Method, apparatus and terminal device for scene generation

Abstract

The application relates to the technical field of smart homes, and discloses a method for scene generation, which comprises the following steps: acquiring relevant information of a current family member, function-related information of smart home appliances in the family, and air standard information; inputting the relevant information of the current family member, the function-related information of the smart home appliances in the family, and the air standard information into a scene arrangement model to obtain scene information output by the scene arrangement model; and showing the scene information output by the scene arrangement model to the current family member, so that the current family member performs scene management based on the scene information. According to the scheme, the flexibility of a user in scene management is remarkably improved, the problem that a user-defined scene function is simple and difficult to match complex and changeable requirements in the prior art is solved, and the user can obtain more accurate and expected scene configuration based on detailed information input. The application further discloses a device for scene generation and a terminal equipment.

Description

Technical Field

[0001] This application relates to the field of smart home technology, such as a method, apparatus, and terminal device for scene generation. Background Technology

[0002] With the advancement of technology and the improvement of living standards, smart homes have become an important part of modern families. The "2023 China Smart Home (AIoH) Development White Paper" details the development history of smart homes, dividing it into three stages. In the 1.0 era, smart homes mainly focused on individual smart devices, allowing users to achieve basic intelligent control through manual operation, voice commands, or remote control. While this stage of smart homes achieved initial intelligence, there was a lack of interconnectivity between devices, resulting in a relatively limited user experience. Entering the 2.0 era, with the rapid development of network communication and IoT technologies, smart homes entered a core development stage focused on diversified scenarios. Through technological integration, overall interconnectivity between home devices was achieved, meeting the diverse needs of families. However, even in the 2.0 era, the level of intelligence in smart homes remained limited, mainly relying on preset scene modes and lacking in-depth exploration of personalized user needs. Entering the 3.0 era, the development of smart homes has reached new heights, with personalized user needs becoming central. Through the deep integration of artificial intelligence and IoT technologies, smart home systems can intelligently manage the entire home environment according to the specific living needs of residents, bringing users a convenient, comfortable, and safe living experience. However, despite the significant technological advancements in smart homes in the 3.0 era, many challenges remain in meeting users' highly personalized scenario needs.

[0003] To address the complexity of smart home scene orchestration procedures, a method and terminal for smart home scene orchestration have been proposed. This method involves an electronic device receiving a user's first operation, responding to the operation, and requesting status information of the smart home devices from a server. Upon receiving the status information, the electronic device generates control rules based on this information to control the smart home devices. When the user performs a second operation to activate the smart home scene, the electronic device controls the smart home devices to enter the state corresponding to the status information, according to the pre-set control rules. This method simplifies the scene orchestration process to some extent and improves the user experience.

[0004] In the process of implementing the embodiments of this disclosure, at least the following problems were found in the related art:

[0005] While related technologies have improved the user experience to some extent, the user-customizable scene functions are relatively simple, mainly relying on preset control rules and status information, which makes it difficult to meet users' growing personalized needs. As the number of smart devices in users' homes increases and their needs become more diverse, users are often unable to customize scenes that perfectly match their expectations.

[0006] It should be noted that the information disclosed in the background section above is only used to enhance the understanding of the background of this application, and therefore may include information that does not constitute prior art known to those skilled in the art. Summary of the Invention

[0007] To provide a basic understanding of some aspects of the disclosed embodiments, a brief summary is given below. This summary is not intended as a general commentary, nor is it intended to identify key / important components or describe the scope of protection of these embodiments, but rather as a prelude to the detailed description that follows.

[0008] This disclosure provides a method, apparatus, and terminal device for scene generation to solve the technical problem that users cannot customize scenes that meet their expectations.

[0009] In some embodiments, the method for scene generation includes: acquiring relevant information of current family members, functional information of smart home appliances in the home, and air quality standard information; inputting the relevant information of current family members, functional information of smart home appliances in the home, and air quality standard information into a scene orchestration model to obtain scene information output by the scene orchestration model; and displaying the scene information output by the scene orchestration model to current family members so that current family members can manage the scene based on the scene information.

[0010] In some embodiments, the method for scene generation includes: acquiring sample data for model training, wherein the sample data consists of various device models pulled from Haiji.com, relevant information of all family members, and historical air quality standard information; and constructing a scene orchestration model based on the sample data.

[0011] In some embodiments, the method for scene generation includes: preprocessing sample data to obtain preprocessed sample data; constructing an initial model according to a machine learning algorithm; and iteratively optimizing and training the initial model using the preprocessed sample data to obtain a scene orchestration model.

