a unit, that is, taking the target home gateway as an example, first conduct an integrated analysis of the viewing data of a single target home gateway, and then merge the target homologous viewing data of multiple terminal devices in the target home gateway that exist across terminal sessions, and then merge the multiple homologous viewing sub-data in the target homologous viewing data, so that the viewing statistical sub-data of users watching a certain target viewing program through different terminal devices can be clearly analyzed.

[0017] Finally, by integrating and counting the multiple audience statistical sub-data of each home gateway, we can obtain the audience statistical data of each target viewing program, thereby accurately analyzing the ratings, market share and other analytical data of the target viewing program in the network platform, which provides a high reference value for subsequent big data platforms to accurately push program preferences to each user in each home gateway, provide streaming media platforms with ratings analysis of each viewing program, and other commercial means. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the following is a brief description of the drawings required for use in the description of the embodiments. Obviously, the drawings described are only part of the embodiments of the present application, not all of the embodiments, and those skilled in the art can also obtain other design solutions and drawings based on these drawings without creative work.

[0019] Figure 1A schematic diagram of an implementation environment of a method for analyzing homologous audience ratings data according to an embodiment of the present application; Figure 2 It is a flowchart of a method for analyzing homologous audience ratings data according to an embodiment of the present application; Figure 3 It is the overall logic diagram of the audience data collection in the existing scheme; Figure 4 The overall logic diagram of audience data collection according to an embodiment of the present application is shown; Figure 5 is a schematic diagram of a system structure of an electronic device according to an embodiment of the present application; Figure 6 is a block diagram of a homologous viewing data analysis device according to an embodiment of the present application; Figure 7 A logic block diagram of audience data analysis according to an embodiment of the present application; Figure 8 It is an overall logic diagram of audience data collection and statistical analysis according to an embodiment of the present application; Fig. 9 It is a schematic diagram of a process of obtaining single viewing statistics sub-data according to an embodiment of the present application; Fig.10 The figure is a schematic diagram of a process for obtaining viewing statistics according to an embodiment of the present application. DETAILED DESCRIPTION

[0020] Example embodiments will now be described more fully with reference to the accompanying drawings. However, example embodiments can be implemented in a variety of forms and should not be construed as limited to the examples set forth herein; rather, these embodiments are provided so that this application will be more comprehensive and complete and fully convey the concept of the example embodiments to those skilled in the art.

[0021] In addition, described feature, structure or characteristic can be combined in one or more embodiments in any suitable manner. In the following description, many specific details are provided to provide a full understanding of the embodiments of the present application. However, those skilled in the art will appreciate that the technical scheme of the present application can be put into practice without one or more of the specific details, or other methods, components, devices, steps, etc. can be adopted. In other cases, known methods, devices, realizations or operations are not shown or described in detail to avoid blurring the various aspects of the application.

[0022] The block diagrams shown in the accompanying drawings are merely functional entities and do not necessarily correspond to physically independent entities. That is, these functional entities may be implemented in software form, or in one or more hardware modules or integrated circuits, or in different networks and / or processor devices and / or micro-control node devices.

[0023] The flowcharts shown in the accompanying drawings are only exemplary and do not necessarily include all the contents and operations / steps, nor must they be executed in the order described. For example, some operations / steps can be decomposed, and some operations / steps can be combined or partially combined, so the actual execution order may change according to actual conditions.

[0024] It should be noted that the "multiple" mentioned in this article refers to two or more. "And / or" describes the association relationship of the associated objects, indicating that there can be three relationships. For example, A and / or B can mean: A exists alone, A and B exist at the same time, and B exists alone. The character " / " generally indicates that the associated objects before and after are in an "or" relationship.

[0025] The following is a detailed introduction to the technical background of the embodiments of the present application: OTT (Over-the-top) refers to a platform or technology that provides various services directly to users through the Internet, without relying on traditional broadcasting methods such as cable or satellite TV. In recent years, with the continuous improvement of network speed and the iterative development of terminal devices (such as smartphones, tablets, Internet TV IPTV and other devices), major OTT streaming media brands have not only launched their own websites and mobile apps, but also released TV versions of applications, and their member VIP subscriptions have also implemented a graded and platform-based system. Through the collaboration of small-screen terminal devices and large-screen terminal devices, users can watch various programs whether using mobile phones, computers or TVs.

[0026] Data is an important productive force. Streaming media viewing data has extremely high commercial value in the field of video consumption. Streaming media ratings analysis can stimulate the use value of massive user data and promote the deep integration of the digital economy and the real economy. In recent years, the commercial value of viewing data in the field of video consumption has continued to increase. However, the current viewing data collection scheme lacks viewing data of users on small-screen terminal devices (such as mobile phones, tablets, computers, etc.), and cannot distinguish the behavior of users watching the same source program (i.e., the target viewing program proposed in the embodiment of this application) across terminals, resulting in incomplete and inaccurate viewing analysis and lack of commercial value.

