System for semantic alignment and adaptation of theatrical performances in cross-cultural contexts

Abstract

This invention discloses a semantic alignment and adaptation system for theatrical performances in a cross-cultural context, belonging to the field of theatrical performance analysis technology. Through systematic extraction of physical symbol features, semantic matching, and ambiguity identification, it addresses the core pain points of distorted cross-cultural semantic transmission of physical symbols and audience misunderstanding, fully preserving the original play's cultural core and symbolic semantics. This invention achieves intelligent and real-time processing of the entire cross-cultural adaptation process for drama, transforming fragmented manual adaptation into a quantifiable, standardized intelligent process, significantly reducing costs and improving efficiency. It meets the real-time adjustment needs of rehearsals and performances through an edge-cloud collaborative architecture; simultaneously, it constructs a dynamic closed loop of real-time audience feedback and performance adaptation.

Description

Technical Field

[0001] This invention relates to the field of theatrical performance analysis technology, and in particular to a theatrical performance semantic alignment and adaptation system in a cross-cultural context. Background Technology

[0002] With the advancement of cultural globalization, drama, as a core art form carrying the essence of national culture, faces an increasingly strong demand for cross-cultural communication and localization. The semantic transmission of theatrical performances relies not only on the text of the dialogue but also on physical symbols such as gestures, facial expressions, body postures, and stylized ritual movements. The semantics of these physical symbols possess strong cultural attributes and are the core pain point for semantic distortion and audience misunderstanding in the cross-cultural communication of drama.

[0003] Current adaptation work for cross-cultural communication of drama still relies primarily on the translation of dialogue texts, supplemented by sporadic manual adjustments to performance movements. This approach suffers from four major unsolvable technical defects: First, it lacks a systematic semantic analysis and cross-cultural adaptation scheme for performance symbols, making it difficult to accurately identify cross-cultural semantic ambiguities in gestures and stylized movements, which can easily lead to the erosion of the original play's cultural core and errors in the transmission of symbolic meaning. Second, its heavy reliance on human experience results in extremely low adaptation efficiency and weak scalability, and it cannot achieve real-time dynamic adjustments during rehearsals and performances. Third, it has not established a closed loop linking audience feedback and performance adaptation, making it impossible to dynamically optimize adaptation parameters based on real-time viewing reactions from live and online audiences, and making it difficult to verify and iterate the adaptation effect in real time. Fourth, it lacks a cross-cultural paradigm adaptation mechanism for the emotional expression in theatrical performances, and cannot solve the problem of audience empathy loss caused by differences in emotional expression styles in different cultural contexts.

[0004] To address the aforementioned technical deficiencies, a high-speed motor control system and method are proposed. Summary of the Invention

[0005] The purpose of this invention is to address the core pain points of distortion in the cross-cultural semantic transmission of physical symbols and audience misunderstanding by systematically extracting physical symbol features, semantic matching, and identifying ambiguities, thereby fully preserving the original cultural core and symbolic semantics of the drama.

[0006] To achieve the above objectives, the present invention adopts the following technical solution:

[0007] A semantic alignment and adaptation system for theatrical performances in a cross-cultural context includes: a performance data acquisition module that collects multimodal raw data of actors' performances and distributes it to a body symbol semantic analysis module; a body symbol semantic module that extracts performance body symbol features through a MediaPipe motion capture model, completes cross-cultural semantic matching and ambiguity identification, and sets a dynamic adjustment interface for semantic mapping weights; an emotion expression paradigm adaptation module that extracts performance emotion features through a BERT-LSTM hybrid architecture model, quantifies the intensity, and calibrates the target culture's emotion expression paradigm, and sets a real-time adjustment interface for emotion expression parameters; and a ritualistic action semantic calibration module that performs ritualistic action symbol semantic analysis and cross-cultural adaptation calibration, and sets a real-time adjustment interface for action calibration coefficients.

[0008] The adaptation effect verification module generates optimization suggestions based on the comprehensive verification of the semantic alignment of the performance adapted to the data from the three adjustment interfaces. The real-time viewing multimodal feedback weight optimization module, based on the data from the three adjustment interfaces, uses a temporal convolutional network real-time weight calculation model to collect real-time viewing behavior data of the audience, complete temporal multi-dimensional feedback weighting, and drive the optimization of adaptation parameters of each module in real time, forming a dynamic adaptation closed loop. The performance guidance instruction generation module works with the real-time viewing multimodal feedback weight optimization module to generate standardized performance guidance instructions.

[0009] Furthermore, the performance data acquisition module is embedded in a portable performance acquisition terminal, which has a built-in high-definition binocular camera, a 3D motion capture sensor, and an omnidirectional audio acquisition array to simultaneously acquire high-definition video, key points of movement, facial expressions, gestures, and multimodal audio data of the actors' performances; the real-time viewing multimodal feedback weight optimization module is connected to the audience behavior acquisition terminal, which has a built-in high-definition camera in the audience seats, an online live broadcast data acquisition interface, and a live pressure sensor array to collect real-time viewing behavior data of the audience on site and online; the output end of the performance guidance instruction generation module is connected to the stage display terminal, which is deployed throughout the stage area to display performance guidance instructions in real time.

[0010] Furthermore, the portable performance acquisition terminal, audience behavior acquisition terminal, and stage display terminal are connected to a local processing terminal that deploys a body symbol semantic analysis module, an adaptation effect verification module, and a real-time viewing multimodal feedback weight optimization module, as well as a cloud computing terminal that deploys an emotional expression paradigm adaptation module and a ritualistic action semantic calibration module.

