Holographic projection image intelligent generation method and system applied to stage virtual interaction

Abstract

The invention provides a holographic projection image intelligent generation method and system applied to stage virtual interaction. The method comprises the following steps: firstly, obtaining stage scene interaction demand information including a stage performance theme, a performance link time node and a virtual interaction triggering condition; constructing a target mapping rule based on the stage scene interaction demand information, and generating an initial holographic projection image element set by using a generative artificial intelligence model; dividing stages according to performance link time nodes, and performing staged dynamic arrangement on an image element set in combination with an interaction triggering condition to obtain a dynamic holographic projection image sequence; collecting stage real-time interaction data, establishing association logic of the stage real-time interaction data and image sequence adjustment, and adjusting parameters to obtain an optimized sequence; and an output parameter system is constructed based on the optimization sequence, and an instruction is generated to drive holographic projection equipment to output an image, so that the visual effect of stage performance and audience immersion are improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of generative artificial intelligence technology, in particular to a holographic projection image intelligent generation method and system applied to stage virtual interaction. BACKGROUND

[0002] In the field of stage performance art, with the continuous development of technology, holographic projection technology has gradually become an important means to enhance the visual effect of the stage and enhance the audience's sense of immersion. However, the existing stage holographic projection application has many limitations.

[0003] On the one hand, traditional methods often lack in-depth consideration of stage scene interaction requirements when generating holographic projection images. Stage performances have rich themes and diverse performance segments, and different segments have specific requirements for the content, timing, and interaction methods of holographic projection. However, existing technologies usually only preset some fixed projection images, and cannot be flexibly adjusted according to key information such as stage performance themes, performance segment time nodes, and virtual interaction trigger conditions, resulting in low integration of holographic projection and stage performance, and difficulty in creating a realistic and creative stage atmosphere.

[0004] On the other hand, in terms of dynamic arrangement and real-time optimization of holographic projection images, existing technologies also have obvious deficiencies. During stage performances, real-time interaction data such as actor performance actions and audience feedback can have a significant impact on the effect of holographic projection. However, traditional methods cannot timely collect these real-time interaction data and establish effective correlation logic between them and holographic projection image adjustments, so that holographic projection images cannot be dynamically optimized according to actual stage conditions, and cannot meet the real-time and interactive requirements of stage performances. SUMMARY

[0005] In view of the above-mentioned problems, in combination with the first aspect of the present application, the embodiments of the present application provide a holographic projection image intelligent generation method applied to stage virtual interaction, which comprises:

[0006] Obtaining stage scene interaction requirement information, the stage scene interaction requirement information including stage performance theme information, performance segment time node information, and virtual interaction trigger condition information;

[0007] Based on the stage scene interaction requirement information, a target mapping rule is constructed, and a generative artificial intelligence model is called to perform image element generation processing according to the target mapping rule to obtain an initial holographic projection image element set, the initial holographic projection image element set including static image elements and dynamic image elements matching the stage performance theme information;

[0008] According to the performance link time node information division performance stage, the initial holographic projection image element set is processed by dynamic arrangement in stages combined with virtual interaction trigger condition information to obtain a dynamic holographic projection image sequence;

[0009] Real-time stage interaction data is collected, and an association logic of real-time interaction data and image sequence adjustment is established, and the dynamic holographic projection image sequence is processed by parameter adjustment according to the association logic to obtain an optimized dynamic holographic projection image sequence;

[0010] A holographic projection output parameter system is constructed based on the optimized dynamic holographic projection image sequence, a holographic projection image generation instruction containing the output parameter system is generated, and the holographic projection image generation instruction is sent to a holographic projection device to drive the holographic projection device to output a holographic projection image.

[0011] In still another aspect, the embodiment of the present application also provides a holographic projection image intelligent generation system applied to stage virtual interaction, which comprises a processor and a machine readable storage medium, the machine readable storage medium is connected with the processor, the machine readable storage medium is used for storing programs, instructions or codes, and the processor is used for executing the programs, instructions or codes in the machine readable storage medium to realize the above-mentioned method.

[0012] Based on the above aspects, the embodiment of the present application can ensure that the generated static and dynamic image elements are highly matched with the stage performance theme by acquiring stage scene interaction demand information, covering key elements such as stage performance theme, performance link time node and virtual interaction trigger condition, constructing target mapping rules based on stage scene interaction demand information, and calling a generative artificial intelligence model to generate an initial holographic projection image element set. According to the performance link time node, the performance stage is divided, and the initial image element set is processed by dynamic arrangement in stages combined with virtual interaction trigger condition, so that the holographic projection image can be presented in order according to the rhythm and demand of the stage performance, and the coherence and logic of the stage performance are enhanced. The real-time parameter adjustment of the dynamic holographic projection image sequence can be realized according to the actual situation of the stage by collecting the stage real-time interaction data and establishing the association logic of the real-time interaction data and the image sequence adjustment, which realizes the dynamic optimization of the holographic projection image and improves the real-time performance and interactivity of the stage performance. Finally, the holographic projection output parameter system is constructed based on the optimized image sequence, and the instruction driving device outputs the image, which ensures the high-quality presentation of the holographic projection image and comprehensively improves the visual effect of the stage performance and the audience's sense of immersion. BRIEF DESCRIPTION OF DRAWINGS

[0013] Figure 1 is the execution flow schematic diagram of the holographic projection image intelligent generation method applied to stage virtual interaction provided by the embodiment of the present application.

[0014] Figure 2 is an exemplary hardware and software components of the holographic projection image intelligent generation system applied to stage virtual interaction provided by an embodiment of the present application. DETAILED DESCRIPTION

[0015] The present application will be described in detail below with reference to the accompanying drawings, Figure 1 is a flowchart of the holographic projection image intelligent generation method applied to stage virtual interaction provided by an embodiment of the present application, which will be described in detail below.

[0016] Step S110: Obtain stage scene interaction requirement information, which includes stage performance theme information, performance link time node information, and virtual interaction trigger condition information.

[0017] This embodiment takes a modern science and technology theme concert as a unified application scene, which integrates modern stage elements such as light show, virtual singer interaction, and audience immersive participation, and needs to present dynamic science and technology image through holographic projection. Three types of core information are obtained through the requirement input module of the stage planning management system.

[0018] The stage performance theme information is explicitly "future technology tour", which includes theme style description (cyberpunk visual style, holographic light fusion, dynamic particle special effect), core performance elements (virtual singer image, digital neon scene, particle composed musical instrument, dynamic data flow), and theme emotional tone (future, immersion, interactivity).

[0019] The performance link time node information is divided according to the concert process, including opening (duration), song performance link (including 5 songs, each song corresponding to independent duration and interlude duration), interaction link (duration), intermission transition (duration), and closing link (duration). Each link is marked with accurate start and end time nodes, and the key rhythm nodes in each link are explicitly marked (such as the prelude, main song, chorus, and interlude time points of the song).

[0020] The virtual interaction trigger condition information covers three types of trigger events: audience interaction trigger (such as the area distribution of the audience holding fluorescent rods reaching the set range, and the on-site decibel value exceeding the set threshold), stage equipment trigger (such as the light switching to a specific mode, and the stage lifting platform reaching the preset height), and performer trigger (such as the singer making a specific gesture, and the dance action reaching the preset posture). Each trigger event is marked with the expected holographic image response requirement after triggering (such as switching scenes, enhancing particle special effects, and synchronizing virtual singer actions).

[0021] For the audience interaction data involved in the collection process (such as the position of the glow stick, the decibel value), anonymization processing is adopted, and the audience's personal information is not associated; data transmission is realized through an encryption protocol, and access permission levels are set when storing, so that only the stage technical team can operate the interaction requirement data, preventing information leakage or tampering.

[0022] Step S120: Based on the stage scene interaction requirement information, a target mapping rule is constructed, a generative artificial intelligence model is called to perform image element generation processing according to the target mapping rule, and an initial holographic projection image element set is obtained, which contains static image elements and dynamic image elements matching the stage performance theme information.

[0023] Based on the theme requirement of "future technology tour", the holographic image element set conforming to the scene is obtained by combining rule construction and AI generation, and the specific process is as follows.

[0024] Step S121: Analyze the stage performance theme information in the stage scene interaction requirement information, extract the core visual style keywords and core element type descriptions in the stage performance theme information, and the core visual style keywords include color tendency description and style genre description, and the core element type description includes scene element category and role element category.

[0025] The theme information is analyzed, and the core visual style keywords are extracted: the color tendency description is "high saturation neon color (blue purple, pink purple, and cyan as the main color), dark background contrast, and strong light and shadow contrast"; the style genre description is "cyberpunk, digital art, and dynamic particle special effect".

[0026] The core element type description is extracted: the scene element category includes "digital city skyline, neon street, virtual stage background, and data flow tunnel"; the role element category includes "virtual singer (3D digital image, style is technology and personification), particle composed dance image, and dynamic symbolic role (such as digital notes and geometric pattern combined role)".

[0027] The extracted keywords and element types are arranged according to the "style-element" correspondence relationship to form a structured extraction list, ensuring that the core vision and element requirements of the theme requirement are covered.

[0028] Step S122: Based on the core visual style keywords and core element type descriptions, a target mapping rule is constructed, which includes the correspondence between visual style keywords and image color parameters, and the correspondence between element type descriptions and image form features.

[0029] Two types of core mapping relationships are constructed: visual style-color parameter mapping and element type-form feature mapping.

[0030] Visual style-color parameter mapping rule: "blue-purple neon color" corresponds to color space parameters (hue range, saturation range, lightness range), "dark background" corresponds to the lightness parameter range of the background color, and "strong light and shadow contrast" corresponds to light and shadow parameters (contrast range, highlight intensity range, shadow density range); At the same time, define color matching rules, such as "the ratio of the main color tone blue-purple and the auxiliary color purple, and the adaptive relationship between the neon color emission intensity parameter and the background lightness".

[0031] Element type-morphology feature mapping rule: "digital city skyline" corresponds to morphology parameters (geometric constraints of building contour, building density distribution, height level relationship), "virtual singer" corresponds to morphology parameters (human proportion constraints, sci-fi texture features of clothing, digital processing standards of facial features), and "particle instrument" corresponds to morphology parameters (particle quantity range, instrument contour accuracy after particle aggregation, physical constraint parameters of particle motion).

[0032] Encode the mapping rule into a structured rule file that can be recognized by generative AI, including four fields of rule identification, input keyword, output parameter range, and constraint condition.

[0033] Step S123: input the target mapping rule into the generative artificial intelligence model, trigger the generative artificial intelligence model to configure image generation basic parameters according to the target mapping rule, and the image generation basic parameters include color configuration parameters, morphology configuration parameters, and texture configuration parameters.

[0034] Call the generative artificial intelligence model based on the diffusion model architecture (trained by millions of stage holographic image data, suitable for dynamic and static element generation), and input the mapping rule file into the rule analysis module of the model.

