Audio and video playing control method

Abstract

The invention relates to the technical field of video transmission, in particular to an audio and video playing control method, which comprises the following steps: acquiring an audio and video file to be played, identifying corresponding scene type data and emotional atmosphere data, initializing player parameters based on the scene type data and the emotional atmosphere data, and playing the audio and video file. Preliminarily adjusting player parameters based on the auditory sensitivity data and the personal health monitoring data of the target user; constructing a three-dimensional virtual environment based on the environment attribute data and the real-time position data, determining a player position and a propagation path in the three-dimensional virtual environment, determining virtual position data of the target user in the three-dimensional virtual environment, and determining a virtual audio and video effect; in the audio and video playing process, actual audio and video data corresponding to the player are obtained, user real-time feedback data of a target user under the actual audio and video data are obtained, and playing parameters of the player are adjusted based on the user real-time feedback data. According to the invention, the accuracy of audio and video playing control can be improved.

Description

Technical Field

[0001] This application relates to the technical field of video dissemination, and in particular, to an audio - video playback control method. Background Art

[0002] In today's multimedia environment, audio - video playback has become an indispensable part. Users rely on various devices such as smartphones, tablets, and smart TVs to enjoy entertainment content. However, current audio - video playback control methods face various challenges.

[0003] Firstly, compatibility issues between different devices limit the seamless experience, resulting in inconsistent functions when users switch devices. Secondly, traditional playback control methods have low interaction efficiency. The user's operation steps are complex and not intuitive enough, making it difficult to achieve a fast - response user experience. In addition, the existing systems lack sufficient intelligence, unable to automatically adjust playback settings according to user habits, and also lack personalized services, unable to meet the unique needs of different users.

[0004] As can be seen from the above, how to improve the accuracy of audio - video playback control still needs to be solved. Summary of the Invention

[0005] In order to improve the accuracy of audio - video playback control, this application provides an audio - video playback control method.

[0006] In a first aspect, this application provides an audio - video playback control method, adopting the following technical solutions:

[0007] An audio - video playback control method includes: obtaining an audio - video file to be played, extracting metadata in the audio - video file, using a machine learning algorithm to identify corresponding scene type data and emotional atmosphere data from the metadata information, initializing the player parameters of the player based on the scene type data and emotional atmosphere data, determining the corresponding target user, retrieving the auditory sensitivity data and personal health monitoring data corresponding to the target user, and preliminarily adjusting the player parameters based on the auditory sensitivity data and personal health monitoring data, wherein the metadata at least includes time code, channel configuration, and scene description; collecting environment attribute data corresponding to the current environment and real - time position data of the target user based on a pre - installed sensor network, constructing a corresponding three - dimensional virtual environment based on the environment attribute data and real - time position data, determining the player position and propagation path in the three - dimensional virtual environment based on the channel configuration and scene description in the metadata, determining the virtual position data of the target user corresponding to the three - dimensional virtual environment, and determining the corresponding virtual audio - video effect based on the player position, propagation path, and virtual position data; during the process of controlling the player to play audio - video based on the player playback parameters, obtaining the actual audio - video data corresponding to the player, obtaining the user real - time feedback data of the target user under the actual audio - video data, adjusting the playback parameters of the player based on the user real - time feedback data, comparing the actual audio - video data with the virtual audio - video effect, obtaining the corresponding comparison result, identifying the abnormal type data in the comparison result, and adjusting the construction parameters of the three - dimensional virtual environment based on the abnormal type data, wherein the actual audio - video data includes the volume, frequency response, and spatial distribution of the audio - video.

[0008] Optionally, after identifying the abnormal type data in the comparison result, the method further includes: retrieving the built - in fault diagnosis algorithm and historical data, obtaining the corresponding current environment information, determining the specific cause data of the abnormality through the fault diagnosis algorithm based on the historical data and the current environment information, and the specific cause data includes player hardware failure, sensor error, and incorrect configuration of the audio processing algorithm; automatically repairing based on the specific cause data through an automatic recovery mechanism, and if the automatic repair fails, generating a corresponding detailed report, wherein the automatic repair includes restarting relevant components, recalibrating sensors, and adjusting audio processing parameters.

[0009] Optionally, during the process of adjusting the construction parameters of the three - dimensional virtual environment based on the abnormal type data, the method further includes: obtaining the weather forecast information for a future period of time, predicting the prediction result of environmental variables through time - series analysis; adjusting the physical parameters of the three - dimensional virtual environment based on the prediction result and the abnormal type data, wherein the physical parameters include air density, sound speed, temperature, humidity, and wind speed; monitoring the actual playback effect after adjusting the physical parameters, and judging whether to adjust the construction parameters based on the actual playback effect.

[0010] Optionally, during the process of real-time adjustment of player parameters, the method further includes: retrieving the precise position data set of the target user, retrieving the historical movement data of the target user, and predicting the predicted movement path of the target user within a certain period of time in the future based on the precise position data set and the historical movement data; calculating the optimal position and propagation path of the virtual sound source based on the predicted movement trajectory, and updating the audio rendering parameters in the three-dimensional environment model; adjusting the corresponding spatialization effect based on the audio rendering parameters, and the spatialization effect includes surround sound and stereo sound.

[0011] Optionally, the method further includes: monitoring the connection status in real time, determining whether a new player joins, and if so, confirming the device type of the corresponding new player through the device identifier, and obtaining the technical specifications of the new player device type, where the technical specifications include the frequency response range, impedance, and maximum volume; analyzing the audio characteristics of the new player device type based on the technical specifications of the device, and determining the optimal audio output parameters corresponding to the new player device type based on the audio characteristics.

[0012] Optionally, after the playback ends, the method further includes: integrating the interaction behavior data and real-time feedback data of the target user during the playback, where the interaction behavior data includes operations such as volume adjustment, scene switching, pause / play, and the real-time feedback data includes physiological signals such as facial expressions and heart rate changes; identifying the common preference data based on the interaction behavior data and the real-time feedback data, collecting the active feedback opinions actively provided by the target user, and forming a user feedback database with the common preference data and the active feedback data; classifying and prioritizing the user feedback database, and generating a corresponding detailed comprehensive report, where the detailed comprehensive report includes various indicators during the playback, user behavior patterns, summary of feedback opinions, and diagnosis of potential problems.

