Control method and device of equipment, equipment, storage medium and program product

Abstract

Embodiments of the present application provide a device control method and device, a device, a storage medium and a program product. The method comprises: obtaining multi-source input data representing a current use state of the device; obtaining a current candidate use scenario based on the multi-source input data, and determining a candidate use scenario that meets a preset switching condition as a target use scenario; obtaining a target control parameter corresponding to the target use scenario, and adjusting a running parameter of the device based on the target control parameter. The present scheme enables the device to automatically switch to a running state suitable for the current use scenario, thereby improving the automation capability of the device.

Description

Technical Field

[0001] This application relates to the field of smart home devices, and more particularly to a control method, apparatus, device, storage medium, and program product for a device. Background Technology

[0002] As the application scenarios for smart terminal devices continue to increase, such as "best friend phones," the same device is often used in different scenarios such as watching movies, studying, video calls, and exercising. Different scenarios often have different requirements for the device's display, audio, and interaction parameters. Therefore, the device needs to have the ability to switch operating states according to the usage scenario. In existing technologies, devices usually rely on users to manually select modes or set operating parameters one by one in different usage scenarios. This results in a low degree of automation during scenario switching and makes it difficult for the device to adaptively adjust operating parameters according to the current usage state.

[0003] Therefore, a method is needed to address the problem that the operating parameters of a device are difficult to adaptively adjust under different usage scenarios. Summary of the Invention

[0004] This application provides a device control method, apparatus, device, storage medium, and program product to solve the problem that the operating parameters of a device are difficult to adaptively adjust under different usage scenarios.

[0005] In a first aspect, embodiments of this application provide a device control method, the method comprising: acquiring multi-source input data characterizing the current usage state of the device; obtaining current candidate usage scenarios based on the multi-source input data, and determining candidate usage scenarios that meet preset switching conditions as target usage scenarios; acquiring target control parameters corresponding to the target usage scenarios, and adjusting the device's operating parameters based on the target control parameters.

[0006] In one possible embodiment, determining the current candidate use scenario based on multi-source input data includes: processing the multi-source input data to obtain scene recognition features; and inputting the scene recognition features into a preset scene recognition model to output at least one candidate use scenario.

[0007] In one possible embodiment, the preset scene recognition model also outputs the scene recognition confidence level corresponding to the candidate use scenario; the candidate use scenario that meets the preset switching conditions is determined as the target use scenario, including: determining the corresponding candidate use scenario and recognition confidence level based on multi-source input data obtained at multiple consecutive time points; when the candidate use scenarios are consistent at multiple consecutive time points and the corresponding recognition confidence levels are all greater than the preset confidence threshold, the candidate use scenario is determined as the target use scenario.

[0008] In one possible embodiment, the method includes: setting a preset scenario parameter configuration set; each scenario corresponds to a set of control parameters; obtaining target control parameters corresponding to the target use scenario, including: reading the control parameters corresponding to the target use scenario from the scenario parameter configuration set as the target control parameters.

[0009] In one possible embodiment, the control parameters include: mandatory parameters and adjustable parameters; based on the target control parameters, the operating parameters of the device are adjusted, including: prioritizing the adjustment of the operating parameters corresponding to the mandatory parameters; and, when the current device state and environmental conditions are met, adjusting the operating parameters corresponding to the adjustable parameters.

[0010] In one possible embodiment, the operating parameters of the device are adjusted based on the target control parameters, including: adjusting at least some of the operating parameters corresponding to the control parameters in stages or continuously according to a preset transition strategy, so that each operating parameter gradually transitions to the target parameter value corresponding to the target use scenario.

[0011] Secondly, embodiments of this application provide a control device for a device, the device comprising: a data acquisition module for acquiring multi-source input data characterizing the current usage state of the device; a scenario determination module for obtaining current candidate usage scenarios based on the multi-source input data, and determining candidate usage scenarios that meet preset switching conditions as target usage scenarios; and a parameter control module for acquiring target control parameters corresponding to the target usage scenario, and adjusting the operating parameters of the device based on the target control parameters.

[0012] Thirdly, embodiments of this application provide an electronic device, including: a memory and a processor; the memory stores computer-executable instructions; the processor executes the computer-executable instructions stored in the memory, causing the processor to perform the method provided above.

[0013] Fourthly, embodiments of this application provide a computer-readable storage medium storing computer-executable instructions, which, when executed by a processor, are used to implement the methods provided above.

[0014] Fifthly, embodiments of this application provide a computer program product, including a computer program that, when executed by a processor, implements the method described above.

[0015] The control method, apparatus, device, storage medium, and program product provided in this application acquire multi-source input data characterizing the current usage state of the device, determine the current candidate usage scenario based on the multi-source input data, and determine the target usage scenario when the candidate usage scenario meets the preset switching conditions; acquire the target control parameters corresponding to the target usage scenario, and adjust the device's operating parameters based on the target control parameters, thereby enabling the device to automatically switch to an appropriate operating state according to the current usage scenario, thereby improving the device's automation capability. Attached Figure Description

[0016] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application.

[0017] Figure 1 A schematic diagram illustrating a scenario for a device control method provided in this application;

[0018] Figure 2 A flowchart illustrating a device control method provided in this application;

[0019] Figure 3 A flowchart illustrating another control method for the device provided in this application;

[0020] Figure 4 A schematic diagram of the structure of a device provided in this application;

[0021] Figure 5 A schematic diagram of the structure of the electronic device provided in this application.

