A method for improving VR video interaction efficiency

Abstract

The present invention provides a method for improving the interaction efficiency of VR videos. The method for improving the interaction efficiency is configured with a focus classification statistics system. The focus classification statistics system includes a VR interactive video library, a screening module, a video element focus screening module, an evaluation statistics module, an anti-malicious score brushing module and a reward distribution module. The screening module is used to screen the basic tags of the VR interactive video content. The video element focus screening module is used to integrate the tags of the VR interactive video content that the user has recently watched to screen out the favorite tags. The present invention can facilitate interactive absorption of feedback to obtain good works, and can perform classification and focus recommendation to obtain videos that are more in line with preferences, so as to solve the problems that the interaction efficiency of existing VR videos is low, the mode is a single viewing mode, multi-person interaction cannot be realized, the classification is single and too vague, and the category that the user wants to watch cannot be accurately recommended.

Description

Technical Field

[0001] The present invention relates to the field of VR video technology, and in particular to a method for improving the efficiency of VR video interaction. Background Art

[0002] VR is the abbreviation of Virtual-Reality, which means virtual reality in Chinese. VR video, also known as panoramic video, refers to a video that can realize three-dimensional space display function when skilled craftsmen use professional VR photography functions to record the on-site environment realistically, and then perform post-processing through a computer.

[0003] In the existing technology, the interactive efficiency of VR videos is low. It is a single viewing mode and can only allow users to select videos independently, which reduces the interactive effect of VR videos. It cannot achieve multi-person interaction, resulting in low viewing efficiency. It cannot allow viewers to interact and give the best feedback, which affects the viewing experience of subsequent viewers. The classification mode is single and does not have intelligent recommendations with multiple emphasis elements. At the same time, the various evaluation labels in the video come from professional film and television producers and cannot interact with ordinary viewers, which reduces the scope of interaction and affects the promotion of VR videos. Summary of the invention

[0004] In view of the shortcomings of the prior art, the purpose of the present invention is to provide a method for improving the efficiency of VR video interaction, which can facilitate interaction to absorb feedback and produce good works, and can classify and focus on recommending videos that are more in line with preferences, so as to solve the problems of low interaction efficiency of existing VR videos, such as a single viewing mode, inability to achieve multi-person interaction, single and vague classification, and inability to accurately recommend the categories you want to watch.

[0005] In order to achieve the above-mentioned purpose, the present invention is implemented through the following technical solutions: a method for improving the efficiency of VR video interaction, the method for interaction efficiency is configured with a focus classification statistics system, the focus classification statistics system includes a VR interactive video library, a screening module, a video element focus screening module, an evaluation statistics module, an anti-malicious score brushing module and a reward distribution module, the screening module is used to screen the basic tags of VR interactive video content, the video element focus screening module is used to integrate the VR interactive video content tags that the user has recently watched to screen out the favorite tags, the evaluation statistics module is used to count the user's scoring content, the anti-malicious score brushing module is used to identify whether the user has malicious evaluations and ensure that the scores are true and effective, and the reward distribution module is used to encourage and enhance the feedback of users after participating in watching VR interactive video content;

[0006] The method comprises the following steps:

[0007] Step S1, loading the VR interactive video script, obtaining the complete VR interactive video content, screening the VR interactive video content tags in each category tag page; filling in the favorite tags for watching the movie;

[0008] Step S2, opening the VR interactive video content selected by screening and watching the video, and during watching the video, the VR interactive video content is scored in real time by focusing on the scoring pop-up window;

[0009] Step S3, the scoring content of the scoring pop-up window is counted through the evaluation statistics module and rewards are given;

[0010] Step S4, extracting tags from the VR interactive video content watched by the user, performing data comparison to obtain a preference value, and then recommending VR interactive video content with a high preference value to the homepage.

