Equipment control method and device, electronic equipment and storage medium

Abstract

The embodiment of the invention provides an equipment control method and device, electronic equipment and a storage medium, and relates to the technical field of smart home, and the method comprises the steps: responding to a first control instruction input by a target user, and if the target user belongs to a family member bound to a control system, executing the first control instruction; if yes, obtaining target user data corresponding to the target user; and performing instruction prediction on the target user according to the target user data, obtaining a second control instruction for the first control instruction, sending the second control instruction to a corresponding first smart home device, and controlling the first smart home device to execute a first device operation corresponding to the second control instruction. Therefore, the control flexibility of the smart home equipment is improved.

Description

Technical Field

[0001] The present 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 Art

[0002] Smart home control systems are currently in a rapid development stage. By integrating a variety of sensors and smart devices, they can achieve intelligent management of the home environment. These systems use advanced data processing and machine learning technologies to analyze the habits and preferences of family members and provide 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 behavior analysis, and smart temperature control systems automatically adjust indoor temperature according to user habits to achieve energy conservation and environmental protection. Although there are still challenges in data privacy, data quality and user acceptance, with the continuous advancement of technology, smart home control systems are gradually improving users' quality of life and living experience.

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

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

[0005] The embodiment of the present invention discloses a device control method, which is applied to a control system. The method includes:

[0006] In response to a first control instruction input by a target user, if the target user is a family member bound to the control system, acquiring target user data corresponding to the target user;

[0007] Perform instruction prediction for the target user according to 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 execute the first device operation corresponding to the second control instruction.

[0008] Some possible implementations also include:

[0009] If the target user is not 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 a second device operation corresponding to the first control instruction.

[0010] In some feasible implementations, after controlling the first smart home device to execute the first device operation corresponding to the second control instruction, the method further includes:

[0011] In response to a withdrawal operation input by the target user within a preset time period after the smart home device performs the device operation, generating a withdrawal instruction for the second control instruction;

[0012] The withdrawal instruction is sent to the first smart home device to control the first smart home device to stop executing the first device operation.

[0013] In some feasible implementations, the performing instruction prediction for the target user according to the target user data to obtain a second control instruction for the first control instruction includes:

[0014] determining an instruction prediction model for the target user data;

[0015] The target user data is input into the instruction prediction model to perform instruction prediction, and a second control instruction for the first control instruction is obtained.

[0016] Some possible implementations also include:

[0017] Acquire local family data and cloud family data, wherein the local family data at least includes first instruction operation habit information of each family member, and the cloud family data at least includes second instruction operation habit information of each family member in other families;

[0018] Model training is performed based on the first instruction operation habit information and the second instruction operation habit information to obtain the instruction prediction model.

[0019] In some feasible implementations, the operation instruction habit information includes at least one of 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.

[0020] In some feasible implementations, in response to the first control instruction input by the target user, if the target user is a family member bound to the control system, obtaining target user data corresponding to the target user includes:

[0021] In response to a first control instruction input by a target user, acquiring an instruction type corresponding to the first control instruction, and determining a user identifier corresponding to the target user based on the instruction type;

[0022] If the user identification belongs to a family member bound to the control system, the target user data corresponding to the target user is obtained.

[0023] In some feasible implementations, the user identification includes a user voiceprint and ID information, and determining the user identification corresponding to the target user based on the instruction type includes:

[0024] If the instruction type is a voice type, recognizing the audio corresponding to the first control instruction to obtain a user voiceprint corresponding to the target user;

[0025] If the instruction type belongs to an application instruction type, the user account to which the first control instruction belongs is obtained, and ID information corresponding to the user account is extracted.

[0026] The embodiment of the present invention further discloses a control device for a device, which is applied to a control system, and the device includes:

[0027] a data acquisition module, configured to respond to a first control instruction input by a target user, and if the target user is a family member bound to the control system, acquire target user data corresponding to the target user;

[0028] A prediction module is used to predict instructions for the target user according to 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.

[0029] Some possible implementations also include:

[0030] A control module is used to send the first control instruction 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 instruction.

[0031] In some feasible implementations, the device further includes

[0032] a withdrawal response module, configured to generate a withdrawal instruction for the second control instruction in response to a withdrawal operation input by the target user within a preset time period after the smart home device performs the device operation;

[0033] The device control module is used to send the withdrawal instruction to the first smart home device to control the first smart home device to stop executing the first device operation.

[0034] In some feasible implementations, the prediction module is specifically used to:

[0035] determining an instruction prediction model for the target user data;

[0036] The target user data is input into the instruction prediction model to perform instruction prediction, and a second control instruction for the first control instruction is obtained.