[0035] The model configures basic parameters according to the rule: the color configuration parameters include the specific color space values of the main color tone, the auxiliary color tone, and the background color, the color transition mode (such as gradient, mutation, and alternate flashing), and the brightness attenuation parameter of the light-emitting element; The morphology configuration parameters include the geometric contour parameters of various elements (such as the height proportion of the virtual singer and the outline line thickness of the building), the scaling coefficient of the element (relative to the stage projection area), and the spatial position constraint between elements (such as the distance range between the virtual singer and the dancer image); The texture configuration parameters include the texture type of the element surface (such as the metal luster texture of the virtual singer's clothing and the neon lamp tube texture of the building surface), the resolution parameter of the texture, and the dynamic change trigger condition of the texture (such as the texture brightness change parameter affected by the light).

[0036] After the parameter configuration is completed, generate a parameter list, and generate a low-resolution parameter effect preview through the preview module of the model to ensure that it meets the mapping rule requirements.

[0037] Step S124: determining an image color matching scheme according to the color configuration parameter, the image color matching scheme including a main color tone parameter, an auxiliary color tone parameter, and a color model-based color tone transition rule.

[0038] Based on the color configuration parameter, a detailed matching scheme is generated: the main color tone parameter is explicitly defined as a specific color space range of blue-violet color, the auxiliary color tone parameter is a color space range of pink purple and cyan, and the use proportion of main and auxiliary color tones is defined (such as the proportion of main color tone, the proportion of auxiliary color tone, and the proportion of decorative color).

[0039] The color tone transition rule based on the HSV (Hue, Saturation, Value) color model is formulated: the color tone transition within the same element adopts a gradient method, and the hue change range, saturation change rate, and value change gradient of the transition interval are explicitly set; the color tone transition between different elements adopts a contrast complementary method to ensure that the color tone difference of adjacent elements meets the visual impact requirement; the color tone transition of dynamic elements is matched with the performance rhythm, such as accelerating the color tone transition rate in the chorus part and slowing down the transition rate in the interlude part.

[0040] The matching scheme also includes color adaptation rules, such as automatically adjusting the value parameter of the color tone according to the stage environment light intensity to avoid conflict between the holographic image and the environment light.

[0041] Step S125: determining the morphological feature parameters and texture detail parameters of the image elements according to the morphological configuration parameters and texture configuration parameters, the morphological feature parameters including contour structure parameters based on geometric constraints and scale size parameters relative to the stage projection area, and the texture detail parameters including texture density parameters based on pixel distribution and texture texture parameters based on material simulation.

[0042] Refine the parameters for each element type: taking the "virtual singer" element as an example, the contour structure parameters in the morphological feature parameters define the geometric contour constraints of the head, torso, and limbs (such as the head being an elliptical contour and the limbs being cylindrical contours), and the angle constraints of the joint parts (such as the elbow joint movement angle range); the scale size parameters define the height proportion of the virtual singer relative to the stage projection area (such as the proportion of the projection area height), and the proportional relationship with other elements (such as the size proportion of the virtual singer and the particle instrument).

[0043] The texture density parameters in the texture detail parameters define the pixel distribution density of the clothing texture (such as the number of texture pixels per unit area), and the texture texture parameters define the material simulation parameters (such as the reflectivity coefficient of metal texture and the wrinkle simulation parameters of cloth texture); at the same time, the hierarchical relationship of the texture is set, such as the face texture resolution of the virtual singer being higher than the clothing texture resolution, to ensure clear details of key parts.

[0044] Step S126: Call the generative artificial intelligence model to perform static image element generation processing according to the image color matching scheme, morphological feature parameters and texture detail parameters, and obtain a static image element matching the core element type description.

[0045] The color matching scheme, morphological and texture parameters are input into the static element generation module of the generative AI, which generates scene class static elements (such as static outlines of digital city skyline, static backgrounds of neon street), role class static elements (such as static standing posture images of virtual singers, static morphologies of symbolic characters), and prop class static elements (such as static outlines of particle musical instruments, static frames of virtual stages) in sequence according to element categories.

[0046] During the generation process, the model ensures that the element morphology meets the parameter requirements through the geometric constraint module, realizes the color matching and transition effect through the color rendering module, and superimposes the texture details through the texture mapping module. After generation, high-resolution static image elements (format is lossless format suitable for holographic projection) are output, each element is labeled with the corresponding element category, parameter source and theme matching degree score.

[0047] Step S127: Based on the dynamic attribute requirements in the core element type description, configure dynamic change rule parameters, which include time dimension change frequency and space dimension change trajectory.

[0048] Analyze the dynamic attribute requirements in the core element type description: such as "virtual singer needs to make dance movements according to the song rhythm", "particle musical instrument needs to produce particle diffusion and aggregation with the melody", "data flow tunnel needs to present a forward advancing effect".

[0049] Configure rule parameters for each dynamic element: time dimension change frequency parameter defines the time interval of element state change (such as the particle diffusion frequency of particle musical instrument synchronized with the song tempo), the duration of state change (such as the completion time of a dance action of virtual singer); space dimension change trajectory parameter defines the motion path of the element (such as the moving path of virtual singer in the stage projection area, represented by a sequence of coordinate points in the stage coordinate system), the spatial constraint of motion (such as not exceeding the coordinate range of the projection area boundary), the posture change parameter during motion (such as the angle change of virtual singer's limbs when moving).

[0050] Parameter configuration needs to match the rhythm nodes in the performance link time node information, to ensure that the dynamic change is synchronized with the performance rhythm.

[0051] Step S128: Call the generative artificial intelligence model to perform dynamic image element generation processing according to the dynamic change rule parameters, and obtain dynamic image elements containing time dimension change law.

[0052] Generate dynamic elements by the following sub-steps:

[0053] Step S1281: Analyze the dynamic attribute requirements in the core element type description to determine the change dimensions of the dynamic image elements, including the position change dimension, the shape change dimension, and the color change dimension.

[0054] Analyze the dynamic attribute requirements to determine the change dimensions of each dynamic element: the virtual singer element includes position change (moving within the stage), shape change (body movement, posture adjustment), and color change (brightness of costume texture changes with light); the particle instrument element includes position change (moving with the virtual singer), shape change (particle aggregation and diffusion), and color change (particle color changes with song mood); the data flow tunnel element includes position change (moving forward), shape change (tunnel cross-section size changes), and color change (data flow color tone alternates).

[0055] Label the priority of each element's change dimension, such as the virtual singer's shape change dimension priority is higher than the position change dimension.

[0056] Step S1282: For the position change dimension, configure the position change rule parameters according to the motion trajectory description in the dynamic attribute requirements, including the space path node coordinates based on the stage coordinate system and the motion speed between nodes based on time units.

[0057] Establish a stage coordinate system (with the lower left corner of the stage as the origin, X-axis as the stage width direction, Y-axis as the height direction, and Z-axis as the depth direction), and configure path node coordinates for the element according to the motion trajectory description: such as the virtual singer's moving path includes start point coordinates, multiple intermediate node coordinates, and end point coordinates, each node corresponds to a specific time point of the performance segment.

[0058] Configure the motion speed parameters between nodes: such as the motion speed from the start point to the first intermediate node, the speed from the first intermediate node to the second intermediate node, the speed value is set according to the performance rhythm (such as the speed is accelerated in the chorus part), and the smooth transition parameters of speed change are defined to avoid motion stuttering.

[0059] Step S1283: For the shape change dimension, configure the shape change rule parameters according to the shape evolution description in the dynamic attribute requirements, including the shape transition node parameters and the shape change amplitude between nodes based on percentage change.

[0060] According to the shape evolution description (such as the virtual singer's "raising hands - waving hands - putting down" action), configure the shape transition node parameters: each node corresponds to a specific shape (such as the raising hands node is a specific angle of the arm and torso, the waving hands node is the angle of arm swing, and the putting down node is the natural angle of arm drop).

[0061] Configure the range of morphological changes between nodes: express the amount of morphological parameter changes in percentage (such as the percentage of angle change from lifting hand to waving hand, the percentage of angle change from waving hand to putting down), the change range is matched with the time interval, ensuring the natural and smooth change of morphology.

[0062] Step S1284: For the color change dimension, configure the color change rule parameters according to the color gradient description in the dynamic attribute requirements, which include color transition node parameters and color change gradient between nodes based on color space.

[0063] According to the color gradient description (such as the particle instrument gradually changing from blue-purple to cyan), configure the color transition node parameters: each node corresponds to a specific color space value (such as the starting node with the HSV value of blue-purple, the intermediate node with the HSV value of blue-cyan mixture, and the end node with the HSV value of cyan).

[0064] Configure the color change gradient between nodes: define the change gradient value of hue, saturation, and lightness (such as the change amount of hue per unit time, the change rate of saturation), ensure that the gradient process meets the visual effect requirements, and is synchronized with the performance emotion.

[0065] Step S1285: Integrate the position change rule parameters, morphological change rule parameters, and color change rule parameters to form a dynamic change rule parameter set.

[0066] Integrate the parameters of the three dimensions according to the element category to form a parameter set: each element's parameter set contains parameter identification, change dimension type, specific parameter value, and associated performance time node, and marks the coordination relationship between parameters (such as the synchronous triggering condition of position change and morphological change).

[0067] The parameter set is stored in XML format, which is convenient for generative AI parsing and calling.

[0068] Step S1286: Input the dynamic change rule parameter set into the generative artificial intelligence model, trigger the generative artificial intelligence model to build a time change model of dynamic image elements, which contains position parameters, morphological parameters, and color parameters corresponding to different time points.

[0069] Input the parameter set into the dynamic modeling module of generative AI, and the module builds a time change model based on the parameters: take the time axis as the reference, assign corresponding position parameters (coordinate values), morphological parameters (outline and posture parameters), and color parameters (HSV values) to each time point (interval consistent with performance rhythm node).

[0070] The model fills in the parameter gaps between adjacent time points through a parameter interpolation algorithm to ensure continuous parameter changes. Meanwhile, the model ensures the synchronization of parameters in different dimensions through a collaborative control module (e.g., when a virtual singer moves to a specific position, the corresponding shape and color changes are triggered simultaneously).

[0071] Step S1287: Based on the time-varying model, generate state data of dynamic image elements at different time points, which includes time stamps, position coordinates, shape parameter values, and color parameter values.

[0072] According to the time-varying model, generate state data in chronological order: each time point corresponds to a piece of state data, which includes a time stamp accurate to milliseconds, position coordinates on the X / Y / Z axes, shape parameters (such as limb angles and particle quantities), and color parameters (HSV values of the main and auxiliary color tones).

[0073] The state data is stored in a sequence, and each piece of data is associated with the corresponding element identifier to ensure traceability to specific dynamic elements.

[0074] Step S1288: Generate a frame sequence of dynamic image elements based on the state data, with each frame corresponding to the state of a dynamic image element at a time point, and the time interval of the frame sequence consistent with the smallest time unit in the performance segment time node information.

[0075] Convert the state data into a frame sequence: generate a frame of image for each time point's state data, with the frame resolution matching the output resolution of the holographic projection device, and the frame format being a holographic projection-specific format (such as one that supports depth information).