[0011] Furthermore, step S3 also includes a scoring method, which is divided into advantage scoring and disadvantage scoring. The advantage scoring is based on a ten-point system. After the advantage scoring, a corresponding reward is issued through a reward distribution module. The disadvantage scoring is based on excessive XX, general XX, and slight XX, where XX is an element category. After the disadvantage scoring, a corresponding reward is issued through a reward distribution module. The element categories include: popularity, evaluation, duration or horror, comedy or love, etc.

[0012] Furthermore, step S3 also includes a reward distribution module, and the reward content of the reward distribution module includes viewing points and evaluation experience. The viewing points and evaluation experience can increase the viewing points of the user after each evaluation, and the viewing points can obtain free viewing rights for paid VR interactive video content.

[0013] Furthermore, step S4 also includes a video element emphasis screening module, which substitutes the elements of the VR interactive video content in the user's historical viewing record into the first algorithm to obtain the more preferred element emphasis, and automatically screens out recommended VR interactive video content.

[0014] Furthermore, the first algorithm is configured as follows: Among them, Ys is the element category, Gc is the total number of movie viewings, and P1 is the element emphasis value. The element emphasis values ​​are arranged in three equal parts from large to small to obtain the corresponding element emphasis order.

[0015] Furthermore, step S4 also includes element emphasis classification, and the element emphasis classification is to arrange the preferred elements according to the first emphasis element, the second emphasis element and the third emphasis element, and the emphasis ratio is based on the first emphasis element to screen the required VR interactive video content, and then the first emphasis element video that has been screened is screened for the second emphasis element, and after the second emphasis element screening is completed, the second element video is screened for the third emphasis element, to obtain a three-element mixed video that the viewer likes, and to accurately improve the viewing effect.

[0016] Furthermore, step S3 also includes an anti-malicious scoring module, which has a duration statistics unit, a preference bias statistics unit, a fast-forward detection unit and a real-name authentication unit. The duration statistics unit is used to detect the viewing time of the viewer. When the VR interactive video content is watched for more than 80% of the total time, accurate scoring can be performed. At the same time, the total duration of the movie at the duration detection point can be used for element emphasis classification. The preference bias statistics unit is used to count the viewer's usual viewing categories, identify the frequently watched element categories, and ensure that there is no malicious swiping of free viewing points. The fast-forward detection unit is used to detect whether the viewer fast-forwards to the scoring time. If no fast-forwarding is detected, free viewing points can be obtained. If fast-forwarding is detected, no reward can be obtained. At the same time, the real-name authentication unit is used to allow users to fill in and confirm their identities to avoid repeated evaluations. The evaluation takes effect after passing the real-name authentication.

[0017] Furthermore, the step S2 also includes evaluation classification, the evaluation classification is divided into evaluated videos and unevaluated videos, the evaluated videos are divided into popular evaluations and ordinary evaluations, the popular evaluations are evaluations by more than a thousand people, and the ordinary evaluations are evaluations by less than a thousand people.

[0018] The beneficial effects of the present invention are as follows: the present invention can perform efficient and accurate element-focused scoring when watching VR interactive video content, avoiding the impact of a single viewing mode on the promotion of VR interactive video content and the speed at which people understand it, thereby improving the efficiency of viewing interaction and the viewing effect.

[0019] The present invention can increase the audience's interest in interaction after watching a movie by setting a scoring method and a reward distribution module, ensure that each work can be attracted by the reward distribution module, and score multiple elements of the VR interactive video content being watched through a scoring method to ensure the proportion of each element and the degree of crowd attraction.

[0020] By setting up an anti-malicious scoring module, the present invention can ensure the authenticity and accuracy of the scoring process, ensure that viewers' evaluations are normal, avoid the existence of malicious scoring, and ensure the quality of VR interactive videos. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] Other features, objects and advantages of the present invention will become more apparent from the detailed description of non-limiting embodiments made with reference to the following drawings:

[0022] Figure 1 is a flow chart of the method of the present invention;

[0023] Figure 2 It is a system principle block diagram of the present invention.