[0022] The accompanying drawings illustrate specific embodiments of this application, which will be described in more detail below. These drawings and descriptions are not intended to limit the scope of the concept in any way, but rather to illustrate the concept of this application to those skilled in the art through reference to particular embodiments. Detailed Implementation

[0023] Exemplary embodiments will now be described in detail, examples of which are illustrated in the accompanying drawings. When the following description relates to the drawings, unless otherwise indicated, the same numbers in different drawings denote the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with this application. Rather, they are merely examples of apparatuses and methods consistent with some aspects of this application as detailed in the appended claims.

[0024] Figure 1This is a schematic diagram illustrating a scenario for the control method of the device provided in this application. As can be seen from this application scenario diagram, the same device, such as the "bestie phone" in this embodiment, may correspond to multiple different usage scenarios in actual use. For example, users can use the bestie phone for applications such as watching movies, learning, video calls, fitness, and cooking. Because users have different needs for the device's operating status under different usage scenarios, the device's display, audio, and interaction parameters usually need to be adjusted accordingly.

[0025] In existing technologies, parameter adjustments for devices in different usage scenarios typically rely on users manually selecting modes or setting operating parameters item by item. This results in low automation during scenario switching and makes it difficult for devices to adaptively adjust operating parameters based on the current usage state. Therefore, a device control method is needed to address the technical problem of the difficulty in adaptively adjusting operating parameters under different usage scenarios.

[0026] In view of the above problems, this application provides a device control method. This method acquires multi-source input data that characterizes the current usage state of the device, determines the current candidate usage scenario based on the multi-source input data, and determines the candidate usage scenario as the target usage scenario when the candidate usage scenario meets the preset switching conditions. Subsequently, the target control parameters corresponding to the target usage scenario are acquired, and the device's operating parameters are adjusted based on the target control parameters, thereby enabling the device to automatically switch to an appropriate operating state according to the current usage scenario, thereby improving the device's automation capability.

[0027] The control method of the device will be described in detail below with specific embodiments to illustrate the technical solution of this application and how the technical solution of this application solves the above-mentioned technical problems. The following specific embodiments can be combined with each other, and the same or similar concepts or processes may not be described again in some embodiments. The embodiments of this application will be described below with reference to the accompanying drawings.

[0028] Example 1

[0029] Figure 2 As shown in Figure 2, a flowchart illustrating a device control method provided in this application embodiment is provided. The device control method in this application embodiment includes:

[0030] S201: Acquire multi-source input data representing the current usage status of the device.

[0031] In this embodiment, the device can be a smart home device, a portable large-screen smart terminal, a display interaction device with a mobile support structure, or other smart devices with display, audio output, sensor acquisition, network communication, and computing control capabilities. The current usage state characterizes the overall situation of the device's operating environment, user interaction, device posture, and resource status at the current moment. Multi-source input data represents a data set from multiple different data sources, different perception dimensions, or different acquisition modules. It is not limited to a single sensor output but includes multiple types of information that reflect the device's current scenario and the user's intentions.

[0032] In one possible implementation, the multi-source input data may include at least one of the following: screen display content data, application usage records, user identity information, user distance information, and user historical preference data.

[0033] In one possible embodiment, the multi-source input data may further include image data, audio data, ambient light data, temperature data, device posture data, and battery status data. Image data is collected by the device's front-facing camera, wide-angle camera, or peripheral vision module to reflect user location, human posture, activity range, number of people in front of the screen, and the surrounding spatial environment. Audio data is collected by a microphone array to reflect voice commands, ambient noise intensity, media playback sound characteristics, and human interaction. Ambient light data is collected by an ambient light sensor to reflect room brightness, day-night variations, and local lighting conditions. Temperature data is collected by a temperature sensor or the main control chip's temperature sensing module to reflect the device's thermal state and the surrounding ambient temperature. Device posture data is collected by a gyroscope, accelerometer, angle sensor, or position detection module to reflect screen rotation, stand tilt, device movement, and changes in placement position. Battery status data is collected by a battery management module to reflect remaining battery power, charging status, discharge rate, and estimated battery life. For example, multi-source input data also includes user touch operation data, voice wake-up records, application running status data, network connection status data, Bluetooth or WiFi (Wireless Fidelity) peripheral connection data, and motion change data in the external environment, to form a complete description of the current usage status.

[0034] In the specific implementation process, the system control module in the device periodically or event-triggeredly sends acquisition commands to each acquisition unit and adds a unified timestamp to data from different sources, thereby ensuring that various input information can be aligned within the same time window during subsequent fusion analysis. Specifically, the camera module can output image frames at a preset frame rate per second, the microphone array can continuously output audio streams at a sampling rate, the ambient light sensor and attitude sensor can output scalar or vector data at a fixed sampling period, and the battery management module can actively report status information when it detects changes in battery level, charging insertion, or abnormal power consumption. For touch operation data, the touch driver layer can record the click position, swipe trajectory, dwell time, and gesture type; for application running status data, the operating system service layer can read the current foreground application category, playback status, window layout, and task switching records; for network connection status data, the network management module can obtain the current network standard, signal strength, bandwidth usage, and latency parameters.

[0035] To reduce noise and improve scene recognition quality, preprocessing operations can be performed after acquiring the raw multi-source input data. These operations include scaling, human region detection, and keypoint extraction for image data; noise reduction, echo cancellation, speech activity detection, and keyword recognition for audio data; moving average filtering for ambient light data; zero-bias correction and pose calculation for posture data; and smoothing estimation and outlier removal for battery status data. Through these methods, a time-consistent, structurally unified set of state inputs that can be directly used for subsequent analysis can be obtained.

[0036] In one possible implementation, such as Figure 3 In the control method of the device shown, the current candidate usage scenario is determined based on multi-source input data, including:

[0037] S2021. Process the multi-source input data to obtain scene recognition features;

[0038] S2022. Input the scene recognition features into the preset scene recognition model to output at least one candidate usage scenario.

