Interactive film watching control method and electronic equipment

Abstract

The invention provides an interactive film watching control method and electronic equipment, and belongs to the technical field of artificial intelligence. According to the method, a user enters a movie and television virtual environment through AR equipment or VR equipment, and a visual simulation effect and an auditory simulation effect are adjusted to generate the best immersion feeling; and then collecting the user behavior and / or the user selection, and adjusting the film and television content according to the user behavior and / or the user selection, so that the film and television content can be dynamically adjusted according to the user behavior and / or the user selection, an interactive film watching experience is provided, and the immersion and participation sense of the user can be improved. And the movie and television content is analyzed through the AI, so that the user can be helped to better understand story backgrounds, role development, plot turning and the like in the movie and television content, and the film watching experience of the user can be enhanced. And according to the real-time feedback data of the user, the environment parameters of the film and television virtual environment and / or the behaviors of the non-player roles are dynamically adjusted, so that more personalized and adaptive film watching experience can be provided.

Description

Technical Field

[0001] The present application relates to the field of artificial intelligence technology, and in particular to an interactive movie viewing control method and electronic device. Background Art

[0002] In the traditional movie and video viewing mode, the user experience is often passive. The audience can only watch the content according to the perspective and plot development preset by the director and screenwriter, lacking a sense of participation and control. This one-way communication method limits the interaction between the audience and the movie content, making the viewing experience lack of personalization and depth.

[0003] In related technologies, some movies attempt to increase audience participation through selective plots or interactive narratives, but these attempts are often limited to preset options and paths and cannot be dynamically adjusted according to the audience's real-time reactions and behaviors, which limits the diversity of film content and the audience's exploration space. Summary of the invention

[0004] The main purpose of the embodiments of the present application is to propose an interactive viewing control method and electronic device, which aims to perform viewing control based on user behavior and feedback to provide a new immersive, interactive and personalized viewing experience.

[0005] To achieve the above-mentioned purpose, a first aspect of an embodiment of the present application proposes an interactive movie viewing control method, the method comprising:

[0006] Use AR or VR devices to allow users to enter a virtual environment and adjust visual and auditory simulation effects to create the best sense of immersion.

[0007] Collecting user behaviors and / or user choices, and adjusting film and television content according to the user behaviors and / or user choices, wherein the film and television content includes film and television plots, changes in film and television virtual environments, or behaviors of non-player characters in the film and television virtual environments;

[0008] Analyze film and television content through AI to provide users with plot analysis and understanding of character motivations, and dynamically adjust the AI's analysis depth based on the user's understanding level;

[0009] Collect real-time feedback data of the user on the current virtual environment state, and dynamically adjust the environmental parameters of the film and television virtual environment and / or the behavior of non-player characters according to the real-time feedback data.

[0010] In one embodiment of the present application, after the user enters the film and television virtual environment through the AR device or the VR device, the method further includes:

[0011] Collect and analyze the user's physiological and behavioral data regarding the complexity of the current environment and / or the current interaction intensity to determine the user's comfort level under the complexity of the current environment and / or the current interaction intensity;

[0012] Calculate the target environment complexity and / or target interaction intensity based on the user's physiological and behavioral data on the current environment complexity and / or the current interaction intensity, wherein the target environment complexity is the environment complexity that can make the user at the best comfort level, and the target interaction intensity is the interaction intensity that can make the user at the best comfort level;

[0013] According to the current environment complexity and the target environment complexity, a first environment complexity is calculated, and the current environment complexity is updated with the first environment complexity, and / or according to the current interaction intensity and the target interaction intensity, a first interaction intensity is calculated, and the current interaction intensity is updated with the first interaction intensity.

[0014] In one embodiment of the present application, after updating the current environment complexity and / or updating the current interaction strength, the method further includes:

[0015] Return to the step of collecting and analyzing the user's physiological and behavioral data on the current environment complexity and / or the current interaction intensity to determine the user's comfort level under the current environment complexity and / or the current interaction intensity, until the current environment complexity is updated to the target environment complexity and / or until the current interaction intensity is updated to the target interaction intensity.

[0016] In one embodiment of the present application, adjusting the film and television content according to user behavior and / or user selection includes:

[0017] Analyze the user behavior and the user selection using AI algorithms to identify user intentions or needs;

[0018] Formulate corresponding film and television content adjustment strategies according to the user's intention or needs;

[0019] In response to the film and television content adjustment strategy, the film and television plot, the film and television virtual environment changes, or the behavior of non-player characters in the film and television virtual environment is adjusted.

[0020] In one embodiment of the present application, after adjusting the film and television content according to the user behavior and / or user selection, the method further includes:

[0021] Collect user behavior data in the film and television virtual environment and quantify the deviation between user behavior and system expected behavior;

[0022] According to the deviation between the user behavior and the expected behavior of the system, the film and television virtual environment and / or the behavior of non-player characters in the film and television virtual environment are adjusted.

[0023] In one embodiment of the present application, the method further includes dynamically adjusting the difficulty of tasks in the film and television according to user behavior data, including:

[0024] Collect and analyze user behavior data to determine the user's skill level, preferences, and current experience status, where the user behavior data includes the user's movement speed, interaction frequency, and the user's success rate in completing tasks;

[0025] Calculate the difficulty of the target task based on the user's skill level, preferences, and current experience state;

[0026] The first task difficulty is calculated according to the current task difficulty and the target task difficulty, and the current task difficulty is updated with the first task difficulty.

[0027] In one embodiment of the present application, dynamically adjusting the analysis depth of the AI ​​according to the user's understanding level includes:

[0028] Collecting user feedback after providing the user with plot analysis and character motivation understanding, and determining the user's understanding of the currently provided plot analysis and character motivation understanding based on the user feedback;

[0029] Calculate a target analysis depth based on the user's understanding of the currently provided plot analysis and character motivation understanding, where the target analysis depth is the analysis depth that allows the user to be at an optimal understanding level;

[0030] Calculate a first analysis depth according to the current analysis depth of the AI ​​and the target analysis depth, and update the current analysis depth with the first analysis depth;

[0031] Return to the step of collecting user feedback after providing the user with plot analysis and character motivation understanding, and determining the user's degree of understanding of the currently provided plot analysis and character motivation understanding based on the user feedback, until the current analysis depth is updated to the target analysis depth.