[0037] Some possible implementations also include:

[0038] A training data acquisition module, used to acquire local family data and cloud family data, wherein the local family data at least includes first instruction operation habit information of each family member, and the cloud family data at least includes second instruction operation habit information of each family member in other families;

[0039] A model training module is used to perform model training based on the first instruction operation habit information and the second instruction operation habit information to obtain the instruction prediction model.

[0040] In some feasible implementations, the operation instruction habit information includes at least one of 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.

[0041] In some feasible implementations, the data acquisition module is specifically used to:

[0042] In response to a first control instruction input by a target user, acquiring an instruction type corresponding to the first control instruction, and determining a user identifier corresponding to the target user based on the instruction type;

[0043] If the user identification belongs to a family member bound to the control system, the target user data corresponding to the target user is obtained.

[0044] In some feasible implementations, the user identification includes a user voiceprint and ID information, and the data acquisition module is specifically used to:

[0045] If the instruction type is a voice type, recognizing the audio corresponding to the first control instruction to obtain a user voiceprint corresponding to the target user;

[0046] If the instruction type belongs to an application instruction type, the user account to which the first control instruction belongs is obtained, and ID information corresponding to the user account is extracted.

[0047] The embodiment of the present invention further discloses an electronic device, comprising a processor, a communication interface, a memory and a communication bus, wherein the processor, the communication interface and the memory communicate with each other via the communication bus;

[0048] The memory is used to store computer programs;

[0049] The processor is used to implement the method described in the embodiment of the present invention when executing the program stored in the memory.

[0050] The embodiment of the present invention further discloses a computer-readable storage medium having instructions stored thereon, which, when executed by one or more processors, enables the processors to execute the method described in the embodiment of the present invention.

[0051] The embodiments of the present invention include the following advantages:

[0052] In an embodiment of the present invention, it can be applied to a control system. In the process of controlling smart home devices, the system can respond to a first control instruction 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, and then the target user is predicted according to the target user data to obtain a second control instruction for the first control instruction, and the second control instruction is sent 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, thereby distinguishing the control instructions issued by different users to achieve personalized device control according to the habits of different users, significantly improving the intelligence level of the system and the flexibility of device control, and ensuring the user experience. BRIEF DESCRIPTION OF THE DRAWINGS

[0053] Figure 1 is a flowchart of a method for controlling a device provided in an embodiment of the present invention;

[0054] Figure 2 is a schematic diagram of a data processing flow provided in an embodiment of the present invention;

[0055] Figure 3 is a structural block diagram of a control device of a device provided in an embodiment of the present invention;

[0056] Figure 4 It is a block diagram of an electronic device provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0057] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the present invention is further described in detail below with reference to the accompanying drawings and specific embodiments.

[0058] As an example, in a smart home scenario, there are often multiple family members, and different family members have different control requirements for devices. However, in the current device control process, it is difficult to flexibly control smart home devices according to the control requirements of different family members, which affects the user experience.

[0059] In this regard, in the present invention, in the process of controlling smart home devices, the system can respond to a first control instruction 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, and then the target user is predicted according to the target user data to obtain a second control instruction for the first control instruction, and the second control instruction is sent 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. Therefore, by distinguishing the control instructions issued by different users, personalized device control according to the habits of different users is achieved, which significantly improves the intelligence level of the system and the flexibility of device control, and ensures the user experience.

[0060] Reference Figure 1 , shows a flow chart of the steps of a control method of a device provided in an embodiment of the present invention, which is applied to a control system and may specifically include the following steps:

[0061] Step 101, in response to a first control instruction input by a target user, if the target user is a family member bound to the control system, acquiring target user data corresponding to the target user;

[0062] In an embodiment of the present 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 program, etc. In a smart home environment, the control system can communicate with smart home devices deployed in the smart home environment to achieve data transmission and device control, etc., and can also communicate with user devices to receive control instructions sent by the user devices, etc. The present invention is not limited to this.

[0063] It should be noted that when users use smart home devices, the system can collect user data generated during the use of smart home devices, subject to user authorization and compliance with local laws and regulations, so as to provide users with personalized services based on the collected user data.

[0064] In the specific implementation, as the user's usage time increases, 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, or the management user can add the corresponding user to the family members. After adding to the family members, in the subsequent control process of smart home devices, the system can provide personalized services for the corresponding family members, such as controlling smart home devices according to the usage habits of family members.

