Intelligent audio playing control method and system, storage medium and equipment

Abstract

The invention relates to the technical field of audio playing control, and discloses an intelligent audio playing control method and system, a storage medium and equipment, and the method comprises the steps: selecting an audio input source and stored playing content, and setting playing parameters which comprise a triggering volume, an equalizer, playing duration and a playing mode; grouping the plurality of audio devices according to presetting, determining a main audio device through a device priority model, setting a task schedule, and presetting a playing task for a specific time period and a playing scene; and in response to the trigger instruction, calling a play control algorithm to execute an audio play operation, constructing a learning optimization model, and dynamically adjusting play and control strategies. Through the above method, an intelligent audio playing control system is realized, advanced setting options are introduced, audio playing control can be customized in a deeper level, multi-device synchronization and group device functions are added, personalized experience is provided, and playing behaviors are customized according to requirements of different scenes.

Description

Technical Field

[0001] The present invention relates to the technical field of audio playback control, and in particular to an intelligent audio playback control method, system, storage medium and device. Background Art

[0002] Currently, most smart audio devices on the market provide a Preset function, which is mainly used to preset and save object properties, allowing users to quickly play commonly used music content through preset buttons. In terms of functionality, it is mainly limited to simple content storage and playback triggering, and users cannot deeply customize it according to personal needs during use. For example, when a user sets a Preset for a playlist on platforms such as Amazon Music, the system always starts playing from the first song each time it is triggered, which does not meet the user's desire to continue from the last playback position. In addition, the audio playback control function usually lacks personalized options such as playback time, volume adjustment, EQ setting, etc., and cannot adapt to the user's various usage scenarios, resulting in a limited user experience.

[0003] This type of audio playback control function is too basic and cannot meet the user's personalized needs for playback behavior. At the same time, it lacks support for multi-device synchronization, and users cannot easily coordinate playback between multiple devices. This simple playback control solution makes users feel limited in actual usage scenarios, especially when it is necessary to manage complex multi-device audio systems through Presets, the shortcomings of the existing technology become more obvious. Therefore, how to improve the customizability of audio playback control functions and adapt to the diverse needs of users in different situations has become a problem that needs to be solved in the current field of intelligent audio control. Summary of the invention

[0004] The purpose of the present invention is to solve the shortcomings of the above-mentioned prior art and to provide an intelligent music recommendation method, system, storage medium and device, aiming to solve the problem that traditional playback control functions are too basic and lack customization.

[0005] On the one hand, an intelligent audio playback control method is provided, characterized in that it includes the following steps: S1: Select an audio input source and stored playback content, and set playback parameters, including trigger volume, equalizer, playback duration, and playback mode; S2: Group multiple audio devices according to presets, determine the main audio device through the device priority model, set task scheduling, and preset playback tasks for specific time periods and playback scenarios; S3: Respond to trigger instructions, call the playback control algorithm to perform audio playback operations, build a learning optimization model, and dynamically adjust the playback and control strategies.

[0006] Furthermore, in step S1, selecting the audio input source and the stored playback content further includes: Selecting an audio input source to provide a specific source of audio in response to the trigger instruction in step S3, wherein the selection of the audio input source includes a music service platform, a local file, and a specified link; Select audio data in various forms as playback content based on your needs and preferences, and manage the audio in an orderly manner.

[0007] Furthermore, in step S2, grouping the multiple audio devices according to the preset further includes: Get the list of added audio devices and group them according to usage scenarios and device functions; Store the device group data and record the status of each device in the device group; Configure audio synchronization playback parameters based on playback delay, volume balance, audio format and sampling rate, determine the communication method and protocol within the device group, and achieve multi-device synchronous playback.

[0008] Preferably, in step S2, determining the primary audio device by the device priority model further comprises: S21: each device is assigned a sound quality score according to its sound quality capability, and all devices are sorted in reverse order according to the sound quality scores to generate a device group sequence; S22: monitor the online status and occupancy of each device to generate real-time device availability status information; S23: constructing the device priority model based on the sound quality score and the device availability status information; S24: Dynamically generate the current highest priority audio playback device and determine it as the main audio device.