[0032] In one embodiment of the present application, dynamically adjusting the environmental parameters of the film and television virtual environment according to the real-time feedback data includes:

[0033] Collecting real-time feedback data of the user on the current virtual environment state, wherein the real-time feedback data includes real-time behavior data and physiological data of the user after entering the current virtual environment state;

[0034] Analyzing the real-time feedback data to identify user emotions or intentions;

[0035] Determining target environment characteristics to be adjusted according to the real-time feedback data;

[0036] Dynamically adjust the environmental parameters corresponding to the target environmental features according to the user's emotions or intentions.

[0037] In one embodiment of the present application, determining the target environment characteristics to be adjusted according to the real-time feedback data includes:

[0038] Obtain real-time user feedback data and corresponding multiple environmental features;

[0039] Calculating the correlation between each of the environmental features and the user's real-time feedback data to extract the environmental feature with the greatest correlation;

[0040] The environmental feature with the greatest correlation is used as the target environmental feature to be adjusted.

[0041] To achieve the above-mentioned purpose, the second aspect of an embodiment of the present application proposes an electronic device, which includes a memory and a processor, the memory stores a computer program, and the processor implements the method described in any embodiment of the present application when executing the computer program.

[0042] In the technical solution provided in an embodiment of the present application, the user is first allowed to enter a film and television virtual environment through an AR device or a VR device, and the visual simulation effect and the auditory simulation effect are adjusted to produce the best immersion. Then, the user behavior and / or user selection are collected to adjust the film and television content according to the user behavior and / or user selection, that is, the film and television content can be dynamically adjusted according to the user behavior and / or user selection, providing an interactive viewing experience, which can improve the user's immersion and participation. Then, the film and television content is analyzed by AI to provide users with plot analysis and character motivation understanding, and the analysis depth of AI is dynamically adjusted according to the user's understanding level, which can help users better understand the story background, character development, and plot twists in the film and television content, thereby enhancing the user's viewing experience. Then, the user's real-time feedback data on the current virtual environment state is collected, and the environmental parameters of the film and television virtual environment and / or the behavior of non-player characters are dynamically adjusted according to the real-time feedback data, that is, the environmental parameters and / or the behavior of non-player characters can be adjusted accordingly according to the user's emotions or intentions, so as to provide a more personalized and adaptive viewing experience. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] Figure 1 is a flow chart of an interactive movie viewing control method provided by an embodiment of the present application;

[0044] Figure 2 It is a flowchart of steps performed after a user enters a film and television virtual environment through an AR device or a VR device, provided by an embodiment of the present application;

[0045] Figure 3is a flowchart of steps for adjusting film and television content according to user behavior and / or user selection provided by an embodiment of the present application;

[0046] Figure 4 is a flowchart of steps performed after adjusting the video content according to user behavior and / or user selection, provided by an embodiment of the present application;

[0047] Figure 5 is a flowchart of the steps of dynamically adjusting the difficulty of tasks in a film and television program according to user behavior data provided by an embodiment of the present application;

[0048] Figure 6 This is a flowchart of the steps of dynamically adjusting the analysis depth of AI according to the user's understanding level provided by an embodiment of the present application;

[0049] Figure 7 is a flowchart of the steps of dynamically adjusting the environmental parameters of the film and television virtual environment according to real-time feedback data provided by an embodiment of the present application;

[0050] Figure 8 is a flowchart of steps for determining target environment characteristics to be adjusted according to real-time feedback data provided by an embodiment of the present application;

[0051] Fig. 9 It is a schematic diagram of the hardware structure of an electronic device provided in one embodiment of the present application. DETAILED DESCRIPTION

[0052] In order to make the purpose, technical solution and advantages of the present application more clearly understood, the present application is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0053] It should be noted that, although the functional modules are divided in the device schematic diagram and the logical order is shown in the flowchart, in some cases, the steps shown or described may be performed in a different order than the module division in the device or the order in the flowchart. The terms "first", "second", etc. in the specification, claims and the above drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence.

[0054] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those commonly understood by those skilled in the art to which this application belongs. The terms used herein are only for the purpose of describing the embodiments of this application and are not intended to limit this application.

[0055] Terminology explanation:

[0056] VR (Virtual Reality): Virtual reality refers to the simulation of a three-dimensional virtual world through computer technology. Users can interact with this virtual world through head-mounted displays (HMDs), handles and other devices to gain an immersive experience.

[0057] AR (Augmented Reality): Augmented reality refers to the use of computer technology to superimpose virtual information (such as images, videos or data) onto the user's real-world vision to enhance the user's perception of reality. This technology is often used in mobile applications, head-mounted devices, etc., allowing virtual content to be seamlessly integrated with the real world.

[0058] AI (Artificial Intelligence): Artificial intelligence refers to intelligent behaviors performed by computer systems, including learning, reasoning, self-correction and other capabilities. AI technology can simulate human cognitive functions, such as visual recognition, language understanding, decision making, etc.

[0059] NPC (Non-Player Character): Non-player character, in the game, refers to a character controlled by a computer. They are not directly controlled by the player, but can interact with the player character according to preset programs or AI algorithms, adding vitality and realism to the game world.

[0060] SDK (Software Development Kit): A software development kit is a collection of tools and libraries that developers use to develop specific functions or applications. SDKs usually include code libraries, documentation, tools, and guidance to help developers quickly integrate and use specific technologies or services.

[0061] NLP (Natural Language Processing): Natural language processing refers to a branch of computer science and artificial intelligence that is dedicated to enabling computers to understand, interpret, and generate content in human language.

