Film and television resource scheduling and management platform

Abstract

The invention relates to the field of information technology and media management, in particular to a movie and television resource scheduling and management platform. Comprising a user terminal, a movie and television resource scheduling and management server and a movie and television resource storage server, the movie and television resource scheduling and management server is used for receiving a movie and television resource playing request sent by the user terminal, processing the request, predicting the requirements of a user according to fields stored in a database, and then formulating a dynamic recommendation strategy according to the requirements; the user terminal sends a scheduling strategy to the video resource storage server, and the video resource storage server selects corresponding video resources from a stored video resource library for caching according to the received scheduling strategy and sends the video resources to the user terminal through the network; and the user terminal receives and can automatically switch the playing source according to the size of the cache and the bandwidth of the network. According to the invention, better, smoother and more personalized online video watching experience is provided for the user, and the operation efficiency and the service stability of the platform are improved at the same time.

Description

Technical Field

[0001] The present invention relates to the fields of information technology and media management, and in particular to a film and television resource scheduling and management platform. Background Art

[0002] With the development of Internet technology, the consumption mode of digital media content has undergone profound changes, especially the consumption of digital media resources with video as the main content, which has shifted from traditional physical channels such as TV and cinema to online video playback services based on the Internet. This change has greatly enriched people's entertainment life, but also put forward higher requirements for the technology of online video platforms.

[0003] Among the numerous online video viewers, everyone has their own unique viewing habits and interest preferences. For example, one user likes to watch action movies at night, while another user may prefer to watch documentaries or educational content during the day. Such diverse user needs have led to an urgent need for personalized recommendation services. Traditional recommendation systems are often based on popular recommendations or simple user tag matching, which is difficult to meet the personalized needs of users, thus affecting the user's viewing experience and satisfaction.

[0004] With the increasing richness of video content and the development of high-definition, panoramic and other technologies, the size of video resources is increasing, which poses a huge challenge to the storage and network transmission of video servers. Especially for large video platforms, how to efficiently manage massive video resources and ensure smooth playback of videos in different time periods and different network environments has become a technical problem. In addition, in order to ensure service quality, it is also necessary to consider issues such as redundant backup and load balancing of video resources to prevent service interruptions caused by server failures or overloads.

[0005] At present, the resource scheduling and management technologies adopted by most video platforms on the market have some shortcomings. For example, the prediction of user preferences is not accurate enough, and the content recommendation is not personalized enough; the resource scheduling algorithm is inefficient and cannot adapt well to changes in network conditions; the server architecture design is unreasonable, lacking effective redundant backup and load balancing mechanisms. Once a failure occurs, it is difficult to restore the service in time, affecting the user experience. Summary of the invention

[0006] The present invention solves the above problems by introducing a more intelligent recommendation algorithm, an optimized resource scheduling strategy and a more stable server architecture, providing users with a better, smoother and more personalized online video viewing experience, while also improving the platform's operational efficiency and service stability.

[0007] The technical solution adopted by the present invention is: a film and television resource scheduling and management platform, including a user terminal, a film and television resource scheduling and management server, and a film and television resource storage server.

[0008] The video resource scheduling and management server is used to receive video resource playback requests from user terminals, process the requests and predict user needs based on the fields stored in the database, and then formulate dynamic recommendation strategies based on the needs, thereby transmitting the scheduling strategies to the video resource storage server.

[0009] The film and television resource storage server selects corresponding film and television resources from the stored film and television resource library for caching according to the received scheduling strategy, and transmits them to the user terminal through the network;

[0010] The user terminal receives and can automatically switch the playback source according to the size of the cache and the bandwidth of the network.

[0011] As a further improvement of the present invention, the fields include but are not limited to the user's historical viewing records, preferred content types, and viewing time periods.

