Virtual reality game video sharing method and system

Abstract

The invention is suitable for the technical field of video generation and sharing, and provides a virtual reality game video sharing method and system, and the method comprises the steps: monitoring player behaviors and game content data in real time, adding timestamps, and carrying out the combined storage. And analyzing the data, performing content grading according to the intensity score, and performing weighted scoring in combination with live broadcast bullet screen information. And setting a video selection rule according to the final score, and editing to generate a first-person video and a third-person video. The system comprises a player game content data acquisition module, a content fierce degree analysis module, a fierce degree weighting module, a video editing and visual angle generation module and a video duration verification management module. According to the method, player behaviors, game picture dynamics and physiological feedback are accurately captured, and key moments in the virtual reality game are comprehensively restored. The rhythm and ornamental value of content selection are optimized, highlight moments of video editing are ensured to be highlighted, and the entertainment and sharing value of game videos are improved.

Description

Technical Field

[0001] The present invention belongs to the technical field of video generation and sharing, and in particular to a virtual reality game video sharing method and system. Background Art

[0002] The generation and sharing of virtual reality game videos is based on the technology of automatic video extraction, editing, and sharing. It mainly focuses on automatically identifying and extracting highlights from the game process through intelligent means, and accurately editing and synthesizing the content to meet the audience's consumption needs for exciting content. The video generation and sharing technology for live content further expands the functions of traditional video automatic extraction and editing, focusing on how to effectively integrate the player's game process with the audience's real-time interactive feedback. This technology automatically identifies and extracts highlights from the game and live broadcast process through intelligent means, and accurately edits and synthesizes the content in combination with multi-dimensional data (such as player behavior, game dynamics, audience barrage feedback, etc.). In particular, for the application of live content, the technology can monitor the audience's barrage density, keyword relevance and other interactive data in real time, and incorporate the audience's needs and emotional reactions into the intensity scoring system, so as to more scientifically screen out clips that are both exciting and effective.

[0003] However, due to the lack of accurate analysis of key behaviors and screen dynamics during the game, traditional methods are difficult to scientifically identify the real highlights of the game, resulting in the generated video failing to effectively highlight the exciting parts. At the same time, existing technologies usually do not have the function of grading the intensity or importance of video content, and lack sufficient data support as a basis for screening, which directly affects the quality of the final video editing.

[0004] In addition, traditional methods fail to fully utilize audience feedback during live broadcasts, such as barrage density and keywords, and ignore the audience's subjective interest and emotional response to certain clips, which may result in some content that is extremely effective in actual viewing experience being unable to be identified and added to the video. The final generated content is difficult to accurately reflect the audience's real-time needs and interests, which greatly limits the video's appeal and dissemination value. Summary of the invention

[0005] The purpose of the present invention is to provide a virtual reality game video sharing method and system, aiming to solve the technical problems existing in the prior art identified in the background technology.

[0006] The present invention is implemented as follows: a method for sharing a virtual reality game video, the method comprising:

[0007] Real-time monitoring, collection and analysis of player behavior data and game content data during the game, adding timestamps to the collected player behavior data and game content data, and jointly storing them with timestamps as data tags;

[0008] Analyze the jointly stored player behavior data and game content data, add intensity scores to the player behavior data and game content data with different timestamps, and classify the player behavior data and game content data according to the intensity scores;

[0009] Continuously monitor the density and keyword information of barrage in the live broadcast, and perform additional weighting on the intensity score of player behavior data and game content data at the same timestamp, and obtain the final intensity score of player behavior data and game content data at each timestamp;

[0010] According to the final intensity score, set the stage score threshold, and set the video selection rules for each stage. According to the selection rules, edit and generate the final game video file, and generate the first-person perspective and third-person perspective videos based on VR characteristics;

[0011] A duration interval is set based on the average video duration of historically uploaded videos, the final generated video file is read, and a comparison is made to see whether the video duration is within the duration interval. If it is within the duration interval, the video is automatically uploaded; if not, the upload is stopped and the video file is set for review.

[0012] As a further solution of the present invention, the real-time monitoring collects and analyzes the player behavior data and game content data during the game, and adds a timestamp to the collected player behavior data and game content data, and stores them jointly with the timestamp as the data tag, specifically including:

[0013] Detect player behavior data through virtual reality equipment, including head movement data, hand operation data, and eye movement data, while recording the player's real-time heart rate and skin conductivity;

[0014] Capture game content data and use it to determine the complexity and dynamic changes of the scene;

[0015] All collected player behavior data and game content data are bound to the timestamp within the game, and all collected player behavior data and game content data are synchronously processed and integrated and stored as a unified data stream.

[0016] As a further solution of the present invention, the head movement data includes: rotation angle, number of rapid movements and frequency of annotation direction changes;

[0017] The hand operation data includes: grip strength, wrist rotation angle, interactive click frequency and swing trajectory;

[0018] The eye movement data includes: fixation duration, fixation frequency and fixation point switching speed;

[0019] The game content data includes: the number, behavior patterns and interactive actions of dynamic objects in the game screen.

[0020] As a further solution of the present invention, the jointly stored player behavior data and game content data are analyzed, intensity scores are added to the player behavior data and game content data of different timestamps, and the player behavior data and game content data are classified according to the intensity scores, specifically including:

[0021] Analyze the player behavior data, quantify the head movements, hand operations and eye movements to obtain the player behavior score, and calculate the player behavior score ;

[0022] Analyze the game screen, quantify the number of dynamic objects, behavior patterns, and interactive actions, and calculate the game screen dynamic score ;

[0023] Calculate physiological data scores based on the player's real-time heart rate and skin conductivity ;

[0024] The calculated data includes: head movement data, eye movement data, player's real-time heart rate and game content data to obtain the intensity score of the data corresponding to each timestamp ;

[0025] Comprehensively calculate all the scoring data, including: player behavior score, game screen dynamic score, physiological data score and intensity score, and obtain the basic intensity score of data with different timestamps ;

[0026] The intensity score is divided into several intervals corresponding to different content levels, including: S segment, A segment and B segment, and according to the intensity scores of different timestamps, the timestamp data corresponding to each intensity score is assigned a corresponding content level.