[0012] In some embodiments, the method for scene generation includes: when a scene orchestration model outputs multiple scene information, filtering out target scene information from the multiple scene information; displaying the target scene information to the current family member so that the current family member can manage the scene based on the scene information.

[0013] In some embodiments, the method for scene generation includes: obtaining scene management behavior information of current family members; and learning and correcting the scene orchestration model based on the scene management behavior information.

[0014] In some embodiments, the method for scene generation includes: if voice information indicating scene incompatibility is received from a current family member, detecting whether the smart home appliances in the home meet the conditions for scene replacement; if it is determined that the smart home appliances in the home meet the conditions for scene replacement, generating new scene information so as to adjust the scene according to the new scene information.

[0015] In some embodiments, the method for scene generation includes: when a new smart home appliance is added to a home, obtaining relevant information about the function of the new smart home appliance; and updating the scene information generated by the scene orchestration model based on the relevant information about the function of the new smart home appliance.

[0016] In some embodiments, the device for scene generation includes: an acquisition module configured to acquire relevant information of current family members, functional information of smart home appliances in the home, and air quality standard information; an output module configured to input the relevant information of current family members, functional information of smart home appliances in the home, and air quality standard information into a scene orchestration model to obtain scene information output by the scene orchestration model; and a display module configured to display the scene information output by the scene orchestration model to current family members so that current family members can manage the scene based on the scene information.

[0017] In some embodiments, the apparatus for scene generation includes a processor and a memory storing program instructions, the processor being configured to execute the aforementioned method for scene generation when the program instructions are executed.

[0018] In some embodiments, the terminal device includes: a terminal device body; and the aforementioned device for scene generation, which is installed on the terminal device body.

[0019] The method, apparatus, and terminal device for scene generation provided in this disclosure can achieve the following technical effects:

[0020] This solution intelligently generates and outputs scene information tailored to the needs of family members by inputting relevant information about current family members, functional information of smart home appliances, and air quality standards into the scene orchestration model. This significantly improves user flexibility in scene management, resolving the previous problem of user-defined scenes being simple in function but difficult to match complex and changing needs. By providing users with detailed information input, this solution allows for more accurate and desired scene configurations, greatly optimizing the user experience and meeting the growing demand for personalized scene management.

[0021] The above general description and the description below are exemplary and illustrative only and are not intended to limit this application. Attached Figure Description

[0022] One or more embodiments are illustrated by way of example with reference to the accompanying drawings. These illustrations and drawings do not constitute a limitation on the embodiments. Elements having the same reference numerals in the drawings are shown as similar elements. The drawings are not to be scaled. And wherein:

[0023] Figure 1 This is a schematic diagram of a method for scene generation provided in an embodiment of this disclosure;

[0024] Figure 2 This is a schematic diagram of a method for constructing a scene orchestration model provided in an embodiment of this disclosure;

[0025] Figure 3 This is a schematic diagram of another method for constructing a scene orchestration model provided in this disclosure embodiment;

[0026] Figure 4 This is a schematic diagram of another method for scene generation provided in this disclosure embodiment;

[0027] Figure 5 This is a schematic diagram of a device for scene generation provided in an embodiment of this disclosure;

[0028] Figure 6 This is a schematic diagram of another device for scene generation provided in an embodiment of this disclosure. Detailed Implementation

[0029] To provide a more detailed understanding of the features and technical content of the embodiments of this disclosure, the implementation of the embodiments of this disclosure will be described in detail below with reference to the accompanying drawings. The accompanying drawings are for illustrative purposes only and are not intended to limit the embodiments of this disclosure. In the following technical description, for ease of explanation, several details are used to provide a full understanding of the disclosed embodiments. However, one or more embodiments may still be implemented without these details. In other cases, well-known structures and devices may be simplified in their depiction to simplify the drawings.

[0030] The terms "first," "second," etc., used in the specification, claims, and accompanying drawings of this disclosure are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate for the embodiments of this disclosure described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion.

[0031] Unless otherwise stated, the term "multiple" means two or more.

[0032] In this embodiment of the disclosure, the character " / " indicates that the objects before and after it are in an "or" relationship. For example, A / B means: A or B.

[0033] The term "and / or" describes an association between objects, indicating that three relationships can exist. For example, A and / or B means: A or B, or A and B.

[0034] The term "correspondence" can refer to an association or binding relationship. The correspondence between A and B means that there is an association or binding relationship between A and B.