[0039] In this implementation, scene recognition features are feature vectors representing the current usage state obtained after cleaning, denoising, normalizing, encoding, and fusing multi-source input data. These features may include human posture features, speech semantic features, ambient light features, device orientation features, application foreground state features, and network connection state features. The preset scene recognition model can be a trained neural network classification model, a rule-based decision model, or a hybrid model combining both. After inputting the feature vector, the model outputs the category probability or scene label corresponding to the current usage context, thereby forming at least one candidate usage scenario. In one possible implementation, the preset scene recognition model can be deployed locally on the device or implemented using a combination of a local model and a cloud model. The local model is used for rapid identification of common usage scenarios to reduce recognition latency; the cloud model is used for auxiliary analysis of complex semantic information, complex user behavior, or multi-turn interaction content.

[0040] In this embodiment, the extraction of scene recognition features can be completed by a feature processing module, which includes a data preprocessing unit and a feature fusion unit. The data preprocessing unit is used to align timestamps and remove outliers from data from different sources, while the feature fusion unit is used to map heterogeneous data to a unified feature space to improve the stability of subsequent recognition. The scene recognition model can be deployed in the device's local computing unit or on an edge server. When using a deep learning model, it can learn the differences in scene features such as watching movies, exercising, working, cooking, and social entertainment in the home environment based on historical labeled samples. When using a rule-based model, it can make joint judgments based on ambient light intensity thresholds, voice keywords, touch frequency, and device posture changes to reduce the misjudgment rate in complex environments. Compared with existing methods that rely solely on a single state variable, this embodiment can uniformly transform information from different channels into identifiable scene features and output multiple candidate usage scenarios, providing a basis for subsequently determining the target usage scenario. This improves the accuracy, real-time performance, and robustness of scene recognition, reduces the burden of manual switching for users, and makes the device more adaptable to the dynamic usage needs within the home space.

[0041] Based on the above analysis, this step continuously acquires input information from multiple dimensions, including device vision, hearing, environment, posture, power supply, and interaction. This avoids the problem of one-sided state representation caused by relying on a single input source, enabling the device to more comprehensively perceive dynamic changes in the home environment. Especially in environments where multiple people share the space, there is frequent movement, lighting changes, and application switching occur simultaneously, multi-source input data can collectively reflect whether the user is currently in different scenarios such as watching a movie, working, exercising, or using daily services. This provides a reliable foundation for accurately identifying candidate usage scenarios, thereby alleviating the technical problems of high human judgment burden, device response lag, and insufficient matching degree of control strategies in existing technologies.

[0042] It should be understood that the above examples are for illustrative purposes only and are not intended to be limiting.

[0043] S202: Based on multi-source input data, obtain the current candidate usage scenarios, and determine the candidate usage scenarios that meet the preset switching conditions as the target usage scenarios.

[0044] In this embodiment of the application, candidate usage scenarios are used to represent one or more possible scenarios that correspond to the current usage state of the device after analysis of current multi-source input data; target usage scenarios are used to represent the scenario finally selected after switching judgment among multiple candidate usage scenarios; preset switching conditions are used to represent the judgment constraints set to avoid accidental switching, jittery switching or short-term misjudgment.

[0045] In practical applications, scene recognition can be performed by the device's local processor, edge computing units, or servers. In home terminal scenarios such as "best friend's phone," to reduce recognition latency and protect privacy, the scene recognition engine is typically deployed within the local system framework. The scene recognition engine can include a feature extraction unit, a temporal fusion unit, a scene inference unit, and a handover determination unit.

[0046] For example, the feature extraction unit is used to generate structured features from different input sources, such as extracting human posture key points, distance between the human body and the screen, facial orientation, number of people, and movement amplitude from image data; extracting speech semantics, volume level, rhythm features, and background noise type from audio data; extracting device rotation angle, movement speed, and stability from posture data; and extracting video playback status, meeting software running status, fitness application activity status, and tutorial page display status from application status. The temporal fusion unit is used to combine features from multiple time slices to identify continuous behavior rather than instantaneous behavior, avoiding misjudgment of the scene due to occasional actions at a certain moment. The scene reasoning unit can use a deep learning classification model, a rule-based decision model, or a hybrid model combining both. The deep learning classification model can output probability values ​​for various scenarios such as movie viewing, fitness, office and study, life service, and social entertainment. The rule-based decision model can make logical judgments based on ambient light intensity, user dwell time, movement frequency, touch frequency, voice keywords, and application type.

[0047] In its implementation, the scene recognition engine first performs feature standardization on the preprocessed multi-source input data to ensure that data of different scales enter a unified analysis space. Then, it generates scene score vectors based on a preset model. For example, image pose features, audio semantic features, pose state features, and application state features at a certain moment can be input into the classification network to obtain a score of 0.78 for a movie-watching scene, 0.12 for a fitness scene, 0.64 for an office / study scene, and 0.20 for a life service scene. Based on these scores, movie-watching and office / study scenes are generated as current candidate usage scenarios.

[0048] To further improve adaptability, a dynamic feature weighting mechanism can be adopted to adjust the weights of different features based on real-time status. For example, when there is high ambient noise, the weight of audio features can be reduced while the weights of visual and application status features can be increased; when the camera is obstructed, the weight of image features can be reduced while the weights of touch, voice, and application behavior features can be increased.

[0049] Based on the aforementioned implementation methods, further, such as Figure 3 As shown, the preset scene recognition model also outputs the scene recognition confidence score corresponding to the candidate usage scene; the candidate usage scenes that meet the preset switching conditions are determined as the target usage scenes, including:

[0050] S2023. Based on the multi-source input data acquired at multiple consecutive time points, determine the corresponding candidate usage scenarios and recognition confidence levels respectively;

[0051] S2024. When the candidate use scenarios are consistent across multiple consecutive time points, and the corresponding recognition confidence scores are all greater than the preset confidence threshold, the candidate use scenario is determined as the target use scenario.