[0027] Please refer to Figure 1 , Figure 1 The schematic diagram of the implementation environment of a method for analyzing homologous viewing data provided in the embodiment of the present application is shown. In the implementation environment, the main software and hardware entities involved include a terminal device 110 and a backend server 120.

[0028] Specifically, the terminal device 110 may be installed with a relevant application, and the background server 120 is the background server of the application. The terminal device 110 and the background server 120 are connected in communication. The same-source viewing data analysis method provided in the embodiment of the present application may be executed on the terminal device 110 side.

[0029] Among them, the terminal device 110 of the above embodiment may include a smart phone, a tablet computer, a laptop computer, a desktop computer, a smart speaker, a smart watch, and a car terminal, but is not limited thereto.

[0030] The backend server 120 may be an independent physical server, or a server cluster or distributed system composed of multiple physical servers. It may also be a cloud server that provides basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communications, middleware services, domain name services, security services, CDN (Content Delivery Network), as well as big data and artificial intelligence platforms.

[0031] In addition, the backend server 120 may also be a node server in the blockchain network.

[0032] The terminal device 110 and the backend server 120 may establish a communication connection through a wireless network or a wired network. The wireless network or wired network uses standard communication technology and / or protocols, and the network may be set to the Internet, or any other network, such as but not limited to a local area network (LAN), a metropolitan area network (MAN), a wide area network (WAN), a mobile, wired or wireless network, a private network or any combination of a virtual private network. Moreover, the above-mentioned software and hardware entities may use the same communication connection mode or different communication connection modes, and this application does not impose any specific restrictions on this.

[0033] Of course, it is understandable that Figure 1 The implementation environment in the embodiment of the present application is only some optional application scenarios of the homologous viewing data analysis method provided in the embodiment of the present application, and the actual application is not fixed. Figure 1 The method provided in the embodiment of the present application can be applied to various technical fields, such as shopping, games, social networking and other fields, and the present application does not make any specific limitation on this.

[0034] Therefore, in order to solve the above problems, this application proposes a homologous viewing data analysis solution. First, refer to Figure 3 As shown, Figure 3This is a logical framework diagram for analyzing user viewing behavior in existing solutions. Figure 4 This is a logical framework diagram for analyzing the viewing behavior of users proposed in the embodiment of the present application. Figure 3 and Figure 4 By comparison, it can be seen that the collection module in the existing solution needs to collect the viewing data of small-screen terminal devices such as mobile phones, tablets, etc. through the local storage data of the set-top box and the additional design of corresponding terminal business applications. This solution undoubtedly brings the cumbersomeness of data collection, and the viewing data collected in this way is reported to the big data platform, which also increases the analysis volume of the big data platform.

[0035] The set-top box collects and locally stores the user's viewing data on large-screen terminals, ignoring the fragmented viewing data of users on small-screen terminals. Or the terminal business application needs to actively send the user's multi-screen viewing data through the middleware message interface, and cannot identify the user's behavior of watching the same source program (the target viewing program from the same source) across terminals. For example, a homologous program is Program 1, and Program 1, a homologous program (target viewing program), can be put on the shelves for playback on various playback platforms. Users can watch it through large-screen terminal devices and small-screen terminal devices. Watching Program 1 through different terminal devices means that users can watch the same source program through different terminal devices such as mobile phones and TV set-top boxes.

[0036] The existing solutions are unable to effectively conduct statistical analysis on the audience data of the same source programs, resulting in the audience analysis system being incomplete, inaccurate, and lacking commercial value. Figure 3 As shown, it is a logical framework diagram for analyzing the user's viewing behavior proposed in the embodiment of the present application, and a viewing data collection system is developed. Through this viewing data collection system, the viewing data collection of each terminal device connected to the home gateway is realized without distinction. That is, there is no need to additionally design terminal business applications to collect viewing data like the existing solutions. The viewing data collection system of the embodiment of the present application can directly upload the collected viewing data to the big data platform, thereby improving the efficiency of data transmission.

[0037] The homologous audience data analysis method in the embodiment of the present application is applied to a data collection and analysis system, which includes a big data platform and a audience data collection system. The audience data collection system is used to collect audience data of each terminal device associated with a home gateway; the method is executed on the big data platform.

[0038] The implementation details of the technical solution of the embodiment of the present application are described in detail below: According to one aspect of an embodiment of the present application, a homologous audience data analysis method is provided, which is applied to a data collection and analysis system, wherein the data collection and analysis system includes a big data platform and a audience data collection system, wherein the audience data collection system is used to collect audience data of each terminal device associated with a home gateway; the method is executed on the big data platform. Figure 2 This is a flow chart of a method for analyzing homologous audience ratings data according to an embodiment of the present application. The method at least includes steps S110 to S120, which are described in detail as follows: In step S110, the viewing data of each home gateway reported by the viewing data collection system is obtained. The viewing data is obtained by the viewing data collection system by collecting the playback data of each terminal device associated with the home gateway.