[0035] In this embodiment of the disclosure, smart home appliances refer to home appliances formed by introducing microprocessors, sensor technology and network communication technology into home appliances. They have the characteristics of intelligent control, intelligent sensing and intelligent application. The operation of smart home appliances often relies on the application and processing of modern technologies such as the Internet of Things, the Internet and electronic chips. For example, smart home appliances can be connected to electronic devices to enable users to remotely control and manage smart home appliances.

[0036] Figure 1 This is a schematic diagram of a method for scene generation provided in an embodiment of this disclosure; combined with Figure 1 As shown, this disclosure provides a method for scene generation, including:

[0037] S11, the terminal device obtains relevant information about current family members, functional information about smart home appliances in the home, and air quality standard information.

[0038] S12, the terminal device inputs the relevant information of the current family members, the functional information of the smart home appliances in the home, and the air quality standard information into the scene orchestration model to obtain the scene information output by the scene orchestration model.

[0039] S13, the terminal device displays the scene information output by the scene orchestration model to the current family member so that the current family member can manage the scene based on the scene information.

[0040] In this embodiment of the disclosure, the terminal device refers to an electronic device with wireless connectivity. The terminal device can communicate with the aforementioned smart home appliances by connecting to the internet, or directly via Bluetooth, Wi-Fi, or other methods. In some embodiments, the terminal device may be, for example, a mobile device, a computer, or an in-vehicle device built into a hovercraft, or any combination thereof. Mobile devices may include, for example, mobile phones, smart home devices, wearable devices, smart mobile devices, virtual reality devices, or any combination thereof. Wearable devices may include, for example, smartwatches, smart bracelets, pedometers, etc.

[0041] In this scheme, the relevant information for current family members includes the number of family members, their height, the distance between family members, their age group, their geographical location, and their behavioral patterns. The age group includes the elderly, children, adult men, and adult women. In an optimized scheme, the relevant information for current family members also includes the season and weather information of their geographical location. Understandably, because the Earth is a tilted, rotating sphere, the degree and duration of direct sunlight received by different latitudes during its revolution around the sun result in different seasons at different geographical locations.

[0042] In this solution, the functional information of smart home appliances includes the model number of the smart home appliance, its MAC (Media Access Control) address, and its capabilities. These capabilities include, but are not limited to, detection and execution capabilities. Detection capabilities include, but are not limited to, temperature and humidity detection, air quality detection, and human presence detection. Execution capabilities include temperature adjustment, humidification, dehumidification, and fresh air intake.

[0043] In this plan, air quality standard information includes real-time environmental parameters as well as current air quality standard information. Current air quality standard information refers to the specific limits or ranges set for various pollutants, temperature, humidity, and other parameters in the air. Specifically, national standards can be used as current air quality standard information. This standard information covers air quality requirements under different regional, seasonal, and weather conditions, and takes into account the behavioral characteristics and health needs of different age groups.

[0044] In this embodiment, the terminal device can utilize various sensors and positioning technologies to comprehensively collect relevant information about current family members, functional information about smart home appliances, and air quality information. For example, temperature and humidity sensors monitor the temperature and humidity of the home environment, air quality sensors detect the concentration of pollutants in the air, human sensors track the activity trajectories and behavioral patterns of family members, and GPS positioning systems and indoor positioning technologies obtain the geographical location information of family members. Simultaneously, the terminal device can also collect information about the model, MAC address, and operational capabilities (such as temperature control, humidification, dehumidification, and ventilation) of smart home appliances through their built-in sensors. This approach enables a deep understanding of the home environment, family member needs, and device capabilities, providing a precise data foundation for determining scenario information.

[0045] Furthermore, the terminal device can input information about current family members, functional information about smart home appliances, and air quality standards into a trained scene orchestration model. The scene orchestration model then outputs corresponding scene information based on the input data. This scene information includes, but is not limited to, scene conditions, scene actions, and linkage rules for smart home appliances. For example, the terminal device collects information about family members' locations, ages, and the current status and functions of smart home appliances, combined with real-time environmental parameters and local air quality standards, such as China's "Ambient Air Quality Standard" (GB 3095-2012). The scene orchestration model then processes the input data and outputs scene information. The output scene conditions include: family members in Beijing are at home and the outdoor temperature is above 30°C; the indoor air quality index shows a PM2.5 concentration exceeding 75 μg / m3. Scene actions include: the smart air conditioner automatically adjusting to 26°C to keep the room cool; the air purifier turning on its high-power mode to reduce indoor PM2.5 concentration; the smart bulb adjusting to a soft, cool light to reduce eye strain; and the smart curtains automatically closing to reduce direct sunlight. The linkage rules for smart home appliances include: if the smart air conditioner is turned on, the smart light bulbs will automatically switch to energy-saving mode; if the air purifier detects an improvement in indoor air quality, the smart curtains will automatically adjust their opening and closing based on the intensity of sunlight.