[0027] As a further solution of the present invention, the player behavior score is calculated :

[0028] ;

[0029] in, is a head action score based on the head rotation angle, number of rapid movements, and frequency of annotated direction changes. The hand operation score is obtained based on hand grip strength, wrist rotation angle, interactive click frequency and swing trajectory. is an eye movement score based on eye fixation duration, fixation frequency, and fixation point switching speed. is the normalization constant;

[0030] Calculate the game screen dynamic score :

[0031] ;

[0032] in, is the number of dynamic objects in the picture, The behavior pattern of dynamic objects in the picture. is the number of interactive objects, For the The interaction weight of each interacting object;

[0033] Calculate physiological data scores :

[0034] ;

[0035] in, is the player's current heart rate, The player's resting heart rate. is the player's maximum heart rate, is the skin conductivity weight coefficient, is the real-time fluctuation amplitude of skin conductivity;

[0036] Get the intensity score of the data corresponding to each timestamp :

[0037] ;

[0038] in, , , ,and The scores of head movement, hand operation, heart rate and number of dynamic objects were The amount of change in time, is the time interval;

[0039] Get the basic intensity score of data with different timestamps :

[0040] ;

[0041] in, is the dynamic weight coefficient.

[0042] As a further solution of the present invention, the method of continuously monitoring the density and keyword information of the bullet screen in the live broadcast, and additionally weighting the intensity score of the player behavior data and game content data at the same timestamp, and obtaining the final intensity score of the player behavior data and game content data at each timestamp, specifically includes:

[0043] Through the bullet screen interface of the live broadcast platform, the bullet screen streaming data of the live broadcast room is collected in real time, and the sending time of each bullet screen is bound to the timestamp of the game content data;

[0044] According to the time window corresponding to each timestamp, count the number of bullet screens per unit time and obtain the real-time bullet screen density. ;

[0045] Preset a keyword list and retrieve the relevance between the bullet comment content and the keyword list in each time window ;

[0046] Increase the base value of intensity score according to the number of bullet comments ;

[0047] According to the relevance of the bullet screen content, the secondary basic value of the intensity score is obtained ;

[0048] The basic value and the secondary basic value are added to the basic intensity score to obtain the final intensity score. :

[0049] .

[0050] As a further solution of the present invention, real-time bullet screen density :

[0051] ;

[0052] in, is the number of bullet comments in the current time window, is the time window length;

[0053] Base value of intensity rating :

[0054] ;

[0055] in, Score the current intensity of this timestamp, is the maximum barrage density;

[0056] Secondary base value of intensity rating :

[0057] ;

[0058] in, is the maximum correlation.

[0059] As a further solution of the present invention, the staged scoring threshold is set according to the final intensity score, and a video selection rule is set for each stage. According to the selection rule, the final game video file is edited and generated, and at the same time, based on the VR characteristics, the first-person perspective and the third-person perspective video are generated, which specifically includes:

[0060] Set scoring thresholds for each interval , , , corresponding to S segment, A segment, and B segment respectively, and ;

[0061] Compare the final intensity ratings and , , The size relationship between them, and get all The corresponding timestamp data is edited and synthesized in the order of the timestamps, and the duration of the synthesized video is recorded;

[0062] The player's real-time perspective data and the virtual world's free perspective data corresponding to the same timestamp are extracted and edited and synthesized respectively.

[0063] As a further solution of the present invention, the duration interval is set based on the average video duration of historical uploaded videos, the finally generated video file is read, and the video duration is compared to see whether it is within the duration interval. If it is within the duration interval, the video is automatically uploaded; if it is not within the duration interval, the upload is stopped and the video file is set to be reviewed, which specifically includes:

[0064] According to the average duration of historically uploaded videos, a duration interval is set to determine whether the recorded video duration is within the duration interval;

[0065] If it is less than the minimum duration of the duration interval, The order of size is from Select from the interval The largest timestamp data is supplemented and synthesized, and the duration of the synthesized video is recalculated;

[0066] If the video duration is greater than the maximum duration of the duration interval, or meets After all timestamp data are synthesized, if the video duration is still less than the minimum duration of the duration interval, the upload and sharing operation will be stopped and the video will be placed under review.

[0067] If the video length is within the time range, the synthesized video will be automatically uploaded and shared.

[0068] Another object of the present invention is to provide a virtual reality game video sharing system, the system comprising:

[0069] The player game content data collection module is used to monitor, collect and analyze the player behavior data and game content data during the game in real time, add timestamps to the collected player behavior data and game content data, and store them jointly with the timestamp as the data tag;

[0070] A content intensity analysis module is used to analyze the jointly stored player behavior data and game content data, add intensity scores to the player behavior data and game content data of different timestamps, and classify the player behavior data and game content data according to the intensity scores;

[0071] The intensity weighting module is used to continuously monitor the density and keyword information of the bullet comments in the live broadcast, and to perform additional weighting on the intensity scores of the player behavior data and game content data at the same timestamp, and to obtain the final intensity score of the player behavior data and game content data at each timestamp;

[0072] The video editing and perspective generation module is used to set the stage scoring threshold according to the final intensity score, and set the video selection rules for each stage. According to the selection rules, the final game video file is edited and generated, and at the same time, based on the VR characteristics, the first-person perspective and the third-person perspective video are generated;

[0073] The video duration verification management module is used to set the duration interval based on the average video duration of historical uploaded videos, read the final generated video file, and compare whether the video duration is within the duration interval. If it is within the duration interval, the video is automatically uploaded. If it is not within the duration interval, the upload is stopped and the video file is set for review.

