Smart home management method, electronic equipment and storage medium

Abstract

The invention discloses a smart home management method, electronic equipment and a storage medium. The method comprises the following steps: in response to a bio-electricity signal and a voice signal of a user acquired by wearable equipment, fusing the bio-electricity signal and the voice signal to obtain emotional state information; generating a home management strategy according to the emotional state information; and according to the home management strategy, adjusting a working mode and an operation parameter of at least one home device. By responding to the bio-electricity signal and the voice signal sent by the wearable device, the signals are fused to obtain the emotional state information, then the home management strategy is generated, operation of the home device is controlled according to the strategy, and the comfort and convenience of the home system can be effectively improved.

Description

Technical Field

[0001] The present application relates to the field of smart home technology, and in particular to a smart home management method, electronic device and storage medium. Background Art

[0002] In traditional smart home systems, the interaction between users and devices is mainly based on preset instructions and rules. Users send instructions to home devices through applications, voice assistants or physical controllers, and these devices perform corresponding operations according to the predetermined logic and rules.

[0003] However, existing smart home systems mainly rely on explicit instructions and preset rules from users, requiring active operation by users to achieve device control. This passive operation mode of smart home systems may cause inconvenience when users are depressed or busy, and it is difficult to automatically provide a comfortable and relaxing environment.

[0004] Therefore, it is necessary to propose a method of smart home management that improves comfort and convenience. Summary of the invention

[0005] The main technical problem solved by the present application is to provide a smart home management method, electronic device and storage medium, which can solve the technical problem of poor comfort and convenience of the home system.

[0006] In order to solve the above technical problems, a technical solution adopted by the present application is: the method is applied to an electronic device, the method is applied to a home system, the home system includes at least one home device, the home system interacts with a wearable device, and the method includes:

[0007] In response to a bioelectric signal and a voice signal of a user acquired by the wearable device, the bioelectric signal and the voice signal are fused to obtain emotional state information;

[0008] generating a home management strategy based on the emotional state information;

[0009] According to the home management strategy, the working mode and operating parameters of at least one home appliance are adjusted.

[0010] Optionally, the step of fusing the bioelectric signal and the voice signal to obtain the emotional state information includes:

[0011] Extracting features from the bioelectric signal to obtain bioelectric features, and extracting features from the speech signal to obtain speech features;

[0012] Using a multimodal data fusion method to fuse the bioelectric feature and the speech feature to obtain fused data;

[0013] Classify the fused data to obtain the emotional state information.

[0014] Optionally, the home system further interacts with a server, and after the step of fusing the bioelectric signal and the voice signal to obtain the emotional state information, further comprises:

[0015] Sending the emotional state information to the server, so that the server analyzes the emotional state information and generates emotional suggestions;

[0016] The emotional suggestion is returned to the wearable device so that the wearable device provides the emotional suggestion to the user.

[0017] Optionally, the step of generating a home management strategy according to the emotional state information includes:

[0018] Analyzing and predicting the emotional state information to obtain the emotional change trend;

[0019] The emotion change trend is mapped with the preset device function to determine the home management strategy.

[0020] Optionally, the step of analyzing and predicting the emotional state information to obtain the emotional change trend includes:

[0021] Inputting the emotional state information into a pre-created prediction model for analysis and prediction to obtain the emotional change trend;

[0022] The method further comprises:

[0023] Get historical sentiment feedback data;

[0024] Reinforcement learning and optimization algorithms are used to combine the emotional state information and historical emotional feedback data to update the parameters and structure of the prediction model.

[0025] Optionally, the step of adjusting the working mode and operating parameters of at least one home appliance according to the home management strategy includes:

[0026] Determine the target home device according to the home management strategy to generate an operation instruction corresponding to the target home device;

[0027] The operation instruction is sent to the target home device to adjust the working mode and the operation parameters of the target home device.

[0028] In order to solve the above technical problems, another technical solution adopted by the present application is: the method is applied to a wearable device, the wearable device interacts with a home system, the home system includes at least one home device, and the method includes:

[0029] Acquire the user's bioelectric signal and voice signal, and send them to the home system;

[0030] The home system responds to the bioelectric signal and the voice signal, fuses the bioelectric signal and the voice signal, and obtains emotional state information; generates a home management strategy based on the emotional state information; and adjusts the working mode and operating parameters of at least one home appliance based on the home management strategy.