[0076] The time interval of the frame sequence strictly matches the smallest time unit of the performance segment (e.g., consistent with the beat interval of a song), ensuring that the motion rhythm of the dynamic elements is synchronized with the performance; at the same time, time stamp identifiers are added to the frame sequence for easy time alignment in subsequent programming.

[0077] Step S1289: Perform inter-frame transition processing on the frame sequence to adjust the state parameter change amplitude between adjacent frames, integrate the processed frame sequence, and form a dynamic image element containing time dimension change rules, with the time length of the dynamic image element matching the time length of the corresponding performance segment in the stage performance theme information.

[0078] Optimize the dynamic effect through inter-frame transition processing, with the specific process as follows:

[0079] Step S1289-1: Extract the dynamic image element state parameters of the adjacent two frames in the frame sequence, which include the position coordinates, shape parameters, and color parameters of the adjacent previous frame, as well as the position coordinates, shape parameters, and color parameters of the adjacent next frame.

[0080] Extract the state parameters of the two adjacent frames (such as frame A and frame B): the position coordinates of frame A, the limb angle parameters of the virtual singer, the dominant HSV value; the position coordinates of frame B, the limb angle parameters, the dominant HSV value.

[0081] Sort the parameters by dimension to form a parameter comparison table of frame A and frame B.

[0082] Step S1289-2: Calculate the Euclidean distance difference value of the position coordinates between the adjacent previous frame and the adjacent next frame to obtain the position difference value; calculate the scalar difference value of the shape parameters between the adjacent previous frame and the adjacent next frame to obtain the shape difference value; calculate the color difference difference value of the color parameters between the adjacent previous frame and the adjacent next frame to obtain the color difference value.

[0083] Calculate the position difference value: calculate the spatial distance of the position coordinates of frame A and frame B by the Euclidean distance formula; calculate the shape difference value: convert the limb angle and other shape parameters into scalar values and calculate the difference value; calculate the color difference value: calculate the color difference of the dominant color of the two frames based on the HSV color space (integrate the differences of hue, saturation, and brightness).

[0084] Step S1289-3: According to the comprehensive size of the position difference value, the shape difference value and the color difference value, determine the transition smoothness requirement between the adjacent frames, the larger the comprehensive difference value, the higher the transition smoothness requirement.

[0085] Sum the three difference values by weight to obtain the comprehensive difference value (such as position difference value weight, shape difference value weight, color difference value weight), according to the size of the comprehensive difference value, divide the smoothness level (such as low, medium, high), the larger the comprehensive difference value, the higher the smoothness level, more transition frames need to be generated to ensure smoothness.

[0086] Step S1289-4: Based on the transition smoothness requirement, determine the transition processing mode between the adjacent frames, the transition processing mode includes linear transition processing, curve transition processing, and segmented transition processing.

[0087] According to the smoothness level to select the processing mode: low smoothness level corresponds to linear transition processing, medium smoothness level corresponds to curve transition processing, and high smoothness level corresponds to segmented transition processing. For example, the virtual singer has slight limb angle changes (small shape difference value) and short position movement distance (small position difference value), the comprehensive difference value is in the low level, and linear transition processing is adopted; the virtual singer moves from the left side of the stage to the right side (large position difference value) and accompanies with large body swing (large shape difference value), the comprehensive difference value is in the high level, and segmented transition processing is adopted.

[0088] Step S1289-5: If linear transition processing is adopted, the state parameters of the transition frames are generated according to the position difference value, the shape difference value and the color difference value by uniformly distributing the difference value change amount according to the inter-frame time interval, and the number of transition frames is determined based on the difference value size and the frame rate requirement.

[0089] When linear transition processing is adopted, the inter-frame time interval (consistent with the performance rhythm node) is first determined, the position difference value is evenly divided into N parts (N is the number of transition frames) according to the time interval, and the position parameter of each transition frame is the position parameter of the previous frame plus the corresponding part of the position change amount; similarly, the shape difference value and the color difference value are also uniformly distributed according to the time interval to generate the shape parameter and the color parameter of each transition frame. The number of transition frames is adjusted according to the difference value size: the smaller the difference value, the fewer the transition frames; the larger the difference value, the more the transition frames, while meeting the frame rate requirement of the holographic projection device to ensure that the transition process has no sense of lag.

[0090] Step S1289-6: If curve transition processing is adopted, a difference value change curve function based on time progress is constructed, the difference value distribution amount at different transition times is calculated based on the curve function, and the state parameters of the transition frames are generated, and the curvature of the curve function is adjusted according to the transition smoothness requirement.

[0091] When curve transition processing is adopted, the time progress (0 to 1, 0 corresponds to the time of the previous frame, and 1 corresponds to the time of the next frame) is taken as the independent variable, and a difference value change curve function (such as a quadratic curve or a cubic curve) is constructed. For example, when a quadratic curve function is constructed, the difference value change amount is small at the initial time, increases in the middle period, and decreases in the later period, so that the transition presents a "slow start-accelerate-slow stop" effect. The curvature of the curve is adjusted according to the smoothness requirement: moderate smoothness corresponds to a flat curvature, and the transition change is uniform; when the smoothness is close to high, a larger curvature is used, and the transition change is more in line with the natural motion law. The position, shape and color difference value distribution amounts at each transition time are calculated based on the curve function to generate the transition frame parameters.

[0092] Step S1289-7: If segmented transition processing is adopted, the difference value is divided into multiple segments based on the change rate, each segment is configured with a corresponding transition rate, the state parameters of each transition frame are calculated based on the segmented transition rate, and the number of segments is positively correlated with the difference value size.

[0093] When using segmented transition processing, the position difference value is divided into multiple segments (such as acceleration segment, uniform speed segment, deceleration segment) according to the change rate, and different transition rates are configured for each segment: the acceleration segment rate gradually increases, the uniform speed segment rate remains stable, and the deceleration segment rate gradually decreases. For example, the position difference value of the virtual singer's movement is divided into 3 segments, the first segment (acceleration segment) rate gradually increases from the initial value to the peak value, the second segment (uniform speed segment) rate remains at the peak value, and the third segment (deceleration segment) rate gradually decreases from the peak value to 0. The number of segments increases as the difference value increases, and when the difference value is large, 4-5 segments can be divided. According to the transition rate and time interval of each segment, the parameter value of each transition frame is calculated to ensure that the movement process is natural and smooth.

[0094] Step S1289-8: Insert the generated transition frame between the adjacent previous frame and the adjacent next frame to form a new frame sequence containing the transition frame.

[0095] The transition frame generated by linear, curve or segmented transition processing is inserted in time sequence between the previous frame and the next frame to form a complete new frame sequence. After insertion, the time stamp of the frame sequence is recalibrated to ensure that the time stamp of each frame is continuous and matches the performance segment time node, and finally a dynamic image element containing time dimension change rule is formed. The total time length of the dynamic image element is completely consistent with the time length of the corresponding performance segment.

[0096] Step S129: Integrate the static image elements and dynamic image elements, and perform quantity verification on the integrated image elements according to the number of categories in the core element type description to ensure that the number of static image elements and dynamic image elements corresponds to the number of element categories mentioned in the core element type description, forming an initial holographic projection image element set.

[0097] The generated static image elements (such as digital city skyline, virtual singer static image) and dynamic image elements (such as virtual singer dynamic frame sequence, particle instrument dynamic effect) are classified and integrated according to element categories (scene, character, prop). Verify the number of categories in the core element type description: for example, the scene element category requires to contain 4 categories (digital city skyline, neon street, virtual stage background, data flow tunnel), and it is necessary to confirm that static and dynamic scene elements cover all 4 categories; the character element category requires to contain 3 categories (virtual singer, particle dancer, symbolic character), and it is necessary to confirm that there is no category missing in the character element. If there is a category missing or the number does not match, return to the generation module to regenerate or supplement the elements; after verification, an initial holographic projection image element set is formed, each element is labeled with category, parameter information and theme matching degree.

[0098] Step S130: According to the performance segment time node information, divide the performance stage, and combine the virtual interaction trigger condition information to perform stage-by-stage dynamic arrangement processing on the initial holographic projection image element set, to obtain a dynamic holographic projection image sequence.

[0099] According to the performance time node and the interaction trigger condition, the image elements are arranged in stages, and the specific process is as follows.

[0100] Step S131: Analyze the performance segment time node information, divide a plurality of continuous performance stages according to the interval characteristics of the time nodes, mark the stage duration information for each performance stage, and each performance stage corresponds to a performance content theme.

[0101] The time node information is analyzed, and the performance stages are divided according to the interval characteristics: the opening stage (from the beginning of the concert to before the prelude of the first song), the duration matches the opening light show duration; Song 1 stage (including prelude, main song 1, chorus 1, interlude, main song 2, chorus 2, and tail), the duration is consistent with the total duration of Song 1; the interaction stage (from the end of Song 1 to before the prelude of Song 2), the duration matches the preset interaction duration; and the subsequent Song 2-5 stages, the intermission transition stage, and the ending stage are divided in turn. Each stage is marked with clear start and end time stamps, and corresponds to a specific performance content theme: for example, the opening stage theme is “technology atmosphere creation”, the Song 1 stage theme is “virtual singer dancing with particle special effects”, and the interaction stage theme is “audience participation in light interaction”.

[0102] Step S132: Classify the static image elements and dynamic image elements in the initial holographic projection image element set, and the classification annotation result includes matching degree information of each performance stage content theme.

[0103] Each image element is added with a classification annotation: the static element “digital city skyline” is annotated as “scene class-atmosphere creation”, the dynamic element “virtual singer dance frame sequence” is annotated as “role class-singing accompaniment”, and the dynamic element “audience glow stick interactive light” is annotated as “interaction class-audience participation”. At the same time, the matching degree of each element and each performance stage theme is calculated through theme keyword matching: for example, the matching degree of “digital city skyline” and the opening stage theme “technology atmosphere creation” is high, and the matching degree of “digital city skyline” and the interaction stage theme “audience participation in light interaction” is low; the matching degree of “audience glow stick interactive light” and the interaction stage theme is high, and the matching degree of “audience glow stick interactive light” and other stages is low. The matching degree is divided into high, medium and low three levels, which is determined based on the coincidence degree of the core features of the elements and the theme requirements.

[0104] Step S133: According to the matching degree information, assign corresponding static image elements and dynamic image elements to each performance stage, make the assigned image elements adapt to the content theme of the performance stage, and form the initial image element group of each performance stage.

[0105] Assign elements to each stage according to matching degree priority: prefer elements with high matching degree, then elements with medium matching degree, and do not assign elements with low matching degree. Assign "digital city skyline" (static), "dynamic data stream background" (dynamic), and "neon particle diffusion effect" (dynamic) to the opening stage to form the initial element group of the opening stage, which adapts to the theme of "technology atmosphere creation"; assign "virtual singer dance frame sequence" (dynamic), "particle instrument dynamic effect" (dynamic), and "virtual stage background" (static) to song 1 stage to form the song 1 element group, which adapts to the theme of "virtual singer dancing with particle special effects"; assign "audience glow stick interactive light" (dynamic) and "dynamic symbolic character (digital note)" (dynamic) to the interactive stage to form the interactive stage element group, which adapts to the theme of "audience participation in light interaction". Each element group is labeled with element identification, element type, and adaptation description to the stage theme.