[0012] As a further improvement of the present invention, the dynamic recommendation strategy is obtained based on training of a machine learning model, and the machine learning model is trained using the user's historical viewing records to predict the user's preferred content type and viewing time period, and generate a dynamic recommendation strategy based on the prediction results.

[0013] As a further improvement of the present invention, the dynamic recommendation strategy generation step includes:

[0014] Step 1: Collect user viewing habit data sent by the user terminal and extract the user's personalized viewing feature information;

[0015] Step 2: Use machine learning algorithms to analyze users’ personalized viewing characteristics and predict users’ demand trends, including the types of content they are interested in and the demand for content in a specific time period.

[0016] Step 3: Convert the prediction results obtained through analysis into a film and television resource scheduling strategy based on a preset recommendation algorithm;

[0017] Step 4: Send the generated dynamic recommendation strategy to the film and television resource storage server, and the storage server will instantly schedule and manage the corresponding film and television resources according to the strategy.

[0018] As a further improvement of the present invention, the film and television resource storage server is configured with a preprocessing module, a distribution module, a redundant backup module, and a load balancing module.

[0019] As a further improvement of the present invention, the preprocessing module is used to parse and process the stored film and television resources, including slicing and compression operations; the distribution module is responsible for distributing the processed film and television resource fragments to the user terminal more quickly; the redundant backup module is used to make some redundant backups of the stored film and television resources to prevent the failure of a single node from causing the film and television resources to be unable to be played; the load balancing module is used to distribute the user's playback request and the processing request of the preprocessing module to different nodes, thereby reducing the pressure on a single node and improving the utilization efficiency of resources.

[0020] As a further improvement of the present invention, the slicing operation is used to convert the received panoramic video resources into multiple continuous small slices, and the small slices can be played and cached independently of other small slices; the compression operation is used to compress the small slices into data in a specific format so as to be distributed to the user terminal more quickly, and different compression ratios can be selected according to different playback quality requirements. A lower compression ratio can provide a higher quality viewing experience, while a higher compression ratio can more efficiently utilize the network bandwidth.

[0021] As a further improvement of the present invention, the user terminal is configured with a request generation module, a playback control module, a content recommendation module and a user feedback module.

[0022] As a further improvement of the present invention, the request generation module is used to receive user requests for playback of film and television resources; the playback control module is used to switch the playback source and control the playback according to the cache size and network bandwidth; the content recommendation module is used to display the content recommendation strategy transmitted by the film and television resource scheduling and management server; the user feedback module is used to collect user feedback and transmit the feedback information back to the server.

[0023] Beneficial effects of the present invention: (1) Improving user experience: The present invention deeply analyzes the user's viewing habits and uses machine learning technology to generate a more accurate dynamic recommendation strategy, which can better meet the user's personalized needs and provide content recommendations that are more in line with their interests. At the same time, the user terminal can automatically switch the playback source according to the network bandwidth and cache size, ensuring that the video can be played smoothly in different network environments, greatly improving the user's viewing experience.

[0024] (2) Optimizing resource management and scheduling: By configuring a preprocessing module, a distribution module, a redundant backup module, and a load balancing module on the video resource storage server side, the present invention achieves efficient management and instant scheduling of video resources. The preprocessing module can process video resources in advance, the distribution module ensures that video clips can be quickly transmitted to user terminals, the redundant backup module improves the stability and reliability of the system, and the load balancing module effectively shares the pressure on the server, improves resource utilization efficiency, and reduces operating costs.

