Control method and device of equipment, electronic equipment and storage medium

Abstract

Embodiments of the present application provide a device control method and device, electronic device and storage medium, and relate to the technical field of smart home, the method comprises: in response to the first control instruction input by the target user, if the target user belongs to the family member bound to the control system, obtaining the target user data corresponding to the target user; according to the target user data, the target user is instructed to predict, obtain the second control instruction for the first control instruction, send the second control instruction to the corresponding first smart home device, control the first smart home device to execute the first device operation corresponding to the second control instruction, thereby improving the control flexibility of the smart home device.

Description

Technical Field

[0001] This invention relates to the field of smart home technology, and in particular to a device control method, a device control apparatus, an electronic device, and a computer-readable storage medium. Background Technology

[0002] Smart home control systems are currently in a phase of rapid development, integrating various sensors and smart devices to achieve intelligent management of the home environment. These systems utilize advanced data processing and machine learning technologies to analyze family members' habits and preferences, providing personalized services and suggestions. For example, smart assistants interact with users through voice recognition and natural language processing, smart security systems improve home safety through image recognition and behavioral analysis, and smart temperature control systems automatically adjust indoor temperature based on user habits, achieving energy conservation and environmental protection. Although challenges remain regarding data privacy, data quality, and user acceptance, with continuous technological advancements, smart home control systems are gradually improving users' quality of life and living experience.

[0003] However, in smart home scenarios, there are often multiple family members, and different family members have different control needs for devices. In the current device control process, it is difficult to flexibly control smart home devices according to the different control needs of family members, which affects the user experience. Summary of the Invention

[0004] The present invention provides a device control method, apparatus, electronic device, and computer-readable storage medium to solve or partially solve the problem of poor control flexibility of smart home devices.

[0005] This invention discloses a device control method applied to a control system, the method comprising: In response to a first control command input by a target user, if the target user is a family member bound to the control system, then target user data corresponding to the target user is acquired; Based on the target user data, the system predicts the target user's instructions to obtain a second control instruction for the first control instruction. The second control instruction is then sent to the corresponding first smart home device, which is then controlled to perform a first device operation corresponding to the second control instruction.

[0006] Among some feasible implementation methods are: If the target user is not a family member bound to the control system, the first control command is sent to the corresponding second smart home device, controlling the second smart home device to perform the second device operation corresponding to the first control command.

[0007] In some feasible implementations, after controlling the first smart home device to execute the first device operation corresponding to the second control command, the method further includes... In response to a withdrawal operation input by the target user within a preset time after the smart home device performs the first device operation, a withdrawal command for the second control command is generated. The withdrawal command is sent to the first smart home device, controlling the first smart home device to stop performing the first device operation.

[0008] In some feasible implementations, the step of predicting instructions from the target user based on the target user data to obtain a second control instruction for the first control instruction includes: Determine the instruction prediction model for the target user data; The target user data is input into the instruction prediction model to predict the instruction, thereby obtaining a second control instruction for the first control instruction.

[0009] Among some feasible implementation methods are: Acquire local family data and cloud-based family data, wherein the local family data includes at least the first instruction operation habit information of each family member, and the cloud-based family data includes at least the second instruction operation habit information of each family member in other families; The instruction prediction model is obtained by training the model based on the first instruction operation habit information and the second instruction operation habit information.

[0010] In some feasible implementations, the instruction operation habit information includes at least the time when the family member executes the instruction operation, whether the family member is on vacation, the device status of each smart home device, whether there is an emergency, and whether there are multiple family members in the smart home environment at the same time.

[0011] In some feasible implementations, in response to a first control command input by a target user, if the target user is a family member bound to the control system, the target user data corresponding to the target user is obtained, including: In response to a first control command input by a target user, the instruction type corresponding to the first control command is obtained, and the user identifier corresponding to the target user is determined based on the instruction type. If the user identifier belongs to a family member bound to the control system, then the target user data corresponding to the target user is obtained.

[0012] In some feasible implementations, the user identifier includes the user's voiceprint and ID information, and determining the user identifier corresponding to the target user based on the instruction type includes: If the instruction type is a voice type, then the audio corresponding to the first control instruction is identified to obtain the user voiceprint corresponding to the target user. If the instruction type is an application instruction type, then obtain the user account to which the first control instruction belongs, and extract the ID information corresponding to the user account.

[0013] This invention also discloses a control device for an equipment, applied to a control system, the device comprising: The data acquisition module is used to respond to a first control command input by the target user. If the target user is a family member bound to the control system, the module acquires the target user data corresponding to the target user. The prediction module is used to predict the target user's instructions based on the target user data, obtain a second control instruction for the first control instruction, send the second control instruction to the corresponding first smart home device, and control the first smart home device to perform a first device operation corresponding to the second control instruction.

[0014] Among some feasible implementation methods are: The control module is configured to send the first control command to the corresponding second smart home device if the target user is not a family member bound to the control system, and control the second smart home device to perform a second device operation corresponding to the first control command.