[0009] Furthermore, in step S2, setting the task schedule further includes: S100: Setting task time parameters, including the time point and periodicity setting of task execution, specifying a specific time to determine the start time of the playback task, and supporting multiple periodicity modes; S200: Setting task priority rules. If the user sets multiple playback tasks in the same time period, the multiple tasks are prioritized and a queue-jumping mechanism for playback tasks marked as urgent tasks is supported. S300: Manage playback scenarios, including organization and scheduling of audio content and device groups.

[0010] Preferably, in step S300, managing the playback scene further includes: S301: Create and manage defined audio playlists, support selecting a single audio, or arranging multiple audios in a customized order, and intelligently recommend relevant audio content based on the user's historical playback records, preferences, and the currently set time period phonemes; S302: Acquire information of all device groups grouped according to different standards, monitor the status of all devices in real time, and select a device group corresponding to the standard for task allocation according to demand when allocating playback tasks; S303: Setting a volume dynamic adjustment rule, and setting the initial volume and the volume change rule over time for each playback task; S304: Configure scene-related audio effect parameters and provide audio effect adjustment options according to different devices and playback content.

[0011] Preferably, the task scheduling also includes working in coordination with other system functions: Read data from other systems, including schedules in the calendar system and preference information in the preference system. Read the schedule in the calendar system, set the playback tasks reasonably according to the user's existing activity plan, and synchronize the set playback tasks to the user's calendar system to perform overall time management and arrangement; Read the preference information in the preference system, recommend audio content that meets the user's preference in a targeted manner based on the user's preference, record the number of set playback tasks, and update the information in the preference system.

[0012] Furthermore, in step S3, responding to the trigger instruction further includes: S31: Obtain the audio input source, the playback content, and the set playback parameters to generate configuration items; S32: The configuration items are comprehensively managed by defining a trigger function. When the user triggers a play operation, the trigger function is executed, and the system outputs corresponding play behavior according to the configuration items.

[0013] Preferably, in step S3, calling the playback control algorithm to perform the audio playback operation further includes: Based on machine learning algorithms, it analyzes users' audio playback habits in different time periods, automatically adjusts default settings, and recommends the most appropriate audio playback control behavior for users based on time and scenario.

[0014] Preferably, based on a machine learning algorithm, analyzing the audio playing habits of users in different time periods further includes: S31: Collect relevant data each time the user triggers a playback behavior, including usage time, playback content, selected device, and volume setting; S32: Obtain the user's historical behavior data, build a user preference model, and summarize the user's preference pattern; S33: Build a prediction model based on the collected trigger data and historical behavior data to generate a personalized recommendation strategy.

[0015] Wherein, in step S33, constructing the prediction model further includes: S331: Select a machine learning algorithm, where: If there is a large amount of labeled data, a supervised learning algorithm is selected to build a prediction model, which predicts new playback scenarios by learning the relationship between features and labels; If there is no labeled data, choose an unsupervised learning algorithm to build a prediction model, and analyze the user's behavior patterns in different time periods and scenarios by analyzing the clustering results; S332: Train the prediction model, and use the trained prediction model to perform classification and regression analysis on the data to predict user behavior.

[0016] Furthermore, in step S332, training the prediction model further includes: S3321: Preprocess the collected data, including data cleaning, removal of outliers, and standardization and normalization of the data; S3322: Select features related to audio playback scenarios and preferences for feature extraction, and further extract combined features; S3323: Divide the data into a training set and a test set, use the training set to train the model, and evaluate the model performance on the test set.

[0017] On the other hand, an intelligent audio playback control system is provided, based on the above method, comprising: A basic setting module, used to select an audio input source and stored playback content, and set playback parameters, including trigger volume, equalizer, playback duration, and playback mode; The group scheduling module is used to group multiple audio devices according to presets, determine the main audio device through the device priority model, set task scheduling, and preset playback tasks for specific time periods and playback scenarios; The trigger and control module is used to respond to trigger instructions, call the playback control algorithm to perform audio playback operations, build a learning optimization model, and dynamically adjust the playback and control strategies.

[0018] In addition, a computer-readable storage medium is provided, on which a computer program is stored, and when the program is executed by a processor, the intelligent audio playback control method described in any one of the above items is implemented.

[0019] At the same time, an electronic device is provided, comprising: one or more processors; a storage device for storing one or more programs, and when the one or more programs are executed by the one or more processors, the one or more processors implement any of the above-mentioned intelligent audio playback control methods.