[0011] Furthermore, the body symbol semantic parsing module is used to extract the core features of four types of body symbols in the performance: gestures, facial expressions, body postures, and spatial interpersonal distance. After completing the feature standardization process, semantic matching is performed with the cross-cultural body symbol semantic database through the cosine similarity model. Cross-cultural semantic ambiguities are identified and marked, and corresponding adaptation and adjustment schemes are generated. At the same time, the semantic mapping weight of body symbols is dynamically adjusted according to the feedback weight issued by the real-time viewing multimodal feedback weight optimization module.

[0012] Furthermore, the emotional expression paradigm adaptation module pre-sets a nonlinear emotional mapping network (NEMN), which completes emotional category identification and intensity quantification based on the extracted multimodal emotional features of the performance. Combining the target culture's emotional expression paradigm parameters and real-time feedback weights, it completes nonlinear calibration of the performance's emotional intensity and expression style, so that the calibrated emotional expression is adapted to the aesthetic paradigm of the target culture.

[0013] Furthermore, the ritualistic action semantic calibration module extracts stylized ritualistic action fragments from the performance, analyzes their core symbolic semantics, and uses thin-plate spline interpolation to construct a nonlinear deformation field to adapt and calibrate the 3D trajectory of the action. During the calibration process, the degree of preservation of the original action trajectory is controlled by the time dynamic retention coefficient.

[0014] Furthermore, the real-time viewing multimodal feedback weight optimization module collects audience eye tracking, departure, cut-out, facial expressions, bullet screen, and comment time sequence data. After data preprocessing, it accurately matches the time sequence IDs of the performance segments to construct an audience emotion propagation graph network and complete real-time weight assignment. Based on the weighting results, it generates optimization instructions to adjust the semantic mapping weights of body symbols, emotional expression parameters, and action calibration coefficients in real time, forming a closed loop for dynamic adaptation of the performance.

[0015] Furthermore, it also includes a data storage and retrieval module, which is connected to each of the functional modules via signals. It interfaces with the local storage medium and cloud storage medium of the end-cloud collaborative distributed storage architecture for the storage, retrieval and synchronization of data throughout the entire process. The cloud storage medium is set to have read and write permissions only for the system terminal, and is configured with a preset periodic data synchronization mechanism, along with supporting data read and write adaptation code, audio and video compression storage mechanism and storage medium anomaly detection program.

[0016] Furthermore, the system is pre-configured with an initial cross-cultural performance semantic database system, including a cross-cultural physical symbol semantic database, a cross-cultural emotional expression paradigm database, a cross-cultural ritualistic action symbol database, and a real-time audience behavior feedback rule database.

[0017] In summary, due to the adoption of the above technical solution, the beneficial effects of the present invention are:

[0018] This invention constructs a comprehensive semantic analysis and cross-cultural adaptation system for theatrical performance body symbols. It breaks through the limitations of traditional theatrical cross-cultural communication that focuses solely on dialogue text. Through systematic extraction of body symbol features, semantic matching, and ambiguity identification, it addresses the core pain points of distorted cross-cultural semantic transmission and audience misunderstanding of body symbols, fully preserving the original play's cultural core and symbolic semantics. This invention achieves intelligent and real-time processing of the entire theatrical cross-cultural adaptation process, transforming fragmented manual adaptation into a quantifiable, standardized intelligent process, significantly reducing costs and improving efficiency. Through an edge-cloud collaborative architecture, it meets the real-time adjustment needs of rehearsals and performances. Simultaneously, it constructs a dynamic closed loop of real-time audience feedback and performance adaptation, using multi-dimensional audience data to reverse-optimize adaptation parameters, overcoming the shortcomings of traditional adaptation methods that lack a long-term iterative mechanism, and enhancing audience acceptance and empathy. This invention also sets up a specialized adaptation module tailored to the characteristics of theatrical art, accurately adapting to cross-cultural emotional expression paradigms, balancing the original play's artistic value with target audience adaptation, and achieving a balance between cultural core transmission and dissemination effectiveness. Attached Figure Description

[0019] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in this invention. For those skilled in the art, other drawings can be obtained based on these drawings.

[0020] Figure 1 This is a schematic diagram of the modules of the present invention; Detailed Implementation

[0021] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0022] Example:

[0023] This invention provides a semantic alignment and adaptation system for theatrical performances in a cross-cultural context.

[0024] I. System Composition and Connections

[0025] The performance data acquisition module is embedded in the portable performance acquisition terminal TC and is connected to the body symbol semantic analysis module. It is used to collect the actor's body movements, facial expressions, spatial position, and audio tone data, and transmit them to the body symbol semantic analysis module for pre-processing.

[0026] The body symbol semantic parsing module is deployed on the local processing terminal TL and is connected to the performance data acquisition module, the emotional expression paradigm adaptation module, the data storage and extraction module, and the real-time viewing multimodal feedback weight optimization module. The core is equipped with the MediaPipe motion capture model, which is used to parse the original semantics of body symbols in the performance, identify cross-cultural semantic ambiguities, set up a dynamic adjustment interface for body symbol semantic mapping weights, support dynamic adjustment of semantic mapping weights, and provide standardized data for subsequent adaptation and calibration.

[0027] The emotional expression paradigm adaptation module is deployed on the cloud computing terminal TB and is connected to the body symbol semantic parsing module, ritualistic action semantic calibration module, data storage and extraction module, and real-time viewing multimodal feedback weight optimization module. The core is equipped with a BERT-LSTM hybrid architecture model to quantify the intensity of performance emotions, adapt to the emotional expression paradigm of the target culture, and set up an interface for real-time adjustment of emotional expression parameters to support real-time adjustment of emotional adaptation parameters and solve the problem of emotional expression adaptation failure.