[0062] 3D sound technology: refers to an audio technology that can simulate the propagation of sound in three-dimensional space, providing a more realistic and immersive sound experience. This technology is often used in movies, games and virtual reality applications to enhance the user's sensory experience.

[0063] Light field display technology: refers to an advanced display technology that can capture and reproduce the propagation of light in three-dimensional space, thereby providing a visual experience with a strong sense of depth and reality. This technology can simulate the scattering and refraction of light in the real world, making virtual images look more natural and three-dimensional.

[0064] In the traditional movie and video viewing mode, the user experience is often passive. The audience can only watch the content according to the perspective and plot development preset by the director and screenwriter, lacking a sense of participation and control. This one-way communication method limits the interaction between the audience and the movie content, making the viewing experience lack of personalization and depth.

[0065] Although some films try to increase audience participation through selective plots or interactive narratives, these attempts are often limited to preset options and paths and cannot be dynamically adjusted according to the audience's real-time reactions and behaviors. This limits the diversity of film content and the audience's exploration space.

[0066] Moreover, everyone's viewing preferences and emotional responses are unique, but existing film and video content often cannot meet this personalized demand. Audiences cannot adjust the plot development or viewing environment according to their own preferences, resulting in a homogenized viewing experience.

[0067] Based on this, an embodiment of the present application proposes an interactive movie viewing control method, which aims to perform movie viewing control based on user behavior and feedback to provide a new immersive, interactive and personalized movie viewing experience.

[0068] Reference Figure 1 , Figure 1 It is a flowchart of an interactive movie viewing control method provided in one embodiment of the present application, including but not limited to steps S110 to S140.

[0069] Step S110, allowing the user to enter a film and television virtual environment through an AR device or a VR device, and adjusting the visual simulation effect and the auditory simulation effect to produce an optimal sense of immersion.

[0070] In the embodiment of the present application, the user can enter the film and television virtual environment by wearing an AR device or a VR device, and can interact with the film and television virtual environment, feel the objects in the virtual environment scene, and even communicate with NPCs in the virtual environment, thereby obtaining a richer and more realistic experience.

[0071] In the embodiment of the present application, in order to create an immersive viewing environment for users, let users feel that they have truly "entered" the scene of the virtual environment of film and television, let users change from traditional passive viewing to active experience, enhance the user's sense of participation and immersion, and produce the best immersion by adjusting the visual simulation effect and the auditory simulation effect. Specifically, a simplified formula can be introduced to describe the user's sense of immersion, namely: S = f(V, A), where S represents the user's sense of immersion; V represents the visual simulation effect, which includes the application of technologies such as stereoscopic display, head and motion tracking, and panoramic sound effects. These technologies work together on the user's visual effects to create a sense of depth and three-dimensional space; A represents the auditory simulation effect, which simulates the direction and distance of sound through spatial sound technology to enhance the user's immersion in the virtual world.

[0072] The specific calculation process involves how to combine visual simulation effects and auditory simulation effects to produce the best sense of immersion. Specifically, in terms of visual simulation, by providing slightly different images to the left and right eyes, a sense of depth is created to form a three-dimensional visual effect. The virtual environment is rendered at a high frame rate (usually over 90FPS) to reduce latency and jitter and improve visual smoothness. Sensors (such as gyroscopes, accelerometers, and laser trackers) are used to accurately track the movement and position of the user's head, and the perspective of the virtual environment is updated in real time accordingly, so that the user's visual experience is synchronized with the actual action. In terms of auditory simulation, spatial sound technology is used to simulate the direction and distance of sound to enhance the user's sense of immersion in the virtual world.

[0073] In the traditional viewing mode, the audience watches the content through a fixed screen, but when using VR or AR devices to enter the virtual environment of the film and television, the audience can move their heads and bodies freely and interact with the virtual environment. This experience is 360 degrees and is not limited by the boundaries of the traditional screen. In addition, VR / AR technology can make every user's action reflected in the virtual environment in real time through head and motion tracking technology. This synchronization greatly enhances the sense of immersion and reality.

[0074] Reference Figure 2 , Figure 2 It is a flowchart of steps performed after a user enters a film and television virtual environment through an AR device or a VR device, provided by an embodiment of the present application, including but not limited to steps S210 to S230.

[0075] Step S210, collecting and analyzing the user's physiological and behavioral data on the current environment complexity and / or the current interaction intensity to determine the user's comfort level under the current environment complexity and / or the current interaction intensity;

[0076] Step S220, calculating the target environment complexity and / or target interaction intensity according to the user's physiological and behavioral data on the current environment complexity and / or the current interaction intensity, wherein the target environment complexity is the environment complexity that can make the user at the best comfort level, and the target interaction intensity is the interaction intensity that can make the user at the best comfort level;

[0077] Step S230, after calculating the first environment complexity according to the current environment complexity and the target environment complexity, and updating the current environment complexity with the first environment complexity, and / or calculating the first interaction intensity according to the current interaction intensity and the target interaction intensity, and updating the current interaction intensity with the first interaction intensity, returns to the step of collecting and analyzing the user's physiological and behavioral data on the current environment complexity and / or the current interaction intensity to determine the user's comfort level under the current environment complexity and / or the current interaction intensity, until the current environment complexity is updated to the target environment complexity and / or until the current interaction intensity is updated to the target interaction intensity.

[0078] In the embodiment of the present application, in order to provide the user with a comfortable viewing experience, the user's physiological and behavioral data on the current environment complexity and / or the current interaction intensity may also be analyzed to determine the user's comfort level with the current environment complexity and / or the current interaction intensity, so as to determine whether the current environment complexity and / or the current interaction intensity is appropriate. If the user's comfort level with the current environment complexity and / or the current interaction intensity is not good, it may be determined that the current environment complexity and / or the current interaction intensity is not appropriate, and therefore the current environment complexity and / or the current interaction intensity needs to be adjusted to provide the user with the most comfortable environment complexity and / or interaction intensity.