[0015] In some feasible implementations, the device further includes The withdrawal response module is used to generate a withdrawal command for the second control command in response to a withdrawal operation input by the target user within a preset time after the smart home device performs the first device operation. The device control module is used to send the withdrawal command to the first smart home device and control the first smart home device to stop performing the first device operation.

[0016] In some feasible implementations, the prediction module is specifically used for: Determine the instruction prediction model for the target user data; The target user data is input into the instruction prediction model to predict the instruction, thereby obtaining a second control instruction for the first control instruction.

[0017] Among some feasible implementation methods are: The training data acquisition module is used to acquire local family data and cloud-based family data. The local family data includes at least the first instruction operation habit information of each family member, and the cloud-based family data includes at least the second instruction operation habit information of each family member in other families. The model training module is used to train the model based on the first instruction operation habit information and the second instruction operation habit information to obtain the instruction prediction model.

[0018] In some feasible implementations, the instruction operation habit information includes at least the time when the family member executes the instruction operation, whether the family member is on vacation, the device status of each smart home device, whether there is an emergency, and whether there are multiple family members in the smart home environment at the same time.

[0019] In some feasible implementations, the data acquisition module is specifically used for: In response to a first control command input by a target user, the instruction type corresponding to the first control command is obtained, and the user identifier corresponding to the target user is determined based on the instruction type. If the user identifier belongs to a family member bound to the control system, then the target user data corresponding to the target user is obtained.

[0020] In some feasible implementations, the user identifier includes the user's voiceprint and ID information, and the data acquisition module is specifically used for: If the instruction type is a voice type, then the audio corresponding to the first control instruction is identified to obtain the user voiceprint corresponding to the target user. If the instruction type is an application instruction type, then obtain the user account to which the first control instruction belongs, and extract the ID information corresponding to the user account.

[0021] This invention also discloses an electronic device, including a processor, a communication interface, a memory, and a communication bus, wherein the processor, the communication interface, and the memory communicate with each other through the communication bus; The memory is used to store computer programs; When the processor executes a program stored in the memory, it implements the method described in the embodiments of the present invention.

[0022] This invention also discloses a computer-readable storage medium storing instructions that, when executed by one or more processors, cause the processors to perform the methods described in this invention.

[0023] The embodiments of the present invention have the following advantages: In this embodiment of the invention, it can be applied to a control system. During the control of smart home devices, the system can respond to a first control command input by a target user. If the target user is a family member bound to the control system, the system obtains the target user data corresponding to the target user. Then, based on the target user data, the system predicts the target user's command to obtain a second control command in response to the first control command. The second control command is then sent to the corresponding first smart home device, controlling the first smart home device to execute a first device operation corresponding to the second control command. By distinguishing the control commands issued by different users, personalized device control based on the habits of different users can be achieved, significantly improving the system's intelligence level and the flexibility of device control, as well as ensuring the user experience. Attached Figure Description

[0024] Figure 1 This is a flowchart of the steps of a device control method provided in an embodiment of the present invention; Figure 2 This is a schematic diagram of the data processing flow provided in an embodiment of the present invention; Figure 3 This is a structural block diagram of a control device for an equipment provided in an embodiment of the present invention; Figure 4 This is a block diagram of an electronic device provided in an embodiment of the present invention. Detailed Implementation

[0025] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0026] As an example, in smart home scenarios, there are often multiple family members, and different family members have different control needs for devices. However, in the current device control process, it is difficult to flexibly control smart home devices according to the different control needs of family members, which affects the user experience.

[0027] In this invention, during the control of smart home devices, the system can respond to a first control command input by a target user. If the target user is a family member bound to the control system, the system obtains the target user data corresponding to the target user. Then, based on the target user data, the system predicts the target user's commands to obtain a second control command in response to the first control command. The second control command is then sent to the corresponding first smart home device, controlling the first smart home device to execute a first device operation corresponding to the second control command. By distinguishing the control commands issued by different users, personalized device control based on the habits of different users can be achieved, significantly improving the system's intelligence level and the flexibility of device control, while ensuring the user experience.

[0028] Reference Figure 1 The diagram illustrates a flowchart of a device control method provided in an embodiment of the present invention, which is applied to a control system and may specifically include the following steps: Step 101: In response to the first control command input by the target user, if the target user is a family member bound to the control system, then obtain the target user data corresponding to the target user; In this embodiment of the invention, the control system (hereinafter referred to as the system) can be a hardware device, a software application, or a control device running a corresponding application. In a smart home environment, the control system can communicate with smart home devices deployed in the smart home environment to realize data transmission and device control, etc. At the same time, it can also communicate with user devices to receive control commands sent by user devices, etc. The present invention does not limit this.

[0029] It should be noted that, during the use of smart home devices, the system may collect user data generated during the user's use of smart home devices, provided that the user has authorized the collection and that local laws and regulations are complied with, in order to provide personalized services to the user based on the collected user data.