[0039] The big data platform can specifically be a Hadoop big data platform, and the big data platform can receive viewing data from each home gateway, that is, the viewing data collection system collects the playback data of each terminal device associated with the home gateway and the viewing data obtained can be reported to the Hadoop big data platform.

[0040] In an embodiment of the present application, the gateway traffic distribution of each region can be counted separately, that is, the gateway traffic distribution of each province can be counted, and the sampling rules can be defined in combination with the population of each province. For example, if the gateway traffic of a province reaches a certain threshold, it proves that the statistical analysis of the viewing data of the province has a high data representativeness, and a gateway MAC sample library is established based on this, and a DPI plug-in is deployed based on the gateway MAC sample library to collect DPI viewing data of users on different terminal devices and report it to the Hadoop big data platform.

[0041] In step S120, the viewing statistics of each home gateway for the target viewing program are determined according to each of the viewing data, and the viewing statistics are obtained according to the viewing statistics sub-data of each of the home gateways.

[0042] Specifically, a viewing statistical analysis is performed on each viewing data based on a home gateway, wherein the target viewing program may be one or more of a large number of film and television programs, such as Program 1, Program 2, and the like. A viewing statistical analysis can be performed on the target viewing program to obtain viewing statistical data of the target viewing program, that is, viewing statistical data of the target viewing program viewed by all home gateways. Further, the market share, ratings, and other indicator data of the target viewing program can be analyzed based on the viewing statistical data.

[0043] Among them, the viewing statistics can be obtained based on the viewing statistics sub-data of each of the home gateways, that is, the target viewing program is first analyzed and counted based on each household, and the obtained viewing statistics sub-data are then analyzed and integrated through the big data platform to obtain the overall analysis data of all home gateways on the target viewing program, which is the viewing statistics described in the embodiment of the present application.

[0044] In one embodiment of the present application, a single viewing statistics sub-data may be obtained by: Fig. 9 The steps S210-S230 shown result in: In step S210, target viewing data of a target home gateway corresponding to the viewing statistical sub-data is obtained.

[0045] Specifically, the target viewing data of the target home gateway corresponding to the viewing statistical sub-data is the viewing data set of all terminal devices connected to the target home gateway. The target viewing data can be used to obtain the viewing sub-data of each terminal device connected to the target home gateway, and it can be analyzed in which time period any terminal device played which viewing program, that is, it can be analyzed in which time period the user watched a certain viewing program.

[0046] In step S220, based on the target viewing data, it is determined that there is target homologous viewing data for cross-terminal sessions in the multiple terminal devices associated with the target home gateway, and the target homologous viewing data includes multiple homologous viewing sub-data, and the target viewing program corresponding to each homologous viewing sub-data is the same.

[0047] Specifically, a cross-terminal session is used to represent that the same target viewing program is played by multiple terminal devices in different time periods. For example, the terminal devices associated with the target home gateway are A, B, C, and D, and the target viewing program can be Program 2, where A watched Program 2 from 11:00 to 11:30 on November 1, B watched Program 2 from 13:20 to 14:30 on November 1, and C watched Program 2 from 19:00 to 20:30 on November 1. Then it can be considered that there are cross-terminal sessions between the three terminal devices A, B, and C. A cross-terminal session means that the user plays and displays the target viewing program through the current interface corresponding to different terminal devices.

[0048] It should be noted that each homologous viewing sub-data is only associated with a single terminal device. A single homologous viewing sub-data represents the viewing behavior of a single terminal device on a target viewing program, and multiple homologous viewing sub-data represent that the target viewing program has a historical playback record in multiple terminal devices. Then, by integrating multiple homologous viewing sub-data, the viewing statistical sub-data of the target viewing program can be obtained, that is, the viewing wide table.

[0049] In one embodiment of the present application, the cross-terminal session is identified by the following steps: Performing data cleaning on the target viewing data to obtain terminal devices corresponding to each of the viewing sub-data in the target viewing data and target viewing programs corresponding to each of the terminal devices; If a plurality of target terminal devices all play the target viewing program, it is determined that the cross-terminal session exists between the plurality of target terminal devices.

[0050] Specifically, one viewing sub-data corresponds to one terminal device. For example, the terminal device A mentioned above only corresponds to one viewing sub-data a. Then, by cleaning the target viewing data, the terminal devices A, B, C, D, E, etc. corresponding to each viewing sub-data can be obtained, as well as the viewing programs watched by the terminal devices A, B, C, D, and E. If any two of the terminal devices play the same target viewing program, then a cross-terminal session exists between the two terminal devices.