[0052] The scene recognition confidence score characterizes the degree of matching between the candidate usage scene and the current multi-source input data. It can be represented by the probability value, score, or normalized weight value output by the model. The preset scene recognition model can consist of a convolutional neural network, a temporal feature model, or a multimodal fusion model. It is used to jointly discriminate touch trajectories, voice commands, ambient light information, device posture information, and application operating status, and output candidate usage scenes and their corresponding confidence scores. The preset confidence threshold can be configured based on the noise level, scene switching frequency, and user habits in the home environment. In practical applications, this threshold can also be dynamically adjusted according to the current load status of the device; this embodiment does not limit this.

[0053] After acquiring multi-source input data at multiple consecutive time points, the system extracts scene recognition features for each time point and inputs them into a preset scene recognition model to obtain candidate usage scenarios and recognition confidence levels for each time point. When the candidate usage scenarios output at multiple consecutive time points are consistent, and the recognition confidence level at each time point is higher than a preset confidence threshold, the system confirms the candidate usage scenario as the target usage scenario, thereby avoiding misjudgments caused by transient noise interference in a single recognition result. If a time window method is used for judgment, multiple consecutive time points can correspond to consecutive sampling periods, consecutive frame data, or consecutive time segments to improve the stability of scene confirmation.

[0054] This implementation introduces confidence level output and continuous consistency judgment, enabling scene switching to be based on stable multi-time recognition results rather than relying on a single recognition result. This approach effectively suppresses scene jitter caused by short-term interference, reduces the probability of erroneous switching between usage scenarios such as watching movies, working, and exercising, and allows the device to maintain a more stable operating state in the complex home environment. Simultaneously, the constraint based on the confidence level threshold improves the reliability of target usage scenario determination, making subsequent control parameter adjustments more accurate, thereby improving user experience and enhancing the robustness of device control.

[0055] Based on the above analysis, this step, through feature extraction, temporal fusion, and switching determination of multi-source input data, achieves a shift from passive rule response to proactive scene recognition control. It not only accurately identifies the actual usage scenario of the device in complex home environments but also suppresses false triggers and short-term jitter through preset switching conditions such as confidence level, duration, and difference thresholds. This improves the stability, real-time performance, and accuracy of scene judgment, laying the foundation for the correct selection of subsequent parameter control strategies. It should be understood that the above example is merely illustrative and not limiting.

[0056] S203: Obtain the target control parameters corresponding to the target usage scenario, and adjust the operating parameters of the device based on the target control parameters.

[0057] In this embodiment, the target control parameters represent a set of control configurations that match a determined target usage scenario. These parameters can be pre-stored in a scenario parameter library or dynamically generated from a basic parameter template combined with the current device status and user historical preferences. The scenario parameter library can be deployed in the device's local storage and parameter mapping relationships can be established according to scenario categories. The operating parameters represent the hardware, software, and interaction parameters currently in effect on the device, including processor frequency, memory resource allocation, background task strategy, display brightness, color temperature, refresh rate, resolution, volume, audio equalization parameters, noise reduction intensity, voice wake-up sensitivity, network connection mode, camera working mode, microphone working mode, and stand posture parameters, etc.

[0058] In one implementation, based on the foregoing embodiments, the method may further include:

[0059] Set a preset set of scenario parameter configurations; each scenario corresponds to a set of control parameters;

[0060] S203 may include: reading the control parameters corresponding to the target usage scenario from the scenario parameter configuration set, and using them as the target control parameters.

[0061] Specifically, the preset scene parameter configuration set can be a scene mapping table, configuration file, database, or local cache stored in memory. The preset scene parameter configuration set can be indexed and managed according to scene identifiers, which can be scene names such as movie watching, fitness, office, meeting, and child companionship, or corresponding scene numbers.

[0062] Control parameters may include at least one of the following: display parameters, audio parameters, computational parameters, network parameters, and interaction parameters. For example, display parameters may include brightness, contrast, color temperature, refresh rate, and display orientation; audio parameters may include volume, channel mode, and noise reduction intensity; computational parameters may include processor frequency limit and task scheduling strategy; network parameters may include wireless connection priority; and interaction parameters may include the operating modes of the camera and microphone.

[0063] Once the target usage scenario is determined, the control module can search the preset scenario parameter configuration set based on the scenario identifier corresponding to the target usage scenario, and read the corresponding set of control parameters as the target control parameters. Subsequently, the device adjusts the corresponding operating parameters based on these target control parameters, enabling the device to switch to a working state that matches the target usage scenario.

[0064] By employing the above methods, once the device identifies the target usage scenario, it can directly invoke preset control parameters without recalculating basic parameters, thereby reducing response latency and improving scenario switching efficiency. Furthermore, different scenarios correspond to different parameter sets, which facilitates stable switching between different usage scenarios and enhances the user experience.

[0065] In this embodiment, the process of acquiring the target control parameters may further include three stages: scenario matching, constraint correction, and control distribution. The scenario matching stage extracts a basic configuration set from the scenario parameter library based on the target usage scenario; the constraint correction stage performs secondary corrections on the basic configuration based on remaining battery power, real-time temperature, network status, user habits, and current application requirements; and the control distribution stage sends the corrected target control parameters to the system control module, display control module, audio control module, power management module, and mechanical execution module, enabling each subsystem to collaboratively complete parameter adjustments.