[0030] In practice, as users spend more time using the system, the system collects corresponding user data. Based on the collected user data, the system can determine whether to add the corresponding user to the family members list, or have the administrator add the corresponding user to the family members list. After being added to the family members list, the system can provide personalized services to the corresponding family members during the subsequent control of smart home devices, such as controlling smart home devices according to the family members' usage habits.

[0031] In one example, the system can preset a minimum number of control commands required. When a user inputs control commands to smart home devices in the smart home environment a number that meets this minimum requirement, the system can either add the user to the family member list or output a corresponding prompt to notify the administrator that the user can be added to the family member list. For example, assuming the minimum number of control commands required is 10, if user A inputs 11 control commands to smart home devices in the smart home environment, exceeding the minimum number of control commands required, the system can either add user A to the family member list or output a corresponding prompt to notify the administrator that user A can be added to the family member list so that the system can provide personalized services to user A after adding them to the family member list.

[0032] Optionally, during the process of managing users to actively add users, the system can display the control records corresponding to the most recent control commands of the added user, in order to help the managing user understand the corresponding user. For example, assuming that the managing user wants to add user A as a family member, the system can display the corresponding control records through the corresponding graphical user interface, as follows: December 1, 2024, 18:30 Equipment: Living room light; Control command: "Turn on the light"; Note: User A turned on the living room light after returning home in the evening.

[0033] December 2, 2024, 07:00 Equipment: Bedroom air conditioner; Control command: "Set the temperature to 24°C"; Note: User A adjusted the bedroom air conditioner temperature when they got up in the morning.

[0034] December 2, 2024, 19:45 Device: Smart TV Control command: "Open and switch to the news channel" Note: User A turned on the TV and switched to the news channel at night.

[0035] 22:00 on December 3, 2024 Equipment: Bedroom lamp Control command: "Turn off the lights" Note: User A turned off the bedroom light before going to bed at night.

[0036] December 4, 2024, 06:30 Equipment: Smart coffee machine Control command: "Turn on the coffee machine" Note: User A turned on the smart coffee machine in the morning and made a cup of coffee.

[0037] ……wait.

[0038] Through the above operation records, management users can intuitively perceive the identity of the users who need to be added.

[0039] In this embodiment of the invention, when the system receives a first control command input by a target user, it can first analyze the identity of the target user based on the first control command, obtain the command type corresponding to the first control command, and determine the user identifier corresponding to the target user based on the command type. If the user identifier belongs to a family member bound to the control system, the target user data corresponding to the target user is obtained; if the user identifier does not belong to a family member bound to the control system, the system can send the first control command to the corresponding smart home device so that the smart home device can perform the corresponding device operation.

[0040] The user identifier includes the user's voiceprint and ID information. Based on the instruction type corresponding to the first control command, if the instruction type is a voice type, the audio corresponding to the first control command is recognized to obtain the user's voiceprint; if the instruction type is an application instruction type, the user account to which the first control command belongs is obtained, and the ID information corresponding to the user account is extracted. Furthermore, based on the obtained user voiceprint and ID information, the system can search for the existence of a corresponding user voiceprint or ID information in the family member group. If it exists, it can be determined that the target user belongs to a family member bound to the system; if it does not exist, it can be determined that the target user is a non-family member user.

[0041] In one example, a user can issue corresponding control commands to the system via voice or an application. After receiving the corresponding control commands, the system can use a deep learning model to identify which family member issued the command for voice commands; for application commands, it can directly identify the user ID logged in on the application. Based on the identification results, it can determine whether the target user is a family member. If the target user is a family member, the system can further obtain the target user's corresponding target user data in order to provide personalized device control based on the target user data.

[0042] Step 102: Based on the target user data, predict the instruction of the target user to obtain a second control instruction for the first control instruction, send the second control instruction to the corresponding first smart home device, and control the first smart home device to perform a first device operation corresponding to the second control instruction.

[0043] In this embodiment of the invention, after determining that the target user belongs to a family member, the system can predict the target user's instructions based on the target user data, obtain a second control instruction in response to the first control instruction, and send the second control instruction to the corresponding first smart home device to control the first smart home device to perform the first device operation corresponding to the second control instruction. If the target user does not belong to a family member bound to the control system, the first control instruction is sent to the corresponding second smart home device to control the second smart home device to perform the second device operation corresponding to the first control instruction. Thus, by distinguishing the control instructions issued by different users, personalized device control based on the habits of different users can be achieved, significantly improving the system's intelligence level and the flexibility of device control, as well as ensuring the user experience.

[0044] In some feasible implementations, the system can first determine the instruction prediction model for the target user data, then input the target user data into the instruction prediction model to predict the instruction, and obtain the second control instruction for the first control instruction. In this way, by distinguishing the control instructions issued by different users, personalized device control can be achieved according to the habits of different users, which significantly improves the intelligence level of the system and the flexibility of device control, and ensures the user experience.

[0045] It should be noted that the command prediction model can be used to predict the next control command corresponding to the control command entered by the user. For example, when the user enters control command ①, the system can predict the next control command ② that the user may enter based on control command ① and the corresponding user data, and then execute control command ②. Thus, by predicting the next control command that the user may enter, the system can provide personalized device control services to the user. On the other hand, by predicting and controlling smart home devices to perform corresponding device operations, the system can effectively reduce the frequency of users manually controlling smart home devices and provide automation and intelligence in device control.

