Live content intelligent creation and distribution system based on big data analysis

Abstract

The invention relates to the technical field of unmanned aerial vehicles, in particular to a live broadcast content intelligent creation and distribution system based on big data analysis, and the system comprises a data collection module which is used for collecting a plurality of data sources from a live broadcast platform, social media, user behavior records and the like; the data analysis and processing module is used for carrying out cleaning, preprocessing and feature extraction on the collected data; a content creation auxiliary module; the intelligent recommendation module is used for recommending the user portraits according to the user portraits and the content feature library; an intelligent distribution module, wherein the intelligent distribution module is used for distributing the recommendation list; and a user feedback and optimization module. Through cooperative use of the data acquisition module, the data analysis processing module, the content creation auxiliary module, the intelligent recommendation module, the intelligent distribution module and the user feedback and optimization module, the purpose of hierarchical user feedback and information collection and analysis can be achieved, and the viewing experience and satisfaction of the user are improved.

Description

Technical Field

[0001] The present invention relates to the field of drone technology, and in particular to a live broadcast content intelligent creation and distribution system based on big data analysis. Background Art

[0002] With the rapid development of Internet technology, the live broadcast industry has rapidly emerged and become an important way of content dissemination. However, traditional live broadcast content creation and distribution methods have problems such as low efficiency, single content, and difficulty in meeting users' personalized needs. Most existing solutions rely on manual planning and distribution, which cannot fully utilize the potential of big data analysis, resulting in a low level of intelligence in content creation and distribution.

[0003] Most of the existing intelligent creation and distribution systems for live content based on big data analysis find it difficult to collect and analyze user feedback and information in a hierarchical manner, which affects the automated creation, recommendation, and distribution of live content. At the same time, some systems are prone to affect users' personalized viewing needs, as well as content and quality.

[0004] To this end, we proposed an intelligent live content creation and distribution system based on big data analysis to solve the above problems. Summary of the invention

[0005] The purpose of the present invention is to provide a live content intelligent creation and distribution system based on big data analysis to solve the problems raised in the above-mentioned background technology.

[0006] To achieve the above purpose, the present invention provides the following technical solutions: a live content intelligent creation and distribution system based on big data analysis, comprising a data acquisition module, A data collection module, which is used to collect user behavior data, viewing history, interest tags and other data from multiple data sources such as live broadcast platforms, social media, and user behavior records, and store these data in a data warehouse; A data analysis and processing module, which cleans, preprocesses and extracts features from the collected data, processes the collected data using big data analysis technology, identifies user preferences, content trends and hot topics, and generates user portraits and a content feature library; A content creation auxiliary module, which automatically generates scripts and materials for live broadcast content, including live broadcast themes, formats, interactive elements, etc., based on user portraits and content feature libraries in combination with machine learning algorithms; Intelligent recommendation module, which generates a personalized live content recommendation list based on user portraits and content feature libraries using collaborative filtering, deep learning and other algorithms; Intelligent distribution module, which distributes live content to corresponding platforms and devices according to the recommendation list, and continuously optimizes the recommendation and distribution strategy through real-time monitoring and feedback mechanism, and realizes personalized recommendation and dynamic distribution of live content based on user portraits and real-time data; The user feedback and optimization module collects user feedback on live content for continuous optimization of system performance and content recommendation strategies.

[0007] Preferably, the data analysis and processing module includes the following steps: user behavior analysis submodule, data cleaning, feature analysis, extraction and user portrait generation content feature library generation, which is convenient for analysis and extraction based on the viewing data and features of different users, so that different content can be delivered to different user targets, making distribution more accurate.

[0008] Preferably, the content creation module uses a machine learning algorithm to automatically generate scripts and materials for live broadcast content. The specific steps include: generating content, creating preliminary scripts and natural language processing technology and combining image recognition and video analysis technology, using multiple algorithms for generation, performing portrait matching for different users, and performing targeted distribution and creation to meet the needs of different users.

[0009] Preferably, the intelligent recommendation module includes the following steps: user portrait matching, content feature matching and recommendation algorithm execution, classifying and matching different portraits of multiple users, adapting to the viewing interests and behaviors of different customers, which can further enhance customers' viewing interests and increase the time users stay, thereby spending more time watching on the platform.

[0010] Preferably, the intelligent distribution module continuously optimizes the recommendation and distribution strategies through real-time monitoring and feedback mechanisms, and continuously optimizes the recommendation and distribution strategies to meet the viewing needs of different users in different time periods, adapt to customer changes and various needs, and allow viewers to stay longer.