[0066] In one possible embodiment, when the target usage scenario is a movie-watching scenario, the basic configuration set includes increasing screen brightness, adjusting the color temperature to a suitable range for video viewing, enabling a high refresh rate display mode, enhancing speaker output, limiting background tasks unrelated to the current video playback, and maintaining stable network bandwidth. When the target usage scenario is a fitness scenario, the basic configuration set includes adjusting the stand height or tilt angle so that the screen faces the user's activity area, automatically switching the display orientation to a direction suitable for following the movements, increasing the external speaker volume, enhancing the microphone pickup range, increasing the priority of voice interaction, and increasing the refresh rate of motion recognition-related sensors. When the target usage scenario is an office or study scenario, the basic configuration set includes switching to landscape display or multi-window layout, enabling eye protection mode, improving touch and input response speed, optimizing the camera's field of view, and enhancing noise reduction and echo cancellation strategies to adapt to the needs of remote conferencing and document processing.

[0067] In the specific implementation process, if the remaining battery power is lower than a preset battery power threshold, the constraint correction phase can perform energy-saving adjustments to the above basic configurations. For example, it can appropriately reduce screen brightness and refresh rate in movie-watching scenarios, limit non-critical background tasks, and shorten the duration of high-frequency sensor data collection, thereby extending battery life while ensuring a good user experience. If the internal temperature of the device is detected to exceed a preset thermal management threshold, the processor frequency limit can be reduced, high-power display modes can be weakened, and background task scheduling can be adjusted to avoid overheating affecting stability. If the current network signal is weak, bandwidth for video conferencing applications can be prioritized in office and study scenarios, while the preloading frequency of non-critical content can be reduced in life service scenarios.

[0068] During the parameter adjustment execution phase, each control module updates its operating parameters in stages according to a unified scheduling strategy. Specifically, the display control module adjusts brightness, contrast, color temperature, refresh rate, and screen orientation based on the target control parameters; the audio control module adjusts volume, noise reduction level, beamforming direction, and output equalization parameters; the power management module adjusts processor frequency, memory usage limit, task scheduling priority, and network connection strategy; the mechanical execution module adjusts the bracket height, pitch angle, and rotation angle; and the system control module coordinates the execution order of each submodule and monitors the completion status of the adjustments. To avoid abrupt changes in user perception, some parameters are adjusted gradually, such as smoothly changing brightness and volume within a time window of hundreds of milliseconds to several seconds; for core parameters affecting interactive functions, a high-priority, real-time adjustment method is used, such as prioritizing the use of camera and microphone optimization parameters when entering video conferencing scenarios. After the adjustments are completed, the system can continue to collect new multi-source input data to verify the adjusted state. If the verification result shows that the current operating parameters do not match the new round of recognition results, a further correction can be triggered to form a closed-loop control mechanism of scene recognition, parameter generation, execution control, and feedback verification.

[0069] In one possible implementation, the control parameters include mandatory parameters and adjustable parameters. Adjusting the equipment's operating parameters based on the target control parameters can include: prioritizing the adjustment of operating parameters corresponding to mandatory parameters; and, when the current equipment state and environmental conditions are met, performing adjustments to operating parameters corresponding to adjustable parameters.

[0070] Among them, mandatory parameters refer to control parameters that are directly related to the target usage scenario and need to be activated first, so as to enable the device to quickly enter a basic working state that matches the target usage scenario. Adjustable parameters refer to control parameters that are selectively activated based on the current device status and environmental conditions without affecting the basic operation of the device, so as to further optimize the user experience of the device. For example, mandatory parameters may include parameters such as display orientation, basic brightness, basic volume, and interaction mode; adjustable parameters may include parameters such as display enhancement parameters, audio enhancement parameters, network resource parameters, and background task strategies.

[0071] Once the target usage scenario is determined, the device prioritizes adjusting the operating parameters corresponding to the essential settings. For example, in a movie-watching scenario, the display orientation, brightness, and basic volume can be adjusted first; in a video call scenario, the camera, microphone, and call display mode can be adjusted first. This allows the device to quickly enter the basic operating state required for the target scenario.

[0072] After the mandatory parameters are adjusted, the device determines whether to adjust the corresponding adjustable parameters based on the current device status and environmental conditions. The current device status may include battery level, temperature, processor load, and memory usage; environmental conditions may include ambient light intensity, ambient noise, and network connection quality. When the device status and environmental conditions meet the requirements, the adjustment of the adjustable parameters is executed; when the conditions are not met, the adjustable parameters may remain unchanged or be postponed to reduce power consumption, heat generation, or system load.

[0073] By dividing control parameters into mandatory and adjustable parameters, and adopting a method of adjusting the mandatory parameters first and then the adjustable parameters, the device can quickly complete the basic state switching after the target scenario is determined, and further optimize the user experience when conditions permit, thereby improving the scenario adaptation speed and operational stability.

[0074] Based on the previous implementation, further, the operating parameters of the device are adjusted based on the target control parameters, including: adjusting at least some of the operating parameters corresponding to the control parameters in stages or continuously according to a preset transition strategy, so that each operating parameter gradually transitions to the target parameter value corresponding to the target use scenario.

[0075] The preset transition strategy is used to limit the way the operating parameters change from their current value to the target parameter value, and may include at least one of the following: adjustment sequence, adjustment duration, adjustment rate, and single adjustment magnitude. Phased adjustment refers to dividing the adjustment process of the operating parameters into multiple stages, so that the operating parameters gradually approach the target parameter value; continuous adjustment refers to continuously changing the operating parameters according to a preset adjustment rate, so that the operating parameters smoothly transition to the target parameter value.