[0106] Step S134: Analyze the virtual interaction trigger condition information to determine the type and trigger time of the virtual interaction trigger event, and label the trigger priority for each virtual interaction trigger event. The type of virtual interaction trigger event includes audience interaction virtual interaction trigger event, stage equipment state virtual interaction trigger event, and performance action virtual interaction trigger event.

[0107] Analyze the trigger condition information to determine three types of trigger events: audience interaction trigger event (such as "audience glow stick raises area covers more than 50% of the stage front area" and "live decibel value exceeds the set threshold"), trigger time is any time within the interactive stage; stage equipment trigger event (such as "light switches to blue neon mode" and "elevator rises to preset height"), trigger time is synchronized with the start time of song 1 chorus 1; performance action trigger event (such as "singer makes right hand high gesture" and "dance team completes specific formation change"), trigger time is synchronized with the start time of song 1 interlude. Label the trigger priority for each event: performance action trigger event has the highest priority (directly related to performance rhythm), stage equipment trigger event has medium priority (assists performance atmosphere), and audience interaction trigger event has medium or high priority according to the needs of the interactive stage.

[0108] Step S135: Based on the type and trigger priority of the virtual interaction trigger event, construct target association rules, which contain image element switching methods and image element switching times corresponding to different virtual interaction trigger events.

[0109] Construct the rules through the following sub-steps:

[0110] Step S1351: Attribute analysis is performed on the type of the virtual interaction trigger event, and the influence range corresponding to each virtual interaction trigger event is determined, including the category range and time influence range of image element switching.

[0111] Analysis of event attributes: the influence range of the performance action trigger event "singer's right hand raised high" is the role class and prop class elements (switching virtual singer action, particle instrument shape), and the time influence range is a short time after the trigger time (matching the hand gesture duration); the influence range of the stage equipment trigger event "light switch to blue neon mode" is the scene class and role class elements (adjusting background tone, virtual singer costume color), and the time influence range is the duration of the light mode; the influence range of the audience interaction trigger event "fluorescent stick covers 50% of the front area" is the interaction class and scene class elements (activating the audience area light and shadow effect, adjusting the background particle density), and the time influence range is the remaining time of the interaction stage.

[0112] Step S1352: According to the influence range of the virtual interaction trigger event and the preset priority determination standard, a trigger priority is assigned to each virtual interaction trigger event, and the priority determination standard includes the size of the influence range and the degree of association with the performance content.

[0113] Preset priority determination standard: events with close association with performance content (such as directly matching singer actions) and clear influence range have high priority; events with wide influence range but moderate association have moderate priority. According to this, the priority is assigned: the priority of the performance action trigger event is 1 (highest), the priority of the stage equipment trigger event is 2 (moderate), and the priority of the audience interaction trigger event is 2 in the interaction stage and 3 (low) in other stages. The smaller the priority value, the more priority the trigger response has.

[0114] Step S1353: Based on the type, trigger priority and influence range of the virtual interaction trigger event, a target association rule is constructed, including: an immediate switching rule corresponding to a virtual interaction trigger event with a first priority, a delayed switching rule corresponding to a virtual interaction trigger event with a second priority, and a conditional trigger switching rule corresponding to a virtual interaction trigger event with a third priority.

[0115] According to the priority, three types of rules are constructed: the first priority (priority 1) corresponds to the immediate switching rule, the second priority (priority 2) corresponds to the delayed switching rule, and the third priority (priority 3) corresponds to the conditional trigger switching rule. The first priority is the highest, the second priority is the second, and the third priority is the lowest.

[0116] Step S1354: The instant switching rule is defined as starting the image element switching process immediately when the virtual interactive trigger event of the first priority occurs, and the response time is not more than the minimum time interval in the performance link time node information.

[0117] The instant switching rule is specifically defined as: when the performance action trigger event (such as the right hand of the singer being raised high) occurs, the switching process is started within the minimum time interval to switch the virtual singer action from "standing" to "waving hands", and the particle instrument from "static" to "glowing and diffusing", ensuring that the image elements are synchronized with the singer's actions without obvious delay.

[0118] Step S1355: The delay switching rule is defined as starting the image element switching process after a preset time when the virtual interactive trigger event of the second priority occurs, and the preset time is determined according to the interval time of adjacent performance stages.

[0119] The delay switching rule is specifically defined as: when the stage equipment trigger event (such as the light switching to the blue neon mode) occurs, after a preset time (such as the fade time of the light switching), the scene element switching is started to switch the virtual stage background color from "purple" to "blue", and the virtual singer's costume texture brightness is simultaneously increased, so that the image switching is coordinated with the light change to avoid visual conflict.

[0120] Step S1356: The condition trigger switching rule is defined as starting the image element switching process after meeting the preset additional trigger condition when the virtual interactive trigger event of the third priority occurs.

[0121] The condition trigger switching rule is specifically defined as: when the audience interaction trigger event (such as the decibel value exceeding the threshold) occurs, the additional trigger condition "currently in the interaction stage" must be met to start the interaction element switching, activate the light and shadow effect of the audience area, and adjust the motion trajectory of the dynamic symbolic role to match the element switching with the interactive scene; if it is not in the interaction stage, even if the decibel value meets the standard, the switching is not started.

[0122] Step S136: According to the stage length information of each performance stage and the trigger time of the virtual interactive trigger event, the initial image element groups of each performance stage are arranged in chronological order to form an initial time axis image sequence.

[0123] Take the time axis as the benchmark, arrange the element groups in the order of performance stages: opening stage element group (corresponding to the opening duration) - song 1 stage element group (corresponding to the song 1 duration) - interaction stage element group (corresponding to the interaction duration) - song 2 stage element group - … - ending stage element group. Mark the start and end time stamps of each element group on the time axis to ensure complete matching with the performance stage duration. For example, the opening stage element group starts from time stamp 0 and ends at time stamp T1 (T1 is the opening duration); the song 1 stage element group starts from T1 and ends at T1+T2 (T2 is the song 1 duration). At the same time, mark the trigger time of the virtual interaction trigger event at the corresponding position on the time axis, such as marking the stage equipment trigger event at the chorus 1 start time stamp T1+T3 of song 1, and marking the performance action trigger event at the interlude start time stamp T1+T4.

[0124] Step S137: According to the target association rule, set an image element switching marker at the time position corresponding to the virtual interaction trigger event in the initial time axis image sequence, define the to-be-switched image element and the target switched image element at the image element switching marker.

[0125] Traverse the initial time axis image sequence, and set a switching marker at the time position corresponding to the trigger event: at the stage equipment trigger event time (T1+T3) of the chorus 1 of song 1, set the marker “equipment-scene switching”, define the to-be-switched image element as “purple virtual stage background” and the target switched image element as “blue virtual stage background”; at the performance action trigger event time (T1+T4) of the interlude of song 1, set the marker “action-role switching”, define the to-be-switched image element as “virtual singer standing frame sequence” and the target switched image element as “virtual singer waving hand frame sequence”; at the audience interaction trigger event time (T1+T2+T5) of the interaction stage, set the marker “audience-interactive switching”, define the to-be-switched image element as “static digital note” and the target switched image element as “dynamic responsive digital note”. Each switching marker contains event type, trigger priority, to-be-switched element ID, target element ID, and switching rule type (immediate / delay / condition).

[0126] Step S138: Perform visual coherence processing on the time axis image sequence containing the image element switching marker, adjust the transition parameters of the to-be-switched image element and the target switched image element at the switching marker, so that the adjacent image elements realize visual smooth transition after switching, integrate the processed time axis image sequence to form a dynamic holographic projection image sequence.

[0127] The elements at the switching mark are processed for continuity: for "device-scene switching" (purple-blue background), the transition parameters are adjusted to "gradient transition", the hue of the background color is gradually transitioned from purple to blue, the saturation and lightness remain stable, and the transition duration is consistent with the light switching duration; for "action-role switching" (standing-waving hands), "interframe transition" is used, 3-5 transition frames are inserted, the virtual singer's arm motion is gradually lifted from drooping to waving posture, and motion jamming is avoided; for "audience-interaction switching" (static-dynamic sound), the "scaling+lighting" transition effect is set, the digital sound gradually enlarges and enhances the lighting intensity from the static state, and the motion trajectory changes are started. After all the switching transition processing is completed, the elements of each stage group, the switching mark and the transition frame are integrated into a complete time axis sequence to form a dynamic holographic projection image sequence, which contains all image elements and interactive response logic from the opening to the end, and the time axis accuracy is completely synchronized with the performance rhythm.

[0128] Step S140: Collecting stage real-time interaction data, establishing the association logic of real-time interaction data and image sequence adjustment, adjusting the parameters of the dynamic holographic projection image sequence according to the association logic to obtain an optimized dynamic holographic projection image sequence.

[0129] Through real-time data collection and analysis, the parameters of the image sequence are adjusted dynamically, and the specific process is as follows.

[0130] Step S141: Collecting stage real-time interaction data through stage data collection equipment, the stage real-time interaction data includes audience interaction data, stage equipment operation data and performer action data, the audience interaction data includes interaction behavior frequency and interaction area distribution, the stage equipment operation data includes equipment working mode and equipment output state, and the performer action data includes action frequency and action amplitude.

[0131] Deploying multiple types of collection devices to obtain real-time data: In the audience area, deploy infrared cameras and decibel sensors to collect audience interaction data, including interaction behavior frequency (the number of times the audience raises a glow stick or waves their hand in a unit of time) and interaction area distribution (the proportion of audience interaction density in different stage areas, such as the front, middle, and back areas). In the stage equipment area, deploy state sensors to collect operation data, including equipment working mode (resolution mode and projection mode for holographic projection equipment; color mode and brightness mode for lighting equipment) and equipment output state (light output intensity and temperature for projection equipment; actual brightness and color deviation for lighting equipment). In the performer area, deploy motion capture cameras and motion sensors to collect motion data, including motion frequency (singer gesture change frequency and dance action beat matching degree) and motion amplitude (space movement distance of gestures and angle change range of limb joints). The collected data is synchronized by millisecond-level timestamp to ensure time sequence consistency.

[0132] Step S142: Feature extraction is performed on the audience interaction data to obtain interaction frequency feature values in a unit of time and spatial distribution features of the interaction area. The display frequency adjustment direction of the dynamic image elements is determined based on the comparison result of the interaction frequency feature values and a preset threshold value, and the display area adjustment direction of the image elements is determined based on the spatial distribution features and the stage area mapping relationship.

[0133] Feature extraction is performed on the audience interaction data: The number of times the glow stick is raised and the hand waving frequency in a unit of time are counted, and after normalization, the interaction frequency feature values are obtained, which comprehensively reflect the participation and activity level of the audience. The interaction area distribution data is converted into spatial distribution features, and through stage area grid division (the stage and audience area are divided into multiple grid units), the proportion of the number of interactions in each grid unit is counted to form a spatial distribution heat map feature.