[0013] In a second aspect, the present application provides an audio-visual playback control device, adopting the following technical solution:

[0014] An audio-visual playback control device, the device includes but is not limited to:

[0015] A preliminary adjustment module, which obtains the audio-visual file to be played, extracts the metadata in the audio-visual file, uses a machine learning algorithm to identify the corresponding scene type data and emotional atmosphere data from the metadata information, initializes the player parameters of the player based on the scene type data and the emotional atmosphere data, determines the corresponding target user, retrieves the auditory sensitivity data and personal health monitoring data of the target user, and uses the auditory sensitivity data and the personal health monitoring data to preliminarily adjust the player parameters, where the metadata at least includes time code, channel configuration, and scene description;

[0016] A virtual audio-visual effect determination module collects environmental attribute data corresponding to the current environment and real-time location data of the target user based on a pre-installed sensor network, constructs a corresponding three-dimensional virtual environment based on the environmental attribute data and real-time location data, determines the player position and propagation path in the three-dimensional virtual environment based on the channel configuration and scene description in the metadata, determines the virtual location data corresponding to the target user in the three-dimensional virtual environment, and uses the player position, propagation path, and virtual location data to determine the corresponding virtual audio-visual effect;

[0017] A comparison result acquisition module, during the process of controlling the player to play audio-visual based on the player's playback parameters, acquires the actual audio-visual data corresponding to the player, acquires the user's real-time feedback data under the actual audio-visual data, adjusts the player's playback parameters based on the user's real-time feedback data, compares the actual audio-visual data with the virtual audio-visual effect to obtain the corresponding comparison result, identifies the abnormal type data in the comparison result, and adjusts the construction parameters of the three-dimensional virtual environment based on the abnormal type data, where the actual audio-visual data includes the volume, frequency response, and spatial distribution of the audio-visual.

[0018] In a third aspect, the present application provides an audio-visual playback control device, adopting the following technical solution:

[0019] An audio-visual playback control device includes a processor, and a program of the audio-visual playback control method described in any one of the above is running in the processor.

[0020] In a fourth aspect, the present application provides a storage medium, adopting the following technical solution:

[0021] A storage medium stores a program of the audio-visual playback control method described in any one of the above.

[0022] In summary, the present application includes at least one of the following beneficial technical effects:

[0023] By deeply analyzing the content and user data, a highly personalized playback experience is achieved. By first decoding the emotional and scene information in the audio-visual file and combining the user's auditory sensitivity and personal health status for customized settings, not only is the accurate configuration of the playback parameters ensured, but also the user's emotional investment and immersion are greatly enhanced, making each playback fit the unique needs of the user.

[0024] During playback, by dynamically responding to environmental changes and user behaviors, continuously optimizing the audio output quality, being able to predict and adapt to the user's movement path in advance, and ensuring a consistent and natural sound localization effect even in complex and changing environments. In addition, by comparing the actual playback effect with the virtual model in real time, quickly identifying and correcting any deviations or anomalies, thus ensuring the stability and consistency during playback and providing a seamless audio-visual enjoyment.

[0025] Finally, through detailed data collection and feedback analysis, a comprehensive report is generated after playback, which not only promotes the continuous optimization of the system and technological iteration, but also provides personalized usage guidance for each user, enhancing user satisfaction. By carefully reviewing and evaluating each playback process, the system can quickly capture and solve potential problems, ensuring the long-term provision of high-quality and precisely controlled audio-visual services. Description of the Drawings

[0026] Figure 1 is a flowchart of an audio-visual playback control method shown according to an exemplary embodiment.

[0027] Figure 2 is a structural block diagram of an audio-visual playback control device shown according to an exemplary embodiment. Detailed Embodiments

[0028] The following details the embodiments of the present application, and the examples of the embodiments are shown in the drawings.

[0029] In the description of this specification, the description with reference to the terms "certain embodiments", "one embodiment", "some embodiments", "illustrative embodiments", "examples", "specific examples", or "some examples" means that the specific features, structures, materials, or characteristics described in connection with the embodiments or examples are included in at least one embodiment or example of the present application. In this specification, the schematic descriptions of the above terms do not necessarily refer to the same embodiments or examples. Moreover, the specific features, structures, materials, or characteristics described can be combined in a suitable manner in any one or more embodiments or examples.

[0030] The embodiments of the present application disclose an audio-visual playback control method, referring to Figure 1 , including:

[0031] S100, obtain the audio - video file to be played, extract the metadata from the audio - video file, use machine - learning algorithms to identify the corresponding scene - type data and emotional - atmosphere data from the metadata information, initialize the player parameters of the player based on the scene - type data and emotional - atmosphere data, determine the corresponding target user, retrieve the auditory sensitivity data and personal health monitoring data of the target user, and preliminarily adjust the player parameters based on the auditory sensitivity data and personal health monitoring data.

[0032] Among them, first obtain the audio - video file selected or recommended by the user from multiple possible sources (such as local storage devices, network streaming media servers, cloud storage, etc.). During the acquisition process, the system will verify the format compatibility and integrity of the file to ensure that it can be correctly parsed and played. Once the audio - video file is successfully loaded, the system will start the metadata analysis module, which can parse various metadata information in the file.

[0033] The metadata information at least includes time code (used to precisely control the playback progress), channel configuration (such as mono, stereo, 5.1 surround sound, etc.), scene description (such as shot transitions, background music changes, etc.); metadata is usually embedded in the file header or dedicated data segments, and the system extracts the required information by reading these parts. In addition, the system will also identify and extract other potential metadata, such as director's notes, actor lists, subtitle information, etc., to enrich the content of subsequent analysis.

[0034] It should be noted here that the extracted metadata is passed to the machine - learning module, which uses a pre - trained model to perform in - depth analysis on the metadata. Based on a large amount of historical data and annotation information, the model can identify the scene type (such as indoor conversation, outdoor sports, concert, etc.) and emotional atmosphere (such as cheerful, sad, tense, etc.) corresponding to the audio - video content.

[0035] To improve the accuracy of recognition, the system may combine multiple feature - extraction techniques, such as natural language processing (NLP) for text description, computer vision for image - frame analysis, and audio - signal processing for sound - feature extraction. Finally, the machine - learning algorithm outputs a high - confidence scene type and emotional - atmosphere label, which will be used to initialize the player parameters.

[0036] Secondly, based on the identified scene type and emotional atmosphere data, the system begins to initialize various parameters of the player. For example, for a vibrant concert scene, the system may enhance the low-frequency response to highlight the drum beats and adjust the equalizer to improve the overall sound quality; while for a quiet conversation scene, it may reduce background noise and optimize speech clarity. The initialized parameters include not only audio settings (such as volume, equalizer, surround sound effect), but also may involve video display parameters (such as brightness, contrast, color correction); in addition, the system will also consider the target user's preference history to ensure that the initial settings are as close as possible to the user's expectations.

[0037] Then, the system determines the current target user through the user account information or device login status. If the system supports multi-user mode, it can also identify which specific user is using the device and call the corresponding personal profile. The system ensures the privacy and security of user data through a secure authentication mechanism. In addition, the system will also record the user's real-time location information for use in constructing a three-dimensional virtual environment in subsequent steps.