[0024] In the figure: 1. Focus on classification statistics system; 2. VR interactive video library; 3. Screening module; 4. Video element focus screening module; 5. Evaluation statistics module; 7. Anti-malicious scoring module; 701. Duration statistics unit; 702. Preference statistics unit; 703. Fast-forward detection unit; 704. Real-name authentication unit; 8. Reward distribution module. DETAILED DESCRIPTION

[0025] In order to make the technical means, creative features, objectives and effects achieved by the present invention easy to understand, the present invention is further explained below in conjunction with specific implementation methods.

[0026] See also Figure 2 , Figure 2 It is a system principle block diagram of the present invention.

[0027] A system for improving the efficiency of VR video interaction, the focus classification and statistics system 1 includes a VR interactive video library 2, a screening module 3, a video element focus screening module 4, an evaluation and statistics module 5, an anti-malicious scoring module 7 and a reward distribution module 8, the screening module 3 is used to screen the basic tags of VR interactive video content, the video element focus screening module 4 is used to integrate the tags of VR interactive video content that the user has recently watched to screen out favorite tags, the evaluation and statistics module 5 is used to count the user's scoring content, the anti-malicious scoring module 7 is used to distinguish whether the user has malicious evaluations and ensure that the scores are true and valid, and the reward distribution module 8 is used to encourage and enhance the feedback of users after participating in watching VR interactive video content.

[0028] The evaluation statistics module 5 includes a scoring method, which is divided into advantage scoring and disadvantage scoring. The advantage scoring is based on a ten-point system. After the advantages are scored, corresponding rewards are issued through the reward distribution module 8. The disadvantages are scored as too XX, general XX and slight XX, where XX is an element category. After the disadvantages are scored, corresponding rewards are issued through the reward distribution module 8, and the element categories include: popularity, evaluation, duration or horror, comedy or love, etc.

[0029] The video element emphasis screening module 4 substitutes the elements of the VR interactive video content in the user's historical viewing record into the first algorithm to obtain the preferred element emphasis, and automatically screens out the recommended VR interactive video content.

[0030] The first algorithm is configured as , where Ys is the element category, Gc is the total number of movie viewings, and P1 is the element emphasis value. The element emphasis values ​​are arranged in three equal parts from large to small to obtain the corresponding element emphasis order.

[0031] The reward content of the reward distribution module 8 includes viewing points and evaluation experience. Viewing points and evaluation experience can increase the user's viewing points after each evaluation. Viewing points can obtain free viewing rights for paid VR interactive video content.

[0032] The evaluation statistics module 5 includes an anti-malicious scoring module 7, which has a duration statistics unit 701, a preference statistics unit 702, a fast-forward detection unit 703 and a real-name authentication unit 704. The duration statistics unit 701 is used to detect the viewing time of the viewer. When the VR interactive video content is viewed for more than 80% of the total duration, accurate scoring can be performed. At the same time, the total duration of the movie at the duration detection point can be used for element emphasis classification. The preference statistics unit 702 is used to count the viewer's usual viewing categories, identify the frequently viewed element categories, and ensure that there is no malicious swiping of free viewing points. The fast-forward detection unit 703 is used to detect whether the viewer fast-forwards to the scoring duration. If no fast-forwarding is detected, free viewing points can be obtained. If fast-forwarding is detected, no reward can be obtained. At the same time, the real-name authentication unit 704 is used to allow users to fill in and confirm their identities to avoid repeated evaluations. The evaluation takes effect after passing the real-name authentication.

[0033] The evaluation statistics module 5 also includes evaluation classification, which is divided into evaluated videos and unevaluated videos. The evaluated videos are divided into popular evaluations and ordinary evaluations. Popular evaluations are evaluations by more than a thousand people, and ordinary evaluations are evaluations by less than a thousand people. This makes it easy for viewers to quickly obtain VR interactive video content in popular evaluations, improve the accuracy of evaluations, and provide more detailed feedback, which is conducive to the development of better works.

[0034] See also Figure 1 , Figure 1 The figure is a flow chart of the method of the present invention.