[0031] Optionally, the wearable device is used to display at least one of the emotional state information, home management strategy, and the working mode and operating parameters of the home equipment.

[0032] In order to solve the above technical problems, another technical solution adopted by the present application is: an electronic device, the electronic device comprising:

[0033] A memory for storing executable program codes;

[0034] A processor is used to call and run the executable program code from the memory, so that the electronic device executes the smart home management method as described in any one of the above.

[0035] To solve the above technical problems, another technical solution adopted in the present application is: a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the smart home management method as described in any one of the above items is implemented.

[0036] Different from the prior art, an embodiment of the present application provides a smart home management method, which includes: responding to the bioelectric signal and voice signal of the user acquired by the wearable device, fusing the bioelectric signal and the voice signal to obtain emotional state information; generating a home management strategy based on the emotional state information; and adjusting the working mode and operating parameters of at least one home appliance based on the home management strategy.

[0037] The beneficial effect of the embodiments of the present application is: by responding to the bioelectric signals and voice signals sent by the wearable device, fusing these signals to obtain emotional state information, and then generating a home management strategy, and controlling the operation of home appliances according to the strategy, the comfort and convenience of the home system can be effectively improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] Figure 1 It is a flowchart of the first embodiment of the smart home management method of the present application;

[0039] Figure 2 It is a flowchart of the second embodiment of the smart home management method of the present application;

[0040] Figure 3 It is a flowchart of the third embodiment of the smart home management method of the present application;

[0041] Figure 4 It is a flowchart of the fourth embodiment of the smart home management method of the present application;

[0042] Figure 5 It is a functional module diagram of the smart home management method of the present application;

[0043] Figure 6 is an exemplary structural block diagram of an electronic device of the smart home management method of the present application;

[0044] Figure 7 It is an exemplary structural block diagram of the computer-readable storage medium of the smart home management method of the present application. DETAILED DESCRIPTION

[0045] The present application is described in detail below with reference to the accompanying drawings and implementation methods.

[0046] See also Figure 1 , Figure 1 It is a flowchart of the first embodiment of the smart home management method of the present application. It should be noted that if there are substantially the same results, the method of the present application is not based on Figure 1 The process sequence shown is limited. Figure 1 As shown, the method is applied to a home system, the home system includes at least one home device, the home system interacts with a wearable device, and the method includes:

[0047] Step S110, in response to the bioelectric signal and the voice signal of the user acquired by the wearable device, fusing the bioelectric signal and the voice signal to obtain emotional state information;

[0048] This embodiment takes into account that the existing smart home system mainly relies on the user's explicit instructions and preset rules, and requires the user to actively operate to achieve home device control. However, this passive operation mode of the existing smart home system may cause inconvenience when the user is depressed or busy, and it is difficult to automatically provide a comfortable and relaxing environment. Therefore, this embodiment provides a smart home emotion recognition system, which realizes a more comfortable and humanized smart home environment management method by actively identifying the user's emotional state.

[0049] Specifically, the home system can be a system composed of different types of home appliances, which can be interconnected through the network and managed and coordinated by a central control module (such as an intelligent gateway) to respond to the user's emotional needs and adjust to provide an automated living environment. Home appliances can be various smart devices installed in the home, such as smart lights, thermostats, smart speakers, smart door locks, etc., which are used to implement home management strategies to adjust the home environment to meet the user's emotional needs.

[0050] Wearable devices can be electronic devices worn by users, such as smart watches and smart bracelets, which can collect bioelectric signals and voice signals to monitor the user's physiological and emotional state in real time and send the data to the home system. Bioelectric signals can be electrical signals collected by wearable devices that reflect the user's physiological state, such as heart rate, skin electrical response, etc., which are used to provide information about the user's current physiological state as an important basis for judging the emotional state; voice signals can be signals generated by the user through voice, which are collected by the microphone of the wearable device.

[0051] When the user's location meets the sensing range of the home system, the home system can respond to the bioelectric signals and voice signals from the wearable device through wireless connection (such as Bluetooth, Wi-Fi) and transmit them to the central control module of the home system. The central control module of the home system can pre-process the bioelectric signals and voice signals, use multimodal data fusion technology to fuse the signals, and then judge the user's emotional state.