[0051] In step S230, a plurality of the homologous viewing sub-data in the target homologous viewing data are integrated to obtain viewing statistical sub-data of the target home gateway viewing the target viewing program.

[0052] Specifically, the target homologous viewing data is a set of viewing data of the target viewing program watched by each terminal device in the cross-terminal session. By integrating multiple homologous viewing sub-data in the homologous viewing data, the viewing statistical sub-data of the target home gateway watching a certain target viewing program is obtained.

[0053] Integrating the multiple homologous sub-data in the homologous viewing data can specifically be merging the multiple homologous sub-data in the homologous viewing data. It should be noted that merging is only a specific implementation method of integration, and the specific implementation method of integration can also be data deduplication, data cleaning and other methods.

[0054] It should be noted that, taking a single home gateway as a unit, one viewing statistics sub-data only represents one target viewing program. Therefore, the big data platform can receive multiple viewing statistics sub-data of the home gateway, that is, multiple viewing statistics sub-data of the home gateway for multiple viewing programs, so as to conduct a comprehensive analysis of the ratings of different viewing programs.

[0055] In one embodiment of the present application, the target viewing data includes a plurality of viewing sub-data, each of which corresponds to each terminal device one by one; and determining, according to the target viewing data, that target homologous viewing data of a cross-terminal session exists in a plurality of target terminal devices, comprises: Searching out each of the viewing sub-data for each homologous viewing sub-data associated with the target viewing program; The target homologous audience rating data is determined according to each of the homologous audience rating sub-data.

[0056] Specifically, the target viewing data includes multiple viewing sub-data, each of which corresponds to the viewing data of a terminal device. For example, if there are terminal devices A, B, C, D, and E, then the target viewing data includes five viewing sub-data of terminal devices A, B, C, D, and E. In each of the viewing sub-data, find out the respective homologous viewing sub-data associated with the target viewing program, that is, find out the data associated with the target viewing program in each of the viewing sub-data, and take this part of the data associated with the target viewing program as the homologous viewing sub-data, and obtain the homologous viewing sub-data, then obtain the target homologous viewing data (representing the viewing behavior of multiple terminal devices for the same target viewing program).

[0057] In one embodiment of the present application, the homologous viewing sub-data has duration information and playback platform information, the duration information is used to characterize the playback duration of the target viewing program by the terminal device corresponding to the homologous viewing sub-data, and the playback platform information is used to characterize the playback platform used by the terminal device corresponding to the homologous viewing sub-data when playing the target viewing program; the integration of multiple homologous viewing sub-data in the target homologous viewing data to obtain the viewing statistical sub-data of the target home gateway viewing the target viewing program includes: The duration information of each of the homologous viewing sub-data and the broadcast platform information are analyzed and counted to obtain the viewing statistical sub-data of the target home gateway viewing the target viewing program.

[0058] Specifically, the same-source viewing sub-data has duration information and playback platform information, that is, the duration information can be used to determine in which specific time period the user played the target viewing program, and the playback platform information can be used to know which streaming media platform the user logged in to play, and what the logged-in user level information is (such as ordinary user, ordinary VIP, SVIP, etc. identity level), then according to the duration information and playback platform information, analysis and statistics can be performed to obtain the viewing statistics sub-data of the target home gateway viewing the target viewing program. Among them, the viewing statistics sub-data can be displayed in the form of a viewing wide table, and the streaming media platform ID (known from the playback platform information), program ID (target viewing program), and viewing time information (known from the duration information) can be used to bind the home gateway MAC address and the terminal device MAC address to facilitate subsequent viewing of viewing data.

[0059] In one embodiment of the present application, the step of determining the viewing statistics of each home gateway for the target viewing program according to each viewing data can be performed by Fig.10 The steps S310-S330 shown result in: Step S310: determining the ratings information of the target viewing program on each of the playback platforms according to the duration information of each of the viewing statistical sub-data and the playback platform information; Step S320: determining the market share information of each of the broadcasting platforms for the target viewing program according to the viewing rate information; Step S330: Generate the audience rating statistics based on the audience rating information and the market share information.

[0060] Specifically, the ratings and market share can be calculated using the ratings indicator calculation formula in Table 1 below:

[0061] Table 1 Furthermore, the ratings information may include the ratings of a certain streaming platform and the total ratings of all platforms added up, which can be obtained by statistics and calculation based on the duration information of each of the viewing statistical sub-data and the playback platform information.

[0062] The viewing duration and average viewing duration of the target viewing program can also be obtained based on the duration information of the viewing statistics sub-data.