[0025] (3) Enhance the robustness and flexibility of the system: The present invention collects the user's experience data during the actual viewing process through the user feedback module of the user terminal, and feeds this data back to the film and television resource scheduling and management server, thereby realizing a closed-loop mechanism for system self-optimization. The server side can continuously adjust the recommendation algorithm and scheduling strategy based on user feedback, so that the platform can more flexibly respond to various complex usage scenarios and network conditions, thereby improving the overall robustness of the system. In addition, by supporting different playback quality requirements, the system can provide the most suitable viewing experience for users in different network environments, further enhancing the platform's adaptability and user satisfaction. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] Figure 1 It is a system block diagram of a film and television resource scheduling and management platform of the present invention;

[0027] Figure 2 It is a flow chart of dynamic recommendation strategy generation of a film and television resource scheduling and management platform of the present invention. DETAILED DESCRIPTION

[0028] In order to make the technical problems, technical solutions and beneficial effects to be solved by the present application more clearly understood, the present application is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0029] The present invention provides a film and television resource scheduling and management platform, including a user terminal, a film and television resource scheduling and management server, and a film and television resource storage server. The film and television resource scheduling and management server is used to receive a film and television resource playback request issued by a user terminal, process the request and predict the user's needs according to the fields stored in a database, and then formulate a dynamic recommendation strategy according to the needs, so as to transmit the scheduling strategy to the film and television resource storage server. The film and television resource storage server selects corresponding film and television resources from a stored film and television resource library for caching according to the received scheduling strategy, and transmits them to the user terminal through a network; the user terminal receives and can automatically switch the playback source according to the size of the cache and the bandwidth of the network.

[0030] The fields in the present invention include but are not limited to the user's historical viewing records, preferred content types, and viewing time periods.

[0031] The dynamic recommendation strategy in the present invention is obtained based on the training of the machine learning model. The machine learning model is trained using the user's historical viewing records to predict the user's favorite content type and viewing time period, and generate a dynamic recommendation strategy based on the prediction results. The steps of generating the dynamic recommendation strategy include: collecting the user's viewing habit data sent by the user terminal, and refining the user's personalized viewing feature information; using the machine learning algorithm to analyze the user's personalized viewing feature information, and predicting the user's demand tendency, including the content type of interest and the demand for content in a specific time period; converting the predicted results obtained by the analysis into a film and television resource scheduling strategy based on a preset recommendation algorithm; sending the generated dynamic recommendation strategy to the film and television resource storage server, and the storage server performs real-time scheduling and management of the corresponding film and television resources based on the strategy.

[0032] The film and television resource storage server of the present invention is configured with a preprocessing module, a distribution module, a redundant backup module, and a load balancing module. The preprocessing module is used to parse and process the stored film and television resources, including slicing and compression operations; the distribution module is responsible for distributing the processed film and television resource fragments to the user terminal faster; the redundant backup module is used to make some redundant backups of the stored film and television resources to prevent the failure of a single node from causing the film and television resources to be unable to be played; the load balancing module is used to distribute the user's playback request and the processing request of the preprocessing module to different nodes, thereby reducing the pressure of a single node and improving the utilization efficiency of resources. The slicing operation is used to convert the received panoramic film and television resources into multiple continuous small slices, and the small slices can be played and cached independently of other small slices; the compression operation is used to compress the small slices into data in a specific format so as to be distributed to the user terminal faster, and different compression ratios can be selected according to different playback quality requirements. A lower compression ratio can provide a higher quality viewing experience, while a higher compression ratio can more efficiently utilize the network bandwidth.

[0033] The user terminal of the present invention is configured with a request generation module, a playback control module, a content recommendation module and a user feedback module. The request generation module is used to receive a user's playback request for a film and television resource; the playback control module is used to switch the playback source and control the playback according to the size of the cache and the network bandwidth; the content recommendation module is used to display the content recommendation strategy transmitted by the film and television resource scheduling and management server; the user feedback module is used to collect user feedback and transmit the feedback information back to the server.

[0034] Example:

[0035] Assume that there is a large online film and television platform named "Video Planet", which uses the film and television resource scheduling and management platform described in the present invention. Video Planet has millions of registered users, processes tens of millions of video playback requests every day, and provides rich high-definition film and television content. The main technical architecture of the platform is as follows:

[0036] (1) User terminal users: Users access the Video Planet application through devices such as smartphones, tablets, or smart TVs. The client software installed on these devices integrates a request generation module, a playback control module, a content recommendation module, and a user feedback module.