[0038] Finally, based on the retrieved auditory sensitivity data and personal health monitoring data, the system further fine-tunes the parameters of the player. For example, if the user is particularly sensitive to high-frequency sounds, the system may reduce the volume of the high-pitched part; if the user is at risk of hearing impairment, the system will automatically adjust the frequency response to avoid overly strong sound stimuli. In addition, the system will also refer to the user's health status (such as heart rate changes, facial expressions, etc.) and dynamically adjust the audio output to reduce auditory fatigue or enhance emotional resonance. This process ensures that the audio output meets the user's physiological needs and protects their hearing health.

[0039] Through the intelligent acquisition and parsing of audio-visual files, in-depth metadata analysis, and the application of machine learning algorithms, the system can accurately identify the scene type and emotional atmosphere of the content, and combine the user's auditory sensitivity and personal health data for personalized player parameter initialization and dynamic adjustment. This method not only ensures that the playback effect highly conforms to the content characteristics and user preferences, but also provides a safe and comfortable audio-visual experience, significantly enhancing the user's immersion and satisfaction.

[0040] S110. Based on the pre-installed sensor network, collect the environmental attribute data corresponding to the current environment and the real-time location data of the target user. Based on the environmental attribute data and real-time location data, construct the corresponding three-dimensional virtual environment. Based on the channel configuration and scene description in the metadata, determine the player position and propagation path in the three-dimensional virtual environment, determine the virtual position data of the target user corresponding to the three-dimensional virtual environment, and determine the corresponding virtual audio-visual effect based on the player position, propagation path, and virtual position data.

[0041] Among them, the system utilizes a pre-installed sensor network, including but not limited to devices such as infrared cameras, ultrasonic rangefinders, Wi-Fi signal strength detectors, and accelerometers, to collect real-time attribute data of the current playback environment. This data covers physical characteristics such as room dimensions, furniture layout, wall materials (such as wood, concrete), temperature, humidity, etc., as well as the precise location information of the target user. The sensor network can provide high-precision spatial mapping and positioning services to ensure the accuracy and real-time nature of the data. For example, an infrared camera can capture the three-dimensional structure of the room, while a Wi-Fi signal strength detector can accurately locate the user's movement trajectory.

[0042] Based on the collected environmental attribute data and the user's real-time location data, the system activates a three-dimensional modeling engine to construct a three-dimensional virtual environment corresponding to the actual environment. The three-dimensional virtual environment includes not only static elements (such as walls, furniture) but also dynamic elements (such as the change in the user's position). The system uses computer vision technology to analyze image frames, generates a detailed environmental map, and combines the distance information provided by the sensors to establish a realistic three-dimensional space.

[0043] Secondly, in the constructed three-dimensional virtual environment, the system determines the specific position of the player and its propagation path according to the channel configuration and scene description in the metadata. For example, for a multi-channel surround sound setting, the system will calculate the optimal placement position of each speaker to achieve the best sound coverage and spatial distribution. The system also considers the characteristics of the audio-visual content (such as a concert, movie dialogue, etc.) and adjusts the propagation path to simulate the real sound wave propagation effect. To ensure the realism of the sound, the system introduces a physical rule-based sound wave propagation model, considering the impact of air density changes on the speed of sound, especially in an environment with a large temperature gradient. In addition, the system will dynamically adjust the propagation path according to the user's real-time position to ensure that even if the user moves, a consistent and natural sound localization effect can be maintained.

[0044] Then, based on the user's real-time location data, the system determines the virtual position of the target user in the three-dimensional virtual environment. This process is not just a simple coordinate mapping but comprehensively considers factors such as the user's movement trajectory and posture changes to ensure the accuracy and real-time nature of the virtual position data.

[0045] Finally, based on the player position, propagation path, and user virtual position data, the system determines the final virtual audio-visual effect. This step involves multiple aspects of adjustment, including but not limited to the sense of direction, distance, spatial distribution, frequency response, etc. of the sound. The system uses adaptive filters and other audio processing technologies to ensure that even in a complex environment, the audio output can remain clear and stable. Ultimately, the virtual audio-visual effect generated by the system can perfectly match the three-dimensional virtual environment, providing the user with an immersive audio-visual experience.

[0046] Through the above steps, the system can construct an accurate three-dimensional virtual environment based on environmental attribute data and real-time user location data, determine the player position and propagation path, and dynamically adjust the virtual audio-visual effects according to the user's virtual position. This method not only ensures the realism and spatialization effect of audio output, but also provides a highly consistent and personalized auditory experience, significantly enhancing the user's immersion and satisfaction.

[0047] S120. During the process of controlling the player to play audio-visual content based on the player's playback parameters, obtain the actual audio-visual data corresponding to the player, obtain the real-time user feedback data of the target user under the actual audio-visual data, adjust the playback parameters of the player based on the real-time user feedback data, compare the actual audio-visual data with the virtual audio-visual effects to obtain the corresponding comparison result, identify the abnormal type data in the comparison result, and adjust the construction parameters of the three-dimensional virtual environment based on the abnormal type data.

[0048] Among them, start the player according to the initialized and preliminarily adjusted playback parameters to start playing audio-visual content. At this time, the player outputs sound according to parameters such as the set audio equalizer, surround sound effect, and volume, and presents images according to the video display parameters (such as brightness, contrast). During the playback process, the system keeps real-time monitoring of the player status to ensure that all parameters are correctly applied and the playback is smooth and error-free. In addition, the system may also enable a buffering mechanism to cope with network latency or device performance issues to ensure the stability of the playback.

[0049] During the playback process, the system continuously collects the actual audio-visual data output by the player. These actual audio-visual data include but are not limited to the volume, frequency response, spatial distribution, etc. of the audio-visual, and the actual audio-visual data can be obtained through built-in audio analysis modules and video capture tools. For example, the audio analysis module can monitor the volume level, spectral distribution, and channel balance in real time, while the video capture tool can record information such as the brightness and color saturation of the picture.

[0050] Use a variety of sensors (such as cameras, heart rate monitors, microphones, etc.) to capture the user's behavior and physiological signals in real time as real-time feedback data. For example, the camera can capture changes in the user's facial expressions, the heart rate monitor can reflect the user's emotional fluctuations, and the microphone can pick up the user's oral feedback (such as comments, instructions). In addition, the system can also receive actively provided feedback opinions (such as ratings, comments) through the user interface. All these feedback data are integrated into a comprehensive database for subsequent analysis and processing. To improve the effectiveness of the feedback data, the system may apply machine learning algorithms to identify key emotional and behavioral patterns, so as to more accurately understand the user's experience and needs.