[0046] Furthermore, the terminal device displays scene information output by the scene orchestration model to current family members, enabling them to manage scenes based on this information. In practical applications, the terminal device can display scene information output by the scene orchestration model to current family members. For example, the display interface can include a clear list or chart showing all automatically generated scenes suitable for the current family environment. Specifically, each scene provides options for editing, enabling / disabling, and deleting, allowing users to manage scenes according to personal preferences and actual needs. In addition, the terminal device also provides a scene preview function, allowing users to preview the effect of each scene before actual application, ensuring it meets the expectations and needs of family members. In this way, users can not only passively receive scene information but also actively participate in the intelligent scene management of the home environment, achieving a truly intelligent home interactive experience.

[0047] The method for scene generation provided in this disclosure allows for the intelligent generation and output of scene information tailored to the needs of family members by inputting relevant information about current family members, functional information about smart home appliances, and air quality standards into a scene orchestration model. This significantly improves user flexibility in scene management, resolving the previous problem that user-defined scenes were simple in function but difficult to match complex and changing needs. By providing detailed information input, users can obtain more accurate and desirable scene configurations, greatly optimizing the user experience and meeting the growing demand for personalized scene management.

[0048] Figure 2 This is a schematic diagram of a method for constructing a scene orchestration model provided in an embodiment of this disclosure; combined with Figure 2 As shown, the scene orchestration model can optionally be constructed in the following ways:

[0049] S21, the terminal device acquires sample data for model training. The sample data consists of various device models pulled from Haiji.com, relevant information of all family members, and historical air quality standard information.

[0050] S22, the terminal device constructs a scene orchestration model based on the sample data.

[0051] In this solution, the terminal device can acquire sample data for model training. Specifically, the terminal device needs to obtain model data of all devices connected to Haier from the Haier Smart Home Network. This data includes detailed functional and status information of devices such as human body sensors, air conditioning systems (floor-standing, wall-mounted, and central), fresh air systems, smart windows, smart pillows, smart bracelets and watches, humidifiers, dehumidifiers, air purifiers, aroma diffusers, and lighting and curtains. Haier Smart Home Network is Haier's open smart home platform, providing full-stack open capabilities and one-stop services for developers. Through core technology support and ecosystem resource sharing, Haier Smart Home Network provides industry-leading smart services to the smart home industry, enabling authorized users to manage, monitor, and control devices. Through this platform, Haier can connect and interact with various smart devices, providing users with a more intelligent and personalized home experience. Simultaneously, the terminal device acquires relevant information about all family members. This information includes, but is not limited to, the number of family members, their height, the distance between family members, their age group, their geographical location, and their behavioral patterns. In one optimized scheme, the relevant information for all family members also includes their geographical location, seasonal information, and weather information. Furthermore, the terminal device needs to acquire historical air quality standard information. This historical air quality standard information includes historical environmental parameters and predefined air quality standards. This allows the terminal device to build a scene orchestration model based on sample data. Using this scheme, by integrating and pulling rich device model data, family member information, and historical air quality standard information from a massive network, a scene orchestration model capable of deep learning user behavior patterns and environmental changes can be constructed. This model can understand the needs of different family members, the functionality of devices, and changes in environmental quality, thereby generating personalized and highly adaptable smart home control scenarios, achieving automated and intelligent management of the home environment, and improving living comfort and energy efficiency.

[0052] Figure 3 This is a schematic diagram of another method for constructing a scene orchestration model provided in this disclosure embodiment; combined with Figure 3 As shown, optionally, in step S22, the terminal device constructs a scene orchestration model based on the sample data, including:

[0053] S31, the terminal device preprocesses the sample data to obtain preprocessed sample data.

[0054] S32, the terminal device constructs an initial model based on machine learning algorithms.

[0055] S33, the terminal device uses the preprocessed sample data to iteratively optimize and train the initial model to obtain the scene orchestration model.

