Self-adaptive dance beauty effect adjusting method and system based on cultural performance content

Abstract

The invention discloses a cultural performance content-based dance beauty effect adaptive adjustment method and system, and the method comprises the steps: collecting stage environment parameters through a stage environment perception intelligent coupling model, building a matrix, obtaining the limb dynamic data of a performance person through a performance limb dynamic feature mapping model, and extracting feature parameters; calling a sound field and light collaborative calibration algorithm, and preliminarily calibrating sound field and light equipment parameters in combination with the limb dynamic characteristic parameters; inputting the environment parameter matrix and the initial calibration parameter set into a multi-mode dance artistic effect analysis platform, constructing an evaluation index system and calculating a real-time numerical value; and dynamically correcting equipment parameters and generating multiple groups of operation parameters according to the evaluation numerical value and the dance beauty effect self-adaptive adjustment parameters, and transmitting the parameters to a control terminal to complete configuration, so that the dance beauty effect self-adaptive adjustment is realized. According to the method, the integrating degree with the performance content is improved, and the viewing experience is optimized.

Description

Technical Field

[0001] This invention relates to the field of cultural performance technology, and in particular to a method and system for adaptive adjustment of stage effects based on the content of cultural performances. Background Technology

[0002] In cultural performances, as audiences' demands for the performance experience continue to rise, the adaptability of stage design to the performance content, as a key element in presenting the performance's meaning and enhancing its impact, is becoming increasingly important. Current cultural performances are diverse, including drama, dance, and concerts, each with different requirements for sound field uniformity, dynamic lighting changes, and environmental adaptability. Simultaneously, the stage environment is affected by factors such as temperature, humidity, and obstacle distribution, and the dynamic changes in performers' movements also alter the stage presentation in real time. Traditional methods relying on manually preset parameters or simple equipment adjustments are no longer sufficient to meet the demands of precise adjustments to stage design in dynamic environments. There is an urgent need for a technical solution that can adaptively adjust stage design based on the stage environment and the dynamic characteristics of the performance, thereby improving the fit between stage design and performance content and optimizing the audience's viewing experience.

[0003] Existing technologies have significant shortcomings in adjusting stage effects for cultural performances. On the one hand, existing technologies struggle to achieve deep coupling and correlation between the stage environment, the dynamic characteristics of performers' limbs, and sound and lighting parameters. They often only adjust parameters for a single factor, ignoring the impact of environmental parameter changes on the operating status of sound and lighting equipment, as well as the synergistic relationship between the dynamic characteristics of performers' limbs and the presentation of stage effects. This results in the adjusted stage effects failing to fully adapt to the real-time changes in the stage scene and performance content. On the other hand, existing technologies lack a comprehensive multimodal stage effect analysis and dynamic correction mechanism. The evaluation of stage effects is often limited to a single dimension, and when a mismatch is found between the stage effects and the performance content, it is impossible to quickly calculate an accurate equipment parameter correction scheme based on the comprehensive evaluation results. This makes it difficult to achieve real-time, adaptive optimization of stage effects, thus affecting the overall presentation quality of cultural performances. Summary of the Invention

[0004] In order to overcome the shortcomings and deficiencies of existing technologies, this invention provides a method and system for adaptive adjustment of stage effects based on cultural performance content.

[0005] The technical solution adopted in this invention is an adaptive adjustment method for stage effects based on cultural performance content, comprising the following steps: S1, collecting temperature, humidity, air velocity, and obstacle distribution data within the stage space through a stage environment perception intelligent coupling model, establishing a multi-dimensional environmental parameter matrix, performing real-time filtering and dynamic correlation analysis on the collected environmental parameters, and determining the mapping relationship between environmental parameters and the operating status of stage equipment; S2, acquiring data on the limb joint movement trajectory, limb movement amplitude, and movement frequency of performers based on a performance limb dynamic feature mapping model, constructing a limb dynamic feature vector set, matching the limb dynamic feature vector set with a preset performance content feature library, and extracting limb dynamic feature parameters whose matching degree meets a set threshold; S3, calling a sound field and lighting collaborative calibration algorithm, and adjusting the frequency response of the stage sound field equipment according to the limb dynamic feature parameters extracted in S2. S1) Initially calibrate the sound pressure level and lighting equipment parameters such as light intensity, color temperature, and beam angle to generate an initial calibration parameter set for the sound field and lighting. S2) Input the environmental parameter matrix obtained in S1 and the initial calibration parameter set for the sound field and lighting generated in S3 into a multimodal stage effect analysis platform. The platform's built-in multi-dimensional feature fusion module performs cross-correlation processing on the parameters to construct a stage effect evaluation index system and calculate the real-time values ​​of each evaluation index. S3) Based on the real-time values ​​of the evaluation indexes calculated in S4, and combined with the adaptive adjustment parameters for the stage effect of the cultural performance content, dynamically correct the parameters of the sound field and lighting equipment to generate multiple sets of corrected stage equipment operating parameters. S4) Transmit the multiple sets of corrected stage equipment operating parameters generated in S5 to the stage equipment control terminal. The control terminal configures the stage equipment parameters according to parameter priority to adaptively adjust the stage effect.

[0006] Furthermore, the expression for the stage environment perception intelligent coupling model is: middle, The intelligent coupling coefficient for stage environment perception. The number of environmental parameter types, Number of obstacle types For the first The weighting coefficients of class environment parameters, For the first Real-time collected values ​​of environmental parameters. For the first Temperature influence coefficient of obstacle-like objects For the first The equivalent temperature difference between the obstacle and the environment. For the first Class environment parameters and the first The correlation coefficient of obstacle types, For the first Class environment parameters and the first Covariance of obstacle distribution density For the first Class environment parameters and the first Coupling adjustment coefficient for obstacle-like structures.

[0007] Furthermore, the expression for the performance limb dynamic feature mapping model is: For the dynamic features of the body in the performance, The number of joints in a limb. The number of types of body movements. For the first Motion weighting coefficients for each joint For the first The speed of movement of each joint For the first The angles of the limb joints in similar movements. For the first The joint and the first The correlation coefficient of action class, For the first The amplitude value of the type of action, The time interval between adjacent action capture frames. For the first The motion frequency coefficient of each joint.

[0008] Furthermore, the expression for the sound field and lighting collaborative calibration algorithm is: ,in, For the sound field and lighting coordination calibration coefficients, To calibrate the weights for the sound field parameters, To calibrate the weights for the lighting parameters, This is the basic value for the sound pressure level of the sound field equipment. This represents the actual frequency response of the sound field equipment. This is the reference frequency response of the sound field device. This is the sound pressure level adjustment factor. This represents the deviation between the actual sound pressure level and the reference sound pressure level. This is the base value for the luminous intensity of the lighting equipment. To match the color temperature of the lights with the performance content, This is the light angle adjustment coefficient. This is the actual value of the light beam angle. This is the actual value of the light color temperature.

[0009] Furthermore, the effect evaluation expression of the multimodal stage design effect analysis platform is as follows: This is a multimodal stage design effect evaluation value. For the number of modal types, The number of performance segments. For the first Evaluation weights for each modality For the first Characteristic parameter values ​​of the modality For the first Modal characteristic parameters and the first The correlation coefficient of each performance segment For the first Type 1 mode and the first The correlation coefficient of the evaluation of individual performance content segments. For the first The actual values ​​of the modal parameters, For the first The standard deviation of the modal parameters For the first The stage design requirements coefficient for each performance segment.

[0010] Furthermore, the calculation expression for the adaptive adjustment parameter of the stage design effect of the cultural performance content is as follows: ,in, The coefficients are adjusted adaptively to enhance the stage design. To adjust the number of parameter types, For the first The weighting coefficients of the class adjustment parameters, For the first The real-time evaluation value corresponding to the class adjustment parameter. For the first The reference evaluation value corresponding to the class adjustment parameter, For the first The dynamic correction coefficient for the adjustment parameters.

[0011] Further, S3 includes the following sub-steps: S31, extracting limb movement amplitude parameters related to the frequency response of the sound field equipment and limb joint motion trajectory parameters related to the light intensity of the lighting equipment from the limb dynamic feature vector set output by the performance limb dynamic feature mapping model, and establishing a parameter association table; S32, inputting the extracted parameters into the preprocessing module of the sound field and lighting collaborative calibration algorithm, filtering the parameters by range, removing abnormal parameters that exceed the preset threshold, and retaining valid parameters that meet the operating requirements of the stage equipment; S33, based on the valid parameters, calling the calculation module of the sound field and lighting collaborative calibration algorithm to calculate the adjustment amount of the frequency response of the sound field equipment, the compensation value of the sound pressure level, and the adjustment amount of the light intensity of the lighting equipment, the correction value of the color temperature, and the offset of the beam angle; S34, integrating the calculated sound field equipment adjustment parameters with the lighting equipment adjustment parameters to generate an initial calibration parameter set for the sound field and lighting, including parameter type, value, and adjustment priority.