[0134] The interaction frequency feature values are compared with the preset threshold value: If the feature value is higher than the threshold value, it indicates that the audience is active in participation, and the display frequency adjustment direction of the dynamic image elements (such as particle special effects and digital notes) is determined to be increased; if the feature value is lower than the threshold value, it indicates that the audience's participation is insufficient, and the adjustment direction is to decrease. Based on the mapping relationship between the spatial distribution features and the stage area (such as the front area of the audience corresponding to the front projection area of the stage, and the middle area corresponding to the middle area of the stage), if the interaction density in a certain area is high, the display area adjustment direction of the image elements corresponding to that area is determined to be expanded in coverage and enhanced in visual prominence; if the interaction density in a certain area is low, the adjustment direction is to reduce the coverage or lower the visual intensity.

[0135] Step S143: Feature analysis is performed on the stage equipment operation data to extract image resolution adaptation parameters corresponding to the equipment operation mode and light output parameters corresponding to the equipment output state, the resolution adjustment direction of the image elements is determined according to the resolution adaptation parameters, and the brightness adjustment direction of the image elements is determined according to the light output parameters.

[0136] Feature analysis is performed on the stage equipment operation data: the resolution adaptation parameters (such as the pixel density parameters corresponding to the current equipment operation resolution level) are extracted from the working mode of the holographic projection equipment, if the equipment switches to a low resolution mode, the resolution adaptation parameters are reduced, and the resolution adjustment direction of the image elements is determined to be reduced (such as simplifying texture details and reducing the number of pixels) to ensure smooth image output; if the equipment switches to a high resolution mode, the resolution adaptation parameters are increased, and the adjustment direction is to increase (such as increasing texture details and improving pixel density).

[0137] The light output parameters (such as the actual light output intensity of the projection equipment and the brightness output value of the lighting equipment) are extracted from the equipment output state: the light output parameters are compared with the rated output parameters of the equipment, if the actual light output intensity is lower than the rated value, the brightness adjustment direction of the image elements is determined to be increased (such as increasing the brightness parameter of the image) to compensate for the insufficient light output; if the actual light output intensity is higher than the rated value, the adjustment direction is to reduce, to avoid overexposure of the picture.

[0138] Step S144: Feature analysis is performed on the performer action data to obtain action frequency characteristic parameters in unit time and action amplitude characteristic parameters based on spatial coordinate changes, the motion rhythm adjustment direction of the image elements is determined based on the deviation of the action frequency characteristic parameters from the preset reference frequency, and the motion range adjustment direction of the image elements is determined based on the deviation of the action amplitude characteristic parameters from the preset reference amplitude.

[0139] Feature analysis is performed on the performer action data: the number of singer gesture changes and the number of dance action beats are counted in unit time, and after standardization, the action frequency characteristic parameters are obtained, which reflect the action rhythm speed of the performer. The spatial coordinate changes of the performer's limb joints are recorded by the motion capture system, the maximum distance and angle range of the coordinate changes are calculated, and the action amplitude characteristic parameters are obtained, which reflect the stretching degree of the action.

[0140] The action frequency characteristic parameter is compared with a preset reference frequency (a standard action frequency set based on the rhythm of a song): if the parameter is higher than the reference frequency, it indicates that the performer's action is accelerated, and it is determined that the motion rhythm adjustment direction of the image element (such as a virtual singer's dance, a dynamic background) is accelerated; if the parameter is lower than the reference frequency, the adjustment direction is slowed down. The action amplitude characteristic parameter is compared with a preset reference amplitude: if the parameter is greater than the reference amplitude, it indicates that the action is stretched, and it is determined that the motion range adjustment direction of the image element is expanded (such as increasing the diffusion range of a particle special effect, the moving distance of a virtual character); if the parameter is less than the reference amplitude, the adjustment direction is reduced.

[0141] Step S145: combining all the above adjustment directions, the association logic of real-time interaction data and image sequence adjustment is constructed, which contains the corresponding relationship between interaction data characteristics and image parameter adjustment amount, the corresponding relationship between device data characteristics and image parameter adjustment amount, and the corresponding relationship between action data characteristics and image parameter adjustment amount.

[0142] The association logic is constructed through the following sub-steps:

[0143] Step S1451: the interaction frequency characteristic value of the audience interaction data is divided into multiple characteristic intervals based on statistical distribution, and a corresponding dynamic image element display frequency adjustment coefficient is assigned to each characteristic interval. The higher the corresponding interaction frequency of the characteristic interval, the larger the assigned display frequency adjustment coefficient.

[0144] The interaction frequency characteristic value is divided into multiple characteristic intervals (such as low active, medium active, and high active) from low to high, and each interval corresponds to a display frequency adjustment coefficient: the low active interval corresponds to a small coefficient (small adjustment amplitude), and the high active interval corresponds to a large coefficient (large adjustment amplitude). For example, the adjustment coefficient of the high active interval can increase the display frequency of the dynamic image element to several times of the original frequency, and the low active interval only fine tunes or does not adjust.

[0145] Step S1452: the interaction area characteristic distribution of the audience interaction data is converted into area weight values based on area importance, and a corresponding image element display area adjustment weight is assigned to different interaction areas. The higher the weight value of the interaction area, the larger the image element display proportion adjustment amount of the corresponding interaction area.

[0146] The area importance is determined according to the spatial distribution characteristics of the interaction area: the weight value of the area with high interaction density (such as the center of the front area) is high, and the weight value of the area with low interaction density (such as the edge of the rear area) is low. Each area is assigned a display area adjustment weight, and the higher the weight value, the larger the image element display proportion adjustment amount of the corresponding area, for example, the image element coverage range of the high weight area can be expanded by several proportions, and the low weight area reduces the coverage range.

[0147] Step S1453: match the device working mode of the stage device running data with the preset resolution adaptation table, determine the resolution reference value corresponding to each working mode, calculate the resolution adjustment coefficient according to the deviation value of the device output state from the reference output state, the greater the deviation value, the greater the absolute value of the resolution adjustment coefficient.

[0148] The preset resolution adaptation table clearly shows the resolution reference values (such as pixel density, texture detail level) corresponding to different device working modes (low, medium, and high resolution modes). The deviation value of the actual output state of the device from the reference output state (such as the difference between the actual resolution and the reference resolution) is calculated, and the resolution adjustment coefficient is calculated based on the deviation value: when the deviation value is positive (the actual resolution is higher than the reference), the coefficient is positive (to increase the image resolution); when the deviation value is negative, the coefficient is negative (to reduce the image resolution), and the greater the absolute value of the deviation value, the greater the absolute value of the coefficient, and the more significant the adjustment amplitude.

[0149] Step S1454: compare the brightness output value in the device output state of the stage device running data with the preset brightness standard value, calculate the brightness deviation ratio, and determine the brightness adjustment amount according to the brightness deviation ratio, if the deviation ratio is positive, reduce the brightness adjustment amount, if the deviation ratio is negative, increase the brightness adjustment amount.

[0150] The brightness deviation ratio is calculated as (actual brightness output value - preset brightness standard value) / preset brightness standard value. If the deviation ratio is positive (the actual brightness is too high), the brightness adjustment amount is determined to be negative (to reduce the image brightness parameter); if the deviation ratio is negative (the actual brightness is too low), the adjustment amount is positive (to increase the image brightness parameter), and the size of the adjustment amount is proportional to the absolute value of the deviation ratio.

[0151] Step S1455: compare the motion frequency characteristic parameter of the performer action data with the image element motion rhythm reference parameter, calculate the rhythm deviation rate, and determine the motion rhythm adjustment amount according to the rhythm deviation rate, if the deviation rate is positive, increase the motion rhythm adjustment amount, if the deviation rate is negative, decrease the motion rhythm adjustment amount.

[0152] Set the image element motion rhythm reference parameter (a standard rhythm value synchronized with the song tempo), and calculate the rhythm deviation rate = (motion frequency characteristic parameter - rhythm reference parameter) / rhythm reference parameter. If the deviation rate is positive (the performer's action is accelerated), the motion rhythm adjustment amount is positive (to accelerate the image element motion speed); if the deviation rate is negative, the adjustment amount is negative (to slow down the motion speed), and the adjustment amount size changes with the absolute value of the deviation rate.

[0153] Step S1456: Compare the action amplitude feature parameter of the performer action data with the image element motion range reference parameter, calculate the range deviation rate, and determine the motion range adjustment amount according to the range deviation rate. The deviation rate is positive, and the motion range adjustment amount is enlarged. The deviation rate is negative, and the motion range adjustment amount is reduced.

[0154] Set the image element motion range reference parameter (such as the standard diffusion radius of the particle special effect, the standard movement range of the virtual character), and calculate the range deviation rate = (action amplitude feature parameter-range reference parameter) / range reference parameter. If the deviation rate is positive (the performer action amplitude is enlarged), the motion range adjustment amount is positive (the image element motion range is enlarged). If the deviation rate is negative, the adjustment amount is negative (the motion range is reduced), and the adjustment amount matches the absolute value of the deviation rate.

[0155] Step S1457: Integrate all the adjustment coefficients, adjustment weights and adjustment amount calculation logic to build the association logic of real-time interaction data and image sequence adjustment. The association logic includes data feature input items, parameter adjustment amount output items and calculation mapping relationship therebetween.

[0156] The data feature input items (such as interaction frequency feature value, resolution deviation value, rhythm deviation rate, etc.) of the audience interaction, device operation and performer action are associated with the corresponding image parameter adjustment amount output items (such as display frequency adjustment amount, resolution adjustment amount, rhythm adjustment amount, etc.) through the calculation mapping relationship to form an association logic table. For example, the input item “interaction frequency feature value (high activity) + rhythm deviation rate (positive)” corresponds to the output item “display frequency adjustment amount (+ large) + motion rhythm adjustment amount (+ medium)”, which ensures that multiple dimensional data features act on image adjustment.

[0157] Step S146: Calculate the parameter adjustment amount of each image element in the dynamic holographic projection image sequence according to the association logic, which includes display frequency adjustment amount, display area adjustment amount, resolution adjustment amount, brightness adjustment amount, motion rhythm adjustment amount and motion range adjustment amount.

[0158] For each image element (such as a virtual singer, a particle instrument and a dynamic background) in the dynamic holographic projection image sequence, according to its element type and current state, match the corresponding input data features from the association logic: for the particle special effect element, match the interaction frequency feature value, action frequency feature parameter and device brightness output value; for the virtual singer element, match the action amplitude feature parameter, device resolution mode and spatial distribution feature.

[0159] According to the matching input features and the calculation mapping relationship in the associated logic, the parameter adjustment amount of each element is calculated one by one: the display frequency adjustment amount is calculated based on the interaction frequency feature value and the adjustment coefficient; the display area adjustment amount is determined based on the interaction area weight value; the resolution adjustment amount is calculated based on the device resolution deviation value and the adjustment coefficient; the brightness adjustment amount is generated based on the brightness deviation ratio; the motion rhythm adjustment amount is derived from the rhythm deviation rate; and the motion range adjustment amount is calculated according to the range deviation rate. All adjustment amounts are marked with adjustment direction (increase / decrease, expand / contract) and adjustment amplitude level (small / medium / large).