[0065] In one example, the system may preset a minimum number of control instructions required. When the number of control instructions input by the corresponding user for the smart home device in the smart home environment reaches the minimum number of control instructions required, the system may add the user to the family members, or output corresponding prompt information to prompt the management user to add the user to the family members. For example, assuming that the minimum number of control instructions required is 10 times, when user A inputs 11 control instructions to the smart home device in the smart home environment, which is greater than the minimum number of control instructions required, the system may add user A to the family members, or output corresponding prompt information to prompt the management user to add user A to the family members, so that after being added to the family members, the system can provide personalized services for user A.

[0066] Optionally, in the process of actively adding users, the system can display the control records corresponding to the latest control instructions of the added users to help the management user understand the corresponding users. For example, assuming that the management user wants to add user A to the family members, the system can display the corresponding control records through the corresponding graphical user interface, as follows:

[0067] December 1, 2024 18:30

[0068] Equipment: Living room lamp;

[0069] Control command: "Turn on the light";

[0070] Note: User A turns on the lights in the living room after returning home at night.

[0071] December 2, 2024 07:00

[0072] Equipment: air conditioning in bedrooms;

[0073] Control command: "Set the temperature to 24℃";

[0074] Note: User A adjusts the temperature of the bedroom air conditioner when he gets up in the morning.

[0075] December 2, 2024 19:45

[0076] Device: Smart TV

[0077] Control command: "Open and switch to the news channel"

[0078] Note: User A turns on the TV in the evening and switches to the news channel.

[0079] December 3, 2024 22:00

[0080] Equipment: bedroom lamp

[0081] Control command: "Turn off the light"

[0082] Note: User A turns off the bedroom light before going to bed at night.

[0083] December 4, 2024 06:30

[0084] Equipment: Smart coffee machine

[0085] Control command: "Turn on the coffee machine"

[0086] Note: User A starts the smart coffee machine in the morning and makes a cup of coffee.

[0087] ……wait.

[0088] Through the above operation records, the management user can intuitively perceive the identity corresponding to the user to be added.

[0089] In an embodiment of the present invention, after the system receives a first control instruction input by a target user, the system may first analyze the identity of the target user based on the first control instruction, obtain the instruction type corresponding to the first control instruction, and determine the user identifier corresponding to the target user based on the instruction 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 may send the first control instruction to the corresponding smart home device so that the smart home device performs the corresponding device operation.

[0090] Among them, the user identification includes the user voiceprint and ID information. According to the instruction type corresponding to the first control instruction, if the instruction type is a voice type, 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, the user account to which the first control instruction 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 corresponding user voiceprints or ID information from the family member group. If so, it can be determined that the target user belongs to a family member bound to the system; if not, it can be determined that the target user belongs to a non-family member user.

[0091] In one example, a user can issue corresponding control instructions to the system through voice or application. After receiving the corresponding control instructions, the system can use a deep learning model to identify the voice instructions and determine which family member issued the instructions. For application instructions, it can be directly identified through the user ID logged in to the application. Based on the identification result, it can be determined whether the target user is a family member. If he is a family member, the system can further obtain the target user data corresponding to the target user, so as to provide personalized device control based on the target user data.

[0092] Step 102: predict instructions for the target user 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.

[0093] In an embodiment of the present invention, after determining that the target user is a family member, the system can predict instructions for the target user 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 the first device operation corresponding to the second control instruction; if the target user is not a family member bound to the control system, the first control instruction is sent to the corresponding second smart home device, and the second smart home device is controlled to perform the second device operation corresponding to the first control instruction, thereby distinguishing the control instructions issued by different users, and realizing personalized device control according to the habits of different users, significantly improving the intelligence level of the system and the flexibility of device control, and ensuring the user experience.

[0094] In some feasible implementations, the system can first determine an instruction prediction model for target user data, then input the target user data into the instruction prediction model for instruction prediction, and obtain a second control instruction for the first control instruction. This can distinguish control instructions issued by different users and achieve personalized device control based on the habits of different users, significantly improving the intelligence level of the system and the flexibility of device control, as well as ensuring the user experience.

[0095] It should be noted that the instruction prediction model can be used to predict the next control instruction corresponding to the control instruction input by the user. For example, when the user enters control instruction ①, the system can predict the control instruction ② that the user may enter next based on control instruction ① and the corresponding user data, and then execute control instruction ②. By predicting the control instruction that the user may enter next, on the one hand, personalized device control services can be provided to users. On the other hand, by predicting instructions and controlling smart home devices to perform corresponding device operations, the frequency of users' manual control of smart home devices can be effectively reduced, providing automation and intelligence of device control.