[0020] Compared with the prior art, the present invention has the following beneficial effects: The present invention introduces advanced setting options to enable users to customize playback control at a deeper level, including support for playback duration setting, volume adjustment, input source selection, output interface configuration, EQ setting, URL link playback, and playback order randomization and Loop mode selection; The present invention adds multi-device synchronization and group device functions, which can adapt to complex audio scenarios. Multi-device synchronization and priority algorithms ensure that audio can be seamlessly coordinated between multiple devices, while giving priority to playing on high-quality audio devices, improving the audio playback effect; The present invention uses a machine learning algorithm to automatically optimize settings according to the user's usage habits, reducing the user's operation complexity and providing a smarter user interaction experience to gradually optimize and adjust trigger and control habits; The present invention introduces scheduling functions, including task scheduling and complex playback scene scheduling, and then works in conjunction with other system functions to provide adjustment options based on preferences, time periods, different devices and playback content. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] The accompanying drawings are used to provide a further understanding of the present invention and constitute a part of the specification. Together with the embodiments of the present invention, they are used to explain the present invention and do not constitute a limitation of the present invention. In the accompanying drawings: Figure 1 This is a flow chart of an intelligent audio playback control method of the present invention; Figure 2 A schematic diagram of a process for determining a main audio device according to the present invention; Figure 3 A flow chart of a method for setting task scheduling of the present invention; Figure 4 A flow chart of a method for managing playback scenes according to the present invention; Figure 5 A flow chart of a collaborative work scheduling method of the present invention; Figure 6 A flow chart of a control algorithm execution method of the present invention; Figure 7 This is a structural block diagram of an intelligent audio playback control system of the present invention; Figure 8 The figure is a schematic diagram of an embodiment of an electronic device of the present invention. DETAILED DESCRIPTION

[0022] In order to make the purpose, technical solution and advantages of the embodiments of the present application clearer, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0023] The present invention introduces a dynamic adjustment mechanism, which dynamically adjusts the audio compression rate and the transmission speed of each speaker by real-time monitoring of the network quality and the speaker buffer status. At the same time, by adjusting the transmission speed of each sub-speaker individually, the overall network resource utilization is effectively balanced, the adaptability and stability of the system are improved, and the overall playback quality is avoided from being affected by a single speaker. Overall, the stability, sound quality and user experience of multi-room audio synchronous playback are improved, and the complex network environment of the home is better adapted.

[0024] By introducing advanced setting options and multi-device synchronization, users can flexibly control playback content and behavior according to their needs. The core of this technical solution is to add personalized options such as volume, EQ, playback time, random playback, loop mode, etc. At the same time, through device priority algorithms and multi-device synchronization, users can play audio collaboratively on different devices. In addition, the system also learns users' playback control usage behavior through machine learning algorithms, automatically recommends the most appropriate playback settings, and further improves the user experience.

[0025] The specific implementation of the present invention is described below with reference to the accompanying drawings and embodiments.

[0026] like Figure 1 As shown, this embodiment provides an intelligent audio playback control method, and the technical solution includes the following steps: S1: Select an audio input source and stored playback content, and set playback parameters, including trigger volume, equalizer, playback duration, and playback mode; S2: Group multiple audio devices according to presets, determine the main audio device through the device priority model, set task scheduling, and preset playback tasks for specific time periods and playback scenarios; S3: Respond to trigger instructions, call the playback control algorithm to perform audio playback operations, build a learning optimization model, and dynamically adjust the playback and control strategies.

[0027] Wherein, in step S1, selecting the audio input source and the stored playback content further includes: Selecting an audio input source to provide a specific source of audio in response to the trigger instruction in step S3, wherein the selection of the audio input source includes a music service platform, a local file, and a specified link; Select audio data in various forms as playback content based on your needs and preferences, and manage the audio in an orderly manner.

[0028] Specifically, the input source can be different music service platforms (such as Spotify, Amazon Music, Tidal, etc.), or a local audio file or a specific URL. By selecting the input source, the system will determine the source of the content played when triggered. The content can be a playlist, a single song, a podcast, etc., which can be freely defined by the user and played by manually triggering the Preset button.

[0029] Specifically, in this embodiment, the user selects Amazon Music as the input source, presets a playlist, enters the path of a local folder, specifies local music as the input source, selects a playlist of 50 songs on Amazon Music, and selects a specific URL that points to a piece of online streaming content.