[0052] Step S120, generating a home management strategy according to the emotional state information;

[0053] Specifically, the home management strategy may include the working mode and operating parameters of the device. The working mode may be the color and brightness of the light, etc. The operating parameters may be the temperature setting of the thermostat, the volume and playback content of the audio system, etc.

[0054] Step S130: adjusting the working mode and operating parameters of at least one home appliance according to the home management strategy.

[0055] Specifically, the central control module can convert the home management strategy into a control command and send it to the relevant home appliances, so that these home appliances can adjust their working modes and operating parameters according to the control instructions, such as adjusting the brightness and color of the lights, playing music suitable for the user's emotional state, adjusting the indoor temperature, etc. In this way, the home environment can be automated and personalized, improving the user's living experience and emotional satisfaction.

[0056] For example, when a user returns home, the smartwatch he is wearing detects that his heart rate is higher than usual, and through voice signal analysis, he finds that his voice tone is rapid, indicating anxiety. The central control module integrates the results of heart rate, galvanic skin response, and voice emotion analysis to confirm that the user is in an anxious state. Based on the anxious emotional state, the following home management strategies are generated: adjust the living room lights to a soft warm tone and lower the brightness; play soothing background music, such as nature sounds or light music; adjust the indoor temperature to a comfortable range; turn off notifications from electronic devices that may cause irritation, such as TVs and mobile phones.

[0057] In another example, a user holds a party with friends at home, and the atmosphere is pleasant. The smart bracelet detects that the user's heart rate is slightly higher than usual, and through voice signal analysis, the user's tone is cheerful, indicating a happy and excited mood. The central control module integrates the heart rate and voice emotion analysis results to confirm that the user is in a happy state. Based on the pleasant emotional state, the following home management strategies are generated: adjust the living room lights to bright warm colors; play cheerful background music and select faster-paced tracks; adjust the indoor temperature to a comfortable range; turn on the smart aromatherapy machine to release a fresh fragrance.

[0058] This embodiment responds to the bioelectric signals and voice signals sent by the wearable device, integrates these signals to obtain emotional state information, and then generates a home management strategy, and controls the operation of home appliances according to the strategy, which can effectively improve the comfort and convenience of the home system.

[0059] See also Figure 2 , Figure 2 1 is a flow chart of the second embodiment of the smart home management method of the present application. In this embodiment, the method includes the following steps:

[0060] Step S210, in response to the bioelectric signal and voice signal of the user acquired by the wearable device, extracting features from the bioelectric signal to obtain bioelectric features, and extracting features from the voice signal to obtain voice features;

[0061] This embodiment takes into account that data of a single modality is easily interfered by specific factors. For example, bioelectric signals may be affected by electromagnetic interference or user motion artifacts, and voice signals may be affected by background noise or changes in user pronunciation. Therefore, this embodiment can perform sentiment analysis from multiple dimensions by fusing bioelectric signals and voice signals, and can obtain both the user's internal emotional response and the user's external emotional expression.

[0062] Specifically, the wearable device can be a smart bracelet, a smart watch, a brain wave detection headband, etc., which is used to collect the user's bioelectric signals and voice signals in real time.

[0063] The bioelectric signal can be data collected by the wearable device at a certain moment or instant, which is used to reflect the user's current emotional state in real time; it can also be data collected continuously over a period of time, which is used to reflect the changes and trends of the user during the period of time. In this embodiment, the bioelectric signal can be preprocessed, and then the feature extraction of the preprocessed bioelectric signal can be performed to obtain the bioelectric feature.

[0064] Among them, the preprocessing methods of bioelectric signals may include but are not limited to: using filters, such as low-pass filters, high-pass filters, band-pass filters, etc., to remove noise and interference in bioelectric signals; normalizing the bioelectric signals to ensure that the signals are within a uniform range; identifying and removing artifacts caused by factors such as user movement and muscle contraction.

[0065] Bioelectric features may include but are not limited to: time domain features, such as extracting heart rate, heart rate variability, peak-to-trough intervals, etc.; frequency domain features, such as extracting signal spectrum features through Fourier transform, such as power spectrum density, main frequency band energy, etc.; nonlinear features, such as computational complexity, entropy value and other nonlinear features.