[0074] The beneficial effects of the present invention are:

[0075] This method can accurately capture player behavior, game screen dynamics and physiological feedback, and fully restore key moments in virtual reality games. The intensity scoring mechanism combines the player's operating behavior, scene complexity and physiological response, quantifies the player's real experience, and scientifically distinguishes the content levels of different intensities in the S segment, A segment and B segment. This data-based content screening method not only optimizes the rhythm and viewing experience of content selection, but also ensures that video editing highlights the highlights, significantly improving the entertainment and sharing value of game videos. At the same time, the application of physiological data makes the scoring model more in line with the user's emotional experience, providing high-quality basic data for subsequent algorithm optimization and personalized recommendations.

[0076] At the same time, by integrating audience participation feedback, the density of bullet screens and keyword relevance are incorporated into the weighted mechanism of intensity scoring, making the video content closer to the user's interests. The statistics of bullet screen density can dynamically reflect the audience's concentrated response to the highlight moments, and the keyword relevance further explores the deep meaning behind the audience's emotions, making the scoring more intelligent and dynamic. Such a mechanism not only improves the sensitivity of video screening to audience needs, but also encourages more interactive behaviors, enhances the stickiness between live broadcasts and audiences, and forms a virtuous circle. This interactive mechanism greatly enhances the attractiveness and dissemination effect of videos, and provides strong support for the healthy development of the sharing ecosystem. BRIEF DESCRIPTION OF THE DRAWINGS

[0077] Figure 1 A flowchart of a virtual reality game video sharing method provided by an embodiment of the present invention;

[0078] Figure 2 A flowchart of real-time monitoring, collecting and analyzing player behavior data and game content data during a game provided by an embodiment of the present invention;

[0079] Figure 3 A flow chart of adding intensity scores to player behavior data and game content data with different timestamps provided by an embodiment of the present invention;

[0080] Figure 4 A flowchart of obtaining a final intensity score for each timestamp data provided by an embodiment of the present invention;

[0081] Figure 5 A flowchart of editing and generating a final game video file provided by an embodiment of the present invention;

[0082] Figure 6 A flowchart for comparing whether a video duration is within a duration interval provided by an embodiment of the present invention;

[0083] Figure 7 A structural block diagram of a virtual reality game video sharing system provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0084] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0085] It is understood that the terms "first", "second", etc. used in this application may be used herein to describe various elements, but unless otherwise specified, these elements are not limited by these terms. These terms are only used to distinguish a first element from another element. For example, without departing from the scope of this application, a first xx script may be referred to as a second xx script, and similarly, a second xx script may be referred to as a first xx script.

[0086] Figure 1 A flowchart of a virtual reality game video sharing method provided by an embodiment of the present invention, such as Figure 1 As shown, the method includes:

[0087] S100, real-time monitoring, collecting and analyzing player behavior data and game content data during the game, adding timestamps to the collected player behavior data and game content data, and jointly storing them with the timestamps as data tags;

[0088] This step uses virtual reality devices to accurately perceive the various behavioral changes of players during the game, and combines the data performance of the game's internal content to form a detailed, multi-dimensional analysis model. This step is divided into two main directions: the integrated collection and analysis of player behavior data and game content data.

[0089] For player behavior data, VR devices use advanced sensors and hardware modules to fully capture players' head movement data, hand operation data, and eye movement data. At the same time, physiological indicators such as heart rate and skin conductivity are also included in the monitoring scope to reflect the real-time psychological and physiological state characteristics of players during the game. The capture of head movement data, especially the rotation angle, number of rapid movements, and frequency of gaze direction changes, can accurately reflect the player's reaction intensity and direction perception ability when facing stimulating scenes in the game. The monitoring of hand operation data, including grip strength, wrist rotation angle, interactive click frequency, and swing trajectory, reveals the depth and flexibility of interaction between players and the game environment. Eye movement data, such as gaze duration, gaze frequency, and gaze point switching speed, can intuitively reflect the player's concentration on specific game content and the dynamic changes of visual focus. In addition, real-time detection of heart rate and skin conductivity provides an important physiological basis for evaluating players' emotional fluctuations, such as the sympathetic nervous system response caused by players' tension, excitement, or involvement.

[0090] For game content data, the system constructs a comprehensive interpretation of the game content by capturing the dynamic features of the game screen, including the number of dynamic objects, behavior patterns and interactive actions, as well as the complexity and dynamic changes of the scene. For example, in a complex battle scene, the number and behavior patterns of dynamic objects can reflect the intensity of the battle, while the frequency of interactive actions reveals the closeness of the interaction between the player and the game environment. The analysis of scene complexity is achieved by judging the diversity of elements in the screen, the concurrency of events, and the frequency of scene switching, which further reveals the challenge and visual impact of game design.

[0091] All collected data is bound to the timestamp in the game to achieve accurate synchronization and integrated storage of data. The application of timestamps ensures that the player's behavior data and game content data can be seamlessly connected based on time, forming a unified data stream through synchronization processing. This method not only reduces the delay in data collection, but also lays the foundation for subsequent data analysis, scoring and content classification.