[0028] The ritualistic action semantic calibration module is deployed on the cloud computing terminal TB and is connected to the emotion expression paradigm adaptation module, the adaptation effect verification module, the data storage and retrieval module, and the real-time viewing multimodal feedback weight optimization module. It is used to analyze the symbolic semantics of ritualistic actions, calibrate the action trajectory and expression form, set up a real-time adjustment interface for action calibration coefficients, support real-time adjustment of action calibration coefficients, and ensure the accurate cross-cultural transmission of core symbolic semantics.

[0029] The adaptation effect verification module is deployed on the local processing terminal TL and is connected to the ritualistic action semantic calibration module, the performance guidance instruction generation module, and the data storage and extraction module, respectively. It is used to verify the semantic alignment of the adapted and calibrated performance and generate optimization and adjustment suggestions.

[0030] The real-time viewing multimodal feedback weight optimization module is deployed on the local processing terminal TL. It is connected to the body symbol semantic parsing module, the emotional expression paradigm adaptation module, the ritualistic action semantic calibration module, and the data storage and extraction module. The core is equipped with a temporal convolutional network (TCN) real-time weight calculation model, which is used to collect real-time viewing behavior data of on-site / online audiences, complete temporal multi-dimensional feedback weighting, and drive the optimization of performance adaptation parameters in real time to form a dynamic performance adaptation closed loop.

[0031] The performance instruction generation module is connected to the adaptation effect verification module and the real-time viewing multimodal feedback weight optimization module. It is used to receive verification results and real-time weight optimization suggestions, generate real-time visual performance instructions, and synchronize them to the stage display terminal TD for actors to adjust in real time.

[0032] The data storage and retrieval modules are connected to each core module and storage medium via signals to realize the storage, retrieval, and synchronization of raw performance data, semantic parsing results, adaptation calibration parameters, real-time feedback weight data, verification reports, and guidance instructions. The storage medium includes local storage media and cloud storage media, which are connected to the data storage and retrieval modules via signals to store all performance data, multimodal model library files, and guidance instruction files of the system, achieving dual backup and secure storage to ensure the traceability and reproduction of performance data.

[0033] The system includes a performance data acquisition module, a body symbol semantic analysis module, an emotional expression paradigm adaptation module, a ritualistic action semantic calibration module, an adaptation effect verification module, a performance guidance instruction generation module, a real-time viewing multimodal feedback weight optimization module, a data storage and retrieval module, storage media and terminal equipment, and signal connections between each module and device. All modules adopt a modular design, operate independently and work together to ensure a smooth and scalable process, adapting to the needs of various scenarios such as stage rehearsals and live performances.

[0034] II. Specific Work Steps (S1-S7)

[0035] S1: System Initialization and Storage Media Deployment

[0036] S101 Terminal and Algorithm Environment Initialization: Complete the deployment of the entire terminal network for performance acquisition, local processing, cloud computing, stage display, and audience behavior acquisition; preset a multimodal data synchronization transmission mechanism to ensure real-time command performance; complete the parameter configuration and dynamic adjustment interface settings for three core algorithm models: body symbol semantic parsing, emotional expression paradigm adaptation, and real-time feedback weight optimization; complete the standardized deployment of dependency libraries; optimize runtime memory usage; and ensure smooth real-time processing.

[0037] S102 Storage Media Initialization Deployment: Build an edge-cloud collaborative distributed dual storage architecture, complete the formatting of local high-speed storage and cloud object storage, dedicated data partitioning planning, configure edge-cloud periodic data synchronization mechanism, data read and write adaptation rules, audio and video compression storage scheme and storage anomaly monitoring and early warning mechanism, realize dual data backup disaster recovery, and balance data security and read and write efficiency.

[0038] S103 Cross-cultural Semantic Library Initialization: Construct and import four core databases: cross-cultural body symbol semantic library, emotional expression paradigm library, ritualized action symbol library, and audience real-time behavior feedback rule library. Complete professional manual annotation and verification of data and model pre-training to provide standardized semantic support and adaptation standards for subsequent semantic parsing and adaptation calibration.

[0039] S2: Performance Data Acquisition and Preprocessing

[0040] S2-1: Performance data collection, the specific operation is as follows:

[0041] Sa1: The preset collection scene is a professional drama rehearsal hall. The collection subjects are two core actors (Du Liniang and Liu Mengmei) of the "Peony Pavilion: A Dream Interrupted" segment. The raw performance data collected is marked as DC. The collection time is 15 minutes, including 3 core scenes and 12 ritualized action segments. Each performance segment is given a unique time sequence ID, and the timestamp accuracy is ±1ms.

[0042] Sa2: The performance data acquisition module is activated. Through the portable performance acquisition terminal TC's four binocular cameras, 3D motion capture sensors, and omnidirectional audio acquisition array, four channels of high-definition video data, 33 human skeleton key point motion data, 478 facial feature point expression data, 21 hand key point gesture data, and 48kHz dual-channel audio data of the actors' performance are acquired simultaneously. All data are given a unified timestamp with a timestamp accuracy of ±1ms to ensure the timing synchronization of multimodal data.

[0043] Sa3: During the acquisition process, the data acquisition frame rate and frame drop rate are monitored in real time to ensure that the video acquisition frame rate is stable at 60fps, the motion capture data acquisition frame rate is stable at 120Hz, the audio sampling rate is stable at 48kHz, and the overall frame drop rate is ≤0.1%, which meets the preset acquisition standards.

[0044] Sa4: The collected raw performance data DC is simultaneously stored and sent to the shape symbol semantic parsing module through the data storage and extraction module.