[0012] Further, step S4 includes the following sub-steps: S41, converting the multi-dimensional environmental parameter matrix obtained in step S1 into a standardized parameter vector, and simultaneously decomposing the initial calibration parameter set for sound field and lighting generated in step S3 into a subset of sound field parameters and a subset of lighting parameters; S42, inputting the standardized environmental parameter vector, the subset of sound field parameters, and the subset of lighting parameters into the multi-dimensional feature fusion module of the multi-modal stage effect analysis platform, and establishing a correlation model between environmental parameters, sound field parameters, lighting parameters, and stage effect through feature cross-operation; S43, based on the correlation model, constructing a stage effect evaluation index system including sound field uniformity, lighting coverage, environmental adaptability, and performance matching degree, and determining the calculation method and weight allocation of each index; S44, according to the set calculation method and combined with the weight of each index, calculating the parameters collected in real time to obtain the real-time value of each evaluation index, and forming an evaluation value report.

[0013] Further, step S5 includes the following sub-steps: S51, retrieve the adjustment parameter type and parameter threshold range corresponding to the evaluation index calculated in step S4 from the adaptive adjustment parameter library of stage effects for cultural performance content; S52, compare the real-time value of the evaluation index with the corresponding parameter threshold range, determine the evaluation index that exceeds the threshold range, and analyze the reasons for the deviation of the sound field or lighting equipment parameters corresponding to the index; S53, according to the reasons for the deviation, call the adaptive adjustment algorithm module to calculate the correction amount of the sound field equipment parameters and the lighting equipment parameters that need to be corrected, to ensure that the corrected parameters can make the evaluation index return to the threshold range; S54, combine the sound field equipment parameters and lighting equipment parameters after multiple corrections to generate multiple sets of stage equipment operating parameters including different parameter combinations, and label the predicted value of the evaluation index corresponding to each set of parameters.

[0014] This system is an adaptive adjustment system for stage effects based on cultural performance content. It utilizes an adaptive adjustment method for stage effects based on cultural performance content and includes: a multi-dimensional stage environment parameter acquisition and coupling analysis unit, which connects to various environmental sensors deployed on the stage. Through an intelligent coupling model of stage environment perception, it processes the temperature, humidity, air velocity, and obstacle distribution data collected by the sensors, outputting a multi-dimensional environmental parameter matrix, which is then transmitted to a multi-modal stage effect integrated processing unit; a performance limb dynamic feature capture and mapping unit, which connects to motion capture equipment deployed on the stage. Through a performance limb dynamic feature mapping model, it processes the captured limb joint motion trajectory, amplitude, and frequency data, outputting a limb dynamic feature vector set, which is transmitted to both a sound field and lighting collaborative calibration unit and a multi-modal stage effect integrated processing unit; and a sound field and lighting collaborative calibration parameter calculation unit, which receives the feature vector set output by the performance limb dynamic feature capture and mapping unit and calls the sound field and lighting collaborative calibration parameter calculation function. The quasi-algorithm performs preliminary calibration of the sound field and lighting equipment parameters, generating an initial calibration parameter set, which is then transmitted to the multimodal stage effect integrated processing unit. This unit receives the environmental parameter matrix from the stage environment multi-dimensional parameter acquisition and coupling analysis unit and the initial calibration parameter set from the sound field and lighting collaborative calibration parameter calculation unit. It then constructs an evaluation index system and calculates real-time values ​​through the multimodal stage effect analysis platform, transmitting the evaluation values ​​to the stage parameter dynamic correction unit. The dynamic correction unit receives the evaluation values ​​from the multimodal stage effect integrated processing unit, combines them with the stage effect of the cultural performance content to adaptively adjust the parameters, calculates the equipment parameter correction amount, generates multiple sets of corrected operating parameters, and transmits these parameters to the stage equipment control and scheduling unit. Finally, the stage equipment control and scheduling unit receives multiple sets of operating parameters from the dynamic correction unit, configures the parameters and monitors the operating status of the stage sound field and lighting equipment according to parameter priority, and performs adaptive adjustments to the stage effect.

[0015] Beneficial Effects: This invention proposes a method and system for adaptive adjustment of stage effects based on cultural performance content. It collects multi-dimensional environmental parameters through a stage environment perception intelligent coupling model and combines this with performer movement data captured by a performance limb dynamic feature mapping model. This achieves deep coupling and correlation between the stage environment, performance dynamic features, and sound and lighting parameters, completely overcoming the limitations of existing technologies that adjust parameters only for a single factor. This ensures that the stage effects accurately adapt to the real-time changes in the stage scene and performance content. Simultaneously, a sound and lighting collaborative calibration algorithm is used to initially calibrate the sound and lighting equipment parameters. Then, a multi-dimensional evaluation index system is constructed and real-time values ​​are calculated through a multi-modal stage effect analysis platform. This, combined with the adaptive adjustment parameters of the stage effects based on the cultural performance content, dynamically corrects the equipment parameters, forming a complete multi-modal stage effect analysis and dynamic correction mechanism. This solves the problems of existing technologies having a single evaluation dimension and being unable to quickly and accurately correct parameters. Ultimately, it achieves real-time adaptive optimization of stage effects, significantly improving the fit between stage effects and performance content, significantly optimizing the audience's viewing experience, and meeting the differentiated needs of different types of cultural performances for stage effects. Attached Figure Description

[0016] Figure 1 This is a flowchart of the method steps of the present invention;

[0017] Figure 2 This is a diagram showing the system unit composition of the present invention. Detailed Implementation

[0018] It should be noted that, unless otherwise specified, the embodiments and features described in this application can be combined with each other. The application will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0019] like Figure 1 As shown, the adaptive adjustment method for stage effects based on the content of a cultural performance includes the following steps:

[0020] S1. Collect data on temperature, humidity, air velocity and obstacle distribution in the stage space through the intelligent coupling model of stage environment perception, establish a multi-dimensional environmental parameter matrix, perform real-time filtering and dynamic correlation analysis on the collected environmental parameters, and determine the mapping relationship between environmental parameters and the operating status of stage equipment.

[0021] Specifically, the implementation process of step S1 is as follows: First, environmental sensors are deployed in the stage space at a preset density. The sensor types include temperature sensors, humidity sensors, air velocity sensors, and obstacle detection sensors. The temperature sensor's acquisition range is set to -10℃ to 50℃, with an acquisition accuracy of ±0.5℃; the humidity sensor's acquisition range is set to 20%RH to 90%RH, with an acquisition accuracy of ±3%RH; the air velocity sensor's acquisition range is set to 0m / s to 5m / s, with an acquisition accuracy of ±0.1m / s; and the obstacle detection sensor uses infrared detection, with a detection distance set to 0.5m to 10m and a detection accuracy of ±0.05m. These sensors collect real-time data on temperature, humidity, airflow velocity, and obstacle distribution in different areas of the stage. The collected data is then transmitted to the data processing module of the intelligent coupling model for stage environmental perception. This module filters the data in real time, removing abnormal data caused by sensor malfunctions or external interference. It then performs dynamic correlation analysis on the filtered data, calculating the correlation coefficients between different environmental parameters, and establishing a multi-dimensional environmental parameter matrix. The matrix dimensions correspond to the parameter types collected by the sensors, and each element in the matrix represents the valid value of the corresponding parameter at a specific collection time. This step accurately obtains the real-time stage environmental status, determines the mapping relationship between environmental parameters and the operating status of stage equipment, and provides basic environmental data support for subsequent adjustments to stage equipment parameters, avoiding adverse effects on the stage performance due to changes in environmental parameters.

[0022] S2. Based on the performance limb dynamic feature mapping model, obtain the limb joint movement trajectory, limb movement amplitude and movement frequency data of the performers, construct a limb dynamic feature vector set, match the limb dynamic feature vector set with the preset performance content feature library, and extract the limb dynamic feature parameters whose matching degree meets the set threshold.