[0092] like Figure 2 As shown, the real-time monitoring collects and analyzes the player behavior data and game content data during the game, adds a timestamp to the collected player behavior data and game content data, and stores them jointly with the timestamp as the data tag, specifically including:

[0093] S110, detecting player behavior data through a virtual reality device, including head movement data, hand operation data, and eye movement data, and recording the player's real-time heart rate and skin conductivity;

[0094] S120, capturing game content data, and judging the complexity and dynamic change degree of the scene based on the game content data;

[0095] S130, all the collected player behavior data and game content data are bound to the timestamp in the game, and all the collected player behavior data and game content data are synchronously processed and integrated and stored as a unified data stream.

[0096] Wherein, the head movement data includes: rotation angle, number of rapid movements and frequency of annotation direction changes;

[0097] The hand operation data includes: grip strength, wrist rotation angle, interactive click frequency and swing trajectory;

[0098] The eye movement data includes: fixation duration, fixation frequency and fixation point switching speed;

[0099] The game content data includes: the number, behavior patterns and interactive actions of dynamic objects in the game screen.

[0100] S200, analyzing the jointly stored player behavior data and game content data, adding intensity scores to the player behavior data and game content data with different time stamps, and classifying the player behavior data and game content data according to the intensity scores;

[0101] This step, through comprehensive analysis of multi-dimensional data, deeply explores the dynamic characteristics and psychological feedback of players in the process of interaction with the game, thus providing a scientific and reliable basis for subsequent video editing and content presentation.

[0102] In the calculation of the score of player behavior data, the head movement score is first calculated by analyzing the head movement data (such as rotation angle, number of rapid movements, and frequency of changes in gaze direction). This data can intuitively reflect the player's perception and reaction speed to environmental changes in high-intensity interactive scenarios. At the same time, the evaluation of hand operation data (such as grip strength, wrist rotation angle, interactive click frequency, and swing trajectory) derives the hand operation score, which is used to characterize the intensity and refinement of the player's interaction with virtual objects in the game. In addition, eye movement data (including gaze duration, gaze frequency, and gaze point switching speed) is converted into an eye movement score, which reflects the player's attention distribution and visual tracking ability for key targets in the picture. The three types of data are comprehensively calculated after normalized weight processing to obtain the player behavior score, thereby comprehensively quantifying the player's behavioral characteristics in the virtual reality environment.

[0103] In the scoring calculation of the game screen dynamic data, the system calculates the game screen dynamic score by analyzing the number of dynamic objects in the screen, the behavior patterns of dynamic objects, and the interaction weights. The number of dynamic objects reflects the complexity of the scene, the behavior patterns describe the movement rules of dynamic elements, and the interaction weights describe the importance of dynamic objects related to player behavior. By performing logarithmic processing and weighted summation on these data, the dynamic complexity of the game scene and the operational challenges that players may face can be accurately evaluated.

[0104] The calculation of physiological data scores is based on the changes in the player's real-time heart rate and skin conductivity. The relationship between the real-time heart rate and the player's resting heart rate and maximum heart rate reflects the player's level of tension or excitement during the game. Skin conductivity fluctuations further reveal the player's emotional fluctuations. The combination of these data not only provides a physiological basis for the calculation of the intensity score, but also makes the scoring model more in line with the player's real experience.

[0105] In the process of comprehensively calculating the intensity score, the multi-dimensional data corresponding to the timestamp is further refined, and the intensity change is calculated by the change in the time interval. The head movements, hand operations, the number of dynamic objects, and the time change rate of physiological data are weighted and summarized to obtain the intensity score corresponding to the timestamp. Finally, the intensity score, player behavior score, game screen dynamic score, and physiological data score are combined using the weight coefficient to calculate the basic intensity score for each timestamp.

[0106] This step accurately captures the real experience of players in virtual reality games through comprehensive quantitative analysis of player behavior, game screen dynamics and physiological feedback. This all-round data integration can not only fully restore the key moments in the game process, but also optimize the rhythm and viewing experience of video clips by distinguishing content levels of different intensity. In addition, the introduction of dynamic change rate enables the intensity score to reflect sudden changes in the game process in real time, which helps to highlight the highlights. At the same time, the application of physiological data allows traditional game content data analysis to incorporate the emotional dimension of players, which is more in line with user experience. The graded content generated by this step not only improves the entertainment and sharing value of game videos, but also provides a high-quality data foundation for subsequent algorithm optimization and personalized recommendations, thereby significantly enhancing the dissemination effect and user recognition of virtual reality game video sharing.

[0107] like Figure 3 As shown, the jointly stored player behavior data and game content data are analyzed, intensity scores are added to the player behavior data and game content data of different timestamps, and the player behavior data and game content data are classified according to the intensity scores, specifically including:

[0108] S210, analyzing the player behavior data, quantifying the head movement, hand operation and eye movement to obtain the player behavior score, and calculating the player behavior score ;

[0109] S220, analyzing the game screen, quantifying the number of dynamic objects, behavior patterns, and interactive actions, and calculating the game screen dynamic score ;

[0110] S230 calculates physiological data scores based on the player's real-time heart rate and skin conductivity ;

[0111] S240, comprehensively calculating the data, including: head movement data, eye movement data, the player's real-time heart rate and game content data, to obtain the intensity score of the data corresponding to each timestamp ;

[0112] S250, comprehensively calculate all the scoring data, including: player behavior score, game screen dynamic score, physiological data score and intensity score, and obtain the basic intensity score of different timestamp data ;

[0113] S260, the intensity score is divided into several intervals corresponding to different content levels, including: S segment, A segment and B segment, and according to the intensity scores of different timestamps, the timestamp data corresponding to each intensity score is assigned a corresponding content level.