[0045] S2-2: Performance data preprocessing, the specific operations are as follows:

[0046] Sb1: Multimodal data timing alignment, based on a unified timestamp, performs timing alignment on video, motion capture, and audio data to eliminate transmission delay deviations from different acquisition devices. The timing alignment error is ≤1ms. The timing alignment accuracy calculation formula is as follows:

[0047]

[0048] in For multimodal data time-series alignment accuracy; The number of data frames with a timing alignment error ≤ 1ms; This represents the total number of data frames.

[0049] Sb2: Denoising of motion capture data. A Kalman filter model is used to denoise and smooth the motion capture, facial expression, and gesture keypoint data, removing jitter noise and environmental interference noise during the acquisition process. The denoised motion data is labeled as DC-1. The smoothness of the keypoint trajectory after denoising is ≥98%. The denoising accuracy is calculated using the following formula:

[0050]

[0051] in Improve the accuracy of noise reduction for motion capture data; The number of key point data points that conform to the laws of human kinematics after noise reduction; This represents the total number of key data points.

[0052] Sb3: Audio data preprocessing. The librosa library is used to perform frame segmentation, windowing, and Mel spectrum extraction on the acquired audio data to remove ambient noise and echo interference. The preprocessed audio data is labeled as DC-2, and the audio signal-to-noise ratio is ≥60dB to ensure the accuracy of audio emotional feature extraction.

[0053] Sb4: Video data preprocessing. OpenCV is used to extract frames, correct distortion, and fuse perspectives from the four channels of high-definition video data to generate 3D panoramic video frames of the actors' performance. The preprocessed video data is labeled as DC-3 to constrain the accuracy of video distortion correction and ensure accurate matching between motion capture data and video footage.

[0054] Sb5: Store and send the preprocessed data (motion data DC-1, audio data DC-2, video data DC-3) to the shape symbol semantic parsing module and the emotion expression paradigm adaptation module.

[0055] S3: Semantic Analysis and Ambiguity Recognition of Shape Symbols

[0056] S3-1: Shape symbol feature extraction, the specific operation is as follows:

[0057] Sa1: The body symbol semantic parsing module receives the preprocessed motion data DC-1, extracts the MediaPipe motion capture model from the storage, and extracts the core features of the body symbols of the actor's performance, labeled as F = {F1, F2, F3, F4}, where: F1 is the gesture feature, such as the three-dimensional coordinates, motion trajectory, speed, and angle of 21 hand key points; F2 is the facial expression feature, such as the displacement changes of 478 facial feature points and micro-expression action sequences; F3 is the limb posture feature, such as the three-dimensional coordinates, joint angles, and relative limb positions of 33 human skeletal key points; and F4 is the spatial interpersonal distance feature, such as the relative distance and position changes between actors and between actors and the stage area. All features are bound to corresponding temporal IDs.

[0058] Sa2: Standardize the extracted feature F to eliminate feature bias caused by differences in actors' height and body shape. The standardization formula is as follows:

[0059]

[0060] in For the first The first feature Standardized values ​​for each dimension; For the first The first feature The original values ​​of each dimension; This represents the minimum value of the corresponding feature dimension; This represents the maximum value of the corresponding feature dimension.

[0061] The standardized data ranges from [0,1], which facilitates subsequent semantic matching and ambiguity identification.

[0062] S3-2: Cross-cultural semantic matching and ambiguity identification, the specific operation is as follows:

[0063] Sb1: Extract the cross-cultural body symbol semantic database D1 from the storage. Use the cosine similarity model to calculate the semantic similarity between the extracted standardized feature F' and the body symbol features of the target country in D1. The cosine similarity calculation formula is as follows:

[0064]

[0065] in To extract features With the semantic library Bar-shaped symbol characteristics The cosine similarity is in the range of [0,1], and the higher the value, the higher the semantic matching degree. For vectors with vector The dot product; For feature vectors The modulus length; semantic library feature vector The length of the module.

[0066] Sb2: Preset semantic matching threshold, if If the shape symbol matches the semantics of the symbol in the semantic database, its original semantic tags and target cultural semantic mappings are extracted; if If the symbol is deemed semantically ambiguous, it requires manual review.

[0067] Sb3: Semantic ambiguity identification. It compares the original semantics of the symbol with the semantics of the target culture, such as Western culture. If there is semantic discrepancy, conflict, or risk of misunderstanding, it is marked as a semantic ambiguity point, bound to a corresponding time-series ID, and a ambiguity resolution report is generated, labeled as R1. The calculation formula is as follows:

[0068]

[0069] in For semantic ambiguity recognition accuracy; The number of correctly identified semantic ambiguities; This represents the total number of semantic ambiguities that actually exist in the text.

[0070] Sb4: For the identified semantic ambiguities, a preliminary adaptation and adjustment scheme is generated in conjunction with the semantic database D1. For example, the original meaning of the action of "covering the face with water sleeves" in traditional Chinese drama is "a shy and reserved emotional expression". In the target culture, it is easily misunderstood as "avoidance and rejection". The adaptation and adjustment scheme is to retain the core trajectory of the water sleeve action, adjust the facial expression to match it, increase eye contact, and at the same time appropriately adjust the range of motion to match the non-verbal expression paradigm of "shyness" in the target culture, so as to ensure that the core semantics are accurately conveyed.

[0071] Sb5: Semantic mapping weight dynamic adjustment mechanism, which sets basic mapping weights and dynamically adjusts them based on real-time feedback, as shown in the following formula:

[0072]

[0073] in Weights for dynamic semantic mapping of shape symbols; The basic mapping weights for the shape symbols are fixed with an initial value of 1. The system provides real-time feedback weighting coefficients to viewers, with an initial value of 1, which are dynamically adjusted based on the feedback results. Content with high feedback automatically receives a higher mapping weight, enhancing semantic adaptation accuracy.