[0063] In one embodiment of the present application, the method further includes: Determine the definition information of the target viewing program when it is played on each of the playback platforms and the identity level information of the user logged in on each of the playback platforms according to the viewing statistics sub-data and the playback platform information of the viewing statistics sub-data; A media information relationship table corresponding to each of the playback platforms is generated based on each of the definition rate information and each of the user identity level information.

[0064] Specifically, the definition information of the target viewing program when it is played on each of the viewing platforms and the identity level information of the user logged in to each of the viewing platforms, i.e., VIP identity information, are determined through the playback platform information of the audience statistics sub-data, and the characteristics of the definition type and value (such as 1920×1080 pixels) when the user plays the target viewing program are analyzed, and a feature rule library of major streaming media platforms is established, so that the definition type and value that each platform can provide can be input into the feature rule library.

[0065] The data of the audience wide table (audience statistics sub-data) and the VIP wide table (media information relationship table) are sorted and written into the relational database, and the data analysis and display platform is built using the front-end framework. Figure 4 The data is displayed in the audience analysis system shown in .

[0066] According to the feature rule library, the VIP type of users in the viewing statistics sub-data (viewing wide table) is matched and identified, and a VIP wide table, that is, a media information relationship table, is generated. In this way, it is possible to clearly view which type of definition each user likes to watch and whether they are VIP users, so as to subsequently classify users into types and push accurate business models, thereby stimulating potential business value.

[0067] In one embodiment of the present application, the viewing data collection system and each terminal device transmit the viewing sub-data through a preset message protocol, and the association relationship between the target home gateway and each terminal device is determined by the following steps: Obtaining the protocol type of the preset message protocol; According to the protocol type and the message characteristics of the viewing sub-data, each terminal device associated with the target home gateway is determined.

[0068] Specifically, the viewing data collection system can collect various viewing sub-data through various protocol messages such as DHCP, MDNS, HTTP, etc., and the data transmission is sent and received in the form of messages. Then the received message is the collected viewing sub-data, which carries message information, that is, the message characteristics (message character header, message field, etc.), and these message characteristics can be used to identify the terminal device under the home gateway.

[0069] Currently, the collection of audience data relies on digital set-top boxes or requires active transmission by terminal business applications. There is a lack of fragmented audience data from small-screen terminal devices, and it is impossible to identify users' behavior of watching the same program across terminals, resulting in incomplete and inaccurate audience analysis and a lack of commercial value.

[0070] like Figure 7 and Figure 8 As shown, the present application uses a viewing data collection system to replace the set-top box and terminal business application module of the original solution to collect the viewing data of the user's multi-screen terminal devices, and adds a homologous data merging module and a VIP user identification module to the big data platform. Combined with the viewing analysis system, the comprehensiveness and accuracy of the viewing analysis are improved, and the commercial value of the viewing data is increased.

[0071] Figure 7 This is a framework diagram of the data collection and analysis system of this application. Regarding the home gateway collection system: statistical sampling is used to select the objects that need to be counted for viewing statistics. The objects can be the terminal devices corresponding to each home gateway in a province. By deploying the DPI plug-in, the DPI viewing behavior data of users in each terminal device can be collected, that is, the playback data described in the embodiment of this application, and the collected data can be reported to the big data platform.

[0072] Regarding the same-source data merging module: Through terminal identification, we can get the terminal devices bound to each home gateway. Through time-related sessions, we can deploy jobs in a distributed computing framework to clean up information such as the terminal device, program ID, and timestamp used for viewing. Combined with terminal identification, we can associate users' cross-terminal sessions, integrate the viewing data of programs from the same source, and generate a wide viewing table, that is, generate the viewing statistical sub-data corresponding to each home gateway. The identification of the terminal device can be obtained through the address identifier of the terminal device.

[0073] Regarding the VIP user identification module: the feature extraction module uses DPI viewing data to analyze the characteristics of the clarity type and clarity value when VIP users play programs, and establishes a media information relationship table for major streaming media platforms; the matching and identification VIP module matches and identifies the user's VIP type in the viewing wide table (viewing statistics sub-data) based on the media information relationship table, and generates a VIP wide table.

[0074] Regarding the audience analysis system: each audience indicator is calculated by the calculation indicator formula in the above Table 1, and then the audience statistics are generated according to each audience indicator, and the audience statistics are displayed.

[0075] Please refer to Figure 8 As shown, Figure 8This is a logical diagram of the audience statistics proposed in the embodiment of the present application. The following is a detailed description of the homologous audience data analysis method proposed in the embodiment of the present application with specific examples: First, the audience data collection system is used to conduct statistical sampling in a certain region or province, and each home gateway in the representative region is identified as the object of this audience statistical analysis. A gateway MAC sample library is established, and then the DPI plug-in is deployed to collect the playback data (audience sub-data) of each terminal device (mobile phone, tablet, computer, TV) associated with each home gateway.