[0066] The voice signal can be data collected by the wearable device at a certain moment or instant for real-time recognition and processing; it can also be data collected continuously over a period of time to reflect the changes and trends of the user's voice during the period of time. In this embodiment, the voice signal can be preprocessed, and then the feature extraction of the preprocessed voice signal can be performed to obtain the voice feature.

[0067] The preprocessing method of the speech signal may include but is not limited to: using speech enhancement technology to reduce the impact of environmental noise on the speech signal; and performing segmentation processing on long speech signals.

[0068] Speech features may include but are not limited to: acoustic features such as frequency cepstrum coefficients, pitch, fundamental frequency, and resonance peaks; through natural language processing technology, analyze the speech content and extract semantic features such as keywords and emotional words; combine the extracted bioelectric features and speech features into a feature vector.

[0069] Step S220, using a multimodal data fusion method to fuse the bioelectric feature and the speech feature to obtain fused data;

[0070] Specifically, the bioelectric features and speech features can be standardized to make the features of different modalities representative. Then, the representative features are selected using a feature selection algorithm, and different weights are assigned to the bioelectric features and speech features according to their importance, and weighted average is performed. In this way, the bioelectric features and speech features can be fused into a comprehensive feature vector to obtain fused data.

[0071] Step S230: classify the fused data to obtain the emotional state information.

[0072] Specifically, emotional state information may include but is not limited to: specific emotional states, such as happiness, sadness, anger, calmness, tension, etc.; the intensity of the emotional state, such as slight happiness, moderate anger, etc.; the duration of the current emotional state; and the speed of change of the emotional state.

[0073] Among them, deep learning algorithms such as support vector machines, random forests, convolutional neural networks, long short-term memory networks, etc. can be used to classify the fused data to identify the user's emotional state and obtain emotional state information.

[0074] Step S240, analyzing and predicting the emotional state information to obtain the emotional change trend;

[0075] Specifically, the trend of emotional changes may include but is not limited to: periodic changes in emotional states, such as daily or hourly emotional fluctuations; long-term changing trends in emotional states, such as the emotional state gradually becoming stable; potential triggers or patterns of emotional changes, such as emotional fluctuations caused by specific time periods or specific events.

[0076] Step S250, mapping the emotion change trend with the preset device function to determine the home management strategy.

[0077] Specifically, a mapping relationship between the emotion change area and the device function can be established based on the correlation between the emotion state information and the device function. Among them, the mapping relationship can set the priority according to the importance and urgency of the emotion state information, such as emergency emotion state and non-emergency emotion state; different response strategies can be implemented according to the priority, such as high-priority emotion state can execute fast response and multi-device linkage.

[0078] Step S260, determining a target home device according to the home management strategy to generate an operation instruction corresponding to the target home device;

[0079] Specifically, the central control module can be provided with a command generation unit, which can convert high-level management strategies into control commands for specific home appliances. Among them, it can be converted into a format that can be recognized and executed by the target device according to the communication protocol of the target home appliance. For example, smart lamps can receive operating instructions through the Zigbee protocol, and the audio system can receive HTTP requests through Wi-Fi. Through the command generation module, the system can accurately control home appliances, and the command formatting process ensures that the commands can be correctly parsed and executed by various smart devices, improving the compatibility and scalability of the system.

[0080] Step S270: sending the operating instruction to the target home appliance to adjust the working mode and the operating parameters of the target home appliance.

[0081] Specifically, the generated operation instruction is sent to the target home appliance through the central control module. After receiving the operation instruction, the target home appliance adjusts the working mode and operation parameters according to the operation instruction.

[0082] Further, the step S240 may include: inputting the emotional state information into a pre-created prediction model for analysis and prediction to obtain the emotional change trend;

[0083] Furthermore, the smart home management method further includes:

[0084] Acquire historical emotion feedback data; adopt reinforcement learning and optimization algorithms, combine the emotion state information and historical emotion feedback data, and update the parameters and structure of the prediction model.