[0023] Specifically, step S2 is implemented as follows: Multiple motion capture cameras are deployed around and above the stage. The sampling frame rate of the motion capture cameras is set to 60 to 120 frames per second, and the shooting resolution is set to 1920×1080 pixels. The capture range covers the entire stage performance area to ensure complete acquisition of the performers' limb movement data. The motion capture cameras capture the performers' limb movements in real time and transmit the image data to the image processing unit of the performance limb dynamic feature mapping model. This unit extracts the skeleton from the image, determines the position coordinates of the performers' limb joints, calculates the displacement of each joint between adjacent frames, and thus obtains the limb joint movement trajectory. At the same time, the amplitude of limb movement is determined by measuring the change in the length of the line connecting the limb joints. The amplitude calculation is based on the initial position of the joint and records the maximum amplitude value during the movement. In addition, the number of repetitions of limb movements per unit time is counted to obtain the movement frequency. The frequency statistics time interval is set to 1 second. Based on these data, a limb dynamic feature vector set is constructed. Each vector in the vector set includes relevant data on joint movement trajectory, movement amplitude, and movement frequency. Subsequently, the vector set is matched with a preset performance content feature library. The preset performance content feature library stores standard data of limb dynamic features corresponding to different performance types (such as dance, drama, and concert) in different performance segments. During the matching process, the matching degree between the real-time vector set and the standard data is calculated. The matching degree threshold is set to 80%. Limb dynamic feature parameters that have reached or exceeded the matching degree are extracted. This provides dynamic data basis for subsequent sound field and lighting parameter calibration to be adapted to the performance content, ensuring that the stage effect can be adjusted according to the changes in the performers' limb movements.

[0024] S3. Call the sound field and lighting collaborative calibration algorithm. Based on the limb dynamic feature parameters extracted in S2, perform preliminary calibration on the frequency response and sound pressure level of the stage sound field equipment and the light intensity, color temperature and beam angle parameters of the lighting equipment to generate the initial calibration parameter set for the sound field and lighting.

[0025] Specifically, step S3 is implemented as follows: First, from the limb dynamic feature parameters extracted in step S2, parameters related to the operating parameters of the sound field equipment and lighting equipment are selected. Parameters related to the sound field equipment include the rate of change of limb movement amplitude and the fluctuation range of movement frequency. Parameters related to the lighting equipment include the coverage area of ​​the limb joint movement trajectory and the maximum extension direction of the movement amplitude. These selected parameters are then input into the parameter input module of the sound field and lighting collaborative calibration algorithm. The algorithm first verifies the validity of the input parameters. After successful verification, it establishes a correspondence between the limb dynamic feature parameters and the sound field and lighting equipment parameters based on preset association rules. For sound field equipment, the frequency response is adjusted according to the rate of change of limb movement amplitude. When the rate of change of movement amplitude increases, the frequency response adjustment range is set to 20Hz to 20000Hz, and the adjustment step is set to 10Hz. At the same time, the sound pressure level is compensated according to the range of movement frequency fluctuation, with the sound pressure level compensation range set to 80dB to 110dB and the compensation step set to 1dB. For lighting equipment, the light intensity is adjusted according to the area covered by the limb joint movement trajectory, with the light intensity adjustment range set to 0cd / m² to 5000cd / m² and the adjustment step set to 50cd / m². The color temperature is corrected according to the maximum extension direction of the movement amplitude, with the color temperature correction range set to 2700K to 6500K and the correction step set to 100K. At the same time, the beam angle is adjusted according to the edge position of the trajectory coverage area, with the beam angle adjustment range set to 10° to 120° and the adjustment step set to 5°. The above calculations generate an initial set of calibration parameters, including the frequency response and sound pressure level of the sound field equipment, and the light intensity, color temperature, and beam angle of the lighting equipment, laying the foundation for subsequent optimization of stage effects.

[0026] S4. Input the environmental parameter matrix obtained in S1 and the initial calibration parameter set of the sound field and lighting generated in S3 into the multimodal stage effect analysis platform. Through the multi-dimensional feature fusion module built into the platform, the parameters are cross-correlated to construct a stage effect evaluation index system and calculate the real-time values ​​of each evaluation index.

[0027] Specifically, step S4 is implemented as follows: First, the multi-dimensional environmental parameter matrix obtained in step S1 is standardized to convert the values ​​of different types of environmental parameters into the same data range, facilitating subsequent fusion analysis with sound field and lighting parameters. Simultaneously, the initial calibration parameter set for sound field and lighting generated in step S3 is categorized and split into a sound field parameter subset and a lighting parameter subset according to the device type to which the parameters belong. The sound field parameter subset includes parameters such as frequency response and sound pressure level, while the lighting parameter subset includes parameters such as light intensity, color temperature, and beam angle. The standardized environmental parameter matrix, sound field parameter subset, and lighting parameter subset are then input into a multimodal stage effect analysis platform. This platform has a built-in multi-dimensional feature fusion module, which uses a weighted fusion algorithm. First, weights are assigned to different types of parameters: environmental parameters are weighted at 0.3, sound field parameters at 0.4, and lighting parameters at 0.3. Then, through cross-correlation calculations between parameters, the comprehensive influence coefficient of different parameter combinations on the stage effect is calculated, thereby constructing a stage effect evaluation index system. The evaluation index system includes four core indicators: sound field uniformity, lighting coverage, environmental adaptability, and performance matching degree. Sound field uniformity is determined by calculating the difference in sound pressure levels between different areas of the stage; the smaller the difference, the higher the uniformity. Lighting coverage is determined by calculating the proportion of the illuminated area to the stage performance area; the larger the proportion, the higher the coverage. Environmental adaptability is determined by calculating the impact of changes in environmental parameters on the operational stability of equipment parameters; the smaller the impact, the higher the adaptability. Performance matching degree is determined by calculating the degree of fit between the stage design parameters and the characteristics of the performance content; the higher the fit, the higher the matching degree. Following the above calculation methods and combining the weights of each indicator, the real-time collected parameters are calculated to obtain real-time values ​​for each evaluation indicator, with the value range set from 0 to 100 points. These values ​​are then compiled into an evaluation value report to provide a basis for evaluating the effectiveness of subsequent equipment parameter adjustments.

[0028] S5. Based on the real-time values ​​of the evaluation indicators calculated by S4, and combined with the stage effects of the cultural performance, the parameters of the sound field and lighting equipment are dynamically corrected to generate multiple sets of corrected stage equipment operating parameters.

[0029] Specifically, the implementation process of step S5 is as follows: First, from the preset cultural performance content stage effect adaptive adjustment parameter library, according to the performance type (such as dance, drama, concert) and the current performance segment determined in step S4, the corresponding adjustment parameter type and parameter threshold range are retrieved. The adjustment parameter types include the sound field equipment frequency response adjustment coefficient, sound pressure level compensation coefficient, and lighting equipment light intensity adjustment coefficient, color temperature correction coefficient, and beam angle offset coefficient. The threshold range of each parameter is set according to the historical performance effect optimization data. For example, the threshold range of the frequency response adjustment coefficient is set to 0.8 to 1.2, the threshold range of the sound pressure level compensation coefficient is set to 0.9 to 1.1, the threshold range of the light intensity adjustment coefficient is set to 0.7 to 1.3, the threshold range of the color temperature correction coefficient is set to 0.85 to 1.15, and the threshold range of the beam angle offset coefficient is set to 0.9 to 1.1. The real-time values ​​of the evaluation indicators calculated in step S4 are compared with the corresponding parameter threshold ranges. If a certain evaluation indicator value exceeds the threshold range, such as a sound field uniformity value below 80 points or a light coverage value below 75 points, the reason for the indicator exceeding the threshold is analyzed to determine whether it is caused by deviations in the sound field equipment parameters or the light equipment parameters. For example, low sound field uniformity may be due to uneven frequency response distribution or insufficient sound pressure level compensation, while low light coverage may be due to insufficient light intensity or an excessively small beam angle coverage range. Based on the cause of the deviation, the adaptive adjustment algorithm module is invoked. This module calculates the required correction amounts for the sound field equipment parameters and the light equipment parameters based on the correlation model between the evaluation indicator deviation value and the equipment parameters. For example, when the sound field uniformity is 10 points below the threshold, the additional adjustment amount for the frequency response is calculated to be 50Hz, and the additional compensation amount for the sound pressure level is 3dB; when the light coverage is 8 points below the threshold, the additional adjustment amount for the light intensity is calculated to be 300cd / m², and the additional offset amount for the beam angle is 10°. The corrected parameters are evaluated and verified again. If the evaluation index returns to the threshold range, the correction is stopped; if it still exceeds the threshold, the above correction process is repeated until the index meets the requirements. Finally, the sound field equipment parameters and lighting equipment parameters after multiple corrections are combined to generate multiple sets of stage equipment operating parameters with different parameter combinations. Each set of parameters is labeled with the corresponding predicted evaluation index value, providing multiple optimization options for subsequent equipment parameter configuration.

[0030] S6. Transmit the multiple sets of corrected stage equipment operating parameters generated in S5 to the stage equipment control terminal. The control terminal configures the parameters of the stage equipment according to the parameter priority order and performs adaptive adjustment of the stage effect.