[0079] Specifically, the system can collect the user's physiological and behavioral data on the current environment complexity and / or the current interaction intensity, such as heart rate, eye movement, handle grip and other data, and then analyze these physiological and behavioral data of the user to determine the user's comfort level with the current environment complexity and / or the current interaction intensity. Then, based on the analysis of the user's physiological and behavioral data on the current environment complexity and / or the current interaction intensity, the target environment complexity that can make the user at the best comfort level, and / or the target interaction intensity that can make the user at the best comfort level can be determined, so that the current environment complexity and / or the current interaction intensity can be further adjusted according to the current environment complexity or the current interaction intensity and the target environment complexity or the target interaction intensity and the adjustment factor. Specifically, the adjustment method can be performed by Formula 1:

[0080] Cnew=Ccurrent+β*(Pcomfort-Ccurrent) (Formula 1);

[0081] In Formula 1, Cnew is the first environment complexity or the first interaction intensity, Ccurrent is the current environment complexity or the current interaction intensity, Pcomfort is the target environment complexity or the target interaction intensity, and β is an adjustment factor used to control the adjustment range. It should be noted that the value of β can be set according to the design of the system and the sensitivity of user feedback.

[0082] In an embodiment of the present application, after calculating the first environment complexity or the first interaction intensity, the current environment complexity can be updated with the first environment complexity, and / or the current interaction intensity can be updated with the first interaction intensity to update the current environment complexity and / or the current interaction intensity. After the update, the user's physiological and behavioral data of the updated environment complexity or interaction intensity can be analyzed again to determine the user's comfort with the updated environment complexity and / or interaction intensity. If the user's comfort with the updated environment complexity and / or interaction intensity is not good, the updated environment complexity or the updated interaction intensity is used as the current environment complexity or current interaction intensity, and the current environment complexity or current interaction intensity is continuously adjusted according to Formula 1 until the current environment complexity is updated to the target environment complexity and / or until the current interaction intensity is updated to the target interaction intensity.

[0083] In the embodiment of the present application, the complexity and / or interaction intensity of the virtual environment can be dynamically adjusted according to the physiological and behavioral data of the user to provide a more personalized and comfortable experience. And by adjusting the factors, a smooth transition of the complexity and interaction intensity of the environment can be achieved to avoid sudden changes that cause discomfort to the user experience.

[0084] Step S120, collecting user behaviors and / or user choices, and adjusting the film and television content according to the user behaviors and / or user choices, the film and television content including the film and television plot, changes in the film and television virtual environment, or behaviors of non-player characters in the film and television virtual environment.

[0085] In the embodiment of the present application, in order to improve the user's interactive experience and enhance the user's sense of immersion and participation, the user behavior and / or user selection are collected, and the film and television content is adjusted according to the user behavior and / or user selection, so that the film and television content can be dynamically adjusted based on the user's intention or needs. Among them, the film and television content may include the film and television plot, the change of the film and television virtual environment, or the behavior of non-player characters in the film and television virtual environment. For example, AI algorithms can be used to analyze user behavior and / or user selection, such as head rotation, gestures, voice commands, etc., and then the NPC's behavior or virtual environment changes can be adjusted according to these data to respond to the user's actions.

[0086] Reference Figure 3 , Figure 3It is a flowchart of steps for adjusting film and television content according to user behavior and / or user selection provided by an embodiment of the present application, including but not limited to steps S310 to S330.

[0087] Step S310, analyzing user behavior and / or user selection using an AI algorithm to identify user intentions or needs;

[0088] Step S320, formulating a corresponding film and television content adjustment strategy according to the user's intention or demand;

[0089] Step S330, in response to the film and television content adjustment strategy, adjusting the film and television plot, the film and television virtual environment change, or the non-player character behavior in the film and television virtual environment.

[0090] In the embodiment of the present application, the user's real-time behavior and user's choice can be collected through sensors and input devices, and the user's behavior and user's choice can be analyzed by AI algorithm to identify the user's intention or need. Based on the user's intention or need, the corresponding film and television content adjustment strategy is formulated through the AI ​​algorithm. Therefore, the film and television plot can be adjusted in real time according to the formulated film and television content adjustment strategy. Or the changes in the film and television virtual environment, such as light, weather, scene layout, etc., can be adjusted according to the adjustment strategy. Or the NPC behavior can be adjusted according to the adjustment strategy, such as changing the NPC dialogue content, expression, action, etc., in response to the user's behavior.

[0091] Specifically, the embodiment of the present application can introduce a formula to describe how to adjust the film and television content according to user behavior and user choice, that is: E = g(B, C) where E represents the film and television content, which may include the film and television plot, changes in the film and television virtual environment, or the behavior of non-player characters in the film and television virtual environment. B represents user behavior, which can be the user's physical actions (such as position movement, gestures), physiological reactions (such as heart rate changes), or even the user's voice commands. C represents user choice, that is, the choice made by the user in the virtual environment, such as choosing a different path or interacting with a specific NPC.

[0092] In the traditional viewing mode, the audience passively receives the film and television content, but in the embodiment of the present application, the audience's behavior and choices can directly affect the direction and outcome of the movie. This makes the movie no longer a one-way narrative, but a two-way interactive experience. In addition, related technologies can increase audience participation through selective plots or interactive narratives, but these attempts are often limited to preset options and paths. In the embodiment of the present application, the film and television content can be adjusted according to user behavior and user choices, so that a more complex and dynamic interactive experience can be achieved. The AI ​​algorithm can learn the user's behavior patterns and make more natural and reasonable adjustments accordingly, making the experience of film and television content richer and more diverse, and each user can enjoy content customized for them.

[0093] Reference Figure 4 , Figure 4 It is a flowchart of steps performed after adjusting the film and television content according to user behavior and / or user selection, provided by an embodiment of the present application, including but not limited to steps S410 to S420.