[0096] In addition, for the instruction prediction model, the local family data and the cloud family data can be obtained, the local family data at least includes the first instruction operation habit information of each family member, and the cloud family data at least includes the second instruction operation habit information of each family member in other families, and then the model is trained according to the first instruction operation habit information and the second instruction operation habit information to obtain the instruction prediction model. Among them, the operation instruction habit information at least includes one of the time point when the family member executes the instruction operation, whether the family member is on vacation, the device status corresponding to 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.

[0097] In one example, the training process of the instruction prediction model may include:

[0098] In order to train the command prediction model, the system needs to collect the following two types of data: local family data: operation habit information of each family member in the current family; cloud family data: operation habit information of each family member in other families. Among them, local family data includes the first command operation habit information of each family member in the current family, for example: the time point when the family member executes the command operation: for example, user A turns on the living room light at 18:30 every night; whether the family member is on vacation: for example, the operation habits of user A during vacation may be different from those on weekdays; the device status corresponding to each smart home device: for example, the living room light is currently off and the air conditioning temperature is 24°C; whether there is an emergency: for example, the operation habits of user A in an emergency (such as a fire alarm); whether there are multiple family members in the smart home environment at the same time: for example, when multiple family members are at home at the same time, the operation habits may be different from when they are alone at home.

[0099] The cloud family data includes the second command operation habits of each family member in other families. The specific content is similar to the local family data, but comes from a different family environment. This data is used to provide a wider reference to help the model learn the common operation habits of different family members.

[0100] For local home data and cloud home data, the collected data needs to be preprocessed before training the model to ensure the quality and consistency of the data. For example, data cleaning, data labeling, data normalization, etc. Specifically, in the data cleaning process, duplicate data or invalid data can be removed, and missing values ​​can be processed, such as filling with average values ​​or interpolation methods; for the data labeling process, operation instructions can be classified, such as "turn on the device", "turn off the device", "adjust the device status", etc., and information such as time points, device status, and family member status can be labeled; in the data normalization process, data from different families can be normalized to ensure that the data is on the same scale, which is convenient for model training.

[0101] After completing the preprocessing of the training data, the instruction prediction model can be trained based on the preprocessed data. Specifically, useful features can be extracted from the data first, for example:

[0102] Time characteristics: the time point of the operation instruction (such as morning, evening).

[0103] Device status characteristics: the current status of the device (such as whether the light is on, the air conditioning temperature).

[0104] Family member status characteristics: whether the family member is on vacation, whether there is an emergency.

[0105] Environmental characteristics: whether there are multiple family members at the same time.

[0106] Next, you can select a suitable machine learning model for training, for example:

[0107] Decision Tree: Used to handle discrete features.

[0108] Random Forest: Used to improve prediction accuracy.

[0109] Neural Networks: Used to handle complex nonlinear relationships.

[0110] Then, local home data and cloud home data can be input into the model for training, and the performance of the model can be evaluated using methods such as cross-validation, and the model parameters can be adjusted to improve the prediction accuracy.

[0111] Finally, after the instruction prediction model is trained, it is deployed to the smart home system. The instruction prediction model can be deployed to a local device (such as a smart gateway or server) to predict the operation instructions of the current family members in real time; or it can be deployed to the cloud to process data from multiple families and provide more extensive prediction support.

[0112] In addition, after predicting the corresponding control instructions through the above process, the system can send the control instructions to the corresponding smart home devices to control the smart home devices to perform the corresponding device operations. Furthermore, if the control instructions predicted by the system do not meet the actual needs of the target user, the target user can input the corresponding withdrawal operation within the preset time after the smart home device successfully executes the control instructions predicted by the system to withdraw the corresponding control instructions. Specifically, the system can generate a withdrawal instruction for the second control instruction in response to the withdrawal operation input by the target user within the preset time after the smart home device performs the device operation, and then send the withdrawal instruction to the first smart home device to control the first smart home device to stop executing the first device operation. In this way, when there is a deviation between the system's prediction results and the user's actual needs, the user can spontaneously revoke the corresponding device control instructions, thereby ensuring the flexibility of device control, avoiding the execution of invalid control instructions, and reducing device energy consumption.

[0113] It should be noted that the embodiments of the present invention include but are not limited to the above examples. It is understandable that those skilled in the art can also make settings according to actual needs under the guidance of the ideas of the embodiments of the present invention, and the present invention is not limited to this.