[0076] For example, when the target usage scenario is watching a movie, the device can first adjust the display orientation, basic brightness, and basic volume, and then gradually adjust parameters such as color temperature, contrast, refresh rate, or sound effect mode; when the target usage scenario is learning, the device can first adjust the display brightness and eye protection mode, and then gradually adjust the color temperature, notification strategy, or background task strategy.

[0077] By employing the above methods, the device can avoid sudden changes in multiple operating parameters during scene switching, reducing issues such as abrupt changes in brightness, volume, or system load, thus improving the smoothness, stability, and user experience of scene switching. Based on the above analysis, this step establishes a correspondence between target usage scenarios and target control parameters, refining these parameters across multiple execution dimensions such as display, audio, power consumption, interaction, and mechanical structure. This allows the device to automatically reconstruct its operating state according to the actual usage scenario, eliminating the need for repeated manual settings by the user. Furthermore, through dynamic adjustments to constraints such as power consumption, temperature, and network speed, the control strategy balances user experience, system stability, and energy efficiency, thereby resolving issues in existing technologies such as reliance on manual parameter switching, mismatch between control strategies and scenarios, and lag in cross-scenario response.

[0078] Furthermore, in one possible implementation, the device can also establish a communication connection with a mobile phone, tablet, personal computer, or other smart terminal, and obtain task status data from the other smart terminal. Task status data may include at least one of the following: playback content, playback progress, document editing status, screen mirroring request, call status, clipboard content, or current application information.

[0079] When a device receives task status data from other smart terminals, it can determine the current candidate usage scenario based on the task status data, or modify the already determined target usage scenario. For example, when a mobile phone is playing a video and sends a screen mirroring request to the device, the device can consider the video viewing scenario as a candidate usage scenario and call the display parameters, audio parameters, and network parameters corresponding to the video viewing scenario; when a personal computer sends a wireless extended screen request to the device, the device can consider the office scenario as a candidate usage scenario and call the display layout parameters and interaction parameters corresponding to the office scenario.

[0080] Through the above methods, the device can combine the task status of other smart terminals to complete content flow and scene continuation, enabling the device to automatically enter the operating state matching the current task during multi-terminal collaborative use. This application embodiment provides a device control method, including: acquiring multi-source input data representing the current usage state of the device; obtaining current candidate usage scenarios based on the multi-source input data, and determining the candidate usage scenarios that meet preset switching conditions as the target usage scenario; acquiring target control parameters corresponding to the target usage scenario, and adjusting the device's operating parameters based on the target control parameters. In this embodiment, by performing multi-dimensional continuous perception of the device's current usage state, fusing and recognizing multi-source input data, and determining the actual scenario based on switching conditions, and then automatically generating and executing corresponding control parameters according to the target scenario, the device can achieve proactive scene recognition and adaptive parameter adjustment in complex environments such as home entertainment, office study, fitness training, and life services. This reduces the burden of manual switching for users, reduces experience fluctuations caused by misjudgments and frequent switching, improves scene switching accuracy, operational control stability, and overall device energy efficiency, thereby providing feasible technical support for the continuous and stable operation of large-screen mobile smart terminals in multi-scenario home environments.

[0081] Example 2

[0082] Figure 4 This is a schematic diagram of the structure of a control device for an apparatus provided in an embodiment of this application, as shown below. Figure 4 As shown, the control unit of this device can be applied to devices with display, audio output, sensor acquisition, network communication, and computational control capabilities. The control unit includes a data acquisition module 41, a scene determination module 42, and a parameter control module 43.

[0083] The data acquisition module 41 acquires multi-source input data representing the current usage status of the device. The scenario determination module 42 obtains current candidate usage scenarios based on the multi-source input data and determines the candidate usage scenarios that meet preset switching conditions as the target usage scenarios. The parameter control module 43 acquires target control parameters corresponding to the target usage scenarios and adjusts the device's operating parameters based on the target control parameters.

[0084] In one possible implementation, the data acquisition module 41 may include at least one of a sensing data acquisition unit, an application status acquisition unit, a content status acquisition unit, a user status acquisition unit, and a communication status acquisition unit. The sensing data acquisition unit acquires sensing data such as image data, audio data, ambient light data, temperature data, and battery status data; the application status acquisition unit acquires application running status data; the content status acquisition unit acquires screen display content data; the user status acquisition unit acquires user interaction data, user identity information, user distance information, and user historical preference data; and the communication status acquisition unit acquires network connection status data and task status data from other smart terminals.

[0085] In one possible implementation, the control device may further include a feature processing module. The feature processing module processes multi-source input data to obtain scene recognition features. The feature processing module may include a data preprocessing unit and a feature fusion unit. The data preprocessing unit performs time alignment, noise reduction, normalization, outlier removal, and format conversion on data from different sources; the feature fusion unit maps data from different sources to a unified feature space to form scene recognition features for scene identification. Through the feature processing module, the consistency and identifiability of the input data can be improved, providing a stable data foundation for subsequent scene judgment.

[0086] In one possible implementation, the scene determination module 42 may include a scene recognition unit and a switching determination unit. The scene recognition unit is used to input scene recognition features into a preset scene recognition model to output at least one candidate usage scenario. The preset scene recognition model can be deployed locally on the device or can be implemented collaboratively by a local model and a cloud model. The local model is used to quickly identify common usage scenarios, while the cloud model is used to assist in the analysis of complex semantic information, complex user behavior, or multi-turn interaction content. In this way, both real-time scene recognition and recognition accuracy in complex scenarios can be achieved.

[0087] In one possible implementation, the preset scene recognition model is also used to output the scene recognition confidence score corresponding to the candidate use scenario. The switching determination unit is used to determine the target use scenario based on the candidate use scenarios and scene recognition confidence scores corresponding to multiple consecutive time points. When the candidate use scenarios are consistent across multiple consecutive time points, and the corresponding scene recognition confidence scores are all greater than a preset confidence threshold, the switching determination unit determines the candidate use scenario as the target use scenario. This approach reduces the probability of erroneous switching due to momentary interference affecting the single recognition result, and improves the stability of target use scenario determination.