[0074] Sb6: The semantic matching results, ambiguity resolution report R1, and preliminary adaptation adjustment plan are synchronously stored and backed up through the data storage and extraction module, and simultaneously sent to the sentiment expression paradigm adaptation module.

[0075] S4: Emotional Expression Paradigm Adaptation and Quantitative Calibration

[0076] S4-1: Extraction and quantification of performance emotional features, the specific operation is as follows:

[0077] Sa1: The emotional expression paradigm adaptation module receives preprocessed audio data DC-2, action data DC-1, and dialogue text data. It extracts the BERT-LSTM hybrid architecture model from storage and extracts multimodal emotional features of the performance, including changes in tone, speed, and volume of the audio, changes in amplitude, speed, and rhythm of the action, and emotional vocabulary and tone features of the text. All features are bound to corresponding time sequence IDs.

[0078] Sa2: The model quantifies the emotional intensity of the performance, denoted as Q, and defines its value range, for example, 0-10 points, where 0 points represents no emotional expression and 10 points represents the peak of emotional expression. It also identifies emotional category labels, such as joy, sadness, longing, grief, shyness, and resolve. The calculation formula is as follows:

[0079]

[0080] in Improve the accuracy of emotion category identification in performances; Identify the correct number of performance segments for each emotion category; This represents the total number of performance segments.

[0081] Sa3: Generates a temporal sequence of emotional expression in the performance, labeled as Qseq, which includes the emotional intensity value, emotional category label, and corresponding temporal ID for each performance time point, and is used for subsequent paradigm adaptation calibration and real-time adjustment.

[0082] S4-2: Calibration of Nonlinear Emotion Mapping Network (NEMN)

[0083] Sb1: Extract the cross-cultural sentiment expression paradigm library D2 from storage, extract the expression paradigm parameters corresponding to the sentiment category of the target culture, and label them as P = {P low , P high , P style}, where P style To express the style vector, eight dimensions can be preset, including indicators such as the range of motion, speech rate, and openness of facial expressions.

[0084] Sb2: Construct a nonlinear sentiment mapping network NEMN, with the following structure: Input layer (sentiment intensity Q) t + Style Vector P style + Feedback weight W feedback(t) A three-layer MLP (hidden layer dimensions of 128, 64, and 32, with Tanh activation function), the output layer is the calibrated sentiment intensity Q'. t The network is trained using a pre-training dataset, and the loss function is mean squared error.

[0085] Sb3: The calibration formula has been upgraded to:

[0086]

[0087] in The emotional intensity value of the performance after time calibration; Here is the nonlinear mapping function of the nonlinear sentiment mapping network NEMN; These are the learning parameters for the nonlinear sentiment mapping network NEMN; for The original emotional intensity value of the performance at any given moment can be set to a range, such as 0-10 points; Vector of target cultural emotional expression style; for The audience provides real-time feedback on the weighting coefficient, which can be set to an initial value of 1 and dynamically updated based on the feedback results.

[0088] Sb4: Independent NEMN subnetworks are trained for each of the six core emotion categories to ensure emotion specificity. After calibration, the emotion intensity sequence... It automatically falls within the target culture threshold range, and the style vector is aligned with the target culture. For example, Du Liniang's original "shy" emotion score is 4.2, and after NEMN calibration, the output score is 5.8, falling into the range of [5.5, 7.5], and the style vector is adjusted from "introverted" to "reserved and extroverted".

[0089] Sb5: Generate the adapted emotional expression time sequence Q'seq and the corresponding performance action and dialogue tone adjustment scheme R2.

[0090] S5: Semantic calibration and adaptation of ritualistic actions

[0091] S5-1: Symbolic semantic analysis of ritualistic actions, the specific steps are as follows:

[0092] Sa1: The ritualistic action semantic calibration module receives the preprocessed action data DC-1, extracts 12 core ritualistic action segments from the 15-minute performance segment, marks them as M1-M12, and binds them with the corresponding time sequence IDs, such as Du Liniang's "Garden Stroll Ceremony" and "Flower Picking Ceremony", Liu Mengmei's "Bowing Ceremony" and other stylized actions;

[0093] Sa2: Extract the cross-cultural ritualistic action symbol library D3 from the storage, extract the 3D motion trajectory, keyframe coordinates, symbolic semantic tags, and cultural ritual script of each ritualistic action, parse the core symbolic semantic layer of the action, and mark it as S to ensure that the core symbolic semantics are not lost;

[0094] Sa3: Compare the ritualistic actions of the original culture with the corresponding symbolic semantic action paradigms of the target culture, identify the differences in action trajectories and expression forms, generate a semantic deviation analysis report, mark it as R3, and bind it to the corresponding time sequence ID.

[0095] S5-2: Nonlinear motion deformation field calibration

[0096] Sb1: Based on the core symbolic semantics of the action, a nonlinear deformation field is constructed using thin-plate spline interpolation to adapt and calibrate the ritualized action trajectory. The calibration formula is as follows:

[0097]

[0098] in for 3D coordinates of key motion points after time calibration; for 3D coordinates of key points in the original motion at any given moment; The nonlinear deformation field constructed for thin plate spline interpolation TPS is calculated by minimizing the bending energy function; for 3D coordinates of key action points corresponding to the symbolic meaning of the target culture at any given moment; The time-dynamic retention coefficient controls the degree to which the original motion trajectory is preserved.

[0099] Sb2: For each ritualistic action segment, select 5-8 keyframes as control points, calculate TPS deformation parameters, and generate a smooth 3D motion trajectory. The calibrated motion retains the core trajectory features of the original motion while naturally integrating the expressive forms of the target culture, such as adjusting the degree of bowing.