[0051] Then, based on the collected real-time feedback data, various parameters of the player are dynamically adjusted. For example, if it is detected that the user shows an unpleasant expression or has an increased heart rate, the system may lower the volume or adjust the audio equalizer to reduce the stimulation; if the user frequently adjusts the screen brightness, the system will automatically optimize the video display parameters to enhance the viewing comfort. The adjustment process not only considers a single feedback point but also combines historical behavior patterns to predict and meet the user's potential needs.

[0052] Compare the actually collected real-time audio-visual data with the preset virtual audio-visual effects to check the differences between the two. The comparison process involves multiple dimensions, including volume consistency, frequency response matching degree, spatial distribution accuracy, etc. The system can use specially designed comparison algorithms to quantify the deviations in each dimension and generate detailed comparison results. These comparison results not only show the advantages and disadvantages of the current playback effect but also point out specific improvement directions. For example, if it is found that the low-frequency response in the actually collected audio-visual data is insufficient, the system will mark this point and suggest increasing the low-frequency gain. In this way, the system can comprehensively evaluate the playback effect and ensure that it is as close as possible to the ideal virtual audio-visual effect.

[0053] Finally, once abnormal type data is identified, the system will correspondingly adjust the construction parameters of the three-dimensional virtual environment to correct the problem and optimize the playback effect. For example, if it is detected that the temperature change affects the air density, the system will recalculate the speed of sound and adjust the propagation path; if it is found that the sensor error causes inaccurate user position data, the system will recalibrate the sensor and update the virtual position data.

[0054] Through the above steps, the system can obtain and analyze the actual audio-visual data of the player and the real-time feedback data of the user in real time during the playback process, dynamically adjust the playback parameters, and optimize the construction parameters of the three-dimensional virtual environment through comparison and anomaly detection. This method not only ensures a high degree of consistency and stability of the playback effect but also provides a personalized user experience, significantly improving the user's satisfaction and immersion.

[0055] The following is a simplified case description: the scenario description of personalized audio-visual playback control in a home theater. Mr. Zhang and his family like to watch movies or listen to music at home on weekend evenings. They have an advanced home theater system that can provide a highly personalized audio-visual experience based on the environment and the real-time state of the user.

[0056] First, one evening, Mr. Zhang selected the science fiction movie "Interstellar" to play through his smart TV. The system first automatically downloaded and verified the file integrity of this movie from the cloud server and parsed its metadata (such as time code, audio channel configuration, scene description, etc.). Based on this information, the machine learning algorithm identified the scene type of the movie as "space adventure" and the emotional atmosphere as "tense and full of hope".

[0057] Secondly, according to the recognition results, the system automatically adjusted the player parameters to match the characteristics of this movie. For example, it enhanced the low-frequency response to highlight the sound of the spaceship engine and optimized the surround sound effect to create a sense of vastness in the cosmic space. At the same time, the system retrieved the auditory sensitivity and personal health monitoring data of Mr. Zhang and his family and found that Mr. Zhang was more sensitive to high-frequency sounds, so it appropriately reduced the volume of the high-frequency part to ensure that he would not feel discomfort.

[0058] Subsequently, the home theater system used the pre-installed sensor network (such as infrared cameras, ultrasonic rangefinders) to collect the environmental attribute data in the room (such as furniture layout, wall materials) and the real-time position information of Mr. Zhang's family. Based on this, the system constructed an accurate three-dimensional virtual environment model. In this virtual environment, the system determined the optimal placement positions and propagation paths of each speaker to ensure that even if the user moves in the room, they can hear a consistent and natural sound localization effect.

[0059] Then, when the movie reached a tense plot, the system detected the increase in Mr. Zhang's heart rate and changes in facial expressions, so it automatically adjusted the audio output, increasing the low-frequency gain to enhance the sense of oppression. In the dialogue scene, the system optimized the speech clarity to make the dialogue easier to understand. In addition, the system continuously monitored the playback effect and compared the actual audio-visual data with the preset virtual audio-visual effects. Once any abnormality was found (such as excessive background noise), it would immediately adjust the construction parameters of the three-dimensional virtual environment to ensure the best audio-visual quality.

[0060] Finally, after the movie ended, the system generated a detailed comprehensive report recording various indicators, user behavior patterns, and diagnoses of potential problems during the playback. This report not only helped the development team improve the system performance but also provided personalized improvement suggestions for Mr. Zhang's family, such as recommending audio configurations more suitable for their home theater setup, further enhancing the future movie-watching experience.

[0061] In the solution of the embodiment of the present application, after identifying the abnormal type data in the recognition comparison result, the method further includes:

[0062] S131, Retrieve the built-in fault diagnosis algorithm and historical data, obtain the corresponding current environmental information, and determine the specific cause data of the abnormality based on the historical data and the current environmental information through the fault diagnosis algorithm.

[0063] During the playback process, the system continuously monitors the difference between the actual audio-visual data and the preset virtual audio-visual effects. When a significant deviation is found between the two, the system marks these abnormal type data and makes a detailed record and classification of them; for example, if there is unexpected noise in the audio output or the image frame rate suddenly drops, the system will capture and record the specific characteristics (such as frequency, intensity, occurrence time) of these abnormal phenomena. To ensure the accuracy of abnormal identification, the system uses a specially designed comparison algorithm to quantify the deviation in each dimension and generate a detailed comparison result.

[0064] After identifying the abnormality, the system starts the built-in fault diagnosis module, which contains a series of optimized fault diagnosis algorithms and a rich historical data set. First, the system obtains the current environmental information, including physical parameters such as room temperature, humidity, air density, and the status report of the sensor network. Then, the system conducts a comparative analysis of the current environmental information and the historical data to find similar historical cases and their solutions. For example, if the temperature changes significantly in the current environment, the system will search for similar problems caused by temperature changes in history and refer to their solutions. In addition, the system will also consider the user behavior pattern and device usage habits to more comprehensively understand the problem background. The fault diagnosis algorithm combines real-time data and historical experience to generate a detailed diagnosis report to help determine the specific cause of the abnormality.

[0065] Based on the above analysis, the system further refines the cause of the abnormality and classifies it into several possible situations, such as player hardware failure, sensor error, incorrect configuration of the audio processing algorithm, etc. For each situation, the system generates specific cause data. For example, if it is a player hardware failure, the system may detect speaker distortion or unstable power supply; if it is a sensor error, the system will find position offset or signal loss; and if it is an incorrect configuration of the audio processing algorithm, the system will identify spectrum imbalance or synchronization problems. To improve the diagnostic accuracy, the system may also apply a machine learning model to predict potential problems to ensure a high confidence level of the diagnostic results. Finally, the system generates a detailed abnormal cause report listing all possible problems and their corresponding solutions.