[0046] Furthermore, for the command prediction model, both local and cloud-based family data can be acquired. Local family data includes at least the first command operation habits of each family member, while cloud-based family data includes at least the second command operation habits of each family member in other households. The model is then trained based on the first and second command operation habits to obtain the command prediction model. The command operation habit information includes at least the time when family members execute commands, whether family members are on vacation, the device status of each smart home device, whether there is an emergency, and whether multiple family members are present simultaneously in the smart home environment.

[0047] In one example, the training process for an instruction prediction model may include: To train the command prediction model, the system needs to collect the following two types of data: local family data: information on the operating habits of each family member in the current household; and cloud-based family data: information on the operating habits of each family member in other households. Local family data includes the first command operating habits of each family member in the current household, such as: the time when a family member executes a command (e.g., user A turns on the living room light at 18:30 every evening); whether a family member is on vacation (e.g., user A's operating habits during vacation may differ from those on weekdays); the device status of each smart home device (e.g., the living room light is currently off, and the air conditioner temperature is 24°C); whether there is an emergency (e.g., user A's operating habits in emergency situations such as fire alarms); and whether multiple family members are present in the smart home environment simultaneously (e.g., operating habits may differ when multiple family members are home at the same time compared to when one family member is home alone).

[0048] Cloud-based family data includes information on the second-instruction operating habits of family members in other households. The specific content is similar to local family data, but it comes from different family environments. This data is used to provide a broader reference, helping the model learn the common operating habits of different family members.

[0049] For both local and cloud-based household data, preprocessing is necessary before training the model to ensure data quality and consistency. This includes data cleaning, data labeling, and data normalization. Specifically, data cleaning removes duplicate or invalid data and handles missing values, such as by using averages or interpolation. Data labeling categorizes operation commands, such as "turn on device," "turn off device," and "adjust device status," and adds information like time points, device status, and family member status. Data normalization normalizes data from different households to ensure the data is on the same scale, facilitating model training.

[0050] After preprocessing the training data, a predictive model can be trained based on the preprocessed data. Specifically, useful features can be extracted from the data first, for example: Time characteristics: the time point of the operation instruction (e.g., morning, evening).

[0051] Equipment status characteristics: The current status of the equipment (e.g., whether the lights are on, the air conditioning temperature).

[0052] Family member status characteristics: whether family members are on vacation, and whether there is an emergency.

[0053] Environmental characteristics: Whether multiple family members exist at the same time.

[0054] Next, you can choose a suitable machine learning model for training, for example: Decision trees: used to handle discrete features.

[0055] Random forest: used to improve prediction accuracy.

[0056] Neural networks are used to handle complex nonlinear relationships.

[0057] Then, local household data and cloud-based household data can be input into the model for training. The model's performance can be evaluated using methods such as cross-validation, and the model parameters can be adjusted to improve prediction accuracy.

[0058] Finally, after the command prediction model is trained, it is deployed to the smart home system. This deployment can be done on local devices (such as smart gateways or servers) to predict the current family member's commands in real time; or it can be deployed to the cloud to process data from multiple households and provide broader predictive support.

[0059] Furthermore, after predicting the corresponding control commands through the above process, the system can send the control commands to the corresponding smart home devices to control the smart home devices to perform the corresponding device operations. Further, if the control commands predicted by the system do not match the actual needs of the target user, the target user can input a corresponding withdrawal operation within a preset time period after the smart home device successfully executes the predicted control commands to withdraw the corresponding control commands. Specifically, in response to the withdrawal operation input by the target user within the preset time period after the smart home device has performed the device operation, the system can generate a withdrawal command for the second control command and then send the withdrawal command to the first smart home device to control the first smart home device to stop executing the first device operation. Thus, in the event of a deviation between the system's prediction result and the user's actual needs, the user can spontaneously withdraw the corresponding device control commands, thereby ensuring the flexibility of device control, avoiding the execution of invalid control commands, and reducing device energy consumption.

[0060] It should be noted that the embodiments of the present invention include, but are not limited to, the examples described above. It is understood that those skilled in the art can make further settings according to actual needs under the guidance of the ideas in the embodiments of the present invention, and the present invention does not limit such settings.

[0061] In this embodiment of the invention, it can be applied to a control system. During the control of smart home devices, the system can respond to a first control command input by a target user. If the target user is a family member bound to the control system, the system obtains the target user data corresponding to the target user. Then, based on the target user data, the system predicts the target user's command to obtain a second control command in response to the first control command. The second control command is then sent to the corresponding first smart home device, controlling the first smart home device to execute a first device operation corresponding to the second control command. By distinguishing the control commands issued by different users, personalized device control based on the habits of different users can be achieved, significantly improving the system's intelligence level and the flexibility of device control, as well as ensuring the user experience.