[0011] Preferably, the user feedback and optimization module also includes: a feedback collection and processing module and a system iteration optimization module, which centrally collect and analyze user opinions and feedback, make adjustments based on the analysis structure, and can make timely adjustments based on the overall data analysis system to meet different viewing needs and improve user experience.

[0012] The present invention provides a live content intelligent creation and distribution system based on big data analysis, which has the following beneficial effects: Through the coordinated use of data collection module, data analysis and processing module, content creation auxiliary module, intelligent recommendation module, intelligent distribution module and user feedback and optimization module, the purpose of hierarchical collection and analysis of user feedback and information can be achieved. By integrating big data analysis and artificial intelligence technology, the automatic creation, recommendation and distribution of live content is realized, which significantly improves the efficiency and intelligence level of content creation and distribution, meets the personalized needs of users, and enhances the user's viewing experience and satisfaction.

[0013] Through the coordinated use of data collection module, data analysis and processing module, content creation auxiliary module, intelligent recommendation module, intelligent distribution module and user feedback and optimization module, the purpose of improving personalization and content quality can be achieved. By analyzing user behavior data and preferences, the system can provide personalized content recommendations for each user. The system can distribute content to the most appropriate target audience based on factors such as the user's geographic location and interests, thereby increasing the exposure and dissemination effect of the content, and improving user satisfaction and participation. Using big data analysis, the system can identify hot trends and user needs, and help creators produce high-quality content that is more in line with market demand. BRIEF DESCRIPTION OF THE DRAWINGS In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0015] Figure 1 It is a schematic diagram of the structural module flow of the present invention.

[0016] In the figure: 1. Data collection module; 2. Data analysis and processing module; 3. Content creation assistance module; 4. Intelligent recommendation module; 5. Intelligent distribution module; 6. User feedback and optimization module. DETAILED DESCRIPTION The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0018] Data collection module 1, which is used to collect user behavior data, viewing history, interest tags and other data from multiple data sources such as live broadcast platforms, social media, user behavior records, and store these data in a data warehouse; Data analysis and processing module 2, which cleans, preprocesses and extracts features from the collected data, processes the collected data using big data analysis technology, identifies user preferences, content trends and hot topics, and generates user portraits and content feature libraries; Data collection: Collects data from various sources, such as user behavior logs, social media, third-party data providers, etc.; a: User behavior analysis submodule: used to analyze data such as user viewing time, interaction frequency, and preference categories; b: Data cleaning: collect the analyzed data, remove duplicates, filter and standardize the collected data; c: Feature analysis and extraction: Conduct sentiment analysis on user-generated content such as bullet comments and reviews, evaluate the popularity of the content, and analyze and extract user interest features, viewing behavior features, content features, etc. from the cleaned data; d: User portrait generation: Generate user interest and behavior portraits based on the extracted features; e: Content feature library generation: Based on the extracted features, a content feature library is generated for subsequent content creation and recommendation.

[0019] Content creation auxiliary module 3, which automatically generates scripts and materials for live broadcast content, including live broadcast theme, format, interactive elements, etc., based on user portraits and content feature libraries in combination with machine learning algorithms; The content creation module uses machine learning algorithms to automatically generate scripts and materials for live broadcast content. The specific steps include: a: Generate a preliminary script for content creation based on user portraits and content feature libraries; b: Optimize and polish the preliminary script through natural language processing technology; c: Combine image recognition and video analysis technology to automatically generate live content materials.

[0020] Intelligent recommendation module 4, the intelligent recommendation module 4 generates a personalized live content recommendation list based on user portraits and content feature libraries using collaborative filtering, deep learning and other algorithms.

[0021] The intelligent recommendation module includes the following steps: a: User portrait matching: Match similar user groups based on user interest portraits and behavior portraits.

[0022] b: Content feature matching: Match content that suits user interests based on the content feature library.

[0023] c: Recommendation algorithm execution: Use collaborative filtering, deep learning and other algorithms to generate personalized recommendation lists.

[0024] Intelligent distribution module 5, the intelligent distribution module 5 distributes the live content to the corresponding platform and device according to the recommendation list, and continuously optimizes the recommendation and distribution strategy through real-time monitoring and feedback mechanism, and realizes personalized recommendation and dynamic distribution of live content based on user portraits and real-time data; the content distribution module continuously optimizes the recommendation and distribution strategy through real-time monitoring and feedback mechanism, and the specific steps include: Monitor users' viewing behavior and feedback in real time, collect real-time data, and dynamically adjust recommendation lists and distribution strategies based on real-time data.