[0066] S132, Perform automatic repair through the automatic recovery mechanism based on the specific cause data. If the automatic repair fails, generate a corresponding detailed report, where the automatic repair includes restarting relevant components, recalibrating sensors, and adjusting audio processing parameters.

[0067] Among them, according to the determined specific cause data, the system starts the corresponding automatic recovery mechanism. For player hardware failures, the system may attempt to restart relevant components, such as resetting the speaker or reconnecting the power supply; for sensor errors, the system will recalibrate the sensor to ensure its normal working state; and for misconfigured audio processing algorithms, the system will adjust relevant parameters, such as resetting the equalizer or optimizing the filter. The system also monitors the effect of automatic repair in real time to evaluate whether the expected goal has been achieved. If the initial repair fails to solve the problem, the system will continue to try other solutions until the problem is completely solved. If all automatic repair attempts fail, the system will automatically generate a detailed fault report recording the entire diagnosis and repair process for technicians to further analyze and process. The automatic recovery mechanism also includes a logging function for subsequent review and optimization.

[0068] Through the execution steps from S131 to S132 above, after the system identifies the abnormal type data in the comparison result, it can determine the specific cause of the abnormality using the built-in fault diagnosis algorithm and historical data, and perform repairs through the automatic recovery mechanism. This method can not only quickly locate and solve problems occurring during playback, ensuring a high degree of consistency and stability of the playback effect, but also provide a detailed fault report for technicians to further optimize the system performance. This intelligent fault handling process greatly improves user satisfaction and system reliability.

[0069] During the process of adjusting the construction parameters of the three-dimensional virtual environment based on the abnormal type data, the method further includes:

[0070] S133, obtain weather forecast information for a future period of time, and predict the prediction results of environmental variables through time series analysis.

[0071] Among them, the system first obtains weather forecast information for a future period of time (such as the next few hours to several days) through the Internet or the built-in meteorological service interface. This information includes, but is not limited to, the changing trends of environmental variables such as temperature, humidity, wind speed, and air pressure. To ensure the accuracy and timeliness of the data, the system will obtain data from multiple authoritative meteorological sources and conduct comprehensive analysis to reduce errors. In addition, the system may also apply machine learning models to train historical weather data to predict the possibility and influence range of future weather changes. This step provides the necessary input data for subsequent time series analysis.

[0072] In addition, based on the obtained weather forecast information, the system activates the time series analysis module, which uses statistical and machine learning algorithms to model and predict the changes in future environmental variables. For example, the system may use technologies such as ARIMA (Autoregressive Integrated Moving Average) and LSTM (Long Short-Term Memory) to predict the changes in physical parameters such as temperature, humidity, air density, etc. By analyzing past data patterns and current trends, the system can generate highly accurate prediction results. In addition, the system also takes into account seasonal factors and regional characteristics to further improve the accuracy of the prediction. Finally, the system outputs a detailed environmental variable prediction report, listing the expected values and their confidence intervals of each variable at different future time periods.

[0073] S134, adjust the physical parameters of the three-dimensional virtual environment based on the prediction results and abnormal type data.

[0074] Among them, combining the prediction results of environmental variables and the previously identified abnormal type data, the system starts to adjust the physical parameters in the three-dimensional virtual environment. Specifically, the system will recalculate the speed and path of sound propagation according to factors such as predicted temperature, humidity, air density, sound speed, and wind speed to ensure the realism of the audio effect.

[0075] For example, if the prediction shows that the temperature will rise, resulting in a decrease in air density and an increase in sound speed, the system will accordingly adjust the placement position and volume settings of the speakers to compensate for the effects brought by these changes. For abnormalities caused by sensor errors or hardware failures, the system will also optimize relevant parameters according to the prediction results, such as recalibrating the sensors or adjusting the audio processing algorithm. All these adjustments are aimed at maintaining the best audiovisual experience and ensuring consistency even under changing environmental conditions.

[0076] S135, monitor the actual playback effect after adjusting the physical parameters, and judge whether it is necessary to adjust the construction parameters based on the actual playback effect.

[0077] Among them, after the adjustment is completed, the system enters the monitoring stage to monitor the actual playback effect after adjustment in real time. The system uses the built-in audio analysis module and video capture tools to continuously collect the actual audio-visual data output by the player, including key indicators such as volume, frequency response, and spatial distribution. At the same time, the system will also monitor the real-time feedback of users through the sensor network, such as physiological signals such as heart rate changes and facial expressions, as well as the scores and comments actively provided by users. All these data are integrated into a comprehensive database for evaluating the effect of the adjustment. The system will compare the changes in the playback effect before and after the adjustment to check whether the expected goals are achieved. For example, whether the audio is clearer and the picture is more stable. If any new problems or unachieved effects are found, the system will record them as the basis for further adjustment.

[0078] Then, based on the monitoring results, the system evaluates whether it is necessary to further adjust the construction parameters of the three-dimensional virtual environment. If the monitoring data shows that the playback effect has been significantly improved and the user feedback is positive, the system will continue to maintain the current settings. However, if it is found that there are still deficiencies in some aspects or new problems arise, the system will initiate a new round of adjustment processes. For example, if the user still feels that the audio is not clear enough or there is a delay in the image, the system may further optimize the position of the speakers or adjust the video encoding parameters. In addition, the system will also consider the impact of long-term environmental changes and regularly update the physical parameters to ensure that the three-dimensional virtual environment always maintains the optimal state. Ultimately, the purpose of the system is to provide a highly consistent and personalized audio-visual experience through continuous optimization and adjustment.

[0079] Through the execution steps from S133 to S135 above, the system can, during the process of adjusting the construction parameters of the three-dimensional virtual environment based on the abnormal type data, obtain and analyze the future weather forecast information in advance, predict the changes in environmental variables, and optimize the physical parameters accordingly. Subsequently, the system continuously monitors the actual playback effect after adjustment, evaluates the effectiveness of the optimization measures, and decides whether further adjustment is needed. This method not only ensures a high degree of consistency and stability of the playback effect but also anticipates and responds to environmental changes, greatly improving user satisfaction and system reliability.

[0080] During the process of real-time adjustment of the player parameters, the method further includes:

[0081] S136, retrieving the accurate position data set of the target user, retrieving the historical movement data of the target user, and predicting the predicted movement path of the target user within a certain period in the future based on the accurate position data set and the historical movement data.

[0082] Among them, the system first obtains the accurate position data of the target user in real time from the sensor network (such as Wi-Fi signal strength detectors, Bluetooth beacons, cameras, etc.). These data can provide the three-dimensional coordinates of the user in the room, ensuring the high precision and real-time nature of the position information. At the same time, the system also retrieves the historical movement data of the user from the database. These data record the movement trajectories of the user in the past period, including movement speed, direction, stay time, etc. By combining the real-time position data and the historical movement data, the system can construct a complete user movement pattern map, providing a solid foundation for subsequent prediction.