[0030] In addition, in the playback parameter settings, users can customize the volume when triggered. The volume setting can be an absolute value (such as 50%) or a relative adjustment (such as increasing the current volume by 10%); Custom EQ (Equalizer) settings allow users to customize the sound effects for different playback content. Users can choose predefined EQ modes (such as bass boost, surround sound effect) or manually adjust the frequency response curve; Users can specify the duration of playback, which is especially suitable for scenarios such as meditation music or sleep audio that need to be stopped at a certain time; Through advanced settings, users can choose whether to enable the loop or shuffle function.

[0031] Specifically, users set the bass enhancement mode for music playback, which is suitable for use in party scenes, manually adjust the frequency, and enhance the treble to improve voice clarity; set the playback of pure music for 30 minutes, after which the system automatically stops playing; set the playback of background music for one hour for work, and then automatically enter silent mode; users activate the Shuffle mode to make the system randomly select songs in the playlist to play, avoiding repeated playback order, and enable the Loop mode to allow the specified music single to play in an infinite loop.

[0032] In addition, in step S2, grouping the plurality of audio devices according to the preset further includes: Get the list of added audio devices and group them according to usage scenarios and device functions; Store the device group data and record the status of each device in the device group; Configure audio synchronization playback parameters based on playback delay, volume balance, audio format and sampling rate, determine the communication method and protocol within the device group, and achieve multi-device synchronous playback.

[0033] Among them, the main audio device is determined by the device priority model. Figure 2 As shown, further comprising: S21: each device is assigned a sound quality score according to its sound quality capability, and all devices are sorted in reverse order according to the sound quality scores to generate a device group sequence; S22: monitor the online status and occupancy of each device to generate real-time device availability status information; S23: constructing the device priority model based on the sound quality score and the device availability status information; S24: Dynamically generate the current highest priority audio playback device and determine it as the main audio device.

[0034] Specifically, users can choose which devices to play audio on synchronously through settings. The system will list the devices available to users, and users can manually select devices or automatically play synchronously on devices on the same network. Multiple audio devices can be grouped to achieve multi-room audio control. The system supports preset device groups, and users can select different device groups for audio playback in different scenarios.

[0035] In this example, the user chooses to play music on the speakers in the living room and the speakers in the bedroom simultaneously. The system automatically detects all the audio devices in the home network and plays the content synchronously after the user confirms. The user presets a "home theater" device group, including the TV speakers and rear speakers in the living room, or creates an "outdoor entertainment" device group, including wireless speakers in the terrace and garden.

[0036] The user sets a high-fidelity speaker as the primary output device, and when that device is available, playback from other devices is automatically downgraded to auxiliary output.

[0037] The priority algorithm formula is as follows: Priority(Device) = Max(QualityScore Availability), Among them, QualityScore (audio device sound quality score) is assigned a score for each device based on its sound quality capabilities (such as frequency response, supported audio formats, etc.), and Availability (device availability) is the availability of the device, which refers to whether the device is online and not occupied by other tasks. If the device is offline or in the process of performing other tasks, its availability score is 0.

[0038] In this embodiment, the user presets the following devices: - Living Room Hi-Fi: `QualityScore = 9` - Bedroom Normal Speaker: `QualityScore = 6` - Kitchen Bluetooth Speaker: `QualityScore = 5`, Current device status: - Living Room Hi-Fi: Online (`Availability = 1`) - Bedroom normal speaker: Online (`Availability = 1`) - Kitchen Bluetooth Speaker: Offline (`Availability = 0`).

[0039] The priority algorithm is implemented as follows: Priority(Device) = Max(9 1, 6 1, 5 0) = Max(9, 6, 0) = 9.

[0040] According to the algorithm, the living room's high-fidelity speakers have the highest priority, so the system will play audio on that device first. If that device is offline or unavailable during playback, the system will automatically switch to the next best device (the bedroom's standard speakers).

[0041] In addition, in step S2, the task schedule is set as follows: Figure 3 As shown, further comprising: S100: Setting task time parameters, including the time point and periodicity setting of task execution, specifying a specific time to determine the start time of the playback task, and supporting multiple periodicity modes; S200: Setting task priority rules. If the user sets multiple playback tasks in the same time period, the multiple tasks are prioritized and a queue-jumping mechanism for playback tasks marked as urgent tasks is supported. S300: Manage playback scenarios, including organization and scheduling of audio content and device groups.