[0056] In this solution, the preprocessing of sample data by the terminal device includes: data cleaning to ensure accuracy and completeness; formatting and standardization to ensure consistent scale and format before model training; data cleaning including, but not limited to, identifying and removing duplicate records, correcting obvious errors and omissions, and handling missing values; and formatting and standardization including, but not limited to, unifying data formats, converting different units and scales, and normalizing or standardizing numerical data. This solution ensures the accuracy and completeness of the dataset through data cleaning, eliminating errors and redundant information. Subsequent formatting and standardization unifies the data scale and format, making the sample data consistent and comparable before model training. This improves the efficiency of model training and the accuracy of predictions, laying a solid data foundation for building an efficient and accurate smart home scene orchestration model.

[0057] Furthermore, the terminal device can construct an initial model based on machine learning algorithms. Here, the choice of algorithm is based on the specific nature of the problem and the characteristics of the data. For example, considering that the scene orchestration model needs to perform complex conditional judgments, understand device functions, and generate actions, we can choose algorithms such as deep neural networks, recurrent neural networks, or reinforcement learning. These algorithms can handle large amounts of input data, capture complex patterns and relationships in the data, and generate accurate outputs. In an embodiment of smart home scene orchestration, the terminal device designs a model architecture comprising an input layer, a processing layer, and an output layer. The input layer is responsible for receiving and integrating relevant information about current family members, functional information about smart home appliances, and air quality standards. The processing layer performs in-depth analysis and processing of the input data, extracting key features and constructing internal representations, enabling the model to understand and learn complex relationships between the data. The output layer generates scene information that meets the user's personalized needs based on the analysis results of the processing layer. With this approach, an initial model can be constructed using the selected machine learning algorithm, which can intelligently process and analyze user family information, smart device status, and real-time air quality standards.

[0058] Furthermore, the terminal device uses preprocessed sample data to iteratively optimize and train the initial model. During training, the model learns how to accurately predict and generate scene information that meets user needs based on various device models, relevant information of all family members, and historical air quality standards. By continuously adjusting model parameters and structure and optimizing the loss function, the model's performance will gradually improve until satisfactory accuracy and generalization ability are achieved, ultimately forming a scene orchestration model that can automatically adapt to different home environments and user preferences. Using this approach, by meticulously iteratively optimizing and training the initial model with preprocessed sample data, a highly accurate and adaptable scene orchestration model is obtained. This model can intelligently identify and respond to the specific needs of the user's family, device status, and environmental changes, thereby achieving automated and personalized smart home environment management, improving the user's living experience and the system's energy efficiency.

[0059] Optionally, in step S13, the terminal device displays scene information output by the scene orchestration model to the current family member, so that the current family member can manage the scene based on the scene information, including:

[0060] When the scene orchestration model outputs multiple scene information, the terminal device selects the target scene information from the multiple scene information.

[0061] The terminal device displays target scene information to the current family members so that they can manage the scene based on the scene information.

[0062] In this solution, target scene information can be filtered from multiple scenarios using various methods. In the first method, the terminal device prioritizes the output scene information based on the specific needs and habits of family members. For example, if there are children or elderly people in the home, the terminal device will assign higher priority to health and safety-related scene information, such as thermostat control and fall detection, thus ensuring that the highest priority scene information is used as the target scene information. This prioritization ensures that the most relevant scene information is noticed by family members first. In the second method, the terminal device also considers the functional importance of smart home appliances. For example, security-related device information, such as security cameras and alarm systems, is usually given high priority because they are crucial to home safety. This also ensures that the highest priority scene information is used as the target scene information. In the third method, the terminal device analyzes the correlation between scene information and the current activities of family members. If it detects that a family member is watching television, the terminal device will prioritize displaying smart home appliance scenes related to the television viewing experience, such as adjusting sound settings and lighting modes to enhance the viewing experience. Simultaneously, if external air quality data indicates poor air quality, the terminal device will prioritize displaying device scene information related to air purification. In the fourth approach, the terminal device can utilize machine learning algorithms to analyze the behavioral patterns of family members and make personalized scenario recommendations based on these patterns. For example, the terminal device might predict and recommend scenario information that family members might need based on their historical activity records at specific times, such as recommending a relaxation mode in the evening and a wake-up mode in the morning. Furthermore, the terminal device displays target scenario information to the current family member, enabling them to manage the scenario based on this information. This solution, by employing multiple filtering mechanisms such as priority ranking, device function importance considerations, activity correlation analysis, and personalized recommendations, allows the terminal device to accurately identify and display the target scenario information that best matches the current needs and habits of family members from a large pool of scenario information. This solution not only improves the response speed and user experience of the smart home system but also enhances the system's intelligence level, making home environment management more automated and personalized, thereby improving living comfort and security.