[0037] (2) User video resource scheduling and management server: As the core part of the platform, the scheduling and management server is responsible for processing user playback requests, predicting demand based on user viewing data, and generating dynamic recommendation strategies to guide the video resource storage server to perform corresponding resource scheduling.

[0038] (3) User video resource storage server users: Contains a large amount of video content, and performs resource preprocessing, distribution, redundant backup, and load balancing according to the scheduling and management server strategy.

[0039] Scenario description:

[0040] The user is a loyal user of Video Planet. He usually watches science fiction movies from 9 to 10 pm on weekdays and likes to watch documentaries on weekends, especially adventure documentaries. The user uses 4G network and has stable fiber broadband at home.

[0041] System operation process:

[0042] (1) User request generation and initial recommended users:

[0043] When a user opens the Video Planet application, the request generation module automatically sends a playback request to the film and television resource scheduling and management server.

[0044] After receiving the user's request, the scheduling and management server immediately queries the user's historical viewing records, preferred content types (science fiction and adventure documentaries), viewing time period (9 to 10 pm on weekdays, all day on weekends), and other information.

[0045] The server uses a pre-trained machine learning model to analyze the user's personalized viewing feature information and concludes that the user may be interested in a recently released science fiction movie or a new adventure documentary.

[0046] (2) User dynamic recommendation strategy generates users:

[0047] Based on the model's prediction results, the server generates a set of dynamic recommendation strategies for users, including putting newly released science fiction movies at the top and recommending adventure documentaries on weekends.

[0048] The dynamic recommendation strategy is then sent to the film and television resource storage server, which manages the resources according to the strategy, such as pre-caching the recommended resource fragments in the edge server closest to the user.

[0049] (3) User resource preprocessing user:

[0050] The film and television resource storage server slices and compresses the recommended film and television resources using a preprocessing module according to the received policy.

[0051] For a new high-definition science fiction movie, it is first converted into multiple continuous small slices, each about 30 seconds or 1 minute. Then, based on the user's viewing history (occasionally watching on a 4G network environment), a slightly higher compression ratio is used to optimize network transmission efficiency while ensuring video quality.

[0052] (4) User resource distribution and playback control users:

[0053] The distribution module quickly distributes the pre-processed video clips to the user's user terminal.

[0054] The playback control module detects that the user is currently using a 4G network and that there is still some space left in the terminal cache. It automatically selects the most suitable segment for playback and caches more segments to be played in the background, reducing buffering time and ensuring smooth playback.

[0055] If the user is using fiber-optic broadband access at home, the playback control module will select less compressed video clips to provide a better viewing experience.

[0056] (5) User feedback and self-optimization users:

[0057] During the viewing process, the user feedback module collects information such as user operation records, playback preferences, and viewing quality.

[0058] These data are transmitted back to the server in a timely manner to update the user's user profile. The machine learning model continuously adjusts the prediction algorithm based on this and optimizes future dynamic recommendation strategies. For example, if the user responds positively to the recommended content, the system will recommend more similar types of content; if the user feedback is not good, the recommendation direction will be adjusted.

[0059] Results and Impact:

[0060] Through the solution of the present invention, users have a more personalized and smooth viewing experience. Video Planet can not only accurately recommend film and television content that users are interested in on demand, but also effectively respond to playback needs in different network environments, reduce playback interruptions caused by network congestion or resource server overload, and significantly improve user satisfaction and platform competitiveness. In addition, the self-optimization mechanism of the system allows the platform to continuously adapt to changes in user needs and technological development, ensuring long-term service quality and user experience improvement.