[0160] Step S147: modifying the parameters of each image element in the dynamic holographic projection image sequence according to the parameter adjustment amount, obtaining the adjusted image element, and recombining the adjusted image element in the original time axis order to form the adjusted dynamic holographic projection image sequence.

[0161] According to the calculated parameter adjustment amount, the parameters of each image element are modified: for the particle special effect element, if the display frequency adjustment amount is "increase-medium", the number of particles generated per unit time and the flicker frequency are increased; if the display area adjustment amount is "expand-front area", the generation range of the particle special effect is extended to the stage projection area corresponding to the front area of the audience. For the virtual singer element, if the motion rhythm adjustment amount is "speed up-small", the frame interval of the dance action is shortened; if the motion range adjustment amount is "expand-left area", the moving path in the stage coordinate system is adjusted to increase the moving distance in the left area. For the dynamic background element, if the resolution adjustment amount is "decrease-medium", the detail level of the background texture is simplified; if the brightness adjustment amount is "increase-large", the brightness parameter value of the background image is increased.

[0162] All the image elements after parameter modification are rearranged and combined in the original time axis order to ensure that the appearance time, switching logic and performance link time node of the elements are consistent, forming the adjusted dynamic holographic projection image sequence.

[0163] Step S148: performing overall visual coordination verification on the adjusted dynamic holographic projection image sequence, fine-tuning the image element parameters based on the verification result, and obtaining the optimized dynamic holographic projection image sequence.

[0164] A visual coordination verification module is started to perform multi-dimensional verification on the adjusted image sequence: verifying the parameter coordination between different elements (such as whether the motion rhythm of the virtual singer is synchronized with the display frequency of the particle special effect, and whether the brightness of the background is adapted to the brightness of the foreground elements); verifying the coordination between the image elements and the stage environment (such as whether the image resolution matches the actual output capability of the projection device, and whether the color style conflicts with the light mode); verifying the coordination between the image sequence and the performance rhythm (such as whether the element switching timing coincides with the prelude and chorus nodes of the song).

[0165] If the verification finds a coordination problem (such as the particle special effect frequency being too fast and being out of sync with the virtual singer's movements, and the background brightness being too high to cover the foreground elements), the corresponding parameters are fine-tuned based on the problem type: the display frequency of the particle special effect is reduced to match the motion rhythm of the virtual singer; the background brightness adjustment amount is reduced to ensure that the foreground elements are clearly visible. After fine-tuning, the verification is performed again until the image sequence is in a coordinated state in terms of visual effects, device adaptation, and rhythm synchronization, and finally an optimized dynamic holographic projection image sequence is formed.

[0166] Step S150: Based on the optimized dynamic holographic projection image sequence, a holographic projection output parameter system is constructed, a holographic projection image generation instruction containing the output parameter system is generated, and the holographic projection image generation instruction is sent to the holographic projection device to drive the holographic projection device to output a holographic projection image.

[0167] Through parameter system construction and instruction generation, the final output of the holographic image is realized, and the specific process is as follows.

[0168] Step S151: Extract the image data information and timestamp information of each image frame in the optimized dynamic holographic projection image sequence, wherein the image data information includes image pixel data, image size data, and image color depth data.

[0169] Iterate through the optimized image sequence to extract the core data of each image frame: the image pixel data includes the color value (in HSV format) and depth value (reflecting the position of the pixel in three-dimensional space) of each pixel in the frame; the image size data is the width and height pixel number of the frame and the corresponding physical size (adapted to the size of the stage projection area); and the image color depth data is the color bit number of each pixel (determining the color expression accuracy). At the same time, the timestamp information (accurate to milliseconds) of each image frame is extracted to determine the playback time point of the frame. The above information is arranged in sequence to form an image frame data list.

[0170] Step S152: Based on the image data information, construct the format adaptation parameters of the holographic projection image, which include pixel arrangement mode, size scaling ratio, and color depth conversion standard, and the format adaptation parameters need to be matched with the input format supported by the holographic projection device.

[0171] According to the technical specification of the holographic projection device, determine the supported input format requirements, build format adaptation parameters: pixel arrangement mode is set to the compatible arrangement order of the device (such as row first or column first), ensure that the pixel data is correctly mapped; the size scaling ratio is calculated according to the projection resolution of the device and the actual projection area size, so that the image frame can be scaled to completely cover the projection area without distortion; the color depth conversion standard converts the color depth data of the image frame to the bit number standard supported by the device (such as converting high color depth to the bit number compatible with the device, while keeping the color without distortion).

[0172] Compatible test is performed on the format adaptation parameters, and the test image frame is processed according to the parameters and input into the device to verify whether the image can be normally displayed. If there is display abnormality (such as color deviation, size misplacement), adjust the adaptation parameters until the adaptation is normal.

[0173] Step S153: According to the timestamp information, build the playing timing parameter of the holographic projection image, which includes the playing start time, playing duration, and frame interval of each image frame. The playing timing parameter needs to be synchronized with the performance link time node information.

[0174] Based on the timestamp information of the image frame and combined with the performance link time node, the playing timing parameter is built: the playing start time of each image frame corresponds to its timestamp, ensuring accurate synchronization with the performance rhythm (such as song beat, action node); the playing duration is determined according to the frame rate of the frame sequence (such as when the frame rate is a certain value, the single frame duration is the inverse of the frame rate); the frame interval is set to be consistent with the duration, ensuring smooth and non-stuttering playing.

[0175] Compare the playing timing parameter with the performance link time node table to check whether the playing time of the key frame (such as the image frame at the beginning of the chorus) matches the node completely. If there is deviation (such as the frame playing time is earlier than the node), adjust the timestamp parameter to make the timing parameter completely synchronized with the performance node.

[0176] Step S154: Integrate the format adaptation parameter and the playing timing parameter to build the holographic projection output parameter system, which also includes image output precision parameter and projection area positioning parameter. The image output precision parameter is associated with the image resolution, and the projection area positioning parameter is associated with the stage space layout information.

[0177] The format adaptation parameter and the playing timing parameter are integrated into a basic parameter layer, two new core parameters are added to form a complete output parameter system: the image output precision parameter is based on image resolution setting, including pixel density, texture detail level and other sub-parameters, the higher the resolution, the higher the output precision parameter setting, to ensure clear image details; the projection area positioning parameter is set according to the stage space layout information (such as the installation position, angle and projection distance of the projection equipment), including projection offset, rotation angle, scaling coefficient and other sub-parameters, so that the image can be accurately projected to the preset stage projection area (such as the central area of the stage and the background wall area).

[0178] The parameter system adopts a hierarchical structure for storage: the basic parameter layer contains format and timing parameters, and the extended parameter layer contains output precision and positioning parameters, each layer of parameters is labeled with parameter name, data type, value range and device mapping relationship, which is convenient for device parsing.

[0179] Step S155: Add parameter identification information to the holographic projection output parameter system, each parameter corresponds to a unique identification code, which is used for parameter identification when the holographic projection device is parsed.

[0180] Each parameter in the parameter system is assigned a unique identification code (such as a combination of numbers and letters), and the coding rule follows the device's parameter identification protocol: for example, "pixel arrangement mode" in the format adaptation parameter corresponds to the code "FMT-PA-001", "play start time" in the playing timing parameter corresponds to the code "SEQ-ST-001", "pixel density" in the image output precision parameter corresponds to the code "PRE-PD-001", and "projection offset" in the projection area positioning parameter corresponds to the code "LOC-OF-001".

[0181] The correspondence between the parameters and the identification codes forms a parameter code table, which is sent to the device together with the parameter system to assist the device in quickly identifying and matching the parameters.

[0182] Step S156: According to the communication protocol requirements of the holographic projection device, the holographic projection output parameter system containing the parameter identification information is packaged into an instruction data structure, the instruction data structure contains an instruction header, a parameter area and a verification area, the instruction header contains an instruction type identification, the parameter area contains the specific data of the output parameter system, and the verification area contains a data integrity verification code.

[0183] According to the communication protocol supported by the device (such as TCP / IP protocol, device-specific communication protocol), the instruction data structure is constructed: the instruction header contains the instruction type identification (such as a specific identification code corresponding to the "holographic image generation instruction"), the instruction length, and the sending timestamp, which is used to identify the instruction type and basic information; the parameter area stores the specific data of the output parameter system in order according to the parameter encoding sequence (such as the combination of parameter identification code + parameter value), and the data format is a binary format compatible with the device; the check area uses the CRC check algorithm to calculate the check code of the parameter area data, which is used to verify whether the data is complete and has not been tampered with after the device receives the instruction.

[0184] The format of the encapsulated instruction data structure is checked to ensure that it meets the frame structure requirements of the communication protocol, and to avoid transmission failure due to format errors.

[0185] Step S157: Generate a holographic projection image generation instruction containing the instruction data structure, and establish a communication link with the holographic projection device after format conversion of the holographic projection image generation instruction.

[0186] The encapsulated instruction data structure is used as the core content to generate a complete holographic projection image generation instruction, and the generated instruction is converted to a transmission format (such as binary stream format) supported by the holographic projection device to ensure that the device can correctly parse the instruction content. Subsequently, a communication connection request is initiated through the device communication interface (such as Ethernet interface, USB interface), the correctness of the device IP address and port number is verified, and after the holographic projection device returns a connection confirmation response, a stable bidirectional communication link is established, which supports real-time interaction of instruction transmission and device state feedback data.

[0187] Step S158: Send the holographic projection image generation instruction to the holographic projection device through the established communication link, so that the holographic projection device receives the holographic projection image generation instruction, loads the corresponding image data according to the output parameter system, and controls the projection component to output the holographic projection image according to the play timing parameter.

[0188] The holographic projection image generation instruction is sent to the holographic projection device through the established communication link in a data packet fragmentation transmission mode. The integrity of each data packet is checked in real time during the transmission process. If a data packet is found to be lost or damaged, a retransmission mechanism is triggered immediately. After receiving the instruction, the holographic projection device starts the instruction analysis module, and the parameter system is parsed according to the parameter identification code. First, the format adaptation parameters are parsed. The image data of the optimized dynamic holographic projection image sequence is format-converted according to the pixel arrangement mode, size scaling ratio, and color depth conversion standard, to ensure that the image data adapts to the input requirements of the device. Then, the play timing parameters are parsed. The play start time, duration, and frame switching interval of each image frame are calibrated with the internal time synchronization module of the device to ensure that the play rhythm is completely consistent with the performance time node. Finally, the image output precision parameters and the projection area positioning parameters are parsed. The projection resolution and light output intensity of the device are adjusted, and the projection angle and focal length of the device are controlled according to the projection area positioning parameters, so that the projection range accurately covers the preset stage projection area.