[0114] In this step, the player behavior score is calculated :

[0115] ;

[0116] in, is a head action score based on the head rotation angle, number of rapid movements, and frequency of annotated direction changes. The hand operation score is obtained based on hand grip strength, wrist rotation angle, interactive click frequency and swing trajectory. is an eye movement score based on eye fixation duration, fixation frequency, and fixation point switching speed. is the normalization constant;

[0117] Calculate the game screen dynamic score :

[0118] ;

[0119] in, is the number of dynamic objects in the picture, The behavior pattern of dynamic objects in the picture. is the number of interactive objects, For the The interaction weight of each interacting object;

[0120] Calculate physiological data scores :

[0121] ;

[0122] in, is the player's current heart rate, The player's resting heart rate. is the player's maximum heart rate, is the skin conductivity weight coefficient, is the real-time fluctuation amplitude of skin conductivity;

[0123] Get the intensity score of the data corresponding to each timestamp :

[0124] ;

[0125] in, , , ,and The scores of head movement, hand operation, heart rate and number of dynamic objects were The amount of change in time, is the time interval;

[0126] Get the basic intensity score of data with different timestamps :

[0127] ;

[0128] in, is the dynamic weight coefficient.

[0129] S300, continuously monitoring the density and keyword information of the bullet comments in the live broadcast, and performing additional weighting on the intensity scores of the player behavior data and game content data at the same timestamp, and obtaining the final intensity score of the player behavior data and game content data at each timestamp;

[0130] This step uses the live broadcast platform's bullet screen interface to collect the bullet screen content and its sending time in real time, and binds it to the timestamp of the game content data. This binding ensures that the bullet screen content can correspond to specific events in the game process, laying an accurate time benchmark for subsequent analysis. In each time window, the number of bullet screens is counted and the real-time bullet screen density is calculated. This indicator reflects the audience's attention to and enthusiasm for the game content in the current time period. The higher the value, the more attractive or impactful the game content in that time period is.

[0131] Then, based on the density of bullet comments in the current time window, the current intensity score is adjusted to calculate the ratio of the base value of the intensity score, which is used to measure the proportional change of the current bullet comment density relative to the maximum bullet comment density, thereby providing a dynamic adjustment factor for the intensity score. If the bullet comment density is close to the maximum value, the base value will increase significantly, reflecting the high attention of the live audience to the content in this time period.

[0132] On the basis of adjusting the basic value, the correlation between the barrage content and the keyword list is further introduced. The keyword list is pre-set and usually contains words that can reflect the audience's emotions or content hotspots, such as "hahaha", "66", etc. The average correlation is obtained by calculating the degree of match between the barrage content and the keyword list in each time window. The secondary basic value based on the correlation is calculated by a formula, which represents the ratio of the current correlation to the maximum correlation, which is used to further amplify the impact of the audience's emotional concentration and hot spot reactions on the intensity score.

[0133] Finally, the basic intensity score, basic value and secondary basic value are calculated comprehensively to get the final intensity score. This score not only reflects the intensity of the game content itself, but also includes the audience's real-time interactive feedback on the content, making the score more comprehensive, dynamic and in line with the actual scene.

[0134] This method can incorporate the audience's real-time participation feedback into the calculation of the intensity score, so that the selection of video content is more in line with the user's interests and emotional needs. By counting the density of bullet screens, the audience's concentrated reaction to the highlight moments can be dynamically captured, greatly improving the sensitivity of the score to the live performance. The correlation analysis further explores the deep information behind the audience's emotions, so that the score can accurately reflect the audience's changes in perception. In addition, by binding the bullet screens with timestamps, the system realizes the seamless integration of live interactive data and game content, providing an accurate basis for subsequent video editing and content push. Compared with the scoring method that relies solely on in-game data, this weighted mechanism that introduces user feedback significantly enhances the dynamics and intelligence of the evaluation system, making the generated video more attractive and valuable. Furthermore, this mechanism can also effectively encourage the audience to actively interact, enhance the stickiness between the live broadcast and the audience, thereby forming a virtuous circle and effectively promoting the healthy development of the virtual reality game video sharing ecosystem.

[0135] like Figure 4 As shown, the density and keyword information of the bullet screen in the live broadcast are continuously monitored, and the intensity score of the player behavior data and game content data at the same timestamp is additionally weighted, and the final intensity score of the player behavior data and game content data at each timestamp is obtained, specifically including:

[0136] S310, collecting the bullet screen streaming data of the live broadcast room in real time through the bullet screen interface of the live broadcast platform, and binding the sending time of each bullet screen with the timestamp of the game content data;

[0137] S320, based on the time window corresponding to each timestamp, count the number of bullet screens in a unit time to obtain the real-time bullet screen density ;

[0138] S330, preset a keyword list, and retrieve the relevance between the bullet comment content in each time window and the keyword list ;

[0139] S340, increase the base value of intensity score according to the number of bullet comments ;

[0140] S350, based on the relevance of the bullet screen content, obtain the secondary basic value of the intensity score ;

[0141] S360, superimpose the basic value and the secondary basic value on the basic intensity score to obtain the final intensity score :

[0142] .

[0143] In this step, the real-time bullet screen density :

[0144] ;

[0145] in, is the number of bullet comments in the current time window, is the time window length;

[0146] Base value of intensity rating :

[0147] ;

[0148] in, Score the current intensity of this timestamp, is the maximum barrage density;

[0149] Secondary base value of intensity rating :

[0150] ;

[0151] in, is the maximum correlation.