[0062] To enable those skilled in the art to better understand the technical solutions in the embodiments of the present invention, the following examples provide exemplary descriptions: For the control system, administrators can set the number of family members. The system automatically recognizes and classifies received voice commands as Member 1, Member 2, etc., while application commands are not included in user habit analysis. As users spend more time using the system, it gradually accumulates data. Once a certain amount is reached (e.g., quantitative analysis is performed by setting a minimum number of control commands required), users can manually bind users in the family to members in the app and name them. (When binding, clicking on the corresponding member allows users to view the recordings of the last five control commands, helping them identify which person the command corresponds to). At this point, when users control devices through the app, the system will also follow the user's habits to provide more personalized services.

[0063] In a specific implementation, the system's processing of user-input control commands may include: Prerequisite: The control system has been operating for some time and has established its own database model.

[0064] When the control system receives a user command, it sends a command data packet. It processes the user-input command, predicts the next command, and sends the corresponding command data packet for the predicted control command (if the model is not yet finalized, it does not send the command directly; a pop-up window notifies the user of the predicted next command, allowing the user to choose whether to execute it). Within ten seconds of the next command being executed, the user can choose to "cancel the operation." At this time, the system sends a data packet showing the previous device state, reverting the state. It also records user feedback and continuously optimizes the command prediction model.

[0065] For example, refer to Figure 2This diagram illustrates the data processing flow provided in this embodiment of the invention. Users can issue commands to the system via voice or a mobile app. For voice commands, the system uses a deep learning model to identify which family member issued the command. For app commands, the system directly identifies the user ID logged in on the app. Next, the system uses the deep learning model to predict the user's next possible command, and then executes the corresponding control command. Users can provide real-time feedback on whether the command meets expectations. The system feeds the results back into the deep learning model to continuously optimize it. Through continuous feedback and training, the system gradually improves the accuracy of recognizing and predicting user habits, thereby enhancing user comfort.

[0066] For deep learning models, the implementation steps are as follows: First, the system requires a large amount of historical dialogue data as a training set. This data comes from real interaction records or manually constructed dialogue scenarios. It is provided by testers and continuously added during the test development process.

[0067] Next, select a suitable deep learning model, such as Convolutional Neural Network (CNN), Recurrent Neural Network (RNN), Long Short-Term Memory Network (LSTM), Transformer, etc., and find the most suitable learning model for this method through continuous testing. Of course, multiple models can also be provided, allowing users to switch to another model if they are not satisfied with the current one.

[0068] The model then extracts key features from the input dialogue history, such as dialogue topic, time, location, climate, and contextual information, to generate a database.

[0069] After a user issues a command, the system generates the next possible command based on the current conversation and historical conversation records. This is typically a probability distribution, representing the probability of each possible command occurring. The system selects the command with the highest probability to execute.

[0070] Finally, there is the feedback mechanism. The system continuously optimizes the model based on real user feedback, improving the accuracy and practicality of predictions.

[0071] For deep learning models, this method provides two databases: Local family database: Stores the command operation habits of each family member, including the time when the corresponding family member performs the command operation, whether they are on vacation, the original state of each device, whether there are any sudden emergencies, and whether other family members are at home (determined by whether other members have sent commands recently).

[0072] Cloud-based family database: Stores the command and operation habits of members in other nearby families, and incorporates them into the training process via network connection as a reference to improve the model's generalization ability and accuracy.

[0073] By differentiating control commands issued by different users, personalized device control can be achieved according to the habits of different users, which significantly improves the intelligence level of the system, the flexibility of device control, and ensures the user experience.

[0074] It should be noted that, for the sake of simplicity, the method embodiments are all described as a series of actions. However, those skilled in the art should understand that the embodiments of the present invention are not limited to the described order of actions, because according to the embodiments of the present invention, some steps can be performed in other orders or simultaneously. Furthermore, those skilled in the art should also understand that the embodiments described in the specification are preferred embodiments, and the actions involved are not necessarily essential to the embodiments of the present invention.

[0075] Reference Figure 3 The diagram illustrates a structural block diagram of a control device for an apparatus provided in an embodiment of the present invention. This device is applied to a control system and may specifically include the following modules: The data acquisition module 301 is used to respond to a first control command input by a target user, and if the target user is a family member bound to the control system, then acquire the target user data corresponding to the target user. Prediction module 302 is used to predict the target user's instructions based on the target user data, obtain a second control instruction for the first control instruction, send the second control instruction to the corresponding first smart home device, and control the first smart home device to perform a first device operation corresponding to the second control instruction.

[0076] Among some feasible implementation methods are: The control module is configured to send the first control command to the corresponding second smart home device if the target user is not a family member bound to the control system, and control the second smart home device to perform a second device operation corresponding to the first control command.

[0077] In some feasible implementations, the device further includes The withdrawal response module is used to generate a withdrawal command for the second control command in response to a withdrawal operation input by the target user within a preset time after the smart home device performs the first device operation. The device control module is used to send the withdrawal command to the first smart home device and control the first smart home device to stop performing the first device operation.