[0094] Step S410, collecting user behavior data in the film and television virtual environment, and quantifying the deviation between the user behavior and the system expected behavior;

[0095] Step S420: adjusting the film and television virtual environment and / or the behavior of non-player characters in the film and television virtual environment according to the deviation between the user behavior and the system expected behavior.

[0096] In the embodiment of the present application, the film and television virtual environment and / or the behavior of non-player characters in the film and television virtual environment can be further adjusted according to the deviation between the user behavior and the expected behavior of the system. Specifically, the embodiment of the present application introduces an abnormality ratio to quantify the deviation between the user behavior and the expected behavior of the system, so as to help the system decide how to adjust the NPC behavior and / or the virtual environment to provide an experience that better meets the user's needs and behavior patterns. The abnormality ratio is calculated by formula 2:

[0097]

[0098] In Formula 2, S represents the anomaly ratio, which indicates the degree of deviation between user behavior and system expected behavior. P represents system expected behavior, which can be predicted based on historical data and learning patterns. A is the actual observed user behavior. Specifically, system expected behavior can be any indicator related to user behavior, such as expected completion time, expected path selection, expected interaction frequency, etc. in a specific task. User behavior is the behavior actually exhibited by the user in the virtual environment, corresponding to the system expected behavior. System expected behavior is the system predicting the user's behavior in a specific situation based on historical data, user portraits, previous behavior patterns and other information. This can be achieved through machine learning models, for example, using regression analysis to predict the time it takes for users to complete a task. Collect user behavior data in the virtual environment in real time, including user movement trajectories, interaction logs, task completion status, etc. For each behavior indicator, calculate the absolute difference between the actual user behavior value A and the system expected behavior value P, and then divide it by the system expected behavior to obtain the anomaly ratio S. The anomaly ratio S is used to quantify the degree of deviation between user behavior and system expected behavior. The larger the anomaly ratio S, the greater the deviation. In the embodiment of the present application, a threshold value may be preset. When the abnormal ratio S exceeds the threshold value, the system considers that the user behavior deviates significantly from expectations and needs to be adjusted.

[0099] According to the size of the abnormal ratio S, the virtual environment and / or NPC behavior are dynamically adjusted to better adapt to the actual behavior of the user. After adjusting the virtual environment and / or NPC behavior, the user's reaction to the adjusted virtual environment and / or NPC behavior can be continuously monitored and analyzed to determine whether the current adjustment is appropriate. That is, based on the analysis of the user's reaction to the adjusted virtual environment and / or NPC, the user's comfort level with the adjusted virtual environment and / or NPC behavior can be determined, so that the virtual environment and / or NPC can be adjusted again according to the user's comfort level.

[0100] In the embodiments of the present application, by quantifying the deviation between the user behavior and the system's expected behavior, the system can more accurately identify the user's needs and preferences, thereby providing an experience that better meets the user's expectations.

[0101] In some embodiments, the film and television content may also include some tasks that require user participation to complete. Figure 5 , Figure 5 It is a flowchart of the steps for dynamically adjusting the difficulty of tasks in movies and TV shows based on user behavior data provided by an embodiment of the present application, including but not limited to steps S510 to S530.

[0102] Step S510, collecting and analyzing user behavior data to determine the user's skill level, preference, and current experience status, wherein the user behavior data includes the user's movement speed, interaction frequency, and the user's success rate in completing tasks;

[0103] Step S520, calculating the difficulty of the target task according to the user's skill level, preference and current experience status;

[0104] Step S530, calculating the first task difficulty according to the current task difficulty and the target task difficulty, and updating the current task difficulty with the first task difficulty.

[0105] In the embodiment of the present application, considering that there may be some tasks in the film and television content that require user participation to complete, at this time, it is necessary to dynamically adjust the difficulty of the tasks in the film and television according to the user behavior data to provide a more personalized and adaptive experience. Specifically, the user's behavior data in the film and television virtual environment is first collected, including the user's movement speed, interaction frequency, success rate of completing tasks and other data, and the user behavior data is analyzed to determine the user's skill level, preferences and current experience status. Thus, the target task difficulty can be calculated based on the user's skill level, preferences and current experience status. Among them, the target task difficulty is the task difficulty that is compatible with the user's skill level, preferences and current experience status, that is, it is not too simple or too challenging for the user. Furthermore, the first task difficulty can be calculated based on the current task difficulty and the target task difficulty, and the current task difficulty can be updated with the first task difficulty. Specifically, the adjustment method can be executed by Formula 3:

[0106] Dnew=Dcurrent+γ*(Suser-Dcurrent) (Formula 3);

[0107] In Formula 3, Dnew is the first task difficulty, Dcurrent is the current task difficulty, Suser is the target task difficulty, and γ is an adjustment factor used to control the magnitude of the adjustment. It should be noted that the value of γ can be set according to the design of the system and the sensitivity of user feedback.

[0108] In the embodiment of the present application, after calculating the first task difficulty, the current task difficulty can be updated to the first task difficulty to update the current task difficulty. After the update, the user's physiological and behavioral data for the updated task difficulty can be analyzed again to determine the user's adaptability to the updated task difficulty. If the user's adaptability to the updated task difficulty is not good, the updated task difficulty needs to be used as the current task difficulty, and the current task difficulty is continuously adjusted according to Formula 3 until the current task difficulty is updated to the target task difficulty.

[0109] In the embodiment of the present application, the task difficulty can be dynamically adjusted according to the user's behavior data to provide a more personalized and adaptive experience. And by adjusting the factor γ, a smooth task difficulty transition can be achieved to avoid sudden changes that cause discomfort to the user experience.

[0110] Step S130, analyzing the film and television content through AI to provide users with plot analysis and character motivation understanding, and dynamically adjust the AI ​​analysis depth according to the user's understanding level.

[0111] In the embodiments of the present application, AI technology is used to deeply analyze the film and television content, thereby providing users with a deeper level of plot analysis and understanding of character motivations. This analysis can help users better understand the story background, character development, and plot twists in the film and television content, thereby enhancing the user's viewing experience. With the assistance of AI, users can obtain a richer and more three-dimensional understanding of the story, making the viewing process not only a visual and auditory enjoyment, but also a deep cognitive and emotional participation.