[0031] Specifically, the implementation process of step S6 is as follows: First, the multiple sets of corrected stage equipment operating parameters generated in step S5 are prioritized. The prioritization is based on the predicted value of the evaluation index corresponding to each set of parameters. The higher the predicted value, the higher the priority of the parameter set. At the same time, the stability of parameter adjustment is taken into consideration to avoid sudden changes in the stage effect due to excessive parameter fluctuations. The priority is divided into three levels: Level 1, Level 2, and Level 3. The Level 1 priority parameter set is the set with the highest predicted evaluation index value and the best parameter stability, followed by Level 2, and then Level 3, which has the lowest priority. The sorted parameter sets are then transmitted to the stage equipment control terminal. The control terminal has a built-in parameter parsing module. This module first parses each parameter in the parameter set to determine the stage equipment number, parameter type, and specific value corresponding to each parameter. For example, a certain parameter corresponds to the sound field device with the number S01, the parameter type is frequency response, and the value is 500Hz. After parsing, the control terminal sends parameter configuration instructions to the stage equipment corresponding to the first-priority parameter group according to parameter priority. These instructions include the equipment number, parameter type, and numerical information. Upon receiving the instructions, the equipment adjusts its operating parameters through its built-in parameter adjustment module. During the adjustment process, the control terminal receives real-time feedback from the equipment regarding the parameter adjustment status to confirm whether the parameters have been adjusted correctly. If the first-priority parameter group is configured successfully and the equipment is operating normally, the second-priority parameter group is configured, and so on, until all priority parameter groups are configured. This step enables precise configuration of stage equipment parameters, allowing the stage effects to adaptively adjust according to the performance content, stage environment, and physical dynamics, ensuring that the stage effects are always optimized during the performance and improving the overall presentation quality.

[0032] Preferably, the expression of the stage environment perception intelligent coupling model is: middle, The intelligent coupling coefficient for stage environment perception. The number of environmental parameter types, Number of obstacle types For the first The weighting coefficients of class environment parameters, For the first Real-time collected values ​​of environmental parameters. For the first Temperature influence coefficient of obstacle-like objects For the first The equivalent temperature difference between the obstacle and the environment. For the first Class environment parameters and the first The correlation coefficient of obstacle types, For the first Class environment parameters and the first Covariance of obstacle distribution density For the first Class environment parameters and the first Coupling adjustment coefficient for obstacle-like structures.

[0033] Specifically, the implementation process of the intelligent coupling model for stage environment perception is as follows: First, determine the number of environmental parameter types and obstacle types required for model calculation. The number of environmental parameter types is set to four categories based on actual stage monitoring needs, corresponding to temperature, humidity, air velocity, and obstacle distribution, respectively. The number of obstacle types is set to three categories, corresponding to fixed stage props, temporary structures, and mobile equipment, respectively. Weighting coefficients are assigned to each type of environmental parameter: temperature parameter weighting coefficient is set to 0.3, humidity parameter weighting coefficient to 0.25, air velocity parameter weighting coefficient to 0.2, and obstacle distribution parameter weighting coefficient to 0.25, ensuring that the total weight of all parameters is 1. Real-time acquisition values ​​of various environmental parameters are obtained, with an acquisition frequency set to 1 time / second. Simultaneously, the equivalent temperature difference between each type of obstacle and the environment is calculated. The equivalent temperature difference is obtained by real-time measurement of the difference between the obstacle surface temperature and the ambient temperature, with a measurement accuracy set to ±0.3℃. The correlation coefficients between each type of environmental parameter and each type of obstacle are determined. These coefficients are set based on historical data statistical analysis, ranging from 0.1 to 0.9. For example, the correlation coefficient between temperature and fixed stage props is set to 0.6, and the correlation coefficient between humidity and temporary structures is set to 0.7. The covariance between each type of environmental parameter and the distribution density of each type of obstacle is calculated. The covariance calculation is based on data collected continuously for 10 consecutive times to ensure the stability of the results. A coupling adjustment coefficient is assigned to each type of environmental parameter and each type of obstacle, ranging from 0.8 to 1.2, dynamically adjusted according to the degree of influence of the parameter on the stage equipment. These parameters are then substituted into the model for calculation to obtain the intelligent coupling coefficient for stage environment perception. The coefficient range is set from 0 to 1; the closer the coefficient is to 1, the higher the coupling degree between the environmental parameter and the obstacle distribution. This model can accurately quantify the comprehensive impact of the stage environment on the stage equipment, providing environmental coupling data support for subsequent equipment parameter adjustments and avoiding deviations in stage effects due to the interaction between the environment and obstacles.

[0034] Preferably, the expression for the performance limb dynamic feature mapping model is: For the dynamic features of the body in the performance, The number of joints in a limb. The number of types of body movements. For the first Motion weighting coefficients for each joint For the first The speed of movement of each joint For the first The angles of the limb joints in similar movements. For the first The joint and the first The correlation coefficient of action class, For the first The amplitude value of the type of action, The time interval between adjacent action capture frames. For the first The motion frequency coefficient of each joint.

[0035] Specifically, the implementation process of the performance limb dynamic feature mapping model is as follows: First, determine the number of limb joints and the number of limb movement types. The number of limb joints is set to 18, including key joints of the head, neck, trunk, and limbs. The number of limb movement types is set to 6 categories, corresponding to extension, bending, rotation, jumping, translation, and pausing movements, respectively. Assign motion weight coefficients to each joint. Core joints such as the hip and shoulder joints have a weight coefficient of 0.15, while secondary joints such as the wrist and ankle joints have a weight coefficient of 0.05, ensuring that the sum of the weights of all joints is 1. Acquire the motion velocity of each joint using motion capture equipment, with a velocity measurement accuracy set to ±0.01 m / s. Simultaneously measure the limb joint angles for each type of movement, with an angle measurement accuracy set to ±1°. Determine the correlation coefficient between each joint and each type of movement. The correlation coefficient ranges from 0.2 to 0.9. For example, the correlation coefficient between the hip joint and jumping movements is set to 0.8, and the correlation coefficient between the shoulder joint and extension movements is set to 0.75. The amplitude of each type of movement was measured, with the initial joint position as the reference and an accuracy set to ±0.02m. The time interval between adjacent movement acquisition frames was set to 0.02 seconds. The movement frequency coefficient of each joint was calculated, with the frequency coefficient set according to the number of movements per unit time, ranging from 0.3 to 1.0. These parameters were then substituted into the model for calculation to obtain the dynamic feature mapping value of the performance limbs. The mapping value ranged from 0 to 10; a higher mapping value indicated a higher degree of fit between the limb dynamic features and the performance content. This model can accurately extract key dynamic features of the performers' limbs, providing dynamic data for subsequent sound and lighting parameter calibration, ensuring that the stage design and limb movements are synchronized and adapted.

[0036] Preferably, the expression for the sound field and lighting collaborative calibration algorithm is: ,in, For the sound field and lighting coordination calibration coefficients, To calibrate the weights for the sound field parameters, To calibrate the weights for the lighting parameters, This is the basic value for the sound pressure level of the sound field equipment. This represents the actual frequency response of the sound field equipment. This is the reference frequency response of the sound field device. This is the sound pressure level adjustment factor. This represents the deviation between the actual sound pressure level and the reference sound pressure level. This is the base value for the luminous intensity of the lighting equipment. To match the color temperature of the lights with the performance content, This is the light angle adjustment coefficient. This is the actual value of the light beam angle. This is the actual value of the light color temperature.

[0037] Specifically, the implementation process of the sound field and lighting coordinated calibration algorithm is as follows: First, weights are assigned to the sound field parameter calibration and lighting parameter calibration. The weight ratio is adjusted according to the performance type. In concert performances, the weight for sound field parameter calibration is set to 0.6, and the weight for lighting parameter calibration is set to 0.4; in dance performances, the weight for sound field parameter calibration is set to 0.45, and the weight for lighting parameter calibration is set to 0.55. The baseline sound pressure level (SPL) of the sound field equipment is determined. The baseline value is set according to the size of the performance venue. In a venue with 500 people, the baseline SPL is set to 90 dB. The actual frequency response and reference frequency response of the sound field equipment are obtained. The reference frequency response is set as a standard frequency curve from 20 Hz to 20000 Hz, and the actual frequency response is measured using a professional audio analyzer. Adjustment coefficients are assigned to the SPL, with the adjustment coefficient range set from 0.8 to 1.2. The deviation between the actual SPL and the reference SPL is calculated, with the deviation measurement accuracy set to ±0.5 dB. The baseline luminous intensity of the lighting equipment is determined based on the stage area requirements; for the main performance area, the baseline luminous intensity is set at 3000 cd / m². The matching angle between the light color temperature and the performance content is calculated, with the matching angle range set from 0° to 90°; a smaller angle indicates a higher color temperature match. Adjustment coefficients are assigned to the light angles, with the adjustment coefficient range set from 0.7 to 1.3. The actual values ​​of the light beam angle and the actual light color temperature are measured, with the beam angle measurement accuracy set at ±1° and the color temperature measurement accuracy set at ±20K. These parameters are then substituted into the algorithm to obtain the sound field and lighting coordination calibration coefficient, with the coefficient range set from 0 to 1; a coefficient closer to 1 indicates better coordination between the sound field and lighting parameters. This algorithm enables the coordinated calibration of sound field and lighting parameters, avoiding parameter conflicts that could lead to uncoordinated stage effects.