[0078] In some feasible implementations, the prediction module 302 is specifically used for: Determine the instruction prediction model for the target user data; The target user data is input into the instruction prediction model to predict the instruction, thereby obtaining a second control instruction for the first control instruction.

[0079] Among some feasible implementation methods are: The training data acquisition module is used to acquire local family data and cloud-based family data. The local family data includes at least the first instruction operation habit information of each family member, and the cloud-based family data includes at least the second instruction operation habit information of each family member in other families. The model training module is used to train the model based on the first instruction operation habit information and the second instruction operation habit information to obtain the instruction prediction model.

[0080] In some feasible implementations, the instruction operation habit information includes at least the time when the family member executes the instruction operation, whether the family member is on vacation, the device status of each smart home device, whether there is an emergency, and whether there are multiple family members in the smart home environment at the same time.

[0081] In some feasible implementations, the data acquisition module 301 is specifically used for: In response to a first control command input by a target user, the instruction type corresponding to the first control command is obtained, and the user identifier corresponding to the target user is determined based on the instruction type. If the user identifier belongs to a family member bound to the control system, then the target user data corresponding to the target user is obtained.

[0082] In some feasible implementations, the user identifier includes the user's voiceprint and ID information, and the data acquisition module 301 is specifically used for: If the instruction type is a voice type, then the audio corresponding to the first control instruction is identified to obtain the user voiceprint corresponding to the target user. If the instruction type is an application instruction type, then obtain the user account to which the first control instruction belongs, and extract the ID information corresponding to the user account.

[0083] As the device embodiment is basically similar to the method embodiment, the description is relatively simple. For relevant details, please refer to the description of the method embodiment.

[0084] In addition, this invention also provides an electronic device, including: a processor, a memory, and a computer program stored in the memory and executable on the processor. When the computer program is executed by the processor, it implements the various processes of the control method embodiments of the above-described device and achieves the same technical effect. To avoid repetition, it will not be described again here.

[0085] This invention also provides a computer-readable storage medium storing a computer program. When the computer program is executed by a processor, it implements the various processes of the control method embodiments of the above-described device and achieves the same technical effects. To avoid repetition, it will not be described again here. The computer-readable storage medium may be a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk, etc.

[0086] Figure 4 A schematic diagram of the hardware structure of an electronic device for implementing various embodiments of the present invention. The electronic device 400 includes, but is not limited to, components such as: a radio frequency unit 401, a network module 402, an audio output unit 403, an input unit 404, a sensor 405, a display unit 406, a user input unit 407, an interface unit 408, a memory 409, a processor 410, and a power supply 411. Those skilled in the art will understand that the electronic device structure involved in the embodiments of the present invention does not constitute a limitation on the electronic device. An electronic device may include more or fewer components than illustrated, or combine certain components, or have different component arrangements. In the embodiments of the present invention, the electronic device includes, but is not limited to, mobile phones, tablet computers, laptop computers, PDAs, in-vehicle terminals, wearable devices, and pedometers.

[0087] It should be understood that, in this embodiment of the invention, the radio frequency unit 401 can be used for receiving and transmitting signals during information transmission or calls. Specifically, it receives downlink data from the base station and processes it with the processor 410; additionally, it transmits uplink data to the base station. Typically, the radio frequency unit 401 includes, but is not limited to, an antenna, at least one amplifier, a transceiver, a coupler, a low-noise amplifier, a duplexer, etc. Furthermore, the radio frequency unit 401 can also communicate with networks and other devices through a wireless communication system.

[0088] The electronic device provides users with wireless broadband internet access through the network module 402, such as helping users send and receive emails, browse web pages, and access streaming media.

[0089] The audio output unit 403 can convert audio data received by the radio frequency unit 401 or the network module 402 or stored in the memory 409 into audio signals and output them as sound. Furthermore, the audio output unit 403 can also provide audio output related to specific functions performed by the electronic device 400 (e.g., call signal reception sound, message reception sound, etc.). The audio output unit 403 includes a speaker, a buzzer, and a receiver, etc.

[0090] Input unit 404 is used to receive audio or video signals. Input unit 404 may include a graphics processing unit (GPU) 4041 and a microphone 4042. The GPU 4041 processes image data of still images or videos acquired by an image capture device (such as a camera) in video capture mode or image capture mode. The processed image frames can be displayed on display unit 406. The image frames processed by GPU 4041 can be stored in memory 409 (or other storage media) or transmitted via radio frequency unit 401 or network module 402. Microphone 4042 can receive sound and process such sound into audio data. The processed audio data can be converted into a format that can be transmitted to a mobile communication base station via radio frequency unit 401 in telephone call mode.