[0112] In an embodiment of the present application, a formula can be introduced to describe how AI analyzes film and television content and provides plot analysis and character motivation understanding, that is: U = h (D, P) where U represents the user's understanding ability, that is, the user's understanding and cognitive depth of the movie video content. D represents film and television content, including scripts, dialogues, scenes, etc. P represents plot analysis and character motivation understanding, that is, the results of in-depth analysis of film and television content using AI technology. Specifically, AI technology can be used to extract key information from film and television content, such as dialogues, scene descriptions, character behaviors, etc. Natural language processing (NLP) technology is then used to analyze the story structure to identify key nodes in the development of the plot. AI is used to analyze the character's behavior and dialogue to infer the character's motivation and psychological state. AI is used to evaluate emotional changes in film and television to understand the relationships and conflicts between characters. Combined with the above AI analysis results, plot analysis and character motivation understanding can be provided to users, thereby enhancing the user's in-depth understanding of the movie.

[0113] In the embodiments of the present application, AI technology can be used to automatically analyze a large amount of film and television content and provide analysis results in real time. Through deep learning and natural language processing technology, AI can understand film and television content more deeply and accurately, including complex plots and character motivations. This can provide users with plot analysis and character motivation understanding, which can not only improve the efficiency and depth of film and television analysis, but also bring users a richer and more personalized viewing experience.

[0114] In some embodiments, to match the user's cognitive level, the AI's analysis depth can be adjusted based on the user's understanding of plot analysis and character motivation. Figure 6 , Figure 6 It is a flowchart of steps for dynamically adjusting the analysis depth of AI according to the user's understanding level provided by an embodiment of the present application, including but not limited to steps S610 to S630.

[0115] Step S610, collecting user feedback after providing the user with plot analysis and character motivation understanding, and determining the user's understanding level of the currently provided plot analysis and character motivation understanding based on the user feedback;

[0116] Step S620, calculating a target analysis depth according to the user's understanding of the currently provided plot analysis and character motivation understanding, the target analysis depth being the analysis depth that can allow the user to be at an optimal understanding level;

[0117] Step S630, calculates the first analysis depth based on the current analysis depth and target analysis depth of the AI, and after updating the current analysis depth with the first analysis depth, returns to the step of collecting user feedback after providing the user with plot analysis and character motivation understanding, and determines the user's understanding level of the currently provided plot analysis and character motivation understanding based on the user feedback, until the current analysis depth is updated to the target analysis depth.

[0118] In the embodiment of the present application, taking into account the different cognitive levels of different users, the analysis depth of AI can be adjusted according to the user's understanding of plot analysis and character motivation understanding, so that the provided plot analysis and character motivation understanding can match the user's cognitive level. Specifically, first collect user feedback after providing plot analysis and character motivation understanding to the user, such as through user interaction, questionnaires or behavioral analysis. Then, based on user feedback, determine the user's understanding of the currently provided plot analysis and character motivation understanding, so that the target analysis depth can be calculated based on the user's understanding of the currently provided plot analysis and character motivation understanding. The target analysis depth is the analysis depth that allows the user to be at the best level of understanding. Furthermore, the first analysis depth can be calculated based on the current analysis depth and target analysis depth of AI, and the current analysis depth can be updated with the first analysis depth. Specifically, the adjustment method can be executed by Formula 4:

[0119] Anew=Acurrent+δ*(Uuser-Acurrent) (Formula 4);

[0120] In Formula 4, Anew is the first analysis depth, Acurrent is the current analysis depth, Uuser is the target analysis depth, and δ is an adjustment factor used to control the amplitude of the adjustment. It should be noted that the value of δ can be set according to the design of the system and the sensitivity of user feedback.

[0121] In the embodiment of the present application, after the first analysis depth is calculated, the current analysis depth can be updated to the first analysis depth to update the current analysis depth. After the update, the user's understanding of the plot analysis and character motivation provided by the updated analysis depth can be analyzed again to determine whether the updated analysis depth matches the user's cognitive level. If the updated analysis depth does not match the user's cognitive level, the updated analysis depth (i.e., the first analysis depth) is used as the current analysis depth, and the current analysis depth is adjusted according to Formula 4 until the current analysis depth is updated to the target analysis depth.

[0122] In the embodiment of the present application, the AI ​​analysis depth can be adjusted according to the user's understanding of the plot analysis and character motivation understanding, so that the provided plot analysis and character motivation understanding can match the user's cognitive level, and can ensure that the user obtains just the right content analysis and character motivation understanding in the virtual environment, while maintaining the flexibility and responsiveness of the system. And by adjusting the factor γ, a smooth transition of depth analysis can be achieved to avoid sudden changes that cause discomfort to the user experience.

[0123] Step S140 , collecting real-time feedback data of the user on the current virtual environment state, and dynamically adjusting the environmental parameters of the film and television virtual environment and / or the behavior of the non-player characters according to the real-time feedback data.

[0124] In the embodiment of the present application, the environmental parameters of the film and television virtual environment and / or the behavior of non-player characters are dynamically adjusted by real-time analysis of the user's real-time feedback data on the current virtual environment state to provide a more personalized and adaptive experience. For example, if the system detects that the user is nervous or uncomfortable with the current virtual environment state, the corresponding environmental parameters (such as light or sound) or NPC behavior can be adjusted to alleviate the user's discomfort.

[0125] Reference Figure 7 , Figure 7 It is a flowchart of the steps for dynamically adjusting the environmental parameters of the film and television virtual environment according to real-time feedback data provided by an embodiment of the present application, including but not limited to steps S710 to S740.