[0083] Then, based on the collected precise location data and historical movement data, the system activates the movement path prediction module. The movement path prediction module uses machine learning algorithms (such as random forest, LSTM, etc.) to model and analyze the user's behavior patterns, and predicts the user's movement path within a certain period in the future. For example, if the user often walks from the living room to the kitchen to get snacks while watching a movie, the system will identify this pattern and predict similar movement behaviors. In addition, the system may also consider the influence of external factors (such as time, activity type) to further improve the accuracy of the prediction. Finally, the system generates a detailed movement path prediction report, listing the expected locations and their probability distributions of the user at different times in the future.

[0084] S137. Calculate the optimal position and propagation path of the virtual sound source based on the predicted movement trajectory, and update the audio rendering parameters in the three-dimensional environment model.

[0085] Among them, according to the predicted movement path, the system recalculates the optimal position and propagation path of the virtual sound source. Specifically, the system dynamically adjusts the position and output angle of each speaker according to the user's predicted position to achieve optimal sound coverage and spatial distribution. For example, if it is predicted that the user will walk from the living room to the balcony, the system will adjust the settings of the speakers near the balcony in advance to ensure that the user can still hear clear and coherent sound after arriving. In addition, the system will also consider the characteristics of the audio-visual content (such as a concert, movie dialogue), and adjust the propagation path to simulate the real sound wave propagation effect. To ensure the realism of the sound, the system introduces a physical rule-based sound wave propagation model, considering the influence of air density changes on the sound speed, especially in an environment with a large temperature gradient.

[0086] Based on the new position and propagation path of the virtual sound source, the system updates the audio rendering parameters in the three-dimensional environment model. This step involves adjustments in multiple aspects, including but not limited to the sense of direction, distance, spatial distribution, frequency response, etc. of the sound. The system uses adaptive filters and other audio processing technologies to ensure that the audio output remains clear and stable even in a complex environment. For example, if the user approaches a certain speaker, the system may enhance the low-frequency response of that speaker to highlight the drum beats; while when the user moves away, the volume is appropriately reduced to avoid overly strong sound stimulation. In addition, the system will also adjust the audio rendering parameters according to the user's auditory preferences and health conditions to provide a more comfortable auditory experience.

[0087] S138. Adjust the corresponding spatialization effect based on the audio rendering parameters, and the spatialization effect includes surround sound and stereo sound.

[0088] Finally, the system adjusts the corresponding spatialization effects according to the updated audio rendering parameters, including surround sound and stereo. For multi-channel surround sound settings, the system ensures that the output of each speaker can accurately reflect the position and movement of the virtual sound source, enabling users to experience a realistic sense of space. For example, in action scenes, the system can simulate the effect of a bullet flying by by adjusting the delay between the left and right channels; while in quiet dialogue scenes, it optimizes voice clarity to make the dialogue more understandable. For stereo settings, the system focuses on enhancing the sound separation in the front, back, left, and right directions, allowing users to feel rich spatial levels even when using headphones. All these adjustments aim to provide a highly consistent and personalized auditory experience, enabling users to enjoy the best sound effects regardless of how they move.

[0089] Through the execution steps of S136 to S138 above, the system can, during the process of real-time adjusting the player parameters, retrieve the accurate position data and historical movement data of the user, predict their future movement path, and accordingly optimize the position and propagation path of the virtual sound source, update the audio rendering parameters in the three-dimensional environment model, and finally adjust the spatialization effect. This method not only ensures a high degree of consistency and naturalness in the audio output but also anticipates the user's movement, greatly enhancing the user's immersion and overall satisfaction. Different from the previous fault diagnosis and automatic recovery mechanisms, the focus here is on providing a personalized auditory experience that always adapts to the user's movement through intelligent prediction and dynamic adjustment.

[0090] During the playback of audio and video by the player, in the embodiments of the present application, the method further includes:

[0091] S141, monitor the connection status in real time, determine whether a new player has joined. If so, confirm the device type of the corresponding new player through the device identifier and obtain the technical specifications of the new player device type.

[0092] Among them, the system starts a network monitoring module to continuously monitor the connection status of all devices in the home network. This module detects in real time whether a new device has joined the current audio and video playback network through methods such as Wi-Fi, Bluetooth, or wired connection. To ensure the accuracy and timeliness of the monitoring, the system regularly sends heartbeat signals (such as ping requests) to confirm the online status of each device and records any new connection events.

[0093] After detecting the addition of a new device, the system starts the device identification module to further determine whether the device is a new player. This process includes checking the device's type and functions. For example, the system will query the device's service ports, supported media formats, and communication protocols. If the device has audio output capabilities (such as speakers, headphone jacks) and supports common streaming media protocols (such as DLNA, AirPlay), the system will identify it as a potential new player. In addition, the system will also verify the manufacturer and model information of the device to determine its specific functions and technical specifications.

[0094] Once it is confirmed that the new device is a player, the system uses device identifiers (such as MAC address, serial number, device ID) to further confirm its specific type. These identifiers are usually embedded in the device's firmware or operating system, and the system obtains this information through standard query protocols (such as UPnP, mDNS). The system compares these identifiers with the pre-stored device database to identify the specific model and manufacturer of the new player. For example, if the new device is a wireless speaker from Sony, the system will match the corresponding entry and extract detailed device information from it. In addition, the system can also update the latest device library through the Internet to ensure that it can identify new models of players.

[0095] Based on the confirmed device type, the system obtains the technical specifications of the new player from the built-in database or the API interface provided by the manufacturer. These technical specifications include but are not limited to key parameters such as frequency response range, impedance, and maximum volume. For example, for a high-fidelity audio system, the system will obtain its frequency response range (such as 20Hz - 20kHz) to ensure the accuracy of audio output; for a portable speaker, the system will focus on its impedance value (such as 8 ohms) to optimize power efficiency and sound quality performance. In addition, the system will also consider the maximum volume limit of the device to prevent damage or distortion caused by overload.

[0096] S142. Analyze the audio characteristics of the new player device type based on the technical specifications of the device, and determine the optimal audio output parameters corresponding to the new player device type based on the audio characteristics.

[0097] Among them, according to the obtained technical specifications, the system starts the audio characteristics analysis module to comprehensively evaluate the audio characteristics of the new player. For example, the system will adjust the equalizer settings according to the frequency response range to ensure that the audio output covers the entire audible frequency band; optimize the current supply according to the impedance value to improve the audio amplification effect; set a reasonable volume upper limit according to the maximum volume to avoid damage to the device caused by user misoperation. In addition, the system will also consider other factors, such as dynamic range, signal-to-noise ratio, etc., to further improve the audio quality. Finally, the system generates a detailed audio characteristics report, listing all optimization suggestions and their expected effects.