[0091] The electronic device 400 also includes at least one sensor 405, such as a light sensor, a motion sensor, and other sensors. Specifically, the light sensor includes an ambient light sensor and a proximity sensor. The ambient light sensor can adjust the brightness of the display panel 4061 according to the ambient light level, and the proximity sensor can turn off the display panel 4061 and / or backlight when the electronic device 400 is moved to the ear. As a type of motion sensor, an accelerometer sensor can detect the magnitude of acceleration in various directions (generally three axes). When stationary, it can detect the magnitude and direction of gravity and can be used to identify the posture of the electronic device (such as landscape / portrait switching, related games, magnetometer posture calibration), vibration recognition related functions (such as pedometer, tapping), etc. The sensor 405 may also include a fingerprint sensor, pressure sensor, iris sensor, molecular sensor, gyroscope, barometer, hygrometer, thermometer, infrared sensor, etc., which will not be described in detail here.

[0092] The display unit 406 is used to display information input by the user or information provided to the user. The display unit 406 may include a display panel 4061, which may be configured in the form of a liquid crystal display (LCD), an organic light-emitting diode (OLED), or the like.

[0093] User input unit 407 can be used to receive input numerical or character information, and to generate key signal inputs related to user settings and function control of electronic devices. Specifically, user input unit 407 includes a touch panel 4071 and other input devices 4072. Touch panel 4071, also known as a touch screen, can collect touch operations performed by the user on or near it (such as operations performed by the user using a finger, stylus, or any suitable object or accessory on or near touch panel 4071). Touch panel 4071 may include two parts: a touch detection device and a touch controller. The touch detection device detects the user's touch position and the signal generated by the touch operation, and transmits the signal to the touch controller; the touch controller receives touch information from the touch detection device, converts it into touch point coordinates, and sends it to the processor 410, which receives and executes commands from the processor 410. In addition, touch panel 4071 can be implemented using various types such as resistive, capacitive, infrared, and surface acoustic wave. Besides touch panel 4071, user input unit 407 may also include other input devices 4072. Specifically, other input devices 4072 may include, but are not limited to, physical keyboards, function keys (such as volume control buttons, power buttons, etc.), trackballs, mice, joysticks, etc., which will not be described in detail here.

[0094] Furthermore, the touch panel 4071 can cover the display panel 4061. When the touch panel 4071 detects a touch operation on or near it, it transmits the information to the processor 410 to determine the type of touch event. Subsequently, the processor 410 provides corresponding visual output on the display panel 4061 according to the type of touch event. It is understood that in one embodiment, the touch panel 4071 and the display panel 4061 are implemented as two independent components to realize the input and output functions of the electronic device. However, in some embodiments, the touch panel 4071 and the display panel 4061 can be integrated to realize the input and output functions of the electronic device. The specific implementation is not limited here.

[0095] Interface unit 408 serves as an interface for connecting external devices to electronic device 400. For example, external devices may include a wired or wireless headphone port, an external power supply (or battery charger) port, a wired or wireless data port, a memory card port, a port for connecting a device with an identification module, an audio input / output (I / O) port, a video I / O port, a headphone port, and so on. Interface unit 408 can be used to receive input from external devices (e.g., data, power, etc.) and transmit the received input to one or more components within electronic device 400, or it can be used to transmit data between electronic device 400 and external devices.

[0096] The memory 409 can be used to store software programs and various data. The memory 409 may primarily include a program storage area and a data storage area. The program storage area may store the operating system, applications required for at least one function (such as sound playback, image playback, etc.), etc.; the data storage area may store data created based on the use of the mobile phone (such as audio data, phonebook, etc.). Furthermore, the memory 409 may include high-speed random access memory, and may also include non-volatile memory, such as at least one disk storage device, flash memory device, or other volatile solid-state storage device.

[0097] The processor 410 is the control center of the electronic device. It connects various parts of the electronic device via various interfaces and lines. By running or executing software programs and / or modules stored in the memory 409, and by calling data stored in the memory 409, it performs various functions and processes data, thereby providing overall monitoring of the electronic device. The processor 410 may include one or more processing units; preferably, the processor 410 may integrate an application processor and a modem processor. The application processor mainly handles the operating system, user interface, and applications, while the modem processor mainly handles wireless communication. It is understood that the modem processor may not be integrated into the processor 410.

[0098] The electronic device 400 may also include a power supply 411 (such as a battery) that supplies power to various components. Preferably, the power supply 411 can be logically connected to the processor 410 through a power management system, thereby enabling functions such as managing charging, discharging, and power consumption through the power management system.

[0099] In addition, the electronic device 400 includes some functional modules not shown, which will not be described in detail here.

[0100] It should be noted that, in this document, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes that element.

[0101] Through the above description of the embodiments, those skilled in the art can clearly understand that the methods of the above embodiments can be implemented by means of software plus necessary general-purpose hardware platforms. Of course, they can also be implemented by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk) and includes several instructions to cause a terminal (which may be a mobile phone, computer, server, air conditioner, or network device, etc.) to execute the methods described in the various embodiments of the present invention.

[0102] The embodiments of the present invention have been described above with reference to the accompanying drawings. However, the present invention is not limited to the specific embodiments described above. The specific embodiments described above are merely illustrative and not restrictive. Those skilled in the art can make many other forms under the guidance of the present invention without departing from the spirit and scope of the claims, and all of these forms are within the protection scope of the present invention.