[0088] In one possible implementation, the scene determination module 42 may further include a feature weight adjustment unit. The feature weight adjustment unit is used to adjust the weights of different input features in the scene recognition process based on the validity of the current multi-source input data or the current environmental state. This method can reduce the impact of corresponding features on the recognition results when the quality of some input data deteriorates, thereby improving the robustness of scene recognition.

[0089] In one possible implementation, the parameter control module 43 may include a parameter configuration storage unit, a parameter retrieval unit, and a parameter execution unit. The parameter configuration storage unit stores a preset scenario parameter configuration set, where each scenario corresponds to a set of control parameters. The parameter retrieval unit reads the corresponding control parameters from the preset scenario parameter configuration set according to the target usage scenario, using them as target control parameters. The parameter execution unit adjusts the device's operating parameters based on the target control parameters.

[0090] In one possible implementation, the parameter control module 43 may further include a parameter correction unit. The parameter correction unit is used to correct the read control parameters based on the current device status, environmental conditions, user identity information, user preference information, or communication status, to obtain target control parameters that better match the current usage state. In this way, the target control parameters can not only match the target usage scenario but also adapt to the current device resource status, external environment, and user-specific needs.

[0091] In one possible implementation, the target control parameters may include at least one of display parameters, audio parameters, computation parameters, network parameters, interaction parameters, and application adaptation parameters. Correspondingly, the parameter execution unit may include at least one of a display control unit, an audio control unit, a computation scheduling unit, a network control unit, an interaction control unit, and an application adaptation unit. The display control unit is used to adjust display-related operating parameters; the audio control unit is used to adjust audio-related operating parameters; the computation scheduling unit is used to adjust computational resources and background task strategies; the network control unit is used to adjust network connection strategies; the interaction control unit is used to adjust interaction-related operating parameters such as camera, microphone, touch response, or voice wake-up; and the application adaptation unit is used to adjust the application display method or interaction method.

[0092] In one possible implementation, the parameter control module 43 may further include a hierarchical control unit. The hierarchical control unit identifies the mandatory and adjustable parameters among the target control parameters, prioritizing the adjustment of the operating parameters corresponding to the mandatory parameters. When the current equipment state and environmental conditions are met, it then adjusts the operating parameters corresponding to the adjustable parameters. In this way, the equipment can quickly complete the basic operating state switch after the target usage scenario is determined, and further optimize the operating state when conditions permit, thereby improving scenario adaptation speed and operational stability.

[0093] In one possible implementation, the parameter control module 43 may further include a transition control unit. The transition control unit is used to adjust at least a portion of the operating parameters corresponding to the target control parameter in stages or continuously according to a preset transition strategy, so that the operating parameters gradually transition to the target parameter values ​​corresponding to the target usage scenario. The preset transition strategy may include at least one of adjustment sequence, adjustment duration, adjustment rate, and single adjustment magnitude. Through the transition control unit, sudden changes in display, audio, interaction, or system load caused by instantaneous changes in multiple operating parameters can be reduced, improving the smoothness and stability of the scene switching process.

[0094] In one possible implementation, the control device may further include a feedback correction module. This module, after the parameter control module 43 has completed adjusting the operating parameters, continues to acquire new multi-source input data and determines whether the adjusted device operating state matches the target usage scenario. If the determination result is a mismatch, the feedback correction module can trigger the scenario determination module 42 to re-determine the target usage scenario, or trigger the parameter control module 43 to correct the already effective operating parameters. This approach forms a closed-loop control process of multi-source input data acquisition, scenario recognition, parameter adjustment, and status feedback, improving the device's adaptability in dynamic usage environments.

[0095] In one possible implementation, the control device may further include a multi-device collaboration module. This module establishes communication connections with other smart terminals and acquires task status data from them. The scenario determination module 42 can determine or modify the target usage scenario based on the task status data, and the parameter control module 43 can acquire corresponding target control parameters based on the modified target usage scenario, enabling the device to enter an operating state that matches the cross-device task. This approach allows for task continuity and scenario continuation between different terminals.

[0096] In one possible implementation, the modules in the control device can be implemented by software programs, hardware circuits, or a combination of both. For example, each module can be implemented by a processor executing computer-executed instructions from memory; it can also be implemented by a controller, sensor interface circuit, communication interface circuit, display control circuit, audio control circuit, network communication circuit, or power management circuit. This application does not limit the specific implementation form of each module.

[0097] The control device provided in this application acquires multi-source input data representing the current usage state of the device through a data acquisition module, determines the target usage scenario through a scenario determination module, and acquires target control parameters and adjusts the device's operating parameters through a parameter control module. This enables the device to automatically enter an operating state matching the target usage scenario based on its current usage state. This reduces the burden of manual switching for users, minimizes experience fluctuations caused by misidentification and frequent switching, and improves the accuracy of scenario switching, the stability of operational control, and the overall adaptability of the device.

[0098] The control device provided in this embodiment can execute the method provided in the above method embodiment. Its implementation principle and technical effect are similar, and will not be described in detail here.

[0099] Example 3

[0100] Figure 5 A schematic diagram of the structure of the electronic device provided in this application. Figure 5 As shown, the electronic device 50 provided in this embodiment includes at least one processor 501 and a memory 502. Optionally, the device 50 further includes a communication component 503. The processor 501, memory 502, and communication component 503 are connected via a bus 504.