[0189] After the device completes the analysis, the converted image data is loaded into the memory buffer, and the projection control module is started to control the laser projection components, spatial light modulators, and other core components to output images frame by frame according to the play timing parameters. During the output process, the state monitoring module of the device collects operation data such as projection brightness, color deviation, and device temperature in real time, and feeds back to the stage control terminal through the communication link. If the feedback data shows an abnormality (such as brightness deviation exceeding the allowed range or device temperature being too high), the stage control terminal can generate parameter adjustment instructions based on the preset abnormality handling rules, send them to the device through the communication link, and adjust the projection parameters in real time to ensure that the holographic projection image always maintains stable and clear output effect, and finally realizes the synchronization of the holographic projection presentation with the performance rhythm of the modern science and technology theme concert and the coordination of the visual effect.

[0190] Figure 2 A schematic diagram of exemplary hardware and software components of the holographic projection image intelligent generation system 100 for stage virtual interaction is shown, which can implement the idea of the present application. For example, the processor 120 can be integrated into the holographic projection image intelligent generation system 100 for stage virtual interaction, and used to execute the functions in the present application.

[0191] The holographic projection image intelligent generation system 100 for stage virtual interaction can be a general server or a special-purpose server, both of which can be used to implement the holographic projection image intelligent generation method for stage virtual interaction of the present application. Although only one server is shown in the present application, for the sake of convenience, the functions described in the present application can be implemented in a distributed manner on multiple similar platforms to balance the processing load.

[0192] For example, the holographic projection image intelligent generation system 100 applied to stage virtual interaction can include a network port 110 connected to a network, one or more processors 120 for executing program instructions, a communication bus 130, and different forms of storage media 140, such as a disk, a ROM, or a RAM, or any combination thereof. Illustratively, the holographic projection image intelligent generation system 100 applied to stage virtual interaction can also include program instructions stored in a ROM, a RAM, or other types of non-transitory storage media, or any combination thereof. The method of the present application can be implemented according to the program instructions. The holographic projection image intelligent generation system 100 applied to stage virtual interaction also includes an I / O interface 150 between the computer and other input and output devices.

[0193] For ease of illustration, only one processor is described in the holographic projection image intelligent generation system 100 applied to stage virtual interaction. However, it should be noted that the holographic projection image intelligent generation system 100 applied to stage virtual interaction in the present application can also include multiple processors, so the steps performed by one processor described in the present application can also be jointly performed or individually performed by multiple processors. For example, if the processor of the holographic projection image intelligent generation system 100 applied to stage virtual interaction performs steps A and B, it should be understood that steps A and B can also be jointly performed by two different processors or individually performed in one processor. For example, a first processor performs step A, a second processor performs step B, or the first processor and the second processor jointly perform steps A and B.

[0194] In addition, the present application also provides a readable storage medium, wherein computer executable instructions are pre-set in the readable storage medium, and when a processor executes the computer executable instructions, the holographic projection image intelligent generation method applied to stage virtual interaction is realized.

[0195] It should be noted that, in order to simplify the description of the present application and to help understand one or more embodiments of the present application, in the foregoing description of the embodiments of the present application, various features are sometimes combined into one embodiment, drawing or description thereof.

Claims

1. A holographic projection image intelligent generation method applied to stage virtual interaction, characterized in that, The method comprises: acquiring stage scene interaction requirement information, the stage scene interaction requirement information including stage performance theme information, performance link time node information, and virtual interaction trigger condition information; constructing a target mapping rule based on the stage scene interaction requirement information, calling a generative artificial intelligence model to perform image element generation processing according to the target mapping rule, and obtaining an initial holographic projection image element set, the initial holographic projection image element set including static image elements and dynamic image elements matched with the stage performance theme information; dividing performance stages according to the performance link time node information, and performing stage-by-stage dynamic arrangement processing on the initial holographic projection image element set in combination with the virtual interaction trigger condition information to obtain a dynamic holographic projection image sequence; collecting stage real-time interaction data, establishing an association logic of real-time interaction data and image sequence adjustment, performing parameter adjustment processing on the dynamic holographic projection image sequence according to the association logic, and obtaining an optimized dynamic holographic projection image sequence; constructing a holographic projection output parameter system based on the optimized dynamic holographic projection image sequence, generating a holographic projection image generation instruction including the output parameter system, and sending the holographic projection image generation instruction to a holographic projection device to drive the holographic projection device to output a holographic projection image.

2. The holographic projection image intelligent generation method applied to stage virtual interaction according to claim 1, characterized in that, The method comprises: parsing the stage performance theme information in the stage scene interaction requirement information, extracting core visual style keywords and core element type descriptions in the stage performance theme information, the core visual style keywords including color tendency descriptions and style genre descriptions, and the core element type descriptions including scene element categories and role element categories; constructing a target mapping rule based on the core visual style keywords and the core element type descriptions, the target mapping rule including a correspondence between visual style keywords and image color parameters and a correspondence between element type descriptions and image form features; inputting the target mapping rule into a generative artificial intelligence model, triggering the generative artificial intelligence model to configure image generation basic parameters according to the target mapping rule, the image generation basic parameters including color configuration parameters, form configuration parameters, and texture configuration parameters; determining an image color matching scheme according to the color configuration parameters, the image color matching scheme including a main color parameter, an auxiliary color parameter, and a color model-based color transition rule; determining form feature parameters and texture detail parameters of image elements according to the form configuration parameters and the texture configuration parameters, the form feature parameters including contour structure parameters based on geometric constraints and scale size parameters relative to a stage projection area, and the texture detail parameters including texture density parameters based on pixel distribution and texture texture parameters based on material simulation. The generative artificial intelligence model is called to generate static image elements according to the image color matching scheme, the morphological feature parameter and the texture detail parameter, and a static image element matching the core element type description is obtained; Based on the dynamic attribute requirement in the core element type description, dynamic change rule parameters are configured, which include time dimension change frequency and space dimension change trajectory; The generative artificial intelligence model is called to generate dynamic image elements according to the dynamic change rule parameters, and a dynamic image element containing time dimension change law is obtained; The static image elements and dynamic image elements are integrated, and the number of the integrated image elements is checked according to the number of the core element type description, to ensure that the number of the static image elements and the dynamic image elements corresponds to the number of the element categories mentioned in the core element type description, forming an initial holographic projection image element set.

3. The holographic projection image intelligent generation method applied to stage virtual interaction according to claim 2, characterized in that, Based on the dynamic attribute requirement in the core element type description, dynamic change rule parameters are configured, which include time dimension change frequency and space dimension change trajectory; The dynamic attribute requirement in the core element type description is analyzed to determine the change dimension of the dynamic image element, which includes position change dimension, morphological change dimension and color change dimension; For the position change dimension, the position change rule parameters are configured according to the motion trajectory description in the dynamic attribute requirement, which include space path node coordinates based on the stage coordinate system and motion speed between nodes based on time unit; For the morphological change dimension, the morphological change rule parameters are configured according to the morphological evolution description in the dynamic attribute requirement, which include morphological transition node parameters and morphological change amplitude between nodes based on percentage change; For the color change dimension, the color change rule parameters are configured according to the color gradient description in the dynamic attribute requirement, which include color transition node parameters and color change gradient between nodes based on color space; The position change rule parameters, the morphological change rule parameters and the color change rule parameters are integrated to form a dynamic change rule parameter set; The dynamic change rule parameter set is input into the generative artificial intelligence model to trigger the generative artificial intelligence model to build a time change model of the dynamic image element, which includes position parameters, morphological parameters and color parameters corresponding to different time points; Based on the time change model, state data of the dynamic image element at different time points is generated, which includes timestamp, position coordinates, morphological parameter value and color parameter value; According to the state data, a frame sequence of the dynamic image element is generated, each frame corresponding to the state of the dynamic image element at a time point, and the time interval of the frame sequence is consistent with the minimum time unit in the performance link time node information; The frame sequence is processed for inter-frame transition, the amplitude of state parameter change between adjacent frames is adjusted, the processed frame sequence is integrated, a dynamic image element containing time dimension change rule is formed, and the time length of the dynamic image element matches the time length of the corresponding performance link in the stage performance theme information.

4. The holographic projection image intelligent generation method applied to stage virtual interaction according to claim 3, characterized in that, The inter-frame transition processing of the frame sequence and the adjustment of the amplitude of state parameter change between adjacent frames include the following steps: extracting dynamic image element state parameters of adjacent two frames in the frame sequence, the dynamic image element state parameters including position coordinates, shape parameters and color parameters of the adjacent previous frame and position coordinates, shape parameters and color parameters of the adjacent next frame; calculating the Euclidean distance difference value of the position coordinates between the adjacent previous frame and the adjacent next frame to obtain a position difference value, calculating the scalar difference value of the shape parameters between the adjacent previous frame and the adjacent next frame to obtain a shape difference value, and calculating the color difference difference value of the color parameters between the adjacent previous frame and the adjacent next frame to obtain a color difference value; determining the transition smoothness requirement between the adjacent frames according to the comprehensive size of the position difference value, the shape difference value and the color difference value, and the larger the comprehensive difference value is, the higher the transition smoothness requirement is; determining the transition processing mode between the adjacent frames based on the transition smoothness requirement, and the transition processing mode includes linear transition processing, curve transition processing and segmented transition processing; if the linear transition processing is adopted, the state parameters of the transition frames are generated by uniformly distributing the difference value change according to the position difference value, the shape difference value and the color difference value, and the number of transition frames is determined based on the difference value size and the frame rate requirement; if the curve transition processing is adopted, a difference value change curve function based on time progress is constructed, the difference value distribution amount at different transition moments is calculated based on the curve function, the state parameters of the transition frames are generated, and the curvature of the curve function is adjusted according to the transition smoothness requirement; if the segmented transition processing is adopted, the difference value is divided into a plurality of segments based on the change rate, a corresponding transition rate is configured for each segment, the state parameters of each transition frame are calculated based on the segmented transition rate, and the number of segments is positively correlated with the difference value size; the generated transition frames are inserted between the adjacent previous frame and the adjacent next frame to form a new frame sequence containing the transition frames.

5. The holographic projection image intelligent generation method applied to stage virtual interaction according to claim 1, characterized in that, The division of the performance stages according to the performance link time node information and the dynamic arrangement processing of the initial holographic projection image element set in stages combined with the virtual interaction trigger condition information to obtain a dynamic holographic projection image sequence include the following steps: analyzing the performance link time node information, dividing a plurality of continuous performance stages according to the interval characteristics of the time nodes, labeling stage time length information for each performance stage, and each performance stage corresponding to a performance content theme; performing category labeling on the static image elements and the dynamic image elements in the initial holographic projection image element set, and the category labeling result containing matching degree information of each performance stage content theme; allocating corresponding static image elements and dynamic image elements for each performance stage according to the matching degree information, so that the allocated image elements adapt to the content theme of the performance stage, and forming an initial image element group of each performance stage; The virtual interaction trigger condition information is analyzed to determine the type and triggering time of the virtual interaction trigger event, and a triggering priority is marked for each virtual interaction trigger event. The type of the virtual interaction trigger event includes an audience interaction virtual interaction trigger event, a stage equipment state virtual interaction trigger event, and a performance action virtual interaction trigger event. Based on the type and triggering priority of the virtual interaction trigger event, a target association rule is constructed, which includes the image element switching mode and image element switching time corresponding to different virtual interaction trigger events. According to the stage time length information of each performance stage and the triggering time of the virtual interaction trigger event, the initial image element groups of each performance stage are arranged in time sequence to form an initial time axis image sequence. According to the target association rule, image element switching markers are set at the time positions corresponding to the virtual interaction trigger events in the initial time axis image sequence, and the image elements to be switched and the target switching image elements at the image element switching markers are defined. The time axis image sequence containing the image element switching markers is subjected to visual coherence processing, the transition parameters of the image elements to be switched and the target switching image elements at the switching markers are adjusted, and after the adjacent image elements realize visual smooth transition during switching, the processed time axis image sequence is integrated to form a dynamic holographic projection image sequence.