[0114] In an embodiment of the present invention, it can be applied to a control system. In the process of controlling smart home devices, the system can respond to a first control instruction 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, and then the target user is predicted according to the target user data to obtain a second control instruction for the first control instruction, and the second control instruction is sent 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, thereby distinguishing the control instructions issued by different users to achieve personalized device control according to the habits of different users, significantly improving the intelligence level of the system and the flexibility of device control, and ensuring the user experience.

[0115] In order to enable those skilled in the art to better understand the technical solutions in the embodiments of the present invention, the following examples are used for exemplary description:

[0116] For the control system, the management user can set the corresponding number of family members. The system automatically recognizes and distinguishes the received voice commands into member 1, member 2, etc., and does not need to participate in the analysis of user habits for application commands. As the user's usage time increases, the system gradually accumulates data. When it reaches a certain amount (for example, setting the corresponding minimum number of required control commands for quantitative analysis), the user can manually choose to bind the users in the APP family to the members and name them (when binding, click on the corresponding member to view the recordings of the last five control commands to help the user determine which person it corresponds to). At this time, when the user controls the device through the APP, the system will also follow the user's habits to link the control and provide more personalized services.

[0117] In a specific implementation, the system may process the control instruction input by the user by:

[0118] Prerequisite: The control system has been working for a period of time and has established its own database model.

[0119] When the control system receives the user's instruction, it sends a command data packet. It processes the instructions input by the user, predicts the next instruction, and sends the command data packet corresponding to the predicted control instruction (it will not be sent directly when the model is not formed, and a pop-up window will notify the user of the predicted next instruction, and the user can choose whether to execute it). Within ten seconds after the next instruction is executed, the user can choose to "withdraw the operation". At this time, the system sends the data packet corresponding to the last device status and rolls back the status. In addition, it records user feedback and continuously optimizes the instruction prediction model.

[0120] For example, refer to Figure 2 , showing a flow chart of data processing provided in an embodiment of the present invention, the user can issue instructions to the system through voice or mobile phone APP. For voice instructions, the system uses deep learning model recognition to determine which family member issued the instruction. For APP instructions, the system directly identifies the user ID logged in on the APP. Then, the system can use the deep learning model to predict the instructions that the user may issue next, and then the system can execute the corresponding control instructions. The user can provide real-time feedback on whether it meets expectations. The system feeds the results back to the deep learning model to continuously optimize the model, so that the system gradually improves the recognition and prediction accuracy of user habits through continuous feedback and training, thereby improving the user's comfort.

[0121] For deep learning models, the implementation steps are as follows:

[0122] First, the system requires a large amount of historical conversation data as a training set. This data comes from real interaction records or manually constructed conversation scenarios. It is provided by testers and is continuously increased during the test development process.

[0123] Then select the appropriate 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, and users can change the current model if they are not satisfied with it.

[0124] The model then extracts key features from the input conversation history, such as conversation topic, time, location, climate, contextual information, etc., to generate a database.

[0125] After the user issues a command, the system generates the next possible command based on the current conversation and historical conversation records. This is generally a probability distribution, indicating the probability of each possible command occurring. The system selects the command with the highest probability to execute.

[0126] Finally, there is the feedback mechanism. The system continuously optimizes the model through real feedback from users to improve the accuracy and practicality of the prediction.

[0127] For deep learning models, this method provides two databases:

[0128] Local family database: saves the command operation habits of each family member, including the time when the corresponding family member executes the command operation, whether he is on vacation, the original status of each device, whether there is any sudden emergency, and whether other family members are at home (determined by whether other members have sent commands recently).

[0129] Cloud-based family database: Saves the command operation habits of members of other nearby families and adds them to the training process as a reference through networking to improve the generalization ability and accuracy of the model.

[0130] By distinguishing the 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 and the flexibility of device control, and ensures the user experience.

[0131] It should be noted that, for the sake of simplicity, the method embodiments are described as a series of action combinations, but those skilled in the art should be aware that the embodiments of the present invention are not limited by the order of the actions described, because according to the embodiments of the present invention, certain steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should also be aware that the embodiments described in the specification are all preferred embodiments, and the actions involved are not necessarily required by the embodiments of the present invention.

[0132] Reference Figure 3, shows a structural block diagram of a control device of a device provided in an embodiment of the present invention, which is applied to a control system and may specifically include the following modules:

[0133] The data acquisition module 301 is used to respond to the first control instruction input by the target user, and if the target user is a family member bound to the control system, acquire the target user data corresponding to the target user;

[0134] The prediction module 302 is used to predict the instructions of the target user according to 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 the first device operation corresponding to the second control instruction.