[0063] Figure 4 This is a schematic diagram of another method for scene generation provided in this disclosure embodiment; combined with Figure 4 As shown, optionally, after the current family member performs scene management based on scene information, the method further includes:

[0064] S41, The terminal device obtains the scene management behavior information of the current family members.

[0065] S42, the terminal device learns and corrects the scene orchestration model based on scene management behavior information.

[0066] In this solution, family members can manage scene information, including viewing, editing, and deleting displayed scene information. Terminal devices capture this scene management behavior information in real time, including the user's specific operational details. This information is used as feedback and directly input into the scene orchestration model. By analyzing these behaviors, the scene orchestration model learns user preferences and habits, thereby adjusting scene recommendations. For example, if a user frequently adjusts the settings of a certain scene, the model will recognize this pattern and consider this personalized need in future recommendations. Furthermore, the scene orchestration model self-corrects and optimizes based on user scene management behavior information. This continuous learning process helps reduce recommendation bias and improve the accuracy of scene recommendations. As the model continuously learns and adapts to user changes, the smart home system can provide scene information that better matches the user's actual needs, thereby improving user experience and satisfaction. This dynamic learning and self-optimization mechanism makes the smart home system more intelligent and human-centered, providing users with a more precise and comfortable living environment.

[0067] Optionally, after the current family member performs scene management based on scene information, the method further includes:

[0068] If the terminal device receives voice information from a family member indicating that the scene is not suitable, it will detect whether the smart home appliances in the home are ready to change the scene.

[0069] Once it is determined that the smart home appliances in the home meet the conditions for scene replacement, the terminal device generates new scene information so that the scene can be adjusted according to the new scene information.

[0070] In this solution, when a family member expresses dissatisfaction with the current scene via voice, and the system receives this voice message indicating an inappropriate scene, the terminal device first checks if the smart home appliances in the home meet the requirements for changing the scene. If the smart home appliances meet the requirements, the terminal device will generate new scene information based on the user's voice command to adjust to a scene that better suits the user's needs. Specifically, when a user makes a specific scene request via voice, such as "I want the temperature at home to be comfortable and the air fresh when I get home from get off work every day," the terminal device will check if the user's smart home appliances and settings support this scene. If the conditions are met, such as if the user has a smart thermostat and an air purification system, the terminal device will automatically generate the corresponding scene conditions and actions. For example, if user A usually gets home at 6 pm every day, the terminal device will predict the arrival time based on the user's real-time location information and automatically start adjusting the indoor temperature and activating the fresh air system half an hour before the user arrives home. If the outdoor air is fresh and the temperature is suitable, the terminal device can also automatically open windows for ventilation to achieve a comfortable and energy-efficient living environment. With this solution, when family members express dissatisfaction with the current home environment via voice feedback, the terminal device can respond promptly. It first checks whether the smart home appliances support scene changing. Once it is confirmed that the devices meet the requirements, it can quickly generate and apply new scene information, automatically adjust the home environment to meet the needs of the family members, thereby achieving a more personalized and automated smart home experience.

[0071] Optionally, when new smart home appliances are added to the home, the terminal device obtains functional information related to the new smart home appliances.

[0072] The terminal device updates the scene information generated by the scene orchestration model based on the functional information of the newly added smart home appliances.

[0073] In this solution, when a new smart home appliance is added to a household, the terminal device automatically acquires relevant functional information about the device, including its MAC address, model, and functional parameters, and transmits this information to the cloud in real time. The cloud system then updates the scene information generated by the scene orchestration model based on the new device's functionality, ensuring that the model fully utilizes the new device's capabilities to optimize home environment management. Specifically, the cloud system establishes an independent scene orchestration library for each user's household, storing all relevant scene information and dynamically updating it based on the user's actual situation and device changes. This solution not only improves the flexibility and adaptability of the scene orchestration model but also ensures that users can fully utilize the functions of the new devices, enhancing the overall smart home experience.

[0074] In one optimized solution, the terminal device allows users to freely create and edit scenes generated by the scene orchestration model according to their personal needs. This includes flexibly setting multiple conditions for scene activation (e.g., when someone is detected at home and the CO2 concentration exceeds 1000ppm) and corresponding actions (such as automatically turning on the fresh air system). It's worth noting that the terminal device supports arbitrary definition of any number of conditions and actions set by the user, and users can also independently set the execution order of these condition groups. Furthermore, the terminal device can incorporate a device snapshot function, which records the original state of related devices such as air conditioners before the user activates the scene and restores the state of these devices by executing a snapshot when needed. This solution, by providing highly customizable scene orchestration and device snapshot functions, significantly enhances users' control and personalization of the smart home environment, enabling users to easily create and manage diverse smart scenes according to their actual needs, thereby greatly improving the convenience, comfort, and enjoyment of home life.