[0038] Preferably, the effect evaluation expression of the multimodal stage design effect analysis platform is: This is a multimodal stage design effect evaluation value. For the number of modal types, The number of performance segments. For the first Evaluation weights for each modality For the first Characteristic parameter values ​​of the modality For the first Modal characteristic parameters and the first The correlation coefficient of each performance segment For the first Type 1 mode and the first The correlation coefficient of the evaluation of individual performance content segments. For the first The actual values ​​of the modal parameters, For the first The standard deviation of the modal parameters For the first The stage design requirements coefficient for each performance segment.

[0039] Specifically, the implementation process of the multimodal stage effect analysis platform is as follows: First, determine the number of modal types and the number of performance content segments. The number of modal types is set to three categories, corresponding to sound field modality, lighting modality, and environmental modality. The number of performance content segments is divided according to the performance duration, with each segment lasting 5 minutes, resulting in 18 segments for a 90-minute performance. An evaluation weight is assigned to each modality: the sound field modality is weighted at 0.4, the lighting modality at 0.35, and the environmental modality at 0.25. Characteristic parameter values ​​for each modality are obtained. Sound field modality characteristic parameters include frequency response uniformity and sound pressure level stability; lighting modality characteristic parameters include light intensity uniformity and color temperature consistency; and environmental modality characteristic parameters include temperature stability and humidity stability. All parameter values ​​are measured in real-time using professional equipment at a frequency of 1 measurement per second. The correlation coefficient between each modality's characteristic parameters and each performance content segment is calculated. This correlation coefficient is obtained through statistical analysis of the matching degree between parameter values ​​and segment content requirements, with a range of -1 to 1. Positive values ​​indicate a positive match between the parameter and segment requirements, while negative values ​​indicate a negative match. The evaluation correlation coefficient between each modality and each performance content segment is determined, with a range of 0.1 to 0.9, set according to the modality's influence on the segment's effect. The actual values ​​and standard deviations of each modality's parameters are calculated, with the standard deviation calculated based on historical best parameter data and an accuracy of ±0.05. A stage design requirement coefficient is assigned to each performance content segment, with a range of 0.5 to 1.0, set according to the segment's importance. These parameters are then substituted into the platform's evaluation expression to obtain a multimodal stage design effect evaluation value, ranging from 0 to 100 points, with higher values ​​indicating better stage design effects. This platform allows for a comprehensive evaluation of stage design effects from multiple dimensions, providing a complete evaluation basis for subsequent parameter adjustments.

[0040] Preferably, the calculation expression for the adaptive adjustment parameter of the stage design effect of the cultural performance content is: ,in, The coefficients are adjusted adaptively to enhance the stage design. To adjust the number of parameter types, For the first The weighting coefficients of the class adjustment parameters, For the first The real-time evaluation value corresponding to the class adjustment parameter. For the first The reference evaluation value corresponding to the class adjustment parameter, For the first The dynamic correction coefficient for the adjustment parameters.

[0041] Specifically, the implementation process of adaptive adjustment parameters for the stage design of cultural performances is as follows: First, determine the number of adjustment parameter types. Five types of adjustment parameters are set, corresponding to sound field frequency response adjustment, sound pressure level compensation, lighting intensity adjustment, color temperature correction, and beam angle offset. Assign weight coefficients to each type of adjustment parameter: sound field frequency response adjustment weight coefficient is set to 0.25, sound pressure level compensation weight coefficient is set to 0.2, lighting intensity adjustment weight coefficient is set to 0.25, color temperature correction weight coefficient is set to 0.15, and beam angle offset weight coefficient is set to 0.15, ensuring the total weight of each type is 1. Obtain the real-time evaluation value and reference evaluation value for each type of adjustment parameter. The real-time evaluation value is calculated using a multimodal stage design analysis platform, and the reference evaluation value is set based on historical best performance data. The evaluation value range is 0 to 100 points. Calculate the deviation ratio between the real-time evaluation value and the reference evaluation value. The deviation ratio is calculated by dividing the absolute value of (real-time evaluation value - reference evaluation value) by the reference evaluation value, with an accuracy set to ±0.01. A dynamic correction coefficient is assigned to each type of adjustment parameter, with the coefficient range set from 0.6 to 1.4. This coefficient is set according to the parameter's sensitivity to the stage design effect; for example, the dynamic correction coefficient for lighting intensity adjustment is set to 1.2, and the dynamic correction coefficient for color temperature correction is set to 1.0. These parameters are then substituted into the adjustment parameter calculation expression to obtain the adaptive adjustment coefficient for the stage design effect. This coefficient ranges from 0.5 to 1.5. An adjustment coefficient greater than 1 indicates that the corresponding parameter adjustment intensity needs to be increased, while a coefficient less than 1 indicates that the adjustment intensity needs to be decreased. Through this adjustment parameter calculation, the adjustment range of each stage equipment parameter can be accurately determined, ensuring that the stage design effect reaches its optimal state after parameter correction and meeting the dynamic needs of different performance content.

[0042] Preferably, step S3 includes the following sub-steps: S31, extracting limb movement amplitude parameters related to the frequency response of the sound field equipment and limb joint motion trajectory parameters related to the light intensity of the lighting equipment from the limb dynamic feature vector set output by the performance limb dynamic feature mapping model, and establishing a parameter association table; S32, inputting the extracted parameters into the preprocessing module of the sound field and lighting collaborative calibration algorithm, filtering the parameters within a range, removing abnormal parameters that exceed a preset threshold, and retaining valid parameters that meet the operating requirements of the stage equipment; S33, based on the valid parameters, calling the calculation module of the sound field and lighting collaborative calibration algorithm to calculate the adjustment amount of the frequency response of the sound field equipment, the compensation value of the sound pressure level, and the adjustment amount of the light intensity of the lighting equipment, the correction value of the color temperature, and the offset of the beam angle; S34, integrating the calculated sound field equipment adjustment parameters with the lighting equipment adjustment parameters to generate an initial calibration parameter set for the sound field and lighting, including parameter type, value, and adjustment priority.

[0043] Specifically, step S3 includes four sub-steps: S31 First, from the set of limb dynamic feature vectors output by the performance limb dynamic feature mapping model, filter out the limb movement amplitude parameters (collection accuracy set to ±0.01 meters) related to the frequency response of the sound field equipment and the limb joint motion trajectory parameters (collection frequency set to 60 frames / second) related to the light intensity of the lighting equipment. Establish a parameter association table through data association software, which includes information such as parameter name, collection time, and corresponding equipment type; S32 Transmit the extracted parameters to the preprocessing module of the sound field and lighting collaborative calibration algorithm. The module has built-in parameter filtering rules, setting the effective range of the movement amplitude parameters to 0.1 meters to 2.0 meters and the effective range of the joint motion trajectory parameters to the coordinate values ​​within the stage performance area. Abnormal parameters exceeding this range are eliminated, and effective parameters that meet the operating requirements of the stage equipment are retained. After filtering, a list of effective parameters is generated; S33 According to the list of effective parameters, call the algorithm's calculation module. The module has preset parameter calculation logic, in which the calculation of the frequency response adjustment of the sound field equipment is based on the rate of change of the movement amplitude parameters. (The calculation interval is set to 0.5 seconds). The sound pressure level compensation value is calculated based on the difference between the maximum and minimum values ​​of the movement amplitude. The light intensity adjustment of the lighting equipment is calculated based on the area covered by the joint movement trajectory (the calculation accuracy is set to ±0.1 square meters). The color temperature correction value is calculated based on the angle of change of the trajectory direction (the calculation accuracy is set to ±1°). The beam angle offset is calculated based on the distance between the trajectory edge and the stage boundary. The adjustment values ​​of each equipment parameter are obtained through the above calculations. S34 imports the calculated sound field equipment adjustment parameters (including frequency response and sound pressure level) and lighting equipment adjustment parameters (including light intensity, color temperature, and beam angle) into the parameter integration module. The module classifies the parameters according to the equipment type and sets the priority according to the degree of influence of the parameters on the stage effect (when the priority of the sound field parameter is higher than that of the lighting parameter, the priority value is set to 1, and otherwise it is set to 2). Finally, an initial calibration parameter set for the sound field and lighting is generated, including parameter type, value, and adjustment priority, which provides basic parameter support for subsequent multimodal analysis and ensures that the sound field and lighting parameters are initially adapted to the dynamic characteristics of the performance limbs.