[0135] Some possible implementations also include:

[0136] A control module is used to send the first control instruction 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 instruction.

[0137] In some feasible implementations, the device further includes

[0138] a withdrawal response module, configured to generate a withdrawal instruction for the second control instruction in response to a withdrawal operation input by the target user within a preset time period after the smart home device performs the device operation;

[0139] The device control module is used to send the withdrawal instruction to the first smart home device to control the first smart home device to stop executing the first device operation.

[0140] In some feasible implementations, the prediction module 302 is specifically used to:

[0141] determining an instruction prediction model for the target user data;

[0142] The target user data is input into the instruction prediction model to perform instruction prediction, and a second control instruction for the first control instruction is obtained.

[0143] Some possible implementations also include:

[0144] A training data acquisition module, used to acquire local family data and cloud family data, wherein the local family data at least includes first instruction operation habit information of each family member, and the cloud family data at least includes second instruction operation habit information of each family member in other families;

[0145] A model training module is used to perform model training based on the first instruction operation habit information and the second instruction operation habit information to obtain the instruction prediction model.

[0146] In some feasible implementations, the operation instruction habit information includes at least one of 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.

[0147] In some feasible implementations, the data acquisition module 301 is specifically used to:

[0148] In response to a first control instruction input by a target user, acquiring an instruction type corresponding to the first control instruction, and determining a user identifier corresponding to the target user based on the instruction type;

[0149] If the user identification belongs to a family member bound to the control system, the target user data corresponding to the target user is obtained.

[0150] In some feasible implementations, the user identification includes a user voiceprint and ID information, and the data acquisition module 301 is specifically used to:

[0151] If the instruction type is a voice type, recognizing the audio corresponding to the first control instruction to obtain a user voiceprint corresponding to the target user;

[0152] If the instruction type belongs to an application instruction type, the user account to which the first control instruction belongs is obtained, and ID information corresponding to the user account is extracted.

[0153] As for the device embodiment, since it is basically similar to the method embodiment, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiment.

[0154] In addition, an embodiment of the present invention further 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, the various processes of the control method embodiment of the above-mentioned device are implemented, and the same technical effect can be achieved. To avoid repetition, it will not be described here.

[0155] The embodiment of the present invention further provides a computer-readable storage medium, on which a computer program is stored, and when the computer program is executed by a processor, each process of the control method embodiment of the above-mentioned device is implemented, and the same technical effect can be achieved. To avoid repetition, it is not repeated here. The computer-readable storage medium is, for example, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk.

[0156] Figure 4 A schematic diagram of the hardware structure of an electronic device for implementing various embodiments of the present invention.

[0157] The electronic device 400 includes but is not limited to: 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 appreciate that the electronic device structure involved in the embodiments of the present invention does not constitute a limitation on the electronic device, and the electronic device may include more or fewer components than shown in the figure, or combine certain components, or arrange components differently. In the embodiments of the present invention, the electronic device includes but is not limited to a mobile phone, a tablet computer, a laptop computer, a PDA, a vehicle-mounted terminal, a wearable device, and a pedometer.

[0158] It should be understood that in the embodiment of the present invention, the radio frequency unit 401 can be used for receiving and sending signals during information transmission or communication. Specifically, after receiving downlink data from the base station, it is sent to the processor 410 for processing; in addition, uplink data is sent to the base station. Generally, 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. In addition, the radio frequency unit 401 can also communicate with the network and other devices through a wireless communication system.

[0159] The electronic device provides users with wireless broadband Internet access through the network module 402, such as helping users to send and receive emails, browse web pages, and access streaming media.

[0160] The audio output unit 403 can convert the audio data received by the RF unit 401 or the network module 402 or stored in the memory 409 into an audio signal and output it as sound. Moreover, the audio output unit 403 can also provide audio output related to a specific function performed by the electronic device 400 (for example, a call signal reception sound, a message reception sound, etc.). The audio output unit 403 includes a speaker, a buzzer, a receiver, etc.

[0161] The input unit 404 is used to receive audio or video signals. The input unit 404 may include a graphics processor (GPU) 4041 and a microphone 4042, and the graphics processor 4041 processes the image data of a static picture or video obtained by an image capture device (such as a camera) in a video capture mode or an image capture mode. The processed image frame can be displayed on the display unit 406. The image frame processed by the graphics processor 4041 can be stored in the memory 409 (or other storage medium) or sent via the radio frequency unit 401 or the network module 402. The microphone 4042 can receive sound and can process such sound into audio data. The processed audio data can be converted into a format output that can be sent to a mobile communication base station via the radio frequency unit 401 in the case of a telephone call mode.