[0152] S400, according to the final intensity score, set the stage score threshold, and set the video selection rule for each stage, edit and generate the final game video file according to the selection rule, and generate the first-person perspective and the third-person perspective video based on the VR characteristics;

[0153] This step classifies the data and sets the staged scoring threshold based on the final intensity score, selects and edits the video content through scientific rules, and generates multi-view videos in combination with virtual reality features. By extracting and finely processing the timestamp data of highlight moments, key scenes and different content levels, it provides the basis for the final presentation of high-quality and diverse game videos.

[0154] This step sets clear scoring thresholds for each content level, corresponding to the S, A, and B segments. The distribution logic is set to ensure that the S segment content is the part with the highest intensity score, which usually contains the most attractive battle scenes, plot climaxes, or high-intensity interactions between players and the environment; the A segment content is the part with medium intensity, showing more important but slower-paced plots; and the B segment is the basic content, which is equivalent to redundant data and is used to supplement and balance the rhythm of the video. The setting of these thresholds is determined based on player behavior data, game content characteristics, and audience feedback, taking into account the importance of the content and the coordination of the rhythm.

[0155] During the editing process, by comparing the final intensity score with the size of each threshold, the system automatically extracts all timestamp data corresponding to the S-segment threshold. This ensures that the selected content is the most exciting and interactive clips. Subsequently, editing and synthesis are performed in the order of timestamps to maintain the coherence and logic of the video plot. During the synthesis process, the system will record the length of the edited video in real time to ensure that the final generated video contains both key content and meets the reasonable range of viewing time.

[0156] By extracting the player's real-time perspective data (first-person perspective) and the free perspective data in the virtual scene (third-person perspective) from the original data matched with the timestamp, the first-person perspective provides a strong sense of immersion by restoring the player's real-time operation and gaze direction, allowing the audience to directly experience the player's subjective perspective in the game. The third-person perspective uses the dynamic capture of the free perspective to show the overall picture and scene layout of the virtual world from a bird's eye view, which can more comprehensively show the game's artistic style and the shock of the scene. After the two perspectives are independently edited and synthesized, different versions of video files can be generated, which is convenient for the audience to choose to watch according to their preferences, and also provides a variety of materials for multi-platform dissemination.

[0157] like Figure 5 As shown, according to the final intensity score, the stage score threshold is set, and the video selection rules are set for each stage. According to the selection rules, the final game video file is edited and generated, and at the same time, based on the VR characteristics, the first-person perspective and the third-person perspective video are generated, which specifically include:

[0158] S410, setting a scoring threshold for each interval , , , corresponding to S segment, A segment, and B segment respectively, and ;

[0159] S420, compare the final intensity score and , , The size relationship between them, and get all The corresponding timestamp data is edited and synthesized in the order of the timestamps, and the duration of the synthesized video is recorded;

[0160] S430, extracting the player's real-time perspective data and the virtual world's free perspective data corresponding to the same timestamp, and performing editing and synthesis respectively.

[0161] S500, sets a duration interval based on the average video duration of historically uploaded videos, reads the final generated video file, compares whether the video duration is within the duration interval, and automatically uploads the video if it is within the duration interval; if not, stops uploading and sets the video file for review.

[0162] This step sets a reasonable duration interval based on the average duration of historically uploaded videos, combined with platform characteristics and user viewing preferences, which includes two thresholds: minimum duration and maximum duration. The generated video duration is directly compared with this interval to determine whether the video meets the requirements. If the video duration is less than the minimum duration of the interval, the system will consider that the current video content is insufficient to present a complete gaming experience. At this time, based on the order of the final intensity score, the highest-scoring content is selected from the timestamp segments of the S and A thresholds for supplementary synthesis. The supplemented content will not only meet the duration requirements through recalculation, but also ensure that the newly added clips are always the most attractive and interactive highlights. This flexible supplementation mechanism avoids the problem of incomplete videos due to insufficient content while maintaining high-quality video output.

[0163] When the video length exceeds the maximum duration of the set interval, the system will determine whether all highlight timestamps that exceed the S segment threshold are synthesized. If this condition is met, it means that the edited content covers all key scenes, and even if it exceeds the duration limit, it can be allowed to be uploaded on the grounds of completeness. However, if the current video length is still less than the minimum duration of the interval, the system will stop the automatic upload operation and mark the video as pending review. This mechanism leaves room for manual intervention to ensure that the final output video can meet the requirements of both audience experience and platform rules.

[0164] If the video length is within the time range, the system will automatically upload and share the final video file. This process does not require manual intervention, and uses intelligent judgment logic to achieve high efficiency and accuracy in video uploading. This automated processing method not only reduces the complexity of manual operations, but also improves the efficiency of content distribution, ensuring that videos can be pushed to relevant platforms in a timely manner and gain wider audience coverage.

[0165] By setting a reasonable duration range, the system can accurately control the length of the video content, so that it can fully present the highlights of the game and meet the audience's expectations for the rhythm and duration of the video. For videos that are not long enough, the content supplement mechanism based on the intensity score ensures the high quality and attractiveness of the newly added clips, avoiding the decline in viewing experience due to thin content. For videos that exceed the duration limit, by judging the completeness of the highlight content, the possibility of losing important content due to editing restrictions in overly long videos is solved, thus achieving a balance between completeness and duration limit.

[0166] like Figure 6 As shown, the duration interval is set based on the average video duration of historical uploaded videos, the final generated video file is read, and the video duration is compared to see whether it is within the duration interval. If it is within the duration interval, the video is automatically uploaded; if it is not within the duration interval, the upload is stopped and the video file is set to be reviewed, specifically including:

[0167] S510, setting a duration interval according to the average duration of historical uploaded videos, and determining whether the recorded video duration is within the duration interval;

[0168] S520: If the duration is less than the minimum duration of the duration interval, The order of size is from Select from the interval The largest timestamp data is supplemented and synthesized, and the duration of the synthesized video is recalculated;

[0169] S530: If the video duration is greater than the maximum duration of the duration interval, or if After all timestamp data are synthesized, if the video duration is still less than the minimum duration of the duration interval, the upload and sharing operation will be stopped and the video will be placed under review.