[0042] Among them, in step S300, the management playback scene is as follows Figure 4 As shown, further comprising: S301: Create and manage defined audio playlists, support selecting a single audio, or arranging multiple audios in a customized order, and intelligently recommend relevant audio content based on the user's historical playback records, preferences, and the currently set time period phonemes; S302: Acquire information of all device groups grouped according to different standards, monitor the status of all devices in real time, and select a device group corresponding to the standard for task allocation according to demand when allocating playback tasks; S303: Setting a volume dynamic adjustment rule, and setting the initial volume and the volume change rule over time for each playback task; S304: Configure scene-related audio effect parameters and provide audio effect adjustment options according to different devices and playback content.

[0043] The task scheduling also includes working with other system functions, such as Figure 5 As shown: Read data from other systems, including schedules in the calendar system and preference information in the preference system. Read the schedule in the calendar system, set the playback tasks reasonably according to the user's existing activity plan, and synchronize the set playback tasks to the user's calendar system to perform overall time management and arrangement; Read the preference information in the preference system, recommend audio content that meets the user's preference in a targeted manner based on the user's preference, record the number of set playback tasks, and update the information in the preference system.

[0044] In step S3, responding to the trigger instruction further includes: S31: Obtain the audio input source, the playback content, and the set playback parameters to generate configuration items; S32: The configuration items are comprehensively managed by defining a trigger function. When the user triggers a play operation, the trigger function is executed, and the system outputs corresponding play behavior according to the configuration items.

[0045] Specifically, when the user presses the trigger button, the system automatically performs a series of operations according to the user's settings through the control algorithm. First, the system identifies the input source and preset content, and then executes the various custom options in the advanced settings, such as volume, EQ, playback time, etc.

[0046] The trigger formula is as follows: Trigger(Preset) = f(Input, Volume, EQ, Duration, Shuffle, Loop,Group), Among them, Input is input source identification. The system selects the input source according to the user's preset content (music service platform or local file); Volume is volume setting. The volume is controlled according to the volume value set by the user or the relative volume adjustment; EQ is the equalizer mode. The frequency response of the audio output is adjusted according to the EQ mode selected by the user; Duration is the playback duration. The duration of the playback content is set, and the system automatically stops playback or switches content; Shuffle is random playback. It determines whether to randomly play the music in the list according to the user's selection; Loop is loop playback. It determines whether to enable loop playback of single songs or playlists according to the user's settings; Group is device group management. When Preset is triggered, you can select which devices to participate in the playback according to the user's settings, supporting multiple devices to play at the same time or a specific device group.

[0047] In this embodiment, the user presets the following conditions: - Input: Amazon Music - Volume: 70% - EQ: Bass Boost - Duration: 30 mins - Shuffle: Enabled - Loop: Disabled - Group: Living Room + Bedroom, Trigger algorithm execution: Trigger(Preset) = f("Amazon Music", "70%", "Bass Boost", "30 mins", "Enabled", "Disabled", "Living Room + Bedroom"), When the user presses the Preset button, the system performs the following actions: - Get user-specified playlists from Amazon Music; - Set the volume to 70%; - Enable EQ mode with bass boost; - Play for 30 minutes and stop after 30 minutes; - Shuffle the music in the playlist; - Disable loop playback; - Play simultaneously on speakers in the living room and bedroom.

[0048] Furthermore, in step S3, calling the playback control algorithm to perform the audio playback operation further includes: Based on machine learning algorithms, it analyzes users' audio playback habits in different time periods, automatically adjusts default settings, and recommends the most appropriate audio playback control behavior for users based on time and scenario.

[0049] To further improve the user experience, machine learning algorithms can be introduced to learn user behavior and optimize it. By analyzing user usage data (time, content, device, etc.), the system can automatically recommend the most suitable configuration.

[0050] Among them, based on machine learning algorithms, the audio playback habits of users in different time periods are analyzed. Figure 6 As shown, further comprising: S31: Collect relevant data each time the user triggers a playback behavior, including usage time, playback content, selected device, and volume setting; S32: Obtain the user's historical behavior data, build a user preference model, and summarize the user's preference pattern; S33: Build a prediction model based on the collected trigger data and historical behavior data to generate a personalized recommendation strategy.