[0025] The user feedback and optimization module 6 collects user feedback on the live content, and is used to continuously optimize system performance and content recommendation strategies.

[0026] a: Feedback collection and processing module, which automatically collects user feedback and performs sentiment analysis through natural language processing technology; b: System iteration optimization module, based on feedback results, regularly updates algorithm models and system functions to improve user experience Working principle: The data collection module 1 collects various data from different platforms, the data analysis and processing module 2 analyzes, cleans, analyzes and extracts the collected data, and the content creation auxiliary module 3 analyzes according to the user portrait and content characteristics. Then the intelligent recommendation module 4 generates and processes the user's portrait and content, and then makes intelligent recommendations based on the generated content, matches the portrait and content, and then recommends algorithms based on the portrait and content. Subsequently, the intelligent distribution module 5 performs intelligent distribution, and finally the distributed data is fed back and analyzed. After the analysis, the user feedback and optimization module 6 adjusts the creation and distribution of big data.

[0027] The above is the entire working principle of the present invention.

[0028] Finally, a few points should be explained: First, in the description of the present application, it should be noted that, unless otherwise specified and limited, the terms "installed", "connected" and "connected" should be understood in a broad sense, and can be mechanical or electrical connections, or internal connectivity between two components, or direct connections. "Up", "down", "left" and "right" are only used to indicate relative position relationships. When the absolute position of the described object changes, the relative position relationship may change; Secondly, in the drawings of the embodiments disclosed in the present invention, only the structures involved in the embodiments disclosed in the present invention are involved. Other structures can refer to the general design. In the absence of conflict, the same embodiment and different embodiments of the present invention can be combined with each other; Finally, the above are only preferred embodiments of the present invention and are not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A live content intelligent creation and distribution system based on big data analysis, comprising a data acquisition module (1), characterized in that: A data collection module (1), the data collection module (1) is used to collect user behavior data, viewing history, interest tags and other data from multiple data sources such as live broadcast platforms, social media, user behavior records, etc., and store these data in a data warehouse; A data analysis and processing module (2), wherein the data analysis and processing module (2) cleans, pre-processes and extracts features from the collected data, processes the collected data using big data analysis technology, identifies user preferences, content trends and hot topics, and generates user portraits and a content feature library; A content creation auxiliary module (3), wherein the content creation auxiliary module (3) automatically generates scripts and materials for live broadcast content, including live broadcast themes, formats, interactive elements, etc., based on user portraits and content feature libraries in combination with machine learning algorithms; An intelligent recommendation module (4), wherein the intelligent recommendation module (4) generates a personalized live content recommendation list based on user portraits and content feature libraries by using collaborative filtering, deep learning and other algorithms; An intelligent distribution module (5), wherein the intelligent distribution module (5) distributes the live broadcast content to corresponding platforms and devices according to the recommendation list, and continuously optimizes the recommendation and distribution strategy through a real-time monitoring and feedback mechanism, and realizes personalized recommendation and dynamic distribution of the live broadcast content based on user portraits and real-time data; A user feedback and optimization module (6), wherein the user feedback and optimization module (6) collects user feedback on live broadcast content, and is used to continuously optimize system performance and content recommendation strategies.

2. According to claim 1, a live content intelligent creation and distribution system based on big data analysis is characterized in that: The data analysis and processing module (2) comprises the following steps: user behavior analysis submodule, data cleaning, feature analysis, extraction and user portrait generation content feature library generation.

3. According to the big data analysis-based intelligent live content creation and distribution system of claim 1, it is characterized by: The content creation auxiliary module (3) uses a machine learning algorithm to automatically generate scripts and materials for live broadcast content. The specific steps include: generating content, creating preliminary scripts and natural language processing technology, and combining image recognition and video analysis technology.

4. According to claim 1, a live broadcast content intelligent creation and distribution system based on big data analysis is characterized in that: The intelligent recommendation module (4) comprises the following steps: user portrait matching, content feature matching and recommendation algorithm execution.

5. According to claim 1, a live broadcast content intelligent creation and distribution system based on big data analysis is characterized in that: The intelligent distribution module (5) continuously optimizes the recommendation and distribution strategies through real-time monitoring and feedback mechanisms.

6. The system for intelligent creation and distribution of live content based on big data analysis according to claim 1, characterized in that: The user feedback and optimization module (6) comprises: a feedback collection and processing module and a system iteration optimization module.

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