[0098] Based on the results of audio feature analysis, the system determines the optimal audio output parameters for the new player. This step involves adjustments in multiple aspects, including but not limited to equalizer settings, surround sound effects, volume control, etc. For example, if the new player has a wide frequency response range, the system may enhance the low-frequency and high-frequency parts to highlight the layers of music; if the impedance of the new player is low, the system will increase the current supply to ensure sufficient volume output. In addition, the system also takes into account the user's auditory preferences and health conditions, and adjusts the audio output parameters personalized to provide a more comfortable auditory experience. All these adjustments aim to give full play to the performance advantages of the new player and ensure that users obtain the best audio-visual enjoyment.

[0099] After the playback ends, the method further includes:

[0100] S143, integrating the interaction behavior data and real-time feedback data of the target user during playback.

[0101] Among them, after the playback ends, the system starts the data integration module to collect and organize the interaction behavior data and real-time feedback data of the target user during playback. The interaction behavior data includes but is not limited to operation records of user-initiated operations such as volume adjustment, scene switching, pause / play operations, etc.; the real-time feedback data covers physiological signals such as facial expressions and heart rate changes, which are captured in real time by the built-in sensor network (such as cameras, heart rate monitors). The system integrates all these data into a unified data structure, ensuring that each data point is accompanied by a timestamp and other necessary metadata, so that the time and background of its occurrence can be accurately traced during subsequent analysis.

[0102] S144, collecting the active feedback opinions provided by the target user based on the common preference data identified from the interaction behavior data and real-time feedback data, and forming a user feedback database with the common preference data and active feedback data.

[0103] Among them, the system uses machine learning algorithms to deeply analyze the integrated data to identify the common preferences shown by the target user during playback. For example, if the user frequently adjusts the volume or the heart rate increases in a specific scenario, the system will mark these behaviors and try to find the patterns among them. To improve the accuracy of identification, the system may apply techniques such as cluster analysis and association rule mining to discover the potential connections between different behaviors. In addition, the system also considers long-term behavior patterns, such as the preference changes of the user at different time periods, to understand the user's habits more comprehensively. Finally, the system generates a detailed preference report listing the operations most frequently performed by the user and their corresponding frequencies and contexts.

[0104] In addition to the automatically recognized preference data, the system also encourages users to provide active feedback. This can be done in various ways, such as pop-up surveys after playback, voice assistant conversations, comment sections within the application, etc. The system is designed with a user-friendly interface to guide users to provide evaluations and suggestions regarding audio-visual content, playback effects, device performance, etc. For each piece of feedback, the system will classify and label it to ensure the convenience of subsequent processing. In addition, the system can also combine natural language processing technology to automatically extract key information from the feedback, such as sentiment tendency, specific problem descriptions, etc., further enriching the content of the user feedback database.

[0105] At the same time, the system integrates the identified common preference data and the collected active feedback into a user feedback database. This database not only contains the specific feedback content but also includes relevant metadata, such as feedback time, device type, playback content, etc. To ensure data consistency and accessibility, the system stores all information in a standardized data format and establishes an efficient data indexing mechanism for convenient subsequent querying and analysis. In addition, the system will regularly update the database by adding new feedback data to ensure that it always reflects the latest user needs and behavior patterns. The user feedback database becomes an important resource library for the continuous improvement of the system.

[0106] S145, classify and prioritize the user feedback database and generate a corresponding detailed comprehensive report.

[0107] Among them, the system starts the data analysis module to classify and prioritize the data in the user feedback database. First, the system classifies the feedback into different types according to its nature, such as sound quality problems, device failures, content recommendations, etc. Then, the system evaluates the importance of each piece of feedback, considering factors including the number of feedbacks, severity, scope of influence, etc. For example, if multiple users report the same problem within the same time period, the system will assign a higher priority; while for the minor dissatisfaction of individual users, it may be classified as a lower priority. In addition, the system will also combine historical data and prediction models to identify potential high-risk problems in advance to ensure timely handling. Finally, the system generates a classified and sorted feedback list to guide subsequent optimization work.

[0108] Based on the classified and sorted user feedback data, the system generates a detailed comprehensive report. This report covers various metrics during playback (such as the number of volume adjustments, the amplitude of heart rate changes), user behavior patterns (such as common operation preferences), a summary of feedback (such as user satisfaction scores, specific suggestions), and the diagnosis of potential problems (such as hardware failures, software errors). To ensure the readability and usability of the report, the system uses various visualization tools such as charts and statistical analysis to intuitively display the data and conclusions. In addition, the system will also put forward targeted improvement suggestions to help the development team clarify the optimization direction. Ultimately, this comprehensive report is not only an important basis for the system's self-evaluation but also provides valuable reference materials for future version iterations.

[0109] An embodiment of the present application discloses an audio-video playback control device, referring to Figure 2 , including but not limited to:

[0110] A preliminary adjustment module 200, which obtains the audio-video file to be played, extracts the metadata in the audio-video file, uses machine learning algorithms to identify the corresponding scene type data and emotional atmosphere data from the metadata information, initializes the player parameters of the player based on the scene type data and emotional atmosphere data, determines the corresponding target user, retrieves the auditory sensitivity data and personal health monitoring data of the target user, and uses the auditory sensitivity data and personal health monitoring data to preliminarily adjust the player parameters. Among them, the metadata at least includes time code, channel configuration, and scene description;

[0111] A virtual audio-video effect determination module 210, which collects the environmental attribute data corresponding to the current environment and the real-time position data of the target user based on the pre-installed sensor network, constructs a corresponding three-dimensional virtual environment based on the environmental attribute data and the real-time position data, determines the player position and propagation path in the three-dimensional virtual environment based on the channel configuration and scene description in the metadata, determines the virtual position data of the target user corresponding to the three-dimensional virtual environment, and uses the player position, propagation path, and virtual position data to determine the corresponding virtual audio-video effect;

[0112] A comparison result acquisition module 220, which, during the process of controlling the player to play the audio-video based on the player playback parameters, obtains the actual audio-video data corresponding to the player, obtains the user real-time feedback data of the target user under the actual audio-video data, adjusts the playback parameters of the player based on the user real-time feedback data, compares the actual audio-video data with the virtual audio-video effect to obtain the corresponding comparison result, identifies the abnormal type data in the comparison result, and adjusts the construction parameters of the three-dimensional virtual environment based on the abnormal type data. Among them, the actual audio-video data includes the volume, frequency response, and spatial distribution of the audio-video.

[0113] An embodiment of the present application also discloses an audio - video playback control device, including a processor, and a program of the audio - video playback control method of any one of the above is running in the processor.