[0051] In step S33, constructing the prediction model further includes: S331: Select a machine learning algorithm, where: If there is a large amount of labeled data, a supervised learning algorithm is selected to build a prediction model, which predicts new playback scenarios by learning the relationship between features and labels; If there is no labeled data, choose an unsupervised learning algorithm to build a prediction model, and analyze the user's behavior patterns in different time periods and scenarios by analyzing the clustering results; S332: Train the prediction model, and use the trained prediction model to perform classification and regression analysis on the data to predict user behavior.

[0052] In step S332, training the prediction model further includes: S3321: Preprocess the collected data, including data cleaning, removal of outliers, and standardization and normalization of the data; S3322: Select features related to audio playback scenarios and preferences for feature extraction, and further extract combined features; S3323: Divide the data into a training set and a test set, use the training set to train the model, and evaluate the model performance on the test set.

[0053] The optimization learning algorithm formula is as follows: Recommendation(Preset) = ML(PresetData, UserPreferences), Among them, PresetData (Preset usage data) is used by the system to record the relevant data each time the user triggers Preset, including the time of use, the content played, the selected device, the volume setting, etc.; UserPreferences (user preferences) is used to summarize the user's preference pattern based on the user's historical behavior data. For example, the user may prefer to play light music in the morning and sleep-inducing audio at night; ML (machine learning model) is used by the system to use supervised learning or unsupervised learning algorithms (such as random forest, KNN algorithm, etc.) to analyze user data and generate recommendations that meet user habits.

[0054] Specifically, this embodiment adopts a supervised learning algorithm using random forest as an example and an unsupervised learning algorithm using KNN as an example. When encountering a new user usage scenario, current time information is obtained as an input feature.

[0055] The supervised learning algorithm model predicts the type of music that the user may like at that time based on the learned decision rules and makes recommendations. When there is a new usage scenario, the unsupervised learning algorithm model calculates the distance between the scenario data (such as the vector corresponding to the current time) and all data points in the historical data set, finds the K nearest neighbors, and analyzes the music type choices of most users among these K neighbors at the corresponding time.

[0056] In this embodiment, assuming that the user has frequently played light music in the morning and meditation music in the evening in the past month, the algorithm is executed as follows: Recommendation(Preset) = ML([Morning Light Music, Evening MeditationMusic], UserPreferences), By analyzing the data, the system learns the user's preset preferences. In the future, it will recommend light music in the morning and automatically recommend meditation music in the evening.

[0057] The introduction of machine learning algorithms can automatically recommend the most appropriate settings by learning and analyzing user usage behaviors, greatly simplifying the user's operation process and improving the user experience.

[0058] On the other hand, this embodiment provides an intelligent audio playback control system based on the above method, such as Figure 7 As shown, including: A basic setting module, used to select an audio input source and stored playback content, and set playback parameters, including trigger volume, equalizer, playback duration, and playback mode; The group scheduling module is used to group multiple audio devices according to presets, determine the main audio device through the device priority model, set task scheduling, and preset playback tasks for specific time periods and playback scenarios; The trigger and control module is used to respond to trigger instructions, call the playback control algorithm to perform audio playback operations, build a learning optimization model, and dynamically adjust the playback and control strategies.

[0059] Figure 7 The functional implementation of each module and unit in the intelligent audio playback control system corresponds to the steps in the above-mentioned embodiment of the intelligent audio playback control method, and its functions and implementation process will not be repeated here one by one.

[0060] This embodiment also provides an electronic device, such as Figure 8 As shown, the electronic device includes a processor 14 and a memory 13. The memory 13 stores machine executable instructions that can be executed by the processor 14. The processor 14 executes the machine executable instructions to implement the above audio control method.

[0061] Further, Figure 8 The electronic device shown further includes a bus 12 and a communication interface 11 , and the processor 14 , the communication interface 11 and the memory 13 are connected via the bus 12 .