[0100] Sb3: The formula for calculating the time dynamic retention factor is as follows:

[0101]

[0102] in The motion trajectory is dynamically preserved by a time retention coefficient to ensure that more of the original trajectory is retained at the beginning and end, and moderate deformation is applied in the middle stages. These are time points within an action sequence; The total duration of ritualized action segments; It is a sine function. It generates adapted 3D motion trajectory data and visualization adjustment guide R4, and binds them to the corresponding timing ID.

[0103] S6: Adaptation Effect Verification and Guidance Instruction Generation

[0104] S6-1: Comprehensive verification of adaptation effect, the specific operation is as follows:

[0105] Sa1: The adaptation effect verification module receives the semantic ambiguity resolution report R1, the emotional adaptation adjustment scheme R2, the semantic deviation analysis report R3, and the action calibration guide R4, and integrates them to generate a comprehensive verification index system for cross-cultural performance semantic alignment. This system includes three core indicators: semantic alignment of body symbols, adaptation of emotional expression paradigm, and accuracy of semantic transmission of ritualistic actions. Constraints are set, such as a maximum score of 100 points for each indicator, and a comprehensive score of ≥85 points is considered a qualified fit.

[0106] Sa2: Calculation of semantic alignment of shape symbols, the formula is as follows:

[0107]

[0108] in is the semantic alignment degree score of body symbols, with a full score of 100 points; is the number of remaining semantic ambiguity points after adaptation adjustment; is the total number of body symbols in the performance.

[0109] Sa3: Calculation of the adaptation degree of emotional expression paradigms. The calculation formula is as follows:

[0110]

[0111] where is the adaptation degree score of emotional expression paradigms; is the calibrated performance emotional intensity value at time is the average emotional intensity of the emotional category corresponding to the target culture; is the total number of performance time nodes; is the summation over all time nodes; is the absolute value operator.

[0112] Sa4: Calculation of the semantic transmission accuracy rate of ritualized actions. The calculation formula is as follows:

[0113]

[0114] where is the semantic transmission accuracy rate score of ritualized actions; is the number of ritualized actions with correct symbolic semantics recognition; is the total number of ritualized actions.

[0115] Sa5: Calculation of the comprehensive score. The formula is as follows:

[0116]

[0117] where is the comprehensive score of cross - cultural performance semantic alignment; is the semantic alignment degree score of body symbols; is the adaptation degree score of emotional expression paradigms; is the semantic transmission accuracy rate score of ritualized actions. When the comprehensive score of cross - cultural performance semantic alignment meets the constraint conditions, it is determined to be qualified. For example, ≥85 points, it is determined that the adaptation is qualified, and an adaptation effect verification report is generated, marked as R5. The report includes the scores of each index, the details of adaptation adjustment, and optimization suggestions, and is sent to the performance guidance instruction generation module.

[0118] S6 - 2: Generation of performance guidance instructions and data storage. The specific operations are as follows:

[0119] Sb1: The performance instruction generation module receives the verification report R5, integrates the semantic adaptation adjustment scheme, the emotional calibration scheme, and the ritualistic action calibration guide, and generates standardized real-time performance instruction, marked as Z. The instruction includes action adjustment details, emotional intensity control, semantic adaptation key points, and is accompanied by a 3D action trajectory comparison diagram and key frame guidance. It is synchronized to the stage display terminal TD for actors to view and adjust in real time.

[0120] Sb2: Sends the guidance instruction Z to the stage display terminal TD and the local processing terminal TL, and displays it in real time on the terminal interface. At the same time, it stores the guidance instruction and log partition of SD1 and the user data storage bucket of SD2.

[0121] Sb3: The data storage and extraction module synchronously backs up and stores the original performance data DC, preprocessed data (motion data DC-1, audio data DC-2, video data DC-3), semantic parsing results, adaptation and calibration scheme, verification report R5, performance guidance instructions Z, and system operation logs to achieve dual data backup, ensuring data security and traceability. The storage formats are uniformly standardized as JSON, MP4, and CSV, corresponding to data extraction from storage, audio and video playback, and parameter statistics scenarios, respectively, which facilitates subsequent performance review, data traceability, and secondary optimization.

[0122] S7: Real-time viewing multimodal feedback weight optimization and performance dynamic adaptation closed loop

[0123] S7-1: Real-time audience viewing behavior data collection and preprocessing, the specific operation is as follows:

[0124] Sa1: The real-time viewing multimodal feedback weight optimization module collects real-time behavior data of the audience on site / online during the performance through the audience behavior collection terminal TA, including audience eye tracking data, exit / cutout data, facial expression data, and on site / online bullet screen / comment timing data. All data is bound to a unified timing ID corresponding to the performance segment, and the original real-time feedback data is marked as F0. The collection frequency is 10Hz to ensure timing matching accuracy.

[0125] Sa2: Perform real-time deduplication and noise reduction on F0, remove invalid data and abnormal interference data, and mark the pre-processed valid real-time feedback data as F1;

[0126] Sa3: Accurately match the effective feedback data F1 with the performance segment time sequence ID to form a "time sequence ID-performance segment-audience behavior data" mapping table, which is used for real-time weight assignment.

[0127] S7-2: Real-time weight assignment based on emotion propagation graph network

[0128] Sb1: Construct an audience emotion propagation graph G = (V, E), where node V represents an individual audience member (either in person or online), uniquely identified by seating arrangement or account ID, and edge E connects pairs of audience members with high emotional similarity. Emotional similarity is calculated using the cosine similarity of linear real-time facial expression features or the emotional tendency of online bullet comments. The similarity is used to judge the effectiveness of the performance feedback and to impose constraints.