[0061] In summary, a film and television resource scheduling and management platform of the present invention achieves accurate grasp and dynamic response to user needs by deeply integrating user feedback and machine learning algorithms. In practical applications, this platform can significantly improve the user's viewing experience, enhance the utilization efficiency of film and television resources, and provide an innovative and efficient management model for the online film and television industry. In the future, with the continuous advancement of technology and the increasing diversification of user needs, this platform will continue to be optimized and improved to bring users a richer, more personalized and high-quality viewing experience.

[0062] The above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features thereof may be replaced by equivalents. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A film and television resource scheduling and management platform, comprising a user terminal, a film and television resource scheduling and management server, and a film and television resource storage server, characterized in that: The video resource scheduling and management server is used to receive video resource playback requests from user terminals, process the requests and predict user needs based on the fields stored in the database, and then formulate dynamic recommendation strategies based on the needs, thereby transmitting the scheduling strategies to the video resource storage server. The film and television resource storage server selects corresponding film and television resources from the stored film and television resource library for caching according to the received scheduling strategy, and transmits them to the user terminal through the network; The user terminal receives and can automatically switch the playback source according to the size of the cache and the bandwidth of the network.

2. A film and television resource scheduling and management platform according to claim 1, characterized in that: The fields include but are not limited to the user's historical viewing records, preferred content types, and viewing time periods.

3. A film and television resource scheduling and management platform according to claim 1, characterized in that: The dynamic recommendation strategy is obtained based on training of a machine learning model, and the machine learning model is trained using the user's historical viewing records to predict the user's preferred content type and viewing time period, and generate a dynamic recommendation strategy based on the prediction results.

4. A film and television resource scheduling and management platform according to claim 3, characterized in that: The dynamic recommendation strategy generation step includes: Step 1: Collect user viewing habit data sent by the user terminal and extract the user's personalized viewing feature information; Step 2: Use machine learning algorithms to analyze users’ personalized viewing characteristics and predict users’ demand trends, including the types of content they are interested in and the demand for content in a specific time period. Step 3: Convert the prediction results obtained through analysis into a film and television resource scheduling strategy based on a preset recommendation algorithm; Step 4: Send the generated dynamic recommendation strategy to the film and television resource storage server, and the storage server will instantly schedule and manage the corresponding film and television resources according to the strategy.

5. A film and television resource scheduling and management platform according to claim 1, characterized in that: The film and television resource storage server is configured with a preprocessing module, a distribution module, a redundant backup module, and a load balancing module.

6. A film and television resource scheduling and management platform according to claim 5, characterized in that: The preprocessing module is used to parse and process the stored film and television resources, including slicing and compression operations; The distribution module is responsible for distributing the processed film and television resource segments to the user terminal more quickly; the redundant backup module is used to make some redundant backups of the stored film and television resources to prevent the failure of a single node to cause the film and television resources to be unable to be played; The load balancing module is used to distribute the user's playback request and the processing request of the pre-processing module to different nodes, thereby reducing the pressure of a single node and improving the utilization efficiency of resources.

7. A film and television resource scheduling and management platform according to claim 6, characterized in that: The slicing operation is used to convert the received panoramic video resources into a plurality of continuous small slices, and the small slices can be played and cached independently of other small slices; The compression operation is used to compress small slices into data in a specific format for faster distribution to user terminals, and different compression ratios can be selected according to different playback quality requirements. A lower compression ratio can provide a higher quality viewing experience, while a higher compression ratio can more efficiently utilize network bandwidth.

8. A film and television resource scheduling and management platform according to claim 1, characterized in that: The user terminal is configured with a request generation module, a playback control module, a content recommendation module and a user feedback module.

9. A film and television resource scheduling and management platform according to claim 8, characterized in that: The request generation module is used to receive a user's request for playing a film and television resource; the playback control module is used to switch the playback source and control the playback according to the cache size and network bandwidth; the content recommendation module is used to display the content recommendation strategy transmitted by the film and television resource scheduling and management server; The user feedback module is used to collect user feedback and transmit the feedback information back to the server.

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