[0044] Preferably, step S4 includes the following sub-steps: S41, converting the multi-dimensional environmental parameter matrix obtained in step S1 into a standardized parameter vector, and simultaneously decomposing the initial calibration parameter set for sound field and lighting generated in step S3 into a subset of sound field parameters and a subset of lighting parameters; S42, inputting the standardized environmental parameter vector, the subset of sound field parameters, and the subset of lighting parameters into the multi-dimensional feature fusion module of the multimodal stage effect analysis platform, and establishing a correlation model between environmental parameters, sound field parameters, lighting parameters, and stage effect through feature cross-operation; S43, based on the correlation model, constructing a stage effect evaluation index system including sound field uniformity, lighting coverage, environmental adaptability, and performance matching degree, and determining the calculation method and weight allocation of each index; S44, according to the set calculation method and combined with the weight of each index, calculating the parameters collected in real time to obtain the real-time value of each evaluation index, and forming an evaluation value report.

[0045] Specifically, step S4 includes four sub-steps: S41 first standardizes the multi-dimensional environmental parameter matrix (including temperature, humidity, air velocity, and obstacle distribution) obtained in step S1. The processing software converts parameters such as temperature (collection range -10℃ to 50℃), humidity (collection range 20%RH to 90%RH), and air velocity (collection range 0m / s to 5m / s) into standardized values ​​of 0 to 1, forming a standardized parameter vector. At the same time, the initial calibration parameter set of the sound field lighting generated in step S3 is decomposed into sound field parameter sub-vectors using parameter decomposition software. The system first sets a subset of environmental parameters (including frequency response and sound pressure level) and a subset of lighting parameters (including light intensity, color temperature, and beam angle), and stores them separately in their respective databases. Then, S42 inputs the standardized environmental parameter vector, the subset of sound field parameters, and the subset of lighting parameters into the multi-dimensional feature fusion module of the multi-modal stage effect analysis platform. The module uses a weighted fusion algorithm, with preset weights of 0.3 for environmental parameters, 0.4 for sound field parameters, and 0.3 for lighting parameters. Through parameter cross-operation (with an interval of 1 second), it establishes a correlation model between environmental parameters, sound field parameters, lighting parameters, and stage effects. The model output parameter combination and effect score correspondence are defined. Based on the correlation model, the S43 platform constructs a stage effect evaluation index system, which includes four indicators: sound field uniformity (calculating the sound pressure level difference in different areas of the stage, accuracy ±0.5dB), lighting coverage (calculating the ratio of lighting area to stage area, accuracy ±1%), environmental adaptability (calculating the impact coefficient of environmental parameter changes on equipment operation stability, range 0 to 1), and performance matching degree (calculating the fit between parameters and performance content characteristics, range 0 to 100 points). The calculation method for each indicator is also determined. The weights are calculated as follows: sound field uniformity weight 0.3, lighting coverage weight 0.25, environmental adaptability weight 0.2, and performance matching weight 0.25. Following the set calculation method and combining the weights of each indicator, the platform calculates the real-time collected parameters (collection frequency 1 time / second) to obtain the real-time value (retaining one decimal place) of each evaluation indicator. The report generation module then organizes the values ​​into an evaluation report including the indicator name, real-time value, standard value, and deviation value, providing a quantitative evaluation basis for subsequent parameter correction and ensuring comprehensive and accurate analysis of the stage effects.

[0046] Preferably, step S5 includes the following sub-steps: S51, retrieving the adjustment parameter type and parameter threshold range corresponding to the evaluation index calculated in step S4 from the adaptive adjustment parameter library of stage effects for cultural performance content; S52, comparing the real-time value of the evaluation index with the corresponding parameter threshold range, identifying the evaluation index that exceeds the threshold range, and analyzing the reasons for the deviation of the sound field or lighting equipment parameters corresponding to the index; S53, based on the reasons for the deviation, calling the adaptive adjustment algorithm module to calculate the correction amount of the sound field equipment parameters and the lighting equipment parameters that need to be corrected, ensuring that the corrected parameters can make the evaluation index return to the threshold range; S54, combining the sound field equipment parameters and lighting equipment parameters after multiple corrections to generate multiple sets of stage equipment operating parameters including different parameter combinations, and labeling the predicted value of the evaluation index corresponding to each set of parameters.

[0047] Specifically, step S5 includes four sub-steps: S51: From the preset cultural performance content stage effect adaptive adjustment parameter library, based on the performance type (such as dance, drama) and the current performance segment, the corresponding adjustment parameter types (including sound field frequency response adjustment coefficient, light intensity adjustment coefficient, etc.) and parameter threshold ranges are retrieved through parameter retrieval software. The frequency response adjustment coefficient threshold is set to 0.8 to 1.2, the sound pressure level compensation coefficient threshold is set to 0.9 to 1.1, the light intensity adjustment coefficient threshold is set to 0.7 to 1.3, the color temperature correction coefficient threshold is set to 0.85 to 1.15, and the beam angle offset coefficient threshold is set to 0.9 to 1.1. The retrieval results are presented in the form of a parameter list. S52: The real-time values ​​of the evaluation indicators calculated in step S4 (such as sound field uniformity and light coverage) are compared with the corresponding parameter threshold ranges. The comparison software uses a difference calculation method. If the indicator values ​​exceed the threshold range (such as sound field uniformity below 80 points and light coverage below 75 points), the deviation cause analysis module is activated. The module determines the deviation is caused by the sound field equipment through parameter correlation analysis. If the deviation is caused by parameters (such as uneven frequency response) or lighting equipment parameters (such as insufficient light intensity), the analysis results will generate a deviation report. S53, based on the deviation report, calls the adaptive adjustment algorithm module. The module has a built-in parameter correction formula and calculates the correction amount of the equipment parameters according to the deviation value (e.g., when the sound field uniformity is 10 points lower, the frequency response is adjusted by an additional 50Hz and the sound pressure level is compensated by an additional 3dB; when the lighting coverage is 8 points lower, the light intensity is adjusted by an additional 300cd / m² and the beam angle is offset by an additional 10°). After the correction amount is calculated, a verification calculation is performed to ensure that the corrected indicators can return to the threshold range. S54 imports the sound field equipment parameters (frequency response, sound pressure level) and lighting equipment parameters (light intensity, color temperature, beam angle) after multiple corrections into the parameter combination module. The module generates multiple sets of stage equipment operating parameters (each set includes 5-8 parameters) according to different parameter combination methods. At the same time, the prediction algorithm calculates the predicted value of the evaluation index corresponding to each set of parameters (accuracy ±2 points) and marks it next to the parameter group, providing multiple optimization schemes for subsequent equipment configuration, ensuring that the stage effect can dynamically adapt to the performance content and environmental changes.

[0048] The intelligent coupling model for stage environment perception in this invention is a model used to collect and process multi-dimensional data of the stage environment and establish the correlation between the environment and stage equipment. Its implementation process is as follows: First, temperature, humidity, air velocity, and obstacle detection sensors are deployed in the stage space at a preset density. The temperature acquisition range is set to -10℃ to 50℃ (accuracy ±0.5℃), humidity to 20%RH to 90%RH (accuracy ±3%RH), air velocity to 0m / s to 5m / s (accuracy ±0.1m / s), and obstacle detection distance to 0.5m to 10m (accuracy ±0.05m). After the sensors collect data in real time, it is transmitted to the model data processing module. The module filters abnormal data and performs dynamic correlation analysis, calculates the correlation coefficients between environmental parameters, constructs a multi-dimensional environmental parameter matrix, and determines the mapping relationship between environmental parameters and the operating status of stage equipment. This model accurately acquires the real-time environmental status of the stage, providing basic environmental data for adjusting the parameters of stage equipment. It avoids adverse effects on the stage effect due to changes in environmental parameters (such as temperature changes affecting the color temperature of lights, and obstacles affecting the propagation of sound fields), ensuring that the stage equipment can still operate stably in a dynamic environment, and laying the foundation for environmental adaptation for subsequent stage effect optimization.

[0049] The performance limb dynamic feature mapping model in this invention is a model used to capture the limb movement data of performers and extract feature parameters that are adapted to stage design adjustments. Its implementation process is as follows: motion capture cameras with a sampling frame rate of 60-120 frames / second and a resolution of 1920×1080 pixels are deployed around and above the stage, covering the entire performance area. The cameras capture limb movement images in real time and transmit them to the model's image processing unit. The unit extracts skeletal coordinates, calculates joint movement trajectories, measures changes in the length of joint lines to determine the range of motion (accuracy ±0.02m), counts the number of movements per unit time to obtain the frequency (statistical interval 1 second), constructs a limb dynamic feature vector set, and then matches it with a preset performance content feature library (storing standard limb data for different performance types), extracting feature parameters with a matching degree ≥80%. This model accurately extracts key features of performers' physical dynamics, providing dynamic data that is compatible with the performance content for the calibration of sound field and lighting parameters. It breaks the problem of traditional stage design adjustments being disconnected from physical movements, enabling the stage effects to adapt in real time to the physical dynamics of the performers (such as increasing the brightness of lights when dancing with large leaps, and focusing the light field when the dramatic body pauses), thereby improving the synergy between the stage effects and the performance content and enhancing the appeal of the live performance.