[0076] Furthermore, by reporting multi-screen viewing data (each viewing sub-data) to the Hadoop big data platform, each viewing sub-data is terminal-identified. The viewing data collection system can collect each viewing sub-data through multiple protocol messages such as DHCP, MDNS, HTTP, etc., and the data is sent and received in the form of messages. Then the received message is the collected viewing sub-data, which carries message information, that is, the message features (message character header, message field, etc.), and the terminal device under the home gateway can be identified through these message features.

[0077] Through the above-mentioned terminal identification and cross-terminal conversations, it can be determined that there is homologous viewing data across terminal conversations, as well as each homologous viewing sub-data in the homologous viewing data. A single homologous viewing sub-data represents the viewing behavior of a single terminal device for a target viewing program, and multiple homologous viewing sub-data represent that the target viewing program has a historical playback record in multiple terminal devices. Then, by integrating multiple homologous viewing sub-data, the viewing statistical sub-data of the target viewing program, that is, the viewing wide table, can be obtained.

[0078] On the other hand, feature extraction can also be performed on the audience sub-data, that is, the clarity information of the target viewing program when it is played on each playback platform and the user identity level information logged in on each playback platform can be determined based on the audience statistical sub-data and the playback platform information of the audience statistical sub-data, that is, the corresponding VIP wide table can be generated based on the clarity information and the user identity level information.

[0079] The generated broadband table and VIP broadband table are input into the audience analysis system, and then each audience indicator is calculated by the calculation indicator formula in the above Table 1, and then audience statistics are generated according to each audience indicator, and the audience statistics are displayed. In this way, the user can clearly see the audience statistics of the target viewing program in a certain area, such as the viewing time of the target viewing program, the playback rate on each playback platform, the market share of each playback platform for the target viewing program and other information.

[0080] Compared with the existing solutions, the technical solution proposed in this application has the following advantages: it can collect viewing behavior data of users' mobile phones, tablets, computers, TVs and other multi-screen terminals, can identify users' cross-terminal viewing behavior, can merge viewing data from the same source, can identify users' VIP types, and help achieve precision marketing.

[0081] Figure 6 This is a block diagram of a homologous audience data analysis device 500 according to an embodiment of the present application. The homologous audience data analysis device 500 according to an embodiment of the present application is applied to a data collection and analysis system, which includes a big data platform and a audience data collection system. The audience data collection system is used to collect audience data of each terminal device associated with a home gateway. The device 500 includes: an acquisition unit 501 and a audience statistics unit 502.

[0082] The acquisition unit 501 is used to acquire the viewing data of each home gateway reported by the viewing data acquisition system. The viewing data is obtained by the viewing data acquisition system by acquiring the playback data of each terminal device associated with the home gateway.

[0083] The audience statistics unit 502 is used to determine the audience statistics of each home gateway for the target viewing program according to each of the audience data, wherein the audience statistics are obtained according to the audience statistics sub-data of each of the home gateways; The single viewing statistics sub-data is obtained through the following steps: Acquire target viewing data of a target home gateway corresponding to the viewing statistics sub-data; According to the target viewing data, determining that there is target same-source viewing data of a cross-terminal session in a plurality of the terminal devices associated with the target home gateway, the target same-source viewing data comprising a plurality of same-source viewing sub-data, each of the same-source viewing sub-data corresponding to the same target viewing program; The plurality of homologous viewing sub-data in the target homologous viewing data are integrated to obtain viewing statistical sub-data of the target home gateway viewing the target viewing program.

[0084] As another aspect, the present application further provides a computer-readable storage medium on which a program product capable of implementing the method provided above in this specification is stored. In some possible implementations, various aspects of the present application may also be implemented in the form of a program product, which includes a program code, and when the program product is run on a terminal device, the program code is used to cause the terminal device to execute the steps according to various exemplary implementations of the present application described in the above "Embodiment Method" section of this specification.

[0085] According to the program product for implementing the above method in the embodiment of the present application, it can adopt a portable compact disk read-only memory (CD-ROM) and include program code, and can be run on a terminal device, such as a personal computer. However, the program product of the present application is not limited thereto. In this document, a readable storage medium can be any tangible medium containing or storing a program, which can be used by or in combination with an instruction execution system, an apparatus or a device.

[0086] The program product may be any combination of one or more readable media. The readable medium may be a readable signal medium or a readable storage medium. The readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or device, or any combination thereof. More specific examples (non-exhaustive list) of readable storage media include: an electrical connection with one or more wires, a portable disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination thereof.

[0087] Computer readable signal media may include data signals propagated in baseband or as part of a carrier wave, in which readable program code is carried. Such propagated data signals may take a variety of forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above. Readable signal media may also be any readable medium other than a readable storage medium, which may send, propagate, or transmit a program for use by or in conjunction with an instruction execution system, apparatus, or device.