[0170] S540: If the video duration is within the duration range, the synthesized video is automatically uploaded and shared.

[0171] Figure 7 A structural block diagram of a virtual reality game video sharing system provided by an embodiment of the present invention, such as Figure 7 As shown, the system comprises:

[0172] The player game content data collection module 100 is used to monitor, collect and analyze the player behavior data and game content data during the game in real time, add timestamps to the collected player behavior data and game content data, and store them jointly with the timestamps as data tags;

[0173] The content intensity analysis module 200 is used to analyze the jointly stored player behavior data and game content data, add intensity scores to the player behavior data and game content data of different timestamps, and classify the player behavior data and game content data according to the intensity scores;

[0174] The intensity weighting module 300 is used to continuously monitor the density and keyword information of the bullet comments in the live broadcast, and to perform additional weighting on the intensity scores of the player behavior data and game content data at the same timestamp, and to obtain the final intensity score of the player behavior data and game content data at each timestamp;

[0175] The video editing and perspective generation module 400 is used to set the stage scoring threshold according to the final intensity score, and set the video selection rules for each stage, edit and generate the final game video file according to the selection rules, and generate the first-person perspective and the third-person perspective video based on the VR characteristics;

[0176] The video duration verification management module 500 is used to set a duration interval based on the average video duration of historically uploaded videos, read the final generated video file, and compare whether the video duration is within the duration interval. If it is within the duration interval, the video is automatically uploaded. If it is not within the duration interval, the upload is stopped and the video file is set for review.

[0177] It should be understood that, although each step in the flow chart of each embodiment of the present invention is shown in sequence according to the indication of the arrow, these steps are not necessarily performed in sequence according to the order indicated by the arrow. Unless there is a clear explanation in this article, the execution of these steps does not have a strict order restriction, and these steps can be performed in other orders. Moreover, at least a portion of the steps in each embodiment may include a plurality of sub-steps or a plurality of stages, and these sub-steps or stages are not necessarily performed at the same time, but can be performed at different times, and the execution order of these sub-steps or stages is not necessarily performed in sequence, but can be performed in turn or alternately with at least a portion of other steps or sub-steps or stages of other steps.

[0178] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the program can be stored in a non-volatile computer-readable storage medium. When the program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media used in the embodiments provided in this application may include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory may include random access memory (RAM) or external cache memory. As an illustration and not limitation, RAM is available in many forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).

[0179] The technical features of the above-described embodiments may be arbitrarily combined. To make the description concise, not all possible combinations of the technical features in the above-described embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0180] The above-mentioned embodiments only express several implementation methods of the present invention, and the description thereof is relatively specific and detailed, but it cannot be understood as limiting the scope of the patent of the present invention. It should be pointed out that, for ordinary technicians in this field, several variations and improvements can be made without departing from the concept of the present invention, which all belong to the protection scope of the present invention. Therefore, the protection scope of the patent of the present invention shall be subject to the attached claims.

[0181] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A method for sharing a virtual reality game video, characterized in that: The method comprises: Real-time monitoring, collection and analysis of player behavior data and game content data during the game, adding timestamps to the collected player behavior data and game content data, and jointly storing them with timestamps as data tags; Analyze the jointly stored player behavior data and game content data, add intensity scores to the player behavior data and game content data with different timestamps, and classify the player behavior data and game content data according to the intensity scores; Continuously monitor the density and keyword information of barrage in the live broadcast, and perform additional weighting on the intensity score of player behavior data and game content data at the same timestamp, and obtain the final intensity score of player behavior data and game content data at each timestamp; According to the final intensity score, set the stage score threshold, and set the video selection rules for each stage. According to the selection rules, edit and generate the final game video file, and generate the first-person perspective and third-person perspective videos based on VR characteristics; A duration interval is set based on the average video duration of historically uploaded videos, the final generated video file is read, and a comparison is made to see whether the video duration is within the duration interval. If it is within the duration interval, the video is automatically uploaded; if not, the upload is stopped and the video file is set for review.

2. The method according to claim 1, characterized in that: The real-time monitoring collects and analyzes the player behavior data and game content data during the game, adds a timestamp to the collected player behavior data and game content data, and stores them jointly with the timestamp as the data tag, specifically including: Detect player behavior data through virtual reality equipment, including head movement data, hand operation data, and eye movement data, while recording the player's real-time heart rate and skin conductivity; Capture game content data and use it to determine the complexity and dynamic changes of the scene; All collected player behavior data and game content data are bound to the timestamp within the game, and all collected player behavior data and game content data are synchronously processed and integrated and stored as a unified data stream.

3. The method according to claim 2, characterized in that The head movement data includes: rotation angle, number of rapid movements and frequency of annotation direction changes; The hand operation data includes: grip strength, wrist rotation angle, interactive click frequency and swing trajectory; The eye movement data includes: fixation duration, fixation frequency and fixation point switching speed; The game content data includes: the number, behavior patterns and interactive actions of dynamic objects in the game screen.