[0075] Figure 5 This is a schematic diagram of a device for scene generation provided in an embodiment of this disclosure; combined with Figure 5 As shown, this embodiment of the disclosure provides a device 200 for scene generation, including an acquisition module 51, an output module 52, and a display module 53. The acquisition module 51 is configured to acquire relevant information of current family members, functional information of smart home appliances in the home, and air quality standard information; the output module 52 is configured to input the relevant information of current family members, functional information of smart home appliances in the home, and air quality standard information into a scene orchestration model to obtain scene information output by the scene orchestration model; the display module 53 is configured to display the scene information output by the scene orchestration model to current family members so that current family members can manage the scene based on the scene information.

[0076] The scene generation apparatus 200 provided in this disclosure allows for the intelligent generation and output of scene information tailored to the needs of family members by inputting relevant information about current family members, functional information about smart home appliances, and air quality standards into a scene orchestration model. This significantly improves user flexibility in scene management, resolving the previous problem that user-defined scenes were simple but difficult to match complex and changing needs. By providing detailed information input, users can obtain more accurate and desirable scene configurations, greatly optimizing the user experience and meeting the growing demand for personalized scene management.

[0077] Figure 6 This is a schematic diagram of another device for scene generation provided in an embodiment of this disclosure. (In conjunction with...) Figure 6As shown, this disclosure provides an apparatus 300 for scene generation, including a processor 301 and a memory 302. Optionally, the apparatus 300 may further include a communication interface 303 and a bus 304. The processor 301, communication interface 303, and memory 302 can communicate with each other via the bus 304. The communication interface 303 can be used for information transmission. The processor 301 can call logical instructions in the memory 302 to execute the scene generation method described in the above embodiment.

[0078] Furthermore, the logic instructions in the aforementioned memory 302 can be implemented as software functional units and, when sold or used as independent products, can be stored in a computer-readable storage medium.

[0079] The memory 302, as a computer-readable storage medium, can be used to store software programs and computer-executable programs, such as program instructions / modules corresponding to the methods in the embodiments of this disclosure. The processor 301 executes functional applications and data processing by running the program instructions / modules stored in the memory 302, that is, it implements the method for scene generation in the above embodiments.

[0080] The memory 302 may include a program storage area and a data storage area. The program storage area may store the operating system and applications required for at least one function; the data storage area may store data created based on the use of the terminal device. Furthermore, the memory 302 may include high-speed random access memory and may also include non-volatile memory.

[0081] This disclosure provides a terminal device, including a terminal device body and the aforementioned scene generation device 200 (300). The scene generation device 200 (300) is mounted on the terminal device body. The mounting relationship described herein is not limited to placement within the terminal device body, but also includes mounting connections with other components of the terminal device, including but not limited to physical connections, electrical connections, or signal transmission connections. Those skilled in the art will understand that the scene generation device 200 (300) can be adapted to any feasible terminal device body to achieve other feasible embodiments.

[0082] This disclosure provides a computer-readable storage medium storing computer-executable instructions configured to perform the above-described method for scene generation.

[0083] The technical solutions of this disclosure can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes one or more 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 method described in this disclosure. The aforementioned storage medium can be a non-transitory storage medium, such as a USB flash drive, external hard drive, read-only memory (ROM), random access memory (RAM), magnetic disk, or optical disk, etc., and other media capable of storing program code.

[0084] The foregoing description and accompanying drawings fully illustrate embodiments of this disclosure to enable those skilled in the art to practice them. Other embodiments may include structural, logical, electrical, procedural, and other changes. The embodiments represent only possible variations. Individual components and functions are optional unless explicitly required, and the order of operation may vary. Parts and features of some embodiments may be included in or replace parts and features of other embodiments. Moreover, the terminology used in this application is for describing embodiments only and is not intended to limit the claims. As used in the description of embodiments and claims, the singular forms “a,” “an,” and “the” are intended to equally include the plural forms unless the context clearly indicates otherwise. Similarly, the term “and / or” as used in this application means including one or more of the associated listed items and all possible combinations thereof. Additionally, when used in this application, the term "comprise" and its variations "comprises" and / or "comprising" refer to the presence of stated features, integrals, steps, operations, elements, and / or components, but do not exclude the presence or addition of one or more other features, integrals, steps, operations, elements, components, and / or groups thereof. Without further limitations, an element defined by the phrase "comprises a..." does not exclude the presence of other identical elements in the process, method, or apparatus that includes said element. In this document, each embodiment may focus on the differences from other embodiments, and similar or identical parts between embodiments can be referred to mutually. For methods, products, etc., disclosed in the embodiments, if they correspond to the method section disclosed in the embodiments, the relevant parts can be referred to the description of the method section.