[0088] The program code embodied on the readable medium may be transmitted using any appropriate medium, including but not limited to wireless, wired, optical cable, RF, etc., or any suitable combination of the foregoing.

[0089] Program code for performing the operations of the present application may be written in any combination of one or more programming languages, including object-oriented programming languages, such as Java, C++, etc., and conventional procedural programming languages, such as "C" or similar programming languages. The program code may be executed entirely on the user computing device, partially on the user device, as a separate software package, partially on the user computing device and partially on a remote computing device, or entirely on a remote computing device or server. In the case of a remote computing device, the remote computing device may be connected to the user computing device through any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computing device (e.g., using an Internet service provider to connect through the Internet).

[0090] As another aspect, the present application also provides an electronic device capable of implementing the above method.

[0091] Those skilled in the art will appreciate that various aspects of the present application may be implemented as a system, method or program product. Therefore, various aspects of the present application may be specifically implemented in the following forms, namely: a complete hardware implementation, a complete software implementation (including firmware, microcode, etc.), or a combination of hardware and software, which may be collectively referred to as "circuit", "module" or "system" herein.

[0092] Refer to the following Figure 5 The electronic device 400 according to this embodiment of the present application is described. Figure 5 The electronic device 400 shown is merely an example and should not bring any limitation to the functions and scope of use of the embodiments of the present application.

[0093] like Figure 5 As shown, the electronic device 400 is in the form of a general computing device. The components of the electronic device 400 may include but are not limited to: at least one processing unit 410, at least one storage unit 420, and a bus 430 connecting different system components (including the storage unit 420 and the processing unit 410).

[0094] The storage unit stores program codes, which can be executed by the processing unit 410, so that the processing unit 410 executes the steps described in the above “Example Method” section of this specification according to various exemplary implementations of the present application.

[0095] The storage unit 420 may include a readable medium in the form of a volatile storage unit, such as a random access memory unit (RAM) 421 and / or a cache memory unit 422 , and may further include a read-only memory unit (ROM) 423 .

[0096] The storage unit 420 may also include a program / utility 424 having a set (at least one) of program modules 425, such program modules 425 including but not limited to: an operating system, one or more application programs, other program modules, and program data, each of which or some combination may include an implementation of a network environment.

[0097] Bus 430 may represent one or more of several types of bus structures, including a memory unit bus or memory unit controller node, a peripheral bus, an accelerated graphics port, a processing unit, or a local bus using any of a variety of bus architectures.

[0098] The electronic device 400 may also communicate with one or more external devices 1200 (e.g., keyboards, pointing devices, Bluetooth devices, etc.), may also communicate with one or more devices that enable a user to interact with the electronic device 400, and / or communicate with any device that enables the electronic device 400 to communicate with one or more other computing devices (e.g., routers, modems, etc.). This communication may be performed through an input / output (I / O) interface 450. In addition, the electronic device 400 may also communicate with one or more networks (e.g., local area networks (LANs), wide area networks (WANs), and / or public networks, such as the Internet) through a network adapter 460. As shown, the network adapter 460 communicates with other modules of the electronic device 400 through a bus 430. It should be understood that, although not shown in the figure, other hardware and / or software modules may be used in conjunction with the electronic device 400, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage systems, etc.

[0099] Through the description of the above implementation methods, it is easy for those skilled in the art to understand that the example implementation methods described here can be implemented by software, or by combining software with necessary hardware. Therefore, the technical solution according to the implementation method of the present application can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (which can be a CD-ROM, a USB flash drive, a mobile hard disk, etc.) or on a network, including instructions to enable a computing device (which can be a personal computer, a server, a terminal device, or a network device, etc.) to execute the method according to the implementation method of the present application.

[0100] In addition, the above-mentioned figures are only schematic illustrations of the processes included in the method according to the exemplary embodiments of the present application, and are not intended to be limiting. It is easy to understand that the processes shown in the above-mentioned figures do not indicate or limit the time sequence of these processes. In addition, it is also easy to understand that these processes can be performed synchronously or asynchronously, for example, in multiple modules.

[0101] It should be understood that the present application is not limited to the precise structures that have been described above and shown in the drawings, and that various modifications and changes may be performed without departing from the scope thereof. The scope of the present application is limited only by the appended claims.