4. The method according to claim 3, characterized in that The jointly stored player behavior data and game content data are analyzed, intensity scores are added to the player behavior data and game content data of different timestamps, and the player behavior data and game content data are classified according to the intensity scores, specifically including: Analyze the player behavior data, quantify the head movements, hand operations and eye movements to obtain the player behavior score, and calculate the player behavior score ; Analyze the game screen, quantify the number of dynamic objects, behavior patterns, and interactive actions, and calculate the game screen dynamic score ; Calculate physiological data scores based on the player's real-time heart rate and skin conductivity ; The calculated data includes: head movement data, eye movement data, player's real-time heart rate and game content data to obtain the intensity score of the data corresponding to each timestamp ; Comprehensively calculate all the scoring data, including: player behavior score, game screen dynamic score, physiological data score and intensity score, and obtain the basic intensity score of data with different timestamps ; The intensity score is divided into several intervals corresponding to different content levels, including: S segment, A segment and B segment, and according to the intensity scores of different timestamps, the timestamp data corresponding to each intensity score is assigned a corresponding content level.

5. The method according to claim 4, characterized in that Calculating Player Behavior Scores : ； in, is a head action score based on the head rotation angle, number of rapid movements, and frequency of annotated direction changes. The hand operation score is obtained based on hand grip strength, wrist rotation angle, interactive click frequency and swing trajectory. is an eye movement score based on eye fixation duration, fixation frequency, and fixation point switching speed. is the normalization constant; Calculate the dynamic score of the game screen : ； in, is the number of dynamic objects in the picture, The behavior pattern of dynamic objects in the picture. is the number of interactive objects, For the The interaction weight of each interacting object; Calculate physiological data scores : ； in, is the player's current heart rate, The player's resting heart rate, is the player's maximum heart rate, is the skin conductivity weight coefficient, is the real-time fluctuation amplitude of skin conductivity; Get the intensity score of the data corresponding to each timestamp : ； in, , , ,and The scores of head movement, hand operation, heart rate and number of dynamic objects were The amount of change in time, is the time interval; Get the basic intensity score of data with different timestamps : ； in, is the dynamic weight coefficient.

6. The method according to claim 4, characterized in that The continuous monitoring of the density and keyword information of the bullet screen in the live broadcast, and additional weighting of the intensity score of the player behavior data and game content data at the same timestamp, and obtaining the final intensity score of the player behavior data and game content data at each timestamp, specifically includes: Through the bullet screen interface of the live broadcast platform, the bullet screen streaming data of the live broadcast room is collected in real time, and the sending time of each bullet screen is bound to the timestamp of the game content data; According to the time window corresponding to each timestamp, count the number of bullet screens per unit time and obtain the real-time bullet screen density. ; Preset a keyword list and retrieve the relevance between the bullet comment content and the keyword list in each time window ; Increase the base value of intensity score according to the number of bullet comments ; According to the relevance of the bullet screen content, the secondary basic value of the intensity score is obtained ; The basic value and the secondary basic value are added to the basic intensity score to obtain the final intensity score. : 。 7. The method according to claim 6, characterized in that The real-time bullet screen density : ； in, is the number of bullet comments in the current time window, is the time window length; Base value of intensity rating : ； in, Score the current intensity of this timestamp, is the maximum barrage density; Secondary base value of intensity rating : ； in, is the maximum correlation.

8. The method according to claim 6, characterized in that The method sets the stage-by-stage scoring thresholds based on the final intensity score, sets video selection rules for each stage, edits and generates the final game video file based on the selection rules, and generates first-person perspective and third-person perspective videos based on VR characteristics, specifically including: Set scoring thresholds for each interval , , , corresponding to S segment, A segment, and B segment respectively, and ; Compare the final intensity ratings and , , The size relationship between them, and get all The corresponding timestamp data is edited and synthesized in the order of the timestamps, and the duration of the synthesized video is recorded; The player behavior data and game content data corresponding to the same timestamp are extracted and edited and synthesized respectively.

9. The method according to claim 7, characterized in that: The duration interval is set based on the average video duration of historical uploaded videos, the final generated video file is read, and the video duration is compared to see whether it is within the duration interval. If it is within the duration interval, the video is automatically uploaded; if it is not within the duration interval, the upload is stopped and the video file is set to be reviewed. Specifically, it includes: According to the average duration of historically uploaded videos, a duration interval is set to determine whether the recorded video duration is within the duration interval; If it is less than the minimum duration of the duration interval, The order of size is Select from the interval The largest timestamp data is supplemented and synthesized, and the duration of the synthesized video is recalculated; If the video duration is greater than the maximum duration of the duration interval, or meets After all timestamp data are synthesized, if the video duration is still less than the minimum duration of the duration interval, the upload and sharing operation will be stopped and the video will be placed under review. If the video length is within the time range, the synthesized video will be automatically uploaded and shared.

10. A virtual reality game video sharing system, characterized in that: The system comprises: The player game content data collection module is used to monitor, collect and analyze the player behavior data and game content data during the game in real time, add timestamps to the collected player behavior data and game content data, and store them jointly with the timestamp as the data tag; A content intensity analysis module is used to analyze the jointly stored player behavior data and game content data, add intensity scores to the player behavior data and game content data of different timestamps, and classify the player behavior data and game content data according to the intensity scores; The intensity weighting module is used to continuously monitor the density and keyword information of the bullet comments in the live broadcast, and to perform additional weighting on the intensity scores of the player behavior data and game content data at the same timestamp, and to obtain the final intensity score of the player behavior data and game content data at each timestamp; The video editing and perspective generation module is used to set the stage scoring threshold according to the final intensity score, and set the video selection rules for each stage. According to the selection rules, the final game video file is edited and generated, and at the same time, based on the VR characteristics, the first-person perspective and the third-person perspective video are generated; The video duration verification management module is used to set the duration interval based on the average video duration of historical uploaded videos, read the final generated video file, and compare whether the video duration is within the duration interval. If it is within the duration interval, the video is automatically uploaded. If it is not within the duration interval, the upload is stopped and the video file is set for review.

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