[0129] Sb2: Employs a Spatiotemporal Graph Convolutional Network (ST-GCN) to model the propagation of audience emotions across the spatiotemporal dimensions. The input consists of audience behavioral features within each time window, such as gaze, departure, facial expressions, and bullet comments. Graph convolution aggregates neighboring audience emotions, and then temporal convolution captures the propagation and evolution. Real-time weight update formula:

[0130]

[0131] The meanings of the letters are explained below. for The audience provides real-time feedback on the weighting value.

[0132] The Sigmoid activation function outputs normalized weights. For nodes All adjacent viewer nodes Summation; For the adjacency matrix of emotion propagation (symmetric normalization); for Moment Viewer behavioral feature vectors; This represents the learning weight matrix of the spatiotemporal graph convolutional network; This is the time-series decay factor.

[0133] Sb3: The formula for calculating the time decay factor is as follows:

[0134]

[0135] This is the time-series decay factor; For time nodes; For the natural constant An exponential function with base 0.

[0136] Sb4: will The weights are mapped to 5 levels, and a real-time weight optimization instruction Rwt is generated and sent to each adaptation module.

[0137] S7-3: Real-time reverse adaptation decision-making and dynamic performance adjustment, the specific operation is as follows:

[0138] Sc1: The shape symbol semantic parsing module dynamically adjusts the semantic mapping weight Wmap of high-priority segments based on the real-time weight optimization instruction Rwt. For segments with Wt≥Wt0, Wfeedback is adjusted to 1.5, and Wmap is updated synchronously to 1.5, thereby enhancing the semantic adaptation accuracy of the segment in real time and eliminating semantic ambiguity.

[0139] Sc2: The emotion expression paradigm adaptation module adjusts the emotion intensity scaling factor k and offset b of high-priority segments in real time according to the real-time weight optimization instruction Rwt. For segments with Wt≥Wt0, the target cultural emotion paradigm parameters are re-matched to optimize the emotion expression intensity and style in real time.

[0140] Sc3: The ritualistic action semantic calibration module adjusts the action trajectory of high-priority segments in real time according to the real-time weight optimization instruction Rwt, retaining the weight coefficient α. For segments with Wt≥Wt0, the α value is appropriately adjusted to optimize the form of action expression and adapt to the target cultural cognition while ensuring the core symbolic semantics.

[0141] Sc4: The performance instruction generation module integrates real-time weight optimization results, updates performance instruction Z, and pushes it to the stage display terminal TD in real time, allowing actors to adjust their performance movements and emotional expressions in real time, forming a dynamic performance adaptation closed loop;

[0142] Sc5: Real-time feedback data, weight assignment results, and dynamic adaptation adjustment process are stored synchronously. Through systematic extraction of physical symbol features, semantic matching, and ambiguity identification, it solves the core pain points of distortion in cross-cultural semantic transmission of physical symbols and audience misunderstanding, fully preserving the original cultural core and symbolic semantics of the drama.

[0143] This invention constructs a comprehensive semantic analysis and cross-cultural adaptation system for non-verbal symbols in theatrical performances. It breaks through the limitations of traditional cross-cultural communication of drama that only focuses on the text of the dialogue. By systematically extracting features, semantically matching and identifying ambiguities of non-verbal symbols such as gestures, facial expressions, body postures and spatial interpersonal distance in the performance, it solves the core pain points of distortion in the cross-cultural semantic transmission of non-verbal symbols and audience misunderstanding, and fully preserves the cultural core and symbolic semantics of the original play.

[0144] This invention realizes the intelligent and real-time full-process cross-cultural adaptation of theatrical performances, transforming the traditional fragmented adaptation work that relies on human experience into a standardized intelligent process that is quantifiable and reproducible, greatly reducing the labor and time costs of adaptation work; at the same time, through the edge-cloud collaborative architecture design, it realizes the real-time adjustment of adaptation parameters and the real-time issuance of guidance instructions during rehearsals and performances, meeting the real-time requirements of the stage.

[0145] This invention constructs a dynamic closed loop for real-time audience feedback and performance adaptation. By collecting multi-dimensional viewing behavior data from both on-site and online audiences in real time, it completes the assignment of temporal weights and drives the optimization of parameters for semantic mapping, emotional expression, and action calibration. This solves the shortcomings of traditional adaptation work, which lacks a long-term iteration mechanism and cannot adapt to the real-time reactions of the audience. It enables real-time verification and dynamic optimization of performance adaptation effects, and significantly improves the audience's acceptance and empathy in cross-cultural communication.

[0146] This invention addresses the artistic characteristics of theatrical performance by establishing specialized modules for adapting emotional expression paradigms and calibrating the semantics of ritualized actions. Through a nonlinear emotional mapping network, it achieves precise adaptation of emotional expression styles in different cultural contexts. By calibrating action trajectories, it preserves the symbolic semantics of stylized ritualized actions and ensures cultural adaptation, thus balancing the preservation of the original play's artistic value with audience adaptation to the target cultural context and achieving a balance between the transmission of cultural core and the dissemination effect.

[0147] The above formulas are all dimensionless calculations. The formulas are derived from software simulations based on a large amount of collected data to obtain the most recent real-world results. The constraint parameters in the formulas, and the example values, can be set by those skilled in the art according to the actual situation. It should be noted that they are all configurable and editable.

[0148] The above description is only a preferred embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any equivalent substitutions or modifications made by those skilled in the art within the scope of the technology disclosed in the present invention, based on the technical solution and inventive concept of the present invention, should be covered within the scope of protection of the present invention.