[0126] Step S710, collecting real-time feedback data of the user on the current virtual environment state, the real-time feedback data including real-time behavior data and physiological data of the user after entering the current virtual environment state;

[0127] Step S720, analyzing the real-time feedback data to identify the user's emotions or intentions;

[0128] Step S730, determining the target environment characteristics to be adjusted according to the real-time feedback data;

[0129] Step S740: dynamically adjust the environmental parameters corresponding to the target environmental features according to the user's emotions or intentions.

[0130] In an embodiment of the present application, real-time feedback data of the user can be collected through sensors and input devices, including real-time behavioral data (such as head rotation, gestures, voice commands, selections, etc.) and physiological data (such as head rotation, gestures) after the user enters the current virtual environment state. By analyzing the real-time feedback data of the user, the user's emotions or intentions can be identified. At the same time, the target environment characteristics to be adjusted can also be determined based on the user's real-time feedback data, wherein the target environment characteristics can be the environmental characteristics most relevant to the user's emotions or intentions. Thus, the environmental parameters corresponding to the target environment characteristics can be dynamically adjusted according to the user's emotions or intentions. If it is identified that the user is nervous about the current virtual environment state, it can be determined that the target environment characteristics to be adjusted can be light. Therefore, the relevant parameters of the light (such as luminous intensity, light saturation, etc.) can be adjusted to create a softer light environment to relieve the user's nervous emotions.

[0131] Reference Figure 8 , Figure 8 It is a flowchart of the steps for determining the target environment characteristics to be adjusted according to the real-time feedback data provided by an embodiment of the present application, including but not limited to steps S810 to S830.

[0132] Step S810, obtaining user real-time feedback data and corresponding multiple environmental features;

[0133] Step S820, calculating the correlation between each environmental feature and the user's real-time feedback data to extract the environmental feature with the greatest correlation;

[0134] Step S830: taking the environmental feature with the greatest correlation as the target environmental feature to be adjusted.

[0135] In an embodiment of the present application, real-time feedback data of users is collected, such as location, interaction, dwell time, and task completion status, and multiple environmental features that may affect user experience are obtained. The correlation between each environmental feature and the real-time feedback data of the user is analyzed, such as by calculating the correlation coefficient between the environmental feature and the real-time feedback data of the user, for example, using the Pearson correlation coefficient to calculate the correlation between each environmental feature and the real-time feedback data of the user. Thus, the environmental feature with the greatest correlation can be used as the target environmental feature to be adjusted. In an embodiment of the present application, a threshold can also be set in advance, and environmental features with correlation coefficients higher than the threshold can be selected as target environmental features to be adjusted.

[0136] In the embodiment of the present application, the environmental parameters can be adjusted individually according to the unique behavior and preferences of each user. The environmental features to be adjusted are selected by calculating the correlation between the environmental features and the user feedback data, so that the system can process data more efficiently, respond to user behavior quickly, and provide a real-time personalized experience.

[0137] The embodiment of the present application also provides an electronic device, the electronic device includes a memory and a processor, the memory stores a computer program, and the processor implements the above-mentioned interactive viewing control method when executing the computer program. The electronic device can be any intelligent terminal including a tablet computer, a car computer, etc.

[0138] See also Fig. 9 , Fig. 9 : is a schematic diagram of the hardware structure of an electronic device provided in an embodiment of the present application, the electronic device includes:

[0139] The processor 901 may be implemented by a general-purpose CPU (Central Processing Unit), a microprocessor, an application-specific integrated circuit (Application Specific Integrated Circuit, ASIC), or one or more integrated circuits, and is used to execute relevant programs to implement the technical solutions provided in the embodiments of the present application;

[0140] The memory 902 can be implemented in the form of a read-only memory (ROM), a static storage device, a dynamic storage device, or a random access memory (RAM). The memory 902 can store an operating system and other application programs. When the technical solution provided in the embodiment of this specification is implemented by software or firmware, the relevant program code is stored in the memory 902, and the processor 901 calls and executes the interactive viewing control method of the embodiment of this application;

[0141] Input / output interface 903, used to implement information input and output;

[0142] Communication interface 904, used to realize communication interaction between the device and other devices, which can be realized by wired mode (such as USB, network cable, etc.) or wireless mode (such as mobile network, WIFI, Bluetooth, etc.);

[0143] A bus 905 that transmits information between various components of the device (e.g., the processor 901, the memory 902, the input / output interface 903, and the communication interface 904);

[0144] The processor 901 , the memory 902 , the input / output interface 903 and the communication interface 904 are connected to each other in communication within the device via a bus 905 .

[0145] The embodiments described in the embodiments of the present application are intended to more clearly illustrate the technical solutions of the embodiments of the present application, and do not constitute a limitation on the technical solutions provided in the embodiments of the present application. Those skilled in the art will appreciate that with the evolution of technology and the emergence of new application scenarios, the technical solutions provided in the embodiments of the present application are also applicable to similar technical problems.

[0146] Those skilled in the art will appreciate that the technical solutions shown in the figures do not constitute a limitation on the embodiments of the present application, and may include more or fewer steps than shown in the figures, or a combination of certain steps, or different steps.

[0147] The device embodiments described above are merely illustrative, and the units described as separate components may or may not be physically separated, that is, they may be located in one place or distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0148] Those skilled in the art will appreciate that all or some of the steps in the methods disclosed above, and the functional modules / units in the systems and devices may be implemented as software, firmware, hardware, or a suitable combination thereof.

[0149] The terms "first", "second", "third", "fourth", etc. (if any) in the specification of the present application and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments of the present application described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any of their variations are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device comprising a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0150] It should be understood that in the present application, "at least one (item)" means one or more, and "plurality" means two or more. "And / or" is used to describe the association relationship of associated objects, indicating that three relationships may exist. For example, "A and / or B" can mean: only A exists, only B exists, and A and B exist at the same time, where A and B can be singular or plural. The character " / " generally indicates that the objects associated before and after are in an "or" relationship. "At least one of the following" or similar expressions refers to any combination of these items, including any combination of single or plural items. For example, at least one of a, b or c can mean: a, b, c, "a and b", "a and c", "b and c", or "a and b and c", where a, b, c can be single or multiple.