[0101] In a specific implementation, at least one processor 501 executes computer execution instructions stored in memory 502, causing at least one processor 501 to perform the above-described method.

[0102] The specific implementation process of processor 501 can be found in the above method embodiments, and its implementation principle and technical effect are similar. It will not be repeated here.

[0103] In the above embodiments, it should be understood that the processor can be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), etc. The general-purpose processor can be a microprocessor or any conventional processor. The steps of the method disclosed in this invention can be directly implemented by a hardware processor, or implemented by a combination of hardware and software modules within the processor.

[0104] The memory may include random access memory (RAM) and may also include non-volatile memory (NVM), such as at least one disk storage device.

[0105] The bus can be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, or an Extended Industry Standard Architecture (EISA) bus, etc. Buses can be categorized as address buses, data buses, control buses, etc. For ease of illustration, the buses shown in the accompanying drawings are not limited to a single bus or a single type of bus.

[0106] This application also provides a computer program product, including a computer program that, when executed by a processor, implements the above-described method.

[0107] This application also provides a computer-readable storage medium storing computer-executable instructions, which, when executed by a processor, implement the above-described method.

[0108] The aforementioned readable storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk. The readable storage medium can be any available medium accessible to a general-purpose or special-purpose computer.

[0109] An exemplary readable storage medium is coupled to a processor, enabling the processor to read information from and write information to the readable storage medium. Of course, the readable storage medium can also be a component of the processor. The processor and the readable storage medium can reside in an Application Specific Integrated Circuit (ASIC). Alternatively, the processor and the readable storage medium can exist as discrete components in the device.

[0110] The division of units is merely a logical functional division; in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be indirect coupling or communication connection through some interfaces, devices, or units, and may be electrical, mechanical, or other forms.

[0111] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0112] In addition, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit.

[0113] If a function is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this invention, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of the various embodiments of this invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0114] Those skilled in the art will understand that all or part of the steps of the above-described method embodiments can be implemented by hardware related to program instructions. The aforementioned program can be stored in a computer-readable storage medium. When executed, the program performs the steps of the above-described method embodiments; and the aforementioned storage medium includes various media capable of storing program code, such as ROM, RAM, magnetic disks, or optical disks.

[0115] Finally, it should be noted that other embodiments of the invention will readily occur to those skilled in the art upon consideration of the specification and practice of the invention disclosed herein. This invention is intended to cover any variations, uses, or adaptations of the invention that follow the general principles of the invention and include common knowledge or customary techniques in the art not disclosed herein, and is not limited to the precise structures described above and shown in the accompanying drawings, and various modifications and changes can be made without departing from its scope. The scope of the invention is limited only by the appended claims.

Claims

1. A method for controlling a device, characterized in that, The method includes: Acquire multi-source input data characterizing the current usage status of the device; Based on the multi-source input data, the current candidate usage scenarios are obtained, and the candidate usage scenarios that meet the preset switching conditions are determined as the target usage scenarios; Obtain the target control parameters corresponding to the target usage scenario, and adjust the operating parameters of the device based on the target control parameters.

2. The control method for the device according to claim 1, characterized in that, The step of determining the current candidate use case based on the multi-source input data includes: Processing multi-source input data to obtain scene recognition features; The scene recognition features are input into a preset scene recognition model to output at least one candidate usage scenario.

3. The control method for the device according to claim 2, characterized in that, The preset scene recognition model also outputs the scene recognition confidence score corresponding to the candidate use scenario; The step of determining the candidate use scenarios that meet the preset switching conditions as the target use scenarios includes: Based on multi-source input data acquired at multiple consecutive time points, the corresponding candidate use cases and recognition confidence levels are determined respectively; When the candidate use scenarios are consistent across multiple consecutive time points, and the corresponding recognition confidence scores are all greater than a preset confidence threshold, the candidate use scenario is determined to be the target use scenario.

4. The control method for the device according to claim 2, characterized in that, The method includes: Set a preset set of scenario parameter configurations; each scenario corresponds to a set of control parameters; The step of obtaining the target control parameters corresponding to the target use scenario includes: reading the control parameters corresponding to the target use scenario from the scenario parameter configuration set, and using them as the target control parameters.

5. The control method for the device according to claim 4, characterized in that, The control parameters include: mandatory parameters and adjustable parameters; the adjustment of the operating parameters of the equipment based on the target control parameters includes: Prioritize adjusting the operating parameters corresponding to the mandatory adjustment parameters; When the current equipment status and environmental conditions are met, the operating parameters corresponding to the adjustable parameters are adjusted.

6. The control method for the device according to any one of claims 1 to 5, characterized in that, The adjustment of the operating parameters of the equipment based on the target control parameters includes: According to the preset transition strategy, at least some of the operating parameters corresponding to the control parameters are adjusted in stages or continuously, so that each operating parameter gradually transitions to the target parameter value corresponding to the target use scenario.

7. A control device for an equipment, characterized in that, The device includes: The data acquisition module is used to acquire multi-source input data that characterizes the current usage status of the device; The scenario determination module is used to obtain the current candidate usage scenario based on the multi-source input data, and determine the candidate usage scenario that meets the preset switching conditions as the target usage scenario; The parameter control module is used to acquire target control parameters corresponding to the target usage scenario, and adjust the operating parameters of the device based on the target control parameters.

8. An electronic device, characterized in that, include: Memory, processor; The memory stores computer-executed instructions; The processor executes computer execution instructions stored in the memory, causing the processor to perform the method as described in any one of claims 1-6.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer-executable instructions, which, when executed by a processor, are used to implement the method as described in any one of claims 1-6.

10. A computer program product, characterized in that, Including computer programs, When executed by a processor, the computer program implements the method described in any one of claims 1-6.

Images