[0062] The memory 13 may include a high-speed random access memory (RAM), and may also include a non-volatile memory, such as at least one disk storage. The communication connection between the system network element and at least one other network element is realized through at least one communication interface 11 (which may be wired or wireless), and the Internet, wide area network, local area network, metropolitan area network, etc. may be used. The bus 12 may be an ISA bus, a PCI bus, or an EISA bus, etc. The bus may be divided into an address bus, a data bus, a control bus, etc. For ease of representation, Figure 8 Only one bidirectional arrow is used in the diagram, but this does not mean that there is only one bus or only one type of bus.

[0063] The processor 14 may be an integrated circuit chip with signal processing capabilities. In the implementation process, each step of the above method can be completed by the hardware integrated logic circuit or software instructions in the processor 14. The above processor 14 can be a general-purpose processor, including a central processing unit (CPU), a network processor (NP), etc.; it can also be a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA) or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components. The methods, steps and logic block diagrams disclosed in this embodiment can be implemented or executed. The general-purpose processor can be a microprocessor or the processor can also be any conventional processor. The steps of the method disclosed in this embodiment can be directly embodied as a hardware decoding processor to be executed, or the hardware and software modules in the decoding processor can be combined and executed. The software module can be located in a mature storage medium in the field such as a random access memory, a flash memory, a read-only memory, a programmable read-only memory or an electrically erasable programmable memory, a register, etc. The storage medium is located in the memory 1001, and the processor 1000 reads the information in the memory 1001 and completes the steps of the audio control method in combination with its hardware.

[0064] The present disclosure also provides a computer-readable storage medium, which may be a non-volatile computer-readable storage medium or a volatile computer-readable storage medium. A computer program is stored in the computer-readable storage medium. When the computer program runs on a computer, the computer executes the steps of the audio control method.

[0065] Finally, it should be noted that the above is only a preferred embodiment of the present invention, and the protection scope of the present invention is not limited to the above embodiments. All technical solutions under the concept of the present invention belong to the protection scope of the present invention. It should be pointed out that for ordinary technicians in this technical field, some improvements and modifications without departing from the principle of the present invention should also be regarded as the protection scope of the present invention.

[0066] The technical features of the above-described embodiments may be arbitrarily combined. To make the description concise, not all possible combinations of the technical features in the above-described embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

Claims

1. An intelligent audio playback control method, characterized in that: The steps include: S1: Select an audio input source and stored playback content, and set playback parameters, including trigger volume, equalizer, playback duration, and playback mode; S2: Group multiple audio devices according to presets, determine the main audio device through the device priority model, set task scheduling, and preset playback tasks for specific time periods and playback scenarios; S3: Respond to trigger instructions, call the playback control algorithm to perform audio playback operations, build a learning optimization model, and dynamically adjust the playback and control strategies.

2. The intelligent audio playback control method according to claim 1, characterized in that: In step S1, selecting an audio input source and stored playback content further includes: Selecting an audio input source to provide a specific source of audio in response to the trigger instruction in step S3, wherein the selection of the audio input source includes a music service platform, a local file, and a specified link; Select audio data in various forms as playback content based on your needs and preferences, and manage the audio in an orderly manner.

3. The intelligent audio playback control method according to claim 1, characterized in that: In step S2, grouping the plurality of audio devices according to the preset further comprises: Get the list of added audio devices and group them according to usage scenarios and device functions; Store the device group data and record the status of each device in the device group; Configure audio synchronization playback parameters based on playback delay, volume balance, audio format and sampling rate, determine the communication method and protocol within the device group, and achieve multi-device synchronous playback.

4. The intelligent audio playback control method according to claim 3, characterized in that: In step S2, determining the primary audio device through the device priority model further includes: S21: each device is assigned a sound quality score according to its sound quality capability, and all devices are sorted in reverse order according to the sound quality scores to generate a device group sequence; S22: monitor the online status and occupancy of each device to generate real-time device availability status information; S23: constructing the device priority model based on the sound quality score and the device availability status information; S24: Dynamically generate the current highest priority audio playback device and determine it as the main audio device.

5. The intelligent audio playback control method according to claim 1, characterized in that: In step S2, setting the task schedule further includes: S100: Setting task time parameters, including the time point and periodicity setting of task execution, specifying a specific time to determine the start time of the playback task, and supporting multiple periodicity modes; S200: Setting task priority rules. If the user sets multiple playback tasks in the same time period, the multiple tasks are prioritized, and a queue-jumping mechanism for playback tasks marked as urgent tasks is supported; S300: Manage playback scenarios, including organization and scheduling of audio content and device groups.