[0050] The sound field and lighting coordinated calibration algorithm in this invention is an algorithm used to adjust the parameters of the sound field and lighting equipment according to the dynamic characteristics of the performance, so as to achieve coordinated adaptation between the two. The implementation process is as follows: First, allocate calibration weights for sound field and lighting parameters according to the performance type (0.6 for concert sound field and 0.4 for lighting; 0.45 for dance sound field and 0.55 for lighting). Obtain the basic value of sound pressure level of the sound field equipment (90dB for a 500-person venue), actual and reference frequency response (reference 20Hz-20000Hz), calculate the sound pressure level deviation (accuracy ±0.5dB), determine the basic value of light intensity of lighting equipment (3000cd / m² for the main performance area), color temperature and performance matching angle (0°-90°), beam angle (accuracy ±1°), and actual values ​​of color temperature (accuracy ±20K). Then, substitute them into the algorithm to calculate the sound field and lighting coordination calibration coefficient (range 0-1, the closer to 1 the better the coordination). Adjust the sound field frequency response (step 10Hz), sound pressure level (step 1dB), light intensity (step 50cd / m²), color temperature (step 100K), and beam angle (step 5°) according to the coefficient. This algorithm enables the linkage calibration of sound field and lighting parameters, avoiding conflicts between the two parameters (such as high sound pressure level combined with low light intensity leading to an unbalanced experience), solving the problem of inconsistent effects caused by traditional separate adjustments of sound field and lighting, and ensuring that the parameters of the two are optimized in synergy according to the dynamics of the performance (such as synchronously increasing sound pressure level and light intensity when the range of body movements increases), thereby improving the overall consistency of the stage effect.

[0051] The multimodal stage effect analysis platform of this invention is a platform for integrating multi-dimensional parameters of environment, sound field, and lighting to construct an evaluation system and quantitatively analyze stage effects. Its implementation process is as follows: First, the environmental parameter matrix is ​​standardized (converted to the 0-1 range), and the initial parameters of the sound field and lighting are split into corresponding subsets. These subsets are then input into the platform's multi-dimensional feature fusion module. The module performs cross-correlation calculations (with a 1-second interval) according to the weights of environment 0.3, sound field 0.4, and lighting 0.3, establishing a parameter-effect correlation model. Next, an evaluation system is constructed, including sound field uniformity (sound pressure level difference value, accuracy ±0.5dB), lighting coverage (illumination area ratio, accuracy ±1%), environmental adaptability (equipment stability influence coefficient 0-1), and performance matching degree (fitness 0-100 points). Weights are assigned (sound field 0.3, lighting 0.25, environment 0.2, matching degree 0.25), and real-time indicator values ​​are calculated according to a set method (retaining one decimal place), generating an evaluation report containing indicator names, real-time values, standard values, and deviation values. This platform comprehensively evaluates stage effects from multiple dimensions, providing quantitative basis for equipment parameter correction. It changes the one-sidedness of traditional single-dimensional (such as only looking at the brightness of lights) evaluation of stage effects. Through multi-parameter fusion analysis (such as combining environmental humidity to judge the cause of sound field uniformity deviation), it accurately locates effect problems, ensuring that subsequent parameter corrections are more targeted, and promotes the transformation of stage effects from "experience adjustment" to "data-driven optimization".

[0052] like Figure 2 As shown, an adaptive adjustment system for stage effects based on cultural performance content is applied to an adaptive adjustment method for stage effects based on cultural performance content. The system includes: a multi-dimensional stage environment parameter acquisition and coupling analysis unit, which connects to various environmental sensors deployed on the stage. Through a stage environment perception intelligent coupling model, it processes the temperature, humidity, airflow velocity, and obstacle distribution data collected by the sensors, outputting a multi-dimensional environmental parameter matrix, which is then transmitted to a multi-modal stage effect integrated processing unit; a performance limb dynamic feature capture and mapping unit, which connects to motion capture equipment deployed on the stage. Through a performance limb dynamic feature mapping model, it processes the captured limb joint motion trajectory, amplitude, and frequency data, outputting a limb dynamic feature vector set, which is then transmitted to a sound field and lighting collaborative calibration unit and a multi-modal stage effect integrated processing unit; and a sound field and lighting collaborative calibration parameter calculation unit, which receives the feature vector set output by the performance limb dynamic feature capture and mapping unit and calls the sound field and lighting collaborative calibration parameter calculation method. The calibration algorithm performs preliminary calibration on the sound field and lighting equipment parameters, generating an initial calibration parameter set, which is then transmitted to the multimodal stage effect integrated processing unit. This unit receives the environmental parameter matrix from the stage environment multi-dimensional parameter acquisition and coupling analysis unit and the initial calibration parameter set from the sound field and lighting collaborative calibration parameter calculation unit. It then constructs an evaluation index system and calculates real-time values ​​through the multimodal stage effect analysis platform, transmitting the evaluation values ​​to the stage parameter dynamic correction unit. The dynamic correction unit receives the evaluation values ​​from the multimodal stage effect integrated processing unit, combines them with the stage effect of the cultural performance content to adaptively adjust the parameters, calculates the equipment parameter correction amount, generates multiple sets of corrected operating parameters, and transmits these parameters to the stage equipment control and scheduling unit. Finally, the stage equipment control and scheduling unit receives multiple sets of operating parameters from the dynamic correction unit, configures the parameters and monitors the operating status of the stage sound field and lighting equipment according to parameter priority, and performs adaptive adjustments to the stage effect.

[0053] A method and system for adaptive adjustment of stage effects based on cultural performance content comprehensively collects environmental data such as temperature, humidity, and obstacle distribution within the stage space through a stage environment perception intelligent coupling model. This data is then combined with dynamic data such as the trajectories, amplitude, and frequency of performers' limb joints captured by a performance limb dynamic feature mapping model. This integrates both environmental parameters and limb dynamic characteristics into the stage effect adjustment logic, rather than processing individual parameters in isolation as in existing technologies. Furthermore, a sound field and lighting collaborative calibration algorithm is invoked, enabling the frequency response and sound pressure level parameters of the sound field equipment to be synchronized with the light intensity, color temperature, and beam angle parameters of the lighting equipment based on environmental and dynamic characteristics. This ensures that the stage effect remains highly adapted to the real-time changes in the stage scene and performance content, completely overcoming the problem of one-sided stage effect adjustments caused by single-parameter adjustments. This method and system effectively solve the shortcomings of existing technologies, such as limited evaluation dimensions and the inability to quickly and accurately correct parameters. The system adaptively adjusts parameters based on the stage design and effects of cultural performances. It can quickly calculate precise equipment parameter corrections for parameter deviations identified during evaluations, generate multiple sets of optimized operating parameters, and configure them according to priority. This forms a closed-loop optimization mechanism of "collection-analysis-evaluation-correction-configuration," ensuring that the stage design and effects can be dynamically adjusted in real time. This significantly improves the fit with the performance content and meets the differentiated needs of various cultural performances.

[0054] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various equivalent changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A method for adaptive adjustment of stage design effects based on cultural performance content, characterized in that, Includes the following steps: S1. Collect data on temperature, humidity, airflow velocity, and obstacle distribution within the stage space using a stage environment perception intelligent coupling model. Establish a multi-dimensional environmental parameter matrix, perform real-time filtering and dynamic correlation analysis on the collected environmental parameters, and determine the mapping relationship between environmental parameters and the operating status of stage equipment. S2. Obtain data on the limb joint movement trajectory, limb movement amplitude, and movement frequency of performers based on a performance limb dynamic feature mapping model. Construct a limb dynamic feature vector set, match the limb dynamic feature vector set with a preset performance content feature library, and extract limb dynamic feature parameters whose matching degree meets a set threshold. S3. Call the sound field and lighting collaborative calibration algorithm. Based on the limb dynamic feature parameters extracted in S2, adjust the frequency response, sound pressure level of the stage sound field equipment, and the light intensity, color temperature, and beam angle of the lighting equipment. S1. Initial calibration of parameters is performed to generate an initial calibration parameter set for the sound field and lighting. S2. The environmental parameter matrix obtained in S1 and the initial calibration parameter set for the sound field and lighting generated in S3 are input into the multimodal stage effect analysis platform. The parameters are cross-correlated through the platform's built-in multi-dimensional feature fusion module to construct a stage effect evaluation index system and calculate the real-time values ​​of each evaluation index. S3. Based on the real-time values ​​of the evaluation indexes calculated in S4, the parameters of the sound field and lighting equipment are dynamically corrected in conjunction with the stage effect of the cultural performance content, generating multiple sets of corrected stage equipment operating parameters. S4. The multiple sets of corrected stage equipment operating parameters generated in S5 are transmitted to the stage equipment control terminal. The control terminal configures the parameters of the stage equipment according to the parameter priority order and performs adaptive adjustments to the stage effect.