[0114] An embodiment of the present application also discloses a storage medium, storing a program of the audio - video playback control method of any one of the above.

[0115] Although the embodiments of the present application have been shown and described above, it can be understood that the above - mentioned embodiments are exemplary and should not be construed as limiting the present application. Those of ordinary skill in the art can make changes, modifications, substitutions, and variations to the above - mentioned embodiments within the scope of the present application.

Claims

1. A method for controlling audio and video playback, characterized in that: include: Acquire an audio or video file to be played, extract metadata from the audio or video file, use a machine learning algorithm to identify corresponding scene type data and emotional atmosphere data from the metadata information, initialize player parameters of the player based on the scene type data and emotional atmosphere data, determine a corresponding target user, retrieve auditory sensitivity data and personal health monitoring data corresponding to the target user, and preliminarily adjust the player parameters based on the auditory sensitivity data and personal health monitoring data, wherein the metadata at least includes time code, channel configuration, and scene description; Based on a pre-installed sensor network, environmental attribute data corresponding to the current environment and real-time location data of the target user are collected, a corresponding three-dimensional virtual environment is constructed based on the environmental attribute data and the real-time location data, a player position and a propagation path in the three-dimensional virtual environment are determined based on the channel configuration and scene description in the metadata, virtual location data corresponding to the target user in the three-dimensional virtual environment are determined, and corresponding virtual audio and video effects are determined based on the player position, the propagation path and the virtual location data; In the process of controlling the player to play audio and video based on the player playback parameters, the actual audio and video data corresponding to the player is obtained, the real-time user feedback data of the target user under the actual audio and video data is obtained, the playback parameters of the player are adjusted based on the real-time user feedback data, the actual audio and video data are compared with the virtual audio and video effects, the corresponding comparison results are obtained, the abnormal type data in the comparison results are identified, and the construction parameters of the three-dimensional virtual environment are adjusted based on the abnormal type data, wherein the actual audio and video data include the volume, frequency response, and spatial distribution of the audio and video.

2. The audio and video playback control method according to claim 1, characterized in that: After identifying the abnormal type data in the comparison result, the method further includes: Retrieving the built-in fault diagnosis algorithm and historical data, obtaining the corresponding current environment information, and determining the specific cause data of the abnormality through the fault diagnosis algorithm based on the historical data and the current environment information, wherein the specific cause data includes player hardware failure, sensor error, and audio processing algorithm misconfiguration; Automatic repair is performed based on the specific cause data through an automatic recovery mechanism. If the automatic repair fails, a corresponding detailed report is generated, wherein the automatic repair includes restarting related components, recalibrating sensors, and adjusting audio processing parameters.

3. The audio and video playback control method according to claim 1, characterized in that: In the process of adjusting the construction parameters of the three-dimensional virtual environment based on the abnormal type data, the method further includes: Obtain weather forecast information for a period of time in the future and predict the results of environmental variables through time series analysis; Adjusting physical parameters of the three-dimensional virtual environment based on the prediction results and the abnormal type data, wherein the physical parameters include air density, speed of sound, temperature, humidity and wind speed; The actual playback effect after the physical parameters are adjusted is monitored, and whether the construction parameters need to be adjusted is determined based on the actual playback effect.

4. The audio and video playback control method according to claim 1, characterized in that: In the process of adjusting the player parameters in real time, the method further includes: Retrieving a precise location data set of a target user, retrieving historical movement data of the target user, and predicting a predicted movement path of the target user in a future period of time based on the precise location data set and the historical movement data; Calculating the optimal position and propagation path of the virtual sound source based on the predicted movement trajectory, and updating the audio rendering parameters in the three-dimensional environment model; The corresponding spatialization effect is adjusted based on the audio rendering parameters, and the spatialization effect includes surround sound and stereo sound.

5. The audio and video playback control method according to claim 1, characterized in that: The method also includes: Monitor the connection status in real time to determine whether a new player has been added. If so, confirm the corresponding new player device type through the device identifier and obtain the technical specifications of the new player device type, wherein the technical specifications include frequency response range, impedance, and maximum volume; The audio characteristics of the new player device type are analyzed based on the technical specifications of the device, and the optimal audio output parameters corresponding to the new player device type are determined based on the audio characteristics.

6. The audio and video playback control method according to claim 1, characterized in that: After the playback is finished, the method also includes: Integrate the target user's interactive behavior data and real-time feedback data during playback. The interactive behavior data includes volume adjustment, scene switching, pause / play operations, and the real-time feedback data includes physiological signals such as facial expressions and heart rate changes. Based on the common preference data identified by the interactive behavior data and the real-time feedback data, active feedback opinions actively provided by the target users are collected, and the common preference data and the active feedback data are formed into a user feedback database; Categorize and prioritize the user feedback database and generate corresponding detailed comprehensive reports, which include various indicators during playback, user behavior patterns, feedback summary, and diagnosis of potential problems.

7. An audio and video playback control device, characterized in that: include: A preliminary adjustment module, which obtains the audio and video files to be played, extracts metadata from the audio and video files, uses a machine learning algorithm to identify corresponding scene type data and emotional atmosphere data from the metadata information, initializes player parameters of the player based on the scene type data and emotional atmosphere data, determines the corresponding target user, retrieves the auditory sensitivity data and personal health monitoring data corresponding to the target user, and preliminarily adjusts the player parameters based on the auditory sensitivity data and personal health monitoring data, wherein the metadata at least includes time code, channel configuration, and scene description; A virtual audio and video effect determination module collects environmental attribute data corresponding to the current environment and real-time position data of the target user based on a pre-installed sensor network, constructs a corresponding three-dimensional virtual environment based on the environmental attribute data and the real-time position data, determines a player position and a propagation path in the three-dimensional virtual environment based on the channel configuration and scene description in the metadata, determines virtual position data corresponding to the target user in the three-dimensional virtual environment, and determines a corresponding virtual audio and video effect based on the player position, the propagation path and the virtual position data; The comparison result acquisition module obtains the actual audio and video data corresponding to the player in the process of controlling the player to play audio and video based on the player playback parameters, obtains the real-time user feedback data of the target user under the actual audio and video data, adjusts the playback parameters of the player based on the real-time user feedback data, compares the actual audio and video data with the virtual audio and video effects to obtain the corresponding comparison results, identifies abnormal type data in the comparison results, and adjusts the construction parameters of the three-dimensional virtual environment based on the abnormal type data, wherein the actual audio and video data includes the volume, frequency response, and spatial distribution of the audio and video.

8. An audio and video playback control device, characterized in that: It includes a processor, in which a program of the audio and video playback control method as described in any one of claims 1 to 6 is running.

9. A storage medium, characterized in that: A program storing the audio and video playback control method as described in any one of claims 1 to 6.

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