6. The intelligent audio playback control method according to claim 3 or 5, characterized in that: In step S300, managing the playback scene further includes: S301: Create and manage defined audio playlists, support selecting a single audio, or arranging multiple audios in a customized order, and intelligently recommend relevant audio content based on the user's historical playback records, preferences, and the currently set time period phonemes; S302: Acquire information of all device groups grouped according to different standards, monitor the status of all devices in real time, and select a device group corresponding to the standard for task allocation according to demand when allocating playback tasks; S303: Setting a volume dynamic adjustment rule, and setting the initial volume and the volume change rule over time for each playback task; S304: Configure scene-related audio effect parameters and provide audio effect adjustment options according to different devices and playback content.

7. The intelligent audio playback control method according to claim 5, characterized in that: The task scheduling also includes working in conjunction with other system functions: Read data from other systems, including schedules in the calendar system and preference information in the preference system. Read the schedule in the calendar system, set the playback tasks reasonably according to the user's existing activity plan, and synchronize the set playback tasks to the user's calendar system to perform overall time management and arrangement; Read the preference information in the preference system, recommend audio content that meets the user's preference in a targeted manner based on the user's preference, record the number of set playback tasks, and update the information in the preference system.

8. The intelligent audio playback control method according to claim 1, characterized in that: In step S3, responding to the trigger instruction further includes: S31: Obtain the audio input source, the playback content, and the set playback parameters to generate configuration items; S32: The configuration items are comprehensively managed by defining a trigger function. When the user triggers a play operation, the trigger function is executed, and the system outputs corresponding play behavior according to the configuration items.

9. The intelligent audio playback control method according to claim 1, characterized in that: In step S3, calling the playback control algorithm to perform the audio playback operation further includes: Based on machine learning algorithms, it analyzes users' audio playback habits in different time periods, automatically adjusts default settings, and recommends the most appropriate audio playback control behavior for users based on time and scenario.

10. The intelligent audio playback control method according to claim 9, characterized in that: Based on the machine learning algorithm, the analysis of users' audio playback habits in different time periods further includes: S31: Collect relevant data each time the user triggers a playback behavior, including usage time, playback content, selected device, and volume setting; S32: Obtain the user's historical behavior data, build a user preference model, and summarize the user's preference pattern; S33: Build a prediction model based on the collected trigger data and historical behavior data to generate a personalized recommendation strategy.

11. The intelligent audio playback control method according to claim 10, characterized in that: In step S33, constructing the prediction model further includes: S331: Select a machine learning algorithm, where: If there is a large amount of labeled data, a supervised learning algorithm is selected to build a prediction model, which predicts new playback scenarios by learning the relationship between features and labels; If there is no labeled data, choose an unsupervised learning algorithm to build a prediction model, and analyze the user's behavior patterns in different time periods and scenarios by analyzing the clustering results; S332: Train the prediction model, and use the trained prediction model to perform classification and regression analysis on the data to predict user behavior.

12. The intelligent audio playback control method according to claim 11, characterized in that: In step S332, training the prediction model further includes: S3321: Preprocess the collected data, including data cleaning, removal of outliers, and standardization and normalization of the data; S3322: Select features related to audio playback scenarios and preferences for feature extraction, and further extract combined features; S3323: Divide the data into a training set and a test set, use the training set to train the model, and evaluate the model performance on the test set.

13. An intelligent audio playback control system based on the method of claims 1 to 12, characterized in that: include: A basic setting module, used to select an audio input source and stored playback content, and set playback parameters, including trigger volume, equalizer, playback duration, and playback mode; The group scheduling module is used to group multiple audio devices according to presets, determine the main audio device through the device priority model, set task scheduling, and preset playback tasks for specific time periods and playback scenarios; The trigger and control module is used to respond to trigger instructions, call the playback control algorithm to perform audio playback operations, build a learning optimization model, and dynamically adjust the playback and control strategies.

14. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by the processor, the intelligent audio playback control method as described in any one of claims 1 to 12 is implemented.

15. An electronic device, characterized in that: include: one or more processors; A storage device for storing one or more programs, when the one or more programs are executed by the one or more processors, enables the one or more processors to implement the intelligent audio playback control method as described in any one of claims 1-12.

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