[0085] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of the embodiments of this disclosure. Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0086] The methods and products (including but not limited to devices and equipment) disclosed in the embodiments herein can be implemented in other ways. For example, the device embodiments described above are merely illustrative. For instance, the division of units may be merely a logical functional division, and 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. In addition, the coupling or direct coupling or communication connection between the shown or discussed units may be through some interfaces, and the indirect coupling or communication connection between devices or units may be electrical, mechanical, or other forms. 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 may be selected to implement this embodiment according to actual needs. Furthermore, the functional units in the embodiments of this disclosure may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.

[0087] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to embodiments of this disclosure. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. In some alternative implementations, the functions marked in the blocks may occur in a different order than that shown in the drawings. For example, two consecutive blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. In the descriptions corresponding to the flowcharts and block diagrams in the accompanying drawings, the operations or steps corresponding to different blocks may also occur in a different order than disclosed in the description, and sometimes there is no specific order between different operations or steps. For example, two consecutive operations or steps may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. Each block in a block diagram and / or flowchart, and combinations of blocks in a block diagram and / or flowchart, can be implemented using a dedicated hardware-based system that performs the specified function or action, or using a combination of dedicated hardware and computer instructions.

Claims

1. A method for scene generation, characterized in that, include: Obtain relevant information about current family members, functional information about smart home appliances in the home, and air quality standards; Input the relevant information of current family members, the functional information of smart home appliances in the home, and the air quality standard information into the scene orchestration model to obtain the scene information output by the scene orchestration model; Display scene information output by the scene orchestration model to the current family members so that they can manage the scene based on the scene information.

2. The method of claim 1, wherein, The scene orchestration model is constructed using the following methods: Obtain sample data for model training, which includes various device models, relevant information of all family members, and historical air quality standard information pulled from Haiji.com; Based on the sample data, a scene orchestration model is constructed.

3. The method of claim 2, wherein, Based on the sample data, a scene orchestration model is constructed, including: The sample data is preprocessed to obtain preprocessed sample data; Build an initial model based on machine learning algorithms; The initial model is iteratively optimized and trained using preprocessed sample data to obtain a scene orchestration model.

4. The method of claim 1, wherein, Display scene information output by the scene orchestration model to the current family members so that they can manage the scene based on this information, including: When the scene orchestration model outputs multiple scene information, the target scene information is selected from the multiple scene information; Display target scene information to current family members so that they can manage the scene based on that information.

5. The method according to any one of claims 1 to 4, characterized in that, After family members manage the scene based on scene information, the method also includes: Obtain current family members' scene management behavior information; The scene orchestration model is learned and corrected based on scene management behavior information.

6. The method according to any one of claims 1 to 4, characterized in that, After family members manage the scene based on scene information, the method also includes: If a voice message indicating that the scene is not suitable is received from a current family member, the system will detect whether the smart home appliances in the home are ready to change the scene. Once it is determined that the smart home appliances in the home meet the conditions for scene replacement, new scene information is generated so that the scene can be adjusted according to the new scene information.

7. The method according to any one of claims 1 to 4, characterized in that, Also includes: When adding smart home appliances to a household, obtain information related to the functions of the newly added smart home appliances; Update the scene information generated by the scene orchestration model based on the functional information of the newly added smart home appliances.

8. An apparatus for scene generation, the apparatus comprising: include: The acquisition module is configured to acquire relevant information about current family members, functional information about smart home appliances in the home, and air quality standards. The output module is configured to input relevant information of current family members, functional information of smart home appliances in the home, and air quality standard information into the scene orchestration model to obtain scene information output by the scene orchestration model; The display module is configured to show scene information output by the scene orchestration model to the current family members, so that the current family members can manage the scene based on the scene information.

9. An apparatus for scene generation, comprising a processor and a memory having stored therein program instructions, the apparatus being characterized by: The processor is configured to perform the method for scene generation as described in any one of claims 1 to 7 when executing the program instructions.

10. A terminal device, comprising: include: Terminal device body; The scene generation apparatus as described in claim 8 or 9 is installed on the terminal device body.

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