[0103] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed in this invention 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 implementations should not be considered beyond the scope of this invention.

[0104] Those skilled in the art will 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.

[0105] In the embodiments provided in this application, it should be understood that the disclosed apparatus and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative. For instance, the division of units is only 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. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between apparatuses or units may be electrical, mechanical, or other forms.

[0106] 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.

[0107] 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.

[0108] If the aforementioned functions are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this invention, essentially, or the part that contributes to the prior art, or a portion 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 described in 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, ROM, RAM, magnetic disks, or optical disks.

[0109] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in the present invention should be included within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A method for controlling a device, characterized in that, Applied to a control system, the method includes: In response to a first control command input by a target user, if the target user is a family member bound to the control system, then target user data corresponding to the target user is acquired; the target user data is used to provide personalized control. Determine the instruction prediction model for the target user data; The target user data is input into the instruction prediction model to predict the next instruction that the target user may issue, thereby obtaining a second control instruction in response to the first control instruction. The second control instruction is then sent to the corresponding first smart home device, which is then controlled to perform a first device operation corresponding to the second control instruction. The instruction prediction model is used to predict the next control instruction corresponding to the control instruction input by the user based on the current dialogue and historical dialogue records. The method further includes: Acquire local family data and cloud-based family data. The local family data includes at least the first instruction operation habit information of each family member, and the cloud-based family data includes at least the second instruction operation habit information of each family member in other families. The instruction operation habit information includes the time when the family member executes the instruction operation, whether the family member is on vacation, the device status of each smart home device, whether there is an emergency, and whether multiple family members are present in the smart home environment at the same time. The instruction prediction model is obtained by training the model based on the first instruction operation habit information and the second instruction operation habit information; The method further includes: When the number of times a user inputs control commands for smart home devices in the smart home environment reaches the preset minimum number of control commands required, the user is added to the family members of the control system.

2. The method according to claim 1, characterized in that, Also includes: If the target user is not a family member bound to the control system, the first control command is sent to the corresponding second smart home device, controlling the second smart home device to perform the second device operation corresponding to the first control command.

3. The method according to claim 1 or 2, characterized in that, After controlling the first smart home device to execute the first device operation corresponding to the second control command, the method further includes: In response to a withdrawal operation input by the target user within a preset time after the smart home device performs the first device operation, a withdrawal command for the second control command is generated. The withdrawal command is sent to the first smart home device, controlling the first smart home device to stop performing the first device operation.

4. The method according to claim 1, characterized in that, In response to a first control command input by a target user, if the target user is a family member bound to the control system, the system acquires target user data corresponding to the target user, including: In response to a first control command input by a target user, the instruction type corresponding to the first control command is obtained, and the user identifier corresponding to the target user is determined based on the instruction type. If the user identifier belongs to a family member bound to the control system, then the target user data corresponding to the target user is obtained.

5. The method according to claim 4, characterized in that, The user identifier includes the user's voiceprint and ID information. Determining the user identifier corresponding to the target user based on the instruction type includes: If the instruction type is a voice type, then the audio corresponding to the first control instruction is identified to obtain the user voiceprint corresponding to the target user. If the instruction type is an application instruction type, then obtain the user account to which the first control instruction belongs, and extract the ID information corresponding to the user account.

6. A control device for an equipment, characterized in that, The device, used in a control system, includes: The data acquisition module is used to respond to a first control command input by a target user. If the target user is a family member bound to the control system, the module acquires target user data corresponding to the target user. The target user data is used to provide personalized control. The prediction module is used to determine an instruction prediction model for the target user data; input the target user data into the instruction prediction model to predict the next instruction that the target user may issue, obtain a second control instruction for the first control instruction, send the second control instruction to the corresponding first smart home device, and control the first smart home device to perform a first device operation corresponding to the second control instruction; the instruction prediction model is used to predict the next control instruction corresponding to the control instruction input by the user based on the current dialogue and historical dialogue records; The device further includes: The training data acquisition module is used to acquire local family data and cloud-based family data. The local family data includes at least the first instruction operation habit information of each family member, and the cloud-based family data includes at least the second instruction operation habit information of each family member in other families. The instruction operation habit information includes the time point when the family member executes the instruction operation, whether the family member is on vacation, the device status of each smart home device, whether there is an emergency, and whether there are multiple family members in the smart home environment at the same time. The model training module is used to train the model based on the first instruction operation habit information and the second instruction operation habit information to obtain the instruction prediction model; The device is also used for: When the number of times a user inputs control commands for smart home devices in the smart home environment reaches the preset minimum number of control commands required, the user is added to the family members of the control system.

7. An electronic device, characterized in that, It includes a processor, a communication interface, a memory, and a communication bus, wherein the processor, the communication interface, and the memory communicate with each other through the communication bus; The memory is used to store computer programs; When the processor executes a program stored in the memory, it implements the method as described in any one of claims 1-5.

8. A computer-readable storage medium having instructions stored thereon that, when executed by one or more processors, cause the processors to perform the method as described in any one of claims 1-5.

Images