[0162] 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, wherein the ambient light sensor can adjust the brightness of the display panel 4061 according to the brightness of the ambient light, and the proximity sensor can turn off the display panel 4061 and / or the backlight when the electronic device 400 is moved to the ear. As a type of motion sensor, the accelerometer sensor can detect the magnitude of acceleration in each direction (generally three axes), and can detect the magnitude and direction of gravity when stationary, which can be used to identify the posture of the electronic device (such as horizontal and vertical screen switching, related games, magnetometer posture calibration), vibration recognition related functions (such as pedometer, tapping), etc.; the sensor 405 can also include a fingerprint sensor, a pressure sensor, an iris sensor, a molecular sensor, a gyroscope, a barometer, a hygrometer, a thermometer, an infrared sensor, etc., which will not be repeated here.

[0163] 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.

[0164] The user input unit 407 can be used to receive input digital or character information, and to generate key signal input related to user settings and function control of the electronic device. Specifically, the user input unit 407 includes a touch panel 4071 and other input devices 4072. The touch panel 4071, also known as a touch screen, can collect the user's touch operation on or near it (such as the user's operation on the touch panel 4071 or near the touch panel 4071 using any suitable object or accessory such as a finger, stylus, etc.). The touch panel 4071 may include two parts: a touch detection device and a touch controller. Among them, the touch detection device detects the user's touch orientation, detects the signal brought by the touch operation, and transmits the signal to the touch controller; the touch controller receives the touch information from the touch detection device, converts it into the contact point coordinates, and then sends it to the processor 410, receives the command sent by the processor 410 and executes it. In addition, the touch panel 4071 can be implemented using various types such as resistive, capacitive, infrared and surface acoustic waves. In addition to the touch panel 4071, the user input unit 407 may also include other input devices 4072. Specifically, other input devices 4072 may include but are not limited to a physical keyboard, function keys (such as volume control keys, switch keys, etc.), a trackball, a mouse, and a joystick, which are not described in detail here.

[0165] Further, the touch panel 4071 may be covered on the display panel 4061. When the touch panel 4071 detects a touch operation on or near it, it is transmitted to the processor 410 to determine the type of the touch event, and then the processor 410 provides a corresponding visual output on the display panel 4061 according to the type of the touch event. It can be understood that in one embodiment, the touch panel 4071 and the display panel 4061 are used as two independent components to implement the input and output functions of the electronic device, but in some embodiments, the touch panel 4071 and the display panel 4061 can be integrated to implement the input and output functions of the electronic device, which is not limited here.

[0166] The interface unit 408 is an interface for connecting an external device to the electronic device 400. For example, the external device may include a wired or wireless headset 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, etc. The interface unit 408 may be used to receive input (e.g., data information, power, etc.) from an external device and transmit the received input to one or more elements within the electronic device 400 or may be used to transmit data between the electronic device 400 and an external device.

[0167] The memory 409 can be used to store software programs and various data. The memory 409 can mainly include a program storage area and a data storage area, wherein the program storage area can store an operating system, at least one application required for a function (such as a sound playback function, an image playback function, etc.), etc.; the data storage area can store data created according to the use of the mobile phone (such as audio data, a phone book, etc.), etc. In addition, the memory 409 can include a high-speed random access memory, and can also include a non-volatile memory, such as at least one disk storage device, a flash memory device, or other volatile solid-state storage devices.

[0168] The processor 410 is the control center of the electronic device. It uses various interfaces and lines to connect various parts of the entire electronic device. It executes various functions of the electronic device and processes data by running or executing software programs and / or modules stored in the memory 409, and calling data stored in the memory 409, so as to monitor the electronic device as a whole. The processor 410 may include one or more processing units; preferably, the processor 410 may integrate an application processor and a modem processor, wherein the application processor mainly processes the operating system, user interface, and application programs, etc., and the modem processor mainly processes wireless communications. It is understandable that the above-mentioned modem processor may not be integrated into the processor 410.

[0169] The electronic device 400 may also include a power supply 411 (such as a battery) for supplying power to each component. Preferably, the power supply 411 may be logically connected to the processor 410 through a power management system, thereby implementing functions such as charging, discharging, and power consumption management through the power management system.

[0170] In addition, the electronic device 400 includes some functional modules not shown, which will not be described in detail here.