[0035] The method comprises the following steps:

[0036] Step S1, loading the VR interactive video script, obtaining the complete VR interactive video content, screening the VR interactive video content tags in each category tag page through the screening module, and filling in the favorite tags for watching the movie;

[0037] Step S2, opening the VR interactive video content selected through screening and watching the film. When watching the film, the VR interactive video content is scored in real time through a scoring pop-up window. The viewer shifts his sight or moves the VR device to the scoring pop-up window, and then selects a scoring method. If many advantages are found during the film, the advantages are selected for scoring. The elements appearing in the film content are scored. If the horror effect of the video is excellent, too horror is selected. If it is not scary enough, slightly horror is selected, so that the evaluation items are detailed.

[0038] Step S3, the scoring content of the scoring pop-up window is counted by the evaluation statistics module 5, and then the scoring situation is detected by the anti-malicious scoring module 7, and the viewing time of the viewer is detected by the time statistics unit 70. When the VR interactive video content is watched for more than 80% of the total time, accurate scoring can be performed, and the preference statistics unit 702 is used to count the viewers' usual viewing categories. For example, if they frequently watch comedy elements, they have more insights into comedy. The advantage scoring also detects whether it is related to comedy, but the focus on the dependent elements is not strong, which can prevent the disadvantage scoring malicious bad reviews, etc., and can accurately put forward the direction of the disadvantages to ensure that there is no There is a malicious attempt to swipe free viewing points. The fast-forward detection unit 703 is used to detect whether the viewer has fast-forwarded to the scoring time. If the fast-forward is not detected, the free viewing points can be obtained. If the fast-forward is detected, the reward cannot be obtained. At the same time, the real-name authentication unit 704 is used to let the user fill in and confirm the identity to avoid repeated evaluation. After the real-name authentication is passed, the evaluation takes effect, and the reward is issued after the evaluation takes effect. The reward content includes viewing points and evaluation experience. Viewing points and evaluation experience can increase the viewing points of users after each evaluation. Viewing points can obtain free viewing rights for paid VR interactive video content;

[0039] Step S4, extract the tags in the VR interactive video content watched by the user, compare the data to obtain the preference value, and then recommend the VR interactive video content with a high preference value to the homepage, so as to help other viewers accurately find the best VR interactive video content with the preferred element style. The first algorithm is used to calculate and sort the proportion of element categories in the total number of times the user watches, and the maximum value is prioritized as the first focus element. The next two element categories are divided into the second focus element and the third focus element, and then the corresponding VR interactive video content is automatically matched and recommended to the user. For example, if the user watches ten times and comedy element videos account for seven of them, then the comedy preference value is If the horror element appears twice, the horror preference value is The love element appears once, and the love preference value is Arranged in order, comedy element videos are recommended at the top, followed by horror elements and finally love elements, thus improving the efficiency of interaction.

[0040] Finally, it should be noted that the above embodiments are only specific implementations of the present invention, which are used to illustrate the technical solutions of the present invention, rather than to limit them. The protection scope of the present invention is not limited thereto. Although the present invention is described in detail with reference to the above embodiments, ordinary technicians in the field should understand that any technician familiar with the technical field can still modify the technical solutions recorded in the above embodiments within the technical scope disclosed by the present invention, or can easily think of changes, or make equivalent replacements for some of the technical features therein; and these modifications, changes or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present invention, and should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention shall be based on the protection scope of the claims.

Claims

1. A method for improving the efficiency of VR video interaction, characterized by: The interactive efficiency method is configured with a focus classification statistics system (1), the focus classification statistics system (1) comprising a VR interactive video library (2), a screening module (3), a video element focus screening module (4), an evaluation statistics module (5), an anti-malicious score brushing module (7) and a reward distribution module (8), wherein the screening module (3) is used to screen out basic tags of VR interactive video content, the video element focus screening module (4) is used to integrate tags of VR interactive video content that users have recently watched to screen out favorite tags, the evaluation statistics module (5) is used to count the user's scoring content, the anti-malicious score brushing module (7) is used to identify whether the user has malicious evaluations and ensure that the scores are authentic and effective, and the reward distribution module (8) is used to encourage and enhance the feedback of users after participating in watching VR interactive video content; The method comprises the following steps: Step S1, loading the VR interactive video script, obtaining the complete VR interactive video content, screening the VR interactive video content tags in each category tag page, and filling in the favorite tags for watching the movie; Step S2, opening the selected VR interactive video content and watching the video, and during watching the video, the VR interactive video content is scored in real time by focusing on the scoring pop-up window; Step S3, the scoring content of the scoring pop-up window is counted through the evaluation statistics module (5) and rewards are given; Step S4, extracting tags from the VR interactive video content watched by the user, performing data comparison to obtain a preference value, and then recommending VR interactive video content with a high preference value to the homepage.