Claims

1. A semantic alignment and adaptation system for theatrical performance in a cross-cultural context, characterized in that, The system includes a performance data acquisition module that collects multimodal raw data of actors' performances and distributes it to a body symbol semantic analysis module; a body symbol semantic module that extracts performance body symbol features through the MediaPipe motion capture model, completes cross-cultural semantic matching and ambiguity identification, and sets up a dynamic adjustment interface for semantic mapping weights; an emotion expression paradigm adaptation module that extracts performance emotion features through a BERT-LSTM hybrid architecture model, quantifies the intensity, and calibrates the target culture's emotion expression paradigm, and sets up a real-time adjustment interface for emotion expression parameters; and a ritualistic action semantic calibration module that performs ritualistic action symbol semantic analysis and cross-cultural adaptation calibration, and sets up a real-time adjustment interface for action calibration coefficients. The adaptation effect verification module generates optimization suggestions based on the comprehensive verification of the semantic alignment of the performance adapted to the data from three adjustment interfaces. The real-time viewing multimodal feedback weight optimization module is based on three types of adjustment interface data. It uses a temporal convolutional network real-time weight calculation model to collect real-time viewing behavior data of the audience, complete temporal multi-dimensional feedback weighting, and drive the optimization of adaptation parameters of each module in real time to form a dynamic adaptation closed loop. The performance guidance instruction generation module works with the real-time viewing multimodal feedback weight optimization module to generate standardized performance guidance instructions.

2. The semantic alignment and adaptation system for theatrical performance in a cross-cultural context according to claim 1, characterized in that, The performance data acquisition module is embedded in a portable performance acquisition terminal. The terminal has a built-in high-definition binocular camera, a 3D motion capture sensor, and an omnidirectional audio acquisition array, which is used to simultaneously acquire high-definition video, key points of movement, facial expressions, gestures, and audio multimodal raw data of the actors' performances. The real-time viewing multimodal feedback weight optimization module is connected to the audience behavior acquisition terminal. The terminal has a built-in high-definition camera in the audience seats, an online live broadcast data acquisition interface, and a live pressure sensor array, which is used to collect real-time viewing behavior data of the audience on site and online. The output of the performance instruction generation module is connected to the stage display terminal, which is deployed throughout the stage area to display performance instruction in real time.

3. The semantic alignment and adaptation system for theatrical performance in a cross-cultural context according to claim 2, characterized in that, Portable performance acquisition terminals, audience behavior acquisition terminals, and stage display terminals are connected to a local processing terminal that deploys a body symbol semantic analysis module, an adaptation effect verification module, and a real-time viewing multimodal feedback weight optimization module, as well as a cloud computing terminal that deploys an emotional expression paradigm adaptation module and a ritualistic action semantic calibration module.

4. The semantic alignment and adaptation system for theatrical performance in a cross-cultural context according to claim 1, characterized in that, The body symbol semantic parsing module is used to extract the core features of four types of body symbols in the performance: gestures, facial expressions, body postures, and spatial interpersonal distance. After completing the feature standardization process, it performs semantic matching with the cross-cultural body symbol semantic database through a cosine similarity model, identifies and marks cross-cultural semantic ambiguities, and generates corresponding adaptation and adjustment schemes. At the same time, it dynamically adjusts the semantic mapping weight of body symbols based on the feedback weight issued by the real-time viewing multimodal feedback weight optimization module.

5. The semantic alignment and adaptation system for theatrical performance in a cross-cultural context according to claim 1, characterized in that, The emotional expression paradigm adaptation module pre-sets a nonlinear emotional mapping network (NEMN), which completes the identification and intensity quantification of emotional categories based on the extracted multimodal emotional features of the performance. Combined with the target culture's emotional expression paradigm parameters and real-time feedback weights, it completes the nonlinear calibration of the performance's emotional intensity and expression style, so that the calibrated emotional expression is adapted to the aesthetic paradigm of the target culture.

6. The semantic alignment and adaptation system for theatrical performance in a cross-cultural context according to claim 1, characterized in that, The ritualistic action semantic calibration module extracts stylized ritualistic action fragments from the performance, analyzes their core symbolic semantics, and uses thin plate spline interpolation to construct a nonlinear deformation field to adapt and calibrate the 3D trajectory of the action. During the calibration process, the degree of preservation of the original action trajectory is controlled by the time dynamic retention coefficient.

7. The semantic alignment and adaptation system for theatrical performance in a cross-cultural context according to claim 1, characterized in that, The real-time viewing multimodal feedback weight optimization module collects audience eye tracking, departure, cutout, facial expressions, bullet screen, and comment time sequence data. After data preprocessing, it accurately matches the time sequence IDs of the performance segments to construct an audience emotion propagation graph network and complete real-time weight assignment. Based on the weighting results, it generates optimization instructions to adjust the semantic mapping weights of body symbols, emotional expression parameters, and action calibration coefficients in real time, forming a closed loop for dynamic adaptation of the performance.

8. The semantic alignment and adaptation system for theatrical performance in a cross-cultural context according to claim 1, characterized in that, It also includes a data storage and retrieval module, which is connected to each of the functional modules by signal, and interfaces with the local storage medium and cloud storage medium of the cloud-coordinated distributed storage architecture for the storage, retrieval and synchronization of data throughout the entire process; The cloud storage media is set to allow only system terminal read and write permissions, and a preset periodic data synchronization mechanism is configured, along with supporting data read and write adaptation code, audio and video compression storage mechanism, and storage media anomaly detection program.

9. The semantic alignment and adaptation system for theatrical performance in a cross-cultural context according to claim 1, characterized in that, The system has a pre-set initial cross-cultural performance semantic database system, including a cross-cultural physical symbol semantic database, a cross-cultural emotional expression paradigm database, a cross-cultural ritualistic action symbol database, and a real-time audience behavior feedback rule database.

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