[0151] In the several embodiments provided in the present application, it should be understood that the disclosed devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of the above units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0152] The units described above as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0153] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0154] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product, and the computer software product is stored in a storage medium, including multiple instructions to enable a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of various embodiments of the present application. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (Read-Only Memory, referred to as ROM), random access memory (Random Access Memory, referred to as RAM), disk or optical disk and other media that can store programs.

[0155] The preferred embodiments of the present invention are described above with reference to the accompanying drawings, but the scope of the rights of the present invention is not limited thereto. Any modification, equivalent substitution and improvement made by a person skilled in the art without departing from the scope and substance of the present invention should be within the scope of the rights of the present invention.

Claims

1. An interactive movie viewing control method, characterized in that: The method comprises: Use AR or VR devices to allow users to enter a virtual environment and adjust visual and auditory simulation effects to create the best sense of immersion. Collecting user behaviors and / or user choices, and adjusting film and television content according to the user behaviors and / or user choices, wherein the film and television content includes film and television plots, changes in film and television virtual environments, or behaviors of non-player characters in the film and television virtual environments; Analyze film and television content through AI to provide users with plot analysis and understanding of character motivations, and dynamically adjust the AI's analysis depth based on the user's understanding level; Collect real-time feedback data of the user on the current virtual environment state, and dynamically adjust the environmental parameters of the film and television virtual environment and / or the behavior of non-player characters according to the real-time feedback data.

2. The method according to claim 1, characterized in that After the user enters the film and television virtual environment through the AR device or the VR device, the method further includes: Collect and analyze the user's physiological and behavioral data regarding the complexity of the current environment and / or the current interaction intensity to determine the user's comfort level under the complexity of the current environment and / or the current interaction intensity; Calculate the target environment complexity and / or target interaction intensity based on the user's physiological and behavioral data on the current environment complexity and / or the current interaction intensity, wherein the target environment complexity is the environment complexity that can make the user at the best comfort level, and the target interaction intensity is the interaction intensity that can make the user at the best comfort level; According to the current environment complexity and the target environment complexity, a first environment complexity is calculated, and the current environment complexity is updated with the first environment complexity, and / or according to the current interaction intensity and the target interaction intensity, a first interaction intensity is calculated, and the current interaction intensity is updated with the first interaction intensity.

3. The method according to claim 2, characterized in that After updating the current environment complexity and / or updating the current interaction strength, the method further includes: Return to the step of collecting and analyzing the user's physiological and behavioral data on the current environment complexity and / or the current interaction intensity to determine the user's comfort level under the current environment complexity and / or the current interaction intensity, until the current environment complexity is updated to the target environment complexity and / or until the current interaction intensity is updated to the target interaction intensity.

4. The method according to claim 1, characterized in that: Adapting content based on user behavior and / or user choices, including: Analyze the user behavior and the user selection using AI algorithms to identify user intentions or needs; Formulate corresponding film and television content adjustment strategies according to the user's intention or needs; In response to the film and television content adjustment strategy, the film and television plot, the film and television virtual environment changes, or the behavior of non-player characters in the film and television virtual environment is adjusted.

5. The method according to claim 1 or 4, characterized in that: After adjusting the film and television content according to the user behavior and / or user selection, the method further includes: Collect user behavior data in the film and television virtual environment and quantify the deviation between user behavior and system expected behavior; According to the deviation between the user behavior and the expected behavior of the system, the film and television virtual environment and / or the behavior of non-player characters in the film and television virtual environment are adjusted.

6. The method according to claim 1, characterized in that The method also includes dynamically adjusting the difficulty of tasks in the film and television according to the user behavior data, including: Collect and analyze user behavior data to determine the user's skill level, preferences, and current experience status, where the user behavior data includes the user's movement speed, interaction frequency, and the user's success rate in completing tasks; Calculate the difficulty of the target task based on the user's skill level, preferences, and current experience state; The first task difficulty is calculated according to the current task difficulty and the target task difficulty, and the current task difficulty is updated with the first task difficulty.

7. The method according to claim 1, characterized in that Dynamically adjust the AI’s analytical depth based on the user’s level of understanding, including: Collecting user feedback after providing the user with plot analysis and character motivation understanding, and determining the user's understanding of the currently provided plot analysis and character motivation understanding based on the user feedback; Calculate a target analysis depth based on the user's understanding of the currently provided plot analysis and character motivation understanding, where the target analysis depth is the analysis depth that allows the user to be at an optimal understanding level; Calculate a first analysis depth according to the current analysis depth of the AI ​​and the target analysis depth, and update the current analysis depth with the first analysis depth; Return to the step of collecting user feedback after providing the user with plot analysis and character motivation understanding, and determining the user's degree of understanding of the currently provided plot analysis and character motivation understanding based on the user feedback, until the current analysis depth is updated to the target analysis depth.

8. The method according to claim 1, characterized in that Dynamically adjusting the environmental parameters of the film and television virtual environment according to the real-time feedback data includes: Collecting real-time feedback data of the user on the current virtual environment state, wherein the real-time feedback data includes real-time behavior data and physiological data of the user after entering the current virtual environment state; Analyzing the real-time feedback data to identify user emotions or intentions; Determining target environment characteristics to be adjusted according to the real-time feedback data; Dynamically adjust the environmental parameters corresponding to the target environmental features according to the user's emotions or intentions.

9. The method according to claim 8, characterized in that The step of determining the target environment characteristics to be adjusted according to the real-time feedback data includes: Obtain real-time user feedback data and corresponding multiple environmental features; Calculating the correlation between each of the environmental features and the user's real-time feedback data to extract the environmental feature with the greatest correlation; The environmental feature with the greatest correlation is used as the target environmental feature to be adjusted.

10. An electronic device, characterized in that: The electronic device comprises a memory and a processor, the memory stores a computer program, and the processor implements the method according to any one of claims 1 to 9 when executing the computer program.

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