6. The holographic projection image intelligent generation method applied to stage virtual interaction according to claim 5, characterized in that, The target association rule is constructed based on the type and triggering priority of the virtual interaction trigger event, and the image element switching markers are set at the time positions corresponding to the virtual interaction trigger events in the initial time axis image sequence according to the target association rule, which includes: The type of the virtual interaction trigger event is subjected to attribute analysis to determine the influence range corresponding to each virtual interaction trigger event, which includes the category range and time influence range of image element switching; According to the influence range of the virtual interaction trigger event and the preset priority determination standard, a triggering priority is assigned to each virtual interaction trigger event, and the priority determination standard includes the size of the influence range and the degree of association with the performance content; Based on the type, triggering priority, and influence range of the virtual interaction trigger event, a target association rule is constructed, which includes: an immediate switching rule corresponding to the virtual interaction trigger event with a first priority, a delayed switching rule corresponding to the virtual interaction trigger event with a second priority, and a conditional trigger switching rule corresponding to the virtual interaction trigger event with a third priority; The immediate switching rule is defined as starting the image element switching process immediately when the virtual interaction trigger event with the first priority occurs, and the response time does not exceed the minimum time interval in the performance link time node information; The delayed switching rule is defined as starting the image element switching process after a preset time when the virtual interaction trigger event with the second priority occurs, and the preset time is determined according to the interval time of adjacent performance stages; The conditional trigger switching rule is defined as starting the image element switching process after satisfying a preset additional trigger condition when the virtual interaction trigger event with the third priority occurs. Traverse the initial timeline image sequence, mark the position corresponding to the trigger time of each virtual interaction trigger event on the timeline, and label the type and priority of each virtual interaction trigger event; According to the target association rule, match the corresponding switching rule for each marked trigger time position, and determine the specified image element class to be switched and the target switching image element class in the switching rule; Set an image element switching marker at the trigger time position, the image element switching marker including the type of the virtual interaction trigger event, the trigger priority, the image element to be switched, the target switching image element identifier, and the switching rule type; Temporarily store the timeline image sequence containing the image element switching marker.

7. The holographic projection image intelligent generation method applied to stage virtual interaction according to claim 1, characterized in that, The stage real-time interaction data is collected, the association logic of the real-time interaction data and the image sequence adjustment is established, the parameter adjustment amount of each image element in the dynamic holographic projection image sequence is calculated according to the association logic, and the optimized dynamic holographic projection image sequence is obtained, including: Collecting stage real-time interaction data through a stage data collection device, the stage real-time interaction data including audience interaction data, stage equipment operation data, and performer action data, the audience interaction data including interaction behavior frequency and interaction area distribution, the stage equipment operation data including equipment working mode and equipment output state, and the performer action data including action frequency and action amplitude; Feature extraction is performed on the audience interaction data to obtain interaction frequency characteristic values within a unit time and spatial distribution characteristics of the interaction area, the display frequency adjustment direction of the dynamic image element is determined based on the comparison result of the interaction frequency characteristic values and a preset threshold, and the display area adjustment direction of the image element is determined based on the spatial distribution characteristics and the stage area mapping relationship; Feature analysis is performed on the stage equipment operation data to extract image resolution adaptation parameters corresponding to the equipment working mode and light output parameters corresponding to the equipment output state, the resolution adjustment direction of the image element is determined according to the resolution adaptation parameters, and the brightness adjustment direction of the image element is determined according to the light output parameters; Feature analysis is performed on the performer action data to obtain action frequency characteristic parameters within a unit time and action amplitude characteristic parameters based on spatial coordinate changes, the motion rhythm adjustment direction of the image element is determined based on the deviation of the action frequency characteristic parameters from a preset reference frequency, and the motion range adjustment direction of the image element is determined based on the deviation of the action amplitude characteristic parameters from a preset reference amplitude; All the adjustment directions are combined to construct the association logic of the real-time interaction data and the image sequence adjustment, the association logic including the correspondence between the interaction data features and the image parameter adjustment amount, the correspondence between the equipment data features and the image parameter adjustment amount, and the correspondence between the action data features and the image parameter adjustment amount; According to the association logic, the parameter adjustment amount of each image element in the dynamic holographic projection image sequence is calculated, the parameter adjustment amount including display frequency adjustment amount, display area adjustment amount, resolution adjustment amount, brightness adjustment amount, motion rhythm adjustment amount, and motion range adjustment amount. According to the parameter adjustment amount, each image element in the dynamic holographic projection image sequence is modified in parameters to obtain an adjusted image element, and the adjusted image elements are recombined in the original time axis sequence to form an adjusted dynamic holographic projection image sequence; The adjusted dynamic holographic projection image sequence is checked for overall visual coordination, and the image element parameters are fine-tuned based on the checking result to obtain an optimized dynamic holographic projection image sequence.

8. The holographic projection image intelligent generation method applied to stage virtual interaction according to claim 7, characterized in that, The above-mentioned all adjustment directions are combined to construct the associated logic of real-time interaction data and image sequence adjustment, and the parameter adjustment amount of each image element in the dynamic holographic projection image sequence is calculated according to the associated logic, including: The interaction frequency characteristic value of the audience interaction data is divided into a plurality of characteristic intervals based on statistical distribution, and a corresponding dynamic image element display frequency adjustment coefficient is assigned to each characteristic interval. The higher the interaction frequency corresponding to the characteristic interval, the larger the assigned display frequency adjustment coefficient; The interaction region feature distribution of the audience interaction data is converted into a region weight value based on the region importance, and a corresponding image element display region adjustment weight is assigned to different interaction regions. The higher the weight value of the interaction region, the larger the image element display proportion adjustment amount of the corresponding interaction region; The device working mode of the stage device running data is matched with the preset resolution adaptation table to determine the resolution reference value corresponding to each working mode, and the resolution adjustment coefficient is calculated according to the deviation value between the device output state and the reference output state. The larger the deviation value, the larger the absolute value of the resolution adjustment coefficient; The luminance output value in the device output state of the stage device running data is compared with the preset luminance standard value to calculate the luminance deviation ratio, and the luminance adjustment amount is determined according to the luminance deviation ratio. If the deviation ratio is positive, the luminance adjustment amount is reduced, and if the deviation ratio is negative, the luminance adjustment amount is increased; The motion frequency characteristic parameter of the performer action data is compared with the image element motion rhythm reference parameter to calculate the rhythm deviation rate, and the motion rhythm adjustment amount is determined according to the rhythm deviation rate. If the deviation rate is positive, the motion rhythm adjustment amount is increased, and if the deviation rate is negative, the motion rhythm adjustment amount is decreased; The motion amplitude characteristic parameter of the performer action data is compared with the image element motion range reference parameter to calculate the range deviation rate, and the motion range adjustment amount is determined according to the range deviation rate. If the deviation rate is positive, the motion range adjustment amount is increased, and if the deviation rate is negative, the motion range adjustment amount is decreased; The calculation logic of all the above-mentioned adjustment coefficients, adjustment weights and adjustment amounts is integrated to construct the associated logic of real-time interaction data and image sequence adjustment, which contains data feature input items, parameter adjustment amount output items and calculation mapping relationships therebetween; The current parameter value of each image element in the dynamic holographic projection image sequence is obtained, which includes the current display frequency, the current display area proportion, the current resolution, the current luminance, the current motion rhythm and the current motion range. The real-time interaction data feature value corresponding to each image element is input into the association logic, and the display frequency adjustment amount, the display area adjustment amount, the resolution adjustment amount, the brightness adjustment amount, the motion rhythm adjustment amount, and the motion range adjustment amount of each image element are calculated through the calculation mapping relationship in the association logic to form a parameter adjustment amount set. 9.The holographic projection image intelligent generation method applied to stage virtual interaction of claim 1, wherein, The output parameter system of the holographic projection is constructed based on the optimized dynamic holographic projection image sequence, a holographic projection image generation instruction containing the output parameter system is generated, and the holographic projection image generation instruction is sent to the holographic projection device to drive the holographic projection device to output the holographic projection image, including: Image data information and timestamp information of each image frame in the optimized dynamic holographic projection image sequence are extracted, and the image data information includes image pixel data, image size data, and image color depth data; A format adaptation parameter of the holographic projection image is constructed based on the image data information, and the format adaptation parameter includes a pixel arrangement mode, a size scaling ratio, and a color depth conversion standard, and the format adaptation parameter needs to be matched with an input format supported by the holographic projection device; A playing time sequence parameter of the holographic projection image is constructed according to the timestamp information, and the playing time sequence parameter includes a playing start time, a playing duration, and a frame switching interval of each image frame, and the playing time sequence parameter needs to be synchronized with the performance link time node information; The format adaptation parameter and the playing time sequence parameter are integrated to construct the output parameter system of the holographic projection, and the output parameter system of the holographic projection further includes an image output precision parameter and a projection area positioning parameter, the image output precision parameter is associated with the image resolution, and the projection area positioning parameter is associated with the stage space layout information; Parameter identification information is added to the output parameter system of the holographic projection, each parameter corresponds to a unique identification code, and the parameter identification information is used for parameter identification when the holographic projection device is parsed; According to the communication protocol requirement of the holographic projection device, the output parameter system of the holographic projection containing the parameter identification information is packaged into an instruction data structure, the instruction data structure includes an instruction header, a parameter area, and a verification area, the instruction header includes an instruction type identification, the parameter area includes specific data of the output parameter system, and the verification area includes a data integrity verification code; A holographic projection image generation instruction containing the instruction data structure is generated, and after the holographic projection image generation instruction is format-converted, a communication link between the holographic projection device and the holographic projection image generation instruction is established, The holographic projection image generation instruction is sent to the holographic projection device through the established communication link, so that the holographic projection device receives the holographic projection image generation instruction, loads corresponding image data according to the output parameter system, and controls the projection component to output the holographic projection image according to the playing time sequence parameter.

10. A holographic projection image intelligent generation system applied to stage virtual interaction, characterized in that, The holographic projection image intelligent generation system applied to stage virtual interaction comprises a processor and a memory, the memory and the processor are connected, the memory is used for storing programs, instructions or codes, and the processor is used for executing the programs, instructions or codes in the memory to realize the holographic projection image intelligent generation method applied to stage virtual interaction in any one of claims 1-9.

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