Claims

1. A method for analyzing homologous audience ratings data, characterized in that: The method comprises: Acquiring viewing data of each home gateway reported by a viewing data collection system, wherein the viewing data is obtained by collecting playback data of each terminal device associated with the home gateway by the viewing data collection system; Determine the viewing statistics of each home gateway for the target viewing program according to each viewing data, wherein the viewing statistics are obtained according to the viewing statistics sub-data of each home gateway; The single viewing statistics sub-data is obtained through the following steps: Acquire target viewing data of a target home gateway corresponding to the viewing statistics sub-data; According to the target viewing data, determining that there is target same-source viewing data of a cross-terminal session in a plurality of the terminal devices associated with the target home gateway, the target same-source viewing data comprising a plurality of same-source viewing sub-data, each of the same-source viewing sub-data corresponding to the same target viewing program; The plurality of homologous viewing sub-data in the target homologous viewing data are integrated to obtain viewing statistical sub-data of the target home gateway viewing the target viewing program.

2. The homologous audience rating data analysis method according to claim 1, characterized in that: The target viewing data includes a plurality of viewing sub-data, each of which corresponds to each terminal device one by one; and determining, according to the target viewing data, the target same-source viewing data having a cross-terminal session among the plurality of terminal devices associated with the target home gateway, includes: Searching out each of the viewing sub-data for each homologous viewing sub-data associated with the target viewing program; The target homologous audience rating data is determined according to each of the homologous audience rating sub-data.

3. The homologous audience ratings data analysis method according to claim 2, characterized in that: The cross-terminal session is identified by the following steps: Performing data cleaning on the target viewing data to obtain terminal devices corresponding to each of the viewing sub-data in the target viewing data and target viewing programs corresponding to each of the terminal devices; If a plurality of target terminal devices all play the target viewing program, it is determined that the cross-terminal session exists between the plurality of target terminal devices.

4. The homologous audience rating data analysis method according to claim 3, characterized in that: The homologous viewing sub-data has duration information and playback platform information, wherein the duration information is used to characterize the playback duration of the target viewing program by the terminal device corresponding to the homologous viewing sub-data, and the playback platform information is used to characterize the playback platform used by the terminal device corresponding to the homologous viewing sub-data when playing the target viewing program; the integration of the plurality of homologous viewing sub-data in the target homologous viewing data to obtain the viewing statistical sub-data of the target home gateway viewing the target viewing program includes: The duration information of each of the homologous viewing sub-data and the broadcast platform information are analyzed and counted to obtain the viewing statistical sub-data of the target home gateway viewing the target viewing program.

5. The homologous audience rating data analysis method according to claim 4, characterized in that: The step of determining the viewing statistics of each home gateway for the target viewing program according to each viewing data includes: Determine the ratings information of the target viewing program on each of the playback platforms according to the duration information of each of the viewing statistical sub-data and the playback platform information; Determine the market share information of each of the broadcasting platforms for the target viewing program according to the viewing rate information; The audience rating statistics are generated based on the audience rating information and the market share information.

6. The homologous audience rating data analysis method according to claim 5, characterized in that: The method further comprises: Determine the definition information of the target viewing program when it is played on each of the playback platforms and the identity level information of the user logged in on each of the playback platforms according to the viewing statistics sub-data and the playback platform information of the viewing statistics sub-data; A media information relationship table corresponding to each of the playback platforms is generated based on each of the definition rate information and each of the user identity level information.

7. The homologous audience ratings data analysis method according to claim 2, characterized in that: The viewing data collection system and each terminal device transmit the viewing sub-data through a preset message protocol, and the association relationship between the target home gateway and each terminal device is determined by the following steps: Obtaining the protocol type of the preset message protocol; According to the protocol type and the message characteristics of the viewing sub-data, each terminal device associated with the target home gateway is determined.

8. A homologous viewing data analysis device, characterized in that: The device comprises: An acquisition unit, used to acquire viewing data of each home gateway reported by a viewing data acquisition system, wherein the viewing data is acquired by the viewing data acquisition system by acquiring playback data of each terminal device associated with the home gateway; A viewing statistics unit, used for determining viewing statistics of each home gateway for a target viewing program according to each viewing data, wherein the viewing statistics are obtained according to the viewing statistics sub-data of each home gateway; The single viewing statistics sub-data is obtained through the following steps: Acquire target viewing data of a target home gateway corresponding to the viewing statistics sub-data; According to the target viewing data, determining that there is target same-source viewing data of a cross-terminal session in a plurality of the terminal devices associated with the target home gateway, the target same-source viewing data comprising a plurality of same-source viewing sub-data, each of the same-source viewing sub-data corresponding to the same target viewing program; The plurality of homologous viewing sub-data in the target homologous viewing data are integrated to obtain viewing statistical sub-data of the target home gateway viewing the target viewing program.

9. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores at least one program code, and the at least one program code is loaded and executed by a processor to implement the operations performed by the method according to any one of claims 1 to 7.

10. An electronic device, characterized in that: The electronic device includes one or more processors and one or more memories, wherein the one or more memories store at least one program code, and the at least one program code is loaded and executed by the one or more processors to implement the operations performed by the method according to any one of claims 1 to 7.

Images