2. The adaptive adjustment method for stage effects based on cultural performance content according to claim 1, characterized in that, The expression for the stage environment perception intelligent coupling model is: middle, The intelligent coupling coefficient for stage environment perception. The number of environmental parameter types, Number of obstacle types For the first The weighting coefficients of class environment parameters, For the first Real-time collected values ​​of environmental parameters. For the first Temperature influence coefficient of obstacle-like objects For the first The equivalent temperature difference between the obstacle and the environment. For the first Class environment parameters and the first The correlation coefficient of obstacle-like objects For the first Class environment parameters and the first Covariance of obstacle distribution density For the first Class environment parameters and the first Coupling adjustment coefficient for obstacle-like structures.

3. The adaptive adjustment method for stage effects based on cultural performance content according to claim 1, characterized in that, The expression for the performance body dynamic feature mapping model is: For the dynamic features of the body in the performance, The number of joints in a limb. The number of types of body movements. For the first Motion weighting coefficients for each joint For the first The speed of movement of each joint For the first The angles of the limb joints in similar movements. For the first The joint and the first The correlation coefficient of action class, For the first The amplitude value of the type of action, The time interval between adjacent action capture frames. For the first The motion frequency coefficient of each joint.

4. The adaptive adjustment method for stage effects based on cultural performance content according to claim 1, characterized in that, The expression for the sound field and lighting collaborative calibration algorithm is: ,in, For the sound field and lighting coordination calibration coefficients, To calibrate the weights for the sound field parameters, To calibrate the weights for the lighting parameters, This is the basic value for the sound pressure level of the sound field equipment. This represents the actual frequency response of the sound field equipment. This is the reference frequency response of the sound field device. This is the sound pressure level adjustment factor. This represents the deviation between the actual sound pressure level and the reference sound pressure level. This is the base value for the luminous intensity of the lighting equipment. To match the color temperature of the lights with the performance content, This is the light angle adjustment coefficient. This is the actual value of the light beam angle. This is the actual value of the light color temperature.

5. The adaptive adjustment method for stage effects based on cultural performance content according to claim 1, characterized in that, The effect evaluation expression of the multimodal stage design effect analysis platform is as follows: This is a multimodal stage design effect evaluation value. For the number of modal types, The number of performance segments. For the first Evaluation weights for each modality For the first Characteristic parameter values ​​of the modality For the first Modal characteristic parameters and the first The correlation coefficient of each performance segment For the first Type 1 mode and the first The correlation coefficient of the evaluation of individual performance content segments. For the first The actual values ​​of the modal parameters, For the first The standard deviation of the modal parameters For the first The stage design requirement coefficient for each performance segment.

6. The adaptive adjustment method for stage effects based on cultural performance content according to claim 1, characterized in that, The calculation expression for the adaptive adjustment parameter of the stage design effect of the cultural performance content is as follows: ,in, The coefficients are adjusted adaptively to enhance the stage design. To adjust the number of parameter types, For the first The weighting coefficients of the class adjustment parameters, For the first The real-time evaluation value corresponding to the class adjustment parameter. For the first The reference evaluation value corresponding to the class adjustment parameter, For the first The dynamic correction coefficient for the adjustment parameters.

7. The adaptive adjustment method for stage effects based on cultural performance content according to claim 1, characterized in that, S3 includes the following sub-steps: S31, extracting limb movement amplitude parameters related to the frequency response of the sound field equipment and limb joint motion trajectory parameters related to the light intensity of the lighting equipment from the limb dynamic feature vector set output by the performance limb dynamic feature mapping model, and establishing a parameter association table; S32, inputting the extracted parameters into the preprocessing module of the sound field and lighting collaborative calibration algorithm, filtering the parameters within a range, removing abnormal parameters that exceed a preset threshold, and retaining valid parameters that meet the operating requirements of the stage equipment; S33, based on the valid parameters, calling the calculation module of the sound field and lighting collaborative calibration algorithm to calculate the adjustment amount of the frequency response of the sound field equipment, the compensation value of the sound pressure level, and the adjustment amount of the light intensity of the lighting equipment, the correction value of the color temperature, and the offset of the beam angle; S34, integrating the calculated sound field equipment adjustment parameters with the lighting equipment adjustment parameters to generate an initial calibration parameter set for the sound field and lighting, including parameter type, value, and adjustment priority.

8. The adaptive adjustment method for stage effects based on cultural performance content according to claim 1, characterized in that, The S4 includes the following sub-steps: S41, converting the multi-dimensional environmental parameter matrix obtained in step S1 into a standardized parameter vector, and simultaneously decomposing the initial calibration parameter set of the sound field and lighting generated in step S3 into a subset of sound field parameters and a subset of lighting parameters; S42. Input the standardized environmental parameter vector, sound field parameter subset, and lighting parameter subset into the multi-dimensional feature fusion module of the multimodal stage effect analysis platform, and establish a correlation model between environmental parameters, sound field parameters, lighting parameters, and stage effects through feature cross-operation; S43. Based on the correlation model, construct a stage effect evaluation index system including sound field uniformity, lighting coverage, environmental adaptability, and performance matching degree, and determine the calculation method and weight allocation of each index; S44. According to the set calculation method, combined with the weight of each index, calculate the parameters collected in real time, obtain the real-time value of each evaluation index, and generate an evaluation value report.

9. The adaptive adjustment method for stage effects based on cultural performance content according to claim 1, characterized in that, S5 includes the following sub-steps: S51, retrieve the adjustment parameter type and parameter threshold range corresponding to the evaluation index calculated in step S4 from the adaptive adjustment parameter library of stage effects for cultural performance content; S52, compare the real-time value of the evaluation index with the corresponding parameter threshold range, identify the evaluation index that exceeds the threshold range, and analyze the reasons for the deviation of the sound field or lighting equipment parameters corresponding to the index; S53, according to the reasons for the deviation, call the adaptive adjustment algorithm module to calculate the correction amount of the sound field equipment parameters and the lighting equipment parameters that need to be corrected, to ensure that the corrected parameters can make the evaluation index return to the threshold range; S54, combine the sound field equipment parameters and lighting equipment parameters after multiple corrections to generate multiple sets of stage equipment operating parameters including different parameter combinations, and label the predicted value of the evaluation index corresponding to each set of parameters.

10. A stage design effect adaptive adjustment system based on cultural performance content, characterized in that: This system is applied to the adaptive adjustment method for stage effects based on cultural performance content as described in claim 1, comprising: a multi-dimensional stage environment parameter acquisition and coupling analysis unit, which is connected to various environmental sensors deployed on the stage, processes the temperature, humidity, air velocity, and obstacle distribution data collected by the sensors through a stage environment perception intelligent coupling model, outputs a multi-dimensional environmental parameter matrix, and transmits the matrix to a multi-modal stage effect integrated processing unit; a performance limb dynamic feature capture and mapping unit, which is connected to motion capture equipment deployed on the stage, processes the captured limb joint motion trajectory, motion amplitude, and frequency data through a performance limb dynamic feature mapping model, outputs a limb dynamic feature vector set, and transmits the vector set to a sound field and lighting collaborative calibration unit and a multi-modal stage effect integrated processing unit respectively; and a sound field and lighting collaborative calibration parameter calculation unit, which receives the feature vector set output by the performance limb dynamic feature capture and mapping unit, and calls a sound field and lighting collaborative calibration algorithm to calculate the sound field and lighting parameters. The lighting equipment parameters undergo preliminary calibration to generate an initial calibration parameter set, which is then transmitted to the multimodal stage effect integrated processing unit. This unit receives the environmental parameter matrix from the stage environment multi-dimensional parameter acquisition and coupling analysis unit and the initial calibration parameter set from the sound field and lighting collaborative calibration parameter calculation unit. It then constructs an evaluation index system and calculates real-time values ​​through the multimodal stage effect analysis platform, transmitting the evaluation values ​​to the stage parameter dynamic correction unit. The dynamic correction unit receives the evaluation values ​​from the multimodal stage effect integrated processing unit, combines them with the stage effect of the cultural performance content to adaptively adjust the parameters, calculates the equipment parameter correction amount, generates multiple sets of corrected operating parameters, and transmits these parameters to the stage equipment control and scheduling unit. Finally, the stage equipment control and scheduling unit receives these operating parameters from the dynamic correction unit, configures the parameters of the stage sound field equipment and lighting equipment according to parameter priority, monitors their operating status, and adaptively adjusts the stage effect.

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