2. A method for improving VR video interaction efficiency according to claim 1, characterized in that: The step S3 also includes a scoring method, which is divided into advantage scoring and disadvantage scoring. The advantage scoring is based on a ten-point scale. After the advantage scoring, a corresponding reward is issued through the reward distribution module (8). The disadvantage scoring is based on excessive XX, general XX and slight XX, where XX is an element category. After the disadvantage scoring, a corresponding reward is issued through the reward distribution module (8). The element categories include: popularity, evaluation, duration or horror, comedy or love.

3. A method for improving VR video interaction efficiency according to claim 1, characterized in that: The step S3 also includes a reward distribution module (8), and the reward content of the reward distribution module (8) includes viewing points and evaluation experience. The viewing points and evaluation experience can increase the viewing points of the user after each evaluation. The viewing points can obtain free viewing rights for paid VR interactive video content.

4. The method for improving the efficiency of VR video interaction according to claim 1, characterized in that: The step S4 further comprises a video element emphasis screening module (4), wherein the video element emphasis screening module (4) substitutes the elements of the VR interactive video content in the user's historical viewing record into the first algorithm to obtain the preferred element emphasis, and automatically screens out the recommended VR interactive video content; The first algorithm is configured as Among them, ys is the element category, Gc is the total number of movie viewings, and p1 is the element emphasis value. The element emphasis values ​​are arranged in three equal parts from large to small to obtain the corresponding element emphasis order.

5. The method for improving the efficiency of VR video interaction according to claim 1, characterized in that: The step S4 also includes element emphasis classification, and the element emphasis classification is to arrange the preferred elements according to the first emphasis element, the second emphasis element and the third emphasis element, and the emphasis ratio is based on the first emphasis element to screen the required VR interactive video content, and then the first emphasis element video that has been screened is screened for the second emphasis element, and after the second emphasis element screening is completed, the second element video is screened for the third emphasis element, so as to obtain the three-element mixed video that the viewer likes, and accurately improve the viewing effect.

6. A method for improving VR video interaction efficiency according to claim 1, characterized in that: The step S3 also includes an anti-malicious scoring module (7), which includes a duration statistics unit (701), a preference statistics unit (702), a fast-forward detection unit (703) and a real-name authentication unit (704). The duration statistics unit (701) detects the viewing time of the viewer. When the VR interactive video content is viewed for more than 80% of the total duration, accurate scoring can be performed. At the same time, the total duration of the movie at the duration detection point can be used for element emphasis classification. The preference statistics unit (702) counts the viewer's usual viewing categories and identifies the frequently viewed element categories to ensure that there is no malicious swiping of free viewing points. The fast-forward detection unit (703) detects whether the viewer fast-forwards to the scoring duration. If no fast-forwarding occurs during the detection, free viewing points can be obtained. If fast-forwarding is detected, no reward can be obtained. At the same time, the real-name authentication unit (704) allows the user to fill in and confirm his identity to avoid repeated evaluation. The evaluation takes effect after passing the real-name authentication.

7. A method for improving VR video interaction efficiency according to claim 1, characterized in that: The step S2 also includes evaluation classification, which is divided into evaluated videos and unevaluated videos. The evaluated videos are divided into popular evaluations and ordinary evaluations. Popular evaluations are evaluations by more than a thousand people, and ordinary evaluations are evaluations by less than a thousand people.

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