[0171] It should be noted that, in this article, the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, an element defined by the sentence "comprises a ..." does not exclude the existence of other identical elements in the process, method, article or device including the element.

[0172] Through the description of the above implementation methods, those skilled in the art can clearly understand that the above-mentioned embodiment methods can be implemented by means of software plus a necessary general hardware platform, and of course by hardware, but in many cases the former is a better implementation method. Based on such an 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, which is stored in a storage medium (such as ROM / RAM, a magnetic disk, or an optical disk), and includes a number of instructions for enabling a terminal (which can be a mobile phone, a computer, a server, an air conditioner, or a network device, etc.) to execute the methods described in each embodiment of the present invention.

[0173] The embodiments of the present invention are described above in conjunction with the accompanying drawings, but the present invention is not limited to the above-mentioned specific implementation modes, which are merely illustrative rather than restrictive. Under the guidance of the present invention, ordinary technicians in this field can also make many forms without departing from the scope of protection of the present invention and the claims, all of which are within the protection of the present invention.

[0174] Those of ordinary skill in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed in the embodiments of the present invention can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of the present invention.

[0175] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0176] In the embodiments provided in the present application, it should be understood that the disclosed devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0177] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0178] In addition, each functional unit in each embodiment of the present invention 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.

[0179] If the functions are implemented in the form of 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 the present invention, or the part that contributes to the prior art, or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium, including several instructions for a computer device (which can be a personal computer, server, or network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: various media that can store program codes, such as USB flash drives, mobile hard disks, ROM, RAM, magnetic disks, or optical disks.

[0180] The above is only a specific embodiment of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art can easily think of changes or substitutions within the technical scope disclosed by the present invention, which should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention should be based on the protection scope of the claims.

Claims

1. A method for controlling a device, characterized in that: Applied to a control system, the method comprises: In response to a first control instruction input by a target user, if the target user is a family member bound to the control system, acquiring target user data corresponding to the target user; Perform instruction prediction for the target user according to 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 execute the first device operation corresponding to the second control instruction.

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 instruction is sent to the corresponding second smart home device to control the second smart home device to perform a second device operation corresponding to the first control instruction.

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 instruction, the method further includes: In response to a withdrawal operation input by the target user within a preset time period after the smart home device performs the device operation, generating a withdrawal instruction for the second control instruction; The withdrawal instruction is sent to the first smart home device to control the first smart home device to stop executing the first device operation.

4. The method according to claim 1, characterized in that: The step of predicting an instruction for the target user according to the target user data to obtain a second control instruction for the first control instruction includes: determining an instruction prediction model for the target user data; The target user data is input into the instruction prediction model to perform instruction prediction, and a second control instruction for the first control instruction is obtained.

5. The method according to claim 4, characterized in that Also includes: Acquire local family data and cloud family data, wherein the local family data at least includes first instruction operation habit information of each family member, and the cloud family data at least includes second instruction operation habit information of each family member in other families; Model training is performed based on the first instruction operation habit information and the second instruction operation habit information to obtain the instruction prediction model.

6. The method according to claim 5, characterized in that The operation instruction habit information includes at least one of the time when the family member executes the instruction operation, whether the family member is on vacation, the device status corresponding to 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.

7. The method according to claim 1, characterized in that In response to the first control instruction input by the target user, if the target user is a family member bound to the control system, acquiring target user data corresponding to the target user includes: In response to a first control instruction input by a target user, acquiring an instruction type corresponding to the first control instruction, and determining a user identifier corresponding to the target user based on the instruction type; If the user identification belongs to a family member bound to the control system, the target user data corresponding to the target user is obtained.

8. The method according to claim 7, characterized in that The user identification includes a user voiceprint and ID information, and the determining the user identification corresponding to the target user based on the instruction type includes: If the instruction type is a voice type, recognizing the audio corresponding to the first control instruction to obtain a user voiceprint corresponding to the target user; If the instruction type belongs to an application instruction type, the user account to which the first control instruction belongs is obtained, and ID information corresponding to the user account is extracted.

9. A control device for an equipment, characterized in that: Applied to a control system, the device comprises: a data acquisition module, configured to respond to a first control instruction input by a target user, and if the target user is a family member bound to the control system, acquire target user data corresponding to the target user; A prediction module is used to predict instructions for the target user according to 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.

10. 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; The processor is used to implement the method according to any one of claims 1 to 8 when executing the program stored in the memory.

11. A computer-readable storage medium having instructions stored thereon, which, when executed by one or more processors, cause the processors to perform the method according to any one of claims 1 to 8.

Images