[0085] Specifically, in order to improve the prediction ability of the prediction model, reinforcement learning and optimization algorithms can be used to dynamically adjust the parameters and structure of the prediction model according to the user's current emotional state information and historical emotional feedback data. Furthermore, the working mode and parameters of smart home devices can also be dynamically adjusted. Among them, the historical emotional feedback data can be the emotional changes of the user over a period of time in the past, which is used to help the prediction model identify long-term trends and emotional patterns. Therefore, by combining emotional state information and historical emotional feedback data, it can be ensured that the prediction model can adapt to the latest emotional change trends and maintain efficient prediction capabilities.

[0086] This embodiment can improve the accuracy and robustness of emotion recognition by fusing bioelectric features and voice features; through the complementarity and comprehensive utilization of multimodal data, the smart home system can effectively respond to the emotional needs of users; by mapping emotional states with home appliances, the decision-making process of home management strategies can be simplified; by updating emotional state information and combining historical emotional feedback data, the prediction model can adapt to the latest emotional changes of users, thereby improving the accuracy of home appliance management.

[0087] See also Figure 3 , Figure 3 1 is a flow chart of the third embodiment of the smart home management method of the present application. In this embodiment, the home system also interacts with the server, and the method includes the following steps:

[0088] Step S310, in response to the bioelectric signal and the voice signal of the user acquired by the wearable device, fusing the bioelectric signal and the voice signal to obtain emotional state information;

[0089] This embodiment takes into account that with the rapid development of smart home technology, more and more families are beginning to use various smart home devices, such as smart lighting systems, smart temperature control systems, smart speakers, etc. However, these home devices mostly rely on the active control of users, and it is difficult to achieve personalized smart home management. Therefore, this embodiment proposes a smart home system based on the user's emotional state, so that smart devices can automatically perceive and respond to the user's emotional changes, and achieve a personalized home environment.

[0090] Step S320, sending the emotional state information to the server, so that the server analyzes the emotional state information and generates emotional suggestions;

[0091] Specifically, since the server has stronger computing power and storage resources, it can centrally process and deeply analyze a large amount of emotional state information. At the same time, complex sentiment analysis and suggestion generation models usually require higher computing resources. Storing the model on the server can reduce the computing burden of the home system and extend the battery life of the device.

[0092] In addition, the emotional suggestions can be encrypted and returned to the wearable device through a secure communication channel, which can ensure the security and privacy of data transmission.

[0093] Step S330, returning the emotional suggestion to the wearable device, so that the wearable device provides the emotional suggestion to the user.

[0094] In this embodiment, the emotional state information is fed back to the server to generate corresponding emotional suggestions and encrypt them, and then return the emotional suggestions to the wearable device. The wearable device provides emotional suggestions to users, which can improve the convenience and usability of the home system and enhance the confidentiality of user data.

[0095] See also Figure 4 , Figure 4 1 is a flow chart of the fourth embodiment of the smart home management method of the present application. In this embodiment, the method is applied to a wearable device, the wearable device interacts with a home system, the home system includes at least one home device, and the method includes the following steps:

[0096] Step S410, obtaining the user's bioelectric signal and voice signal, and sending them to the home system;

[0097] Step S420, the home system responds to the bioelectric signal and the voice signal, fuses the bioelectric signal and the voice signal, and obtains emotional state information; generates a home management strategy based on the emotional state information; and adjusts the working mode and operating parameters of at least one home appliance based on the home management strategy.

[0098] The above steps S110 to S130 may be performed and will not be described in detail herein.

[0099] Furthermore, the wearable device is used to display at least one of the emotional state information, the home management strategy, and the working mode and operating parameters of the home equipment.

[0100] This embodiment adopts the human-computer interaction design concept. The wearable device may include a graphical interface and voice interaction mode, so that the user can intuitively understand his or her emotional state through the wearable device and perform intuitive adjustment and feedback operations.

[0101] Specifically, see Figure 5 , Figure 5 This is a functional module diagram of the smart home management method of this application. Figure 5 It is shown that the specific functions of human-computer interaction may include: emotional state feedback function, after the home system adjusts the working mode and operating parameters of at least one home appliance according to the home management strategy, the user can provide emotional feedback through wearable devices; log recording and analysis function, so that the user can view personal emotional changes through the panel; health management function, which can share emotional state information with experts to obtain corresponding professional advice; personalized emotional management function, which can provide personalized emotional reminders and suggestions according to the different emotions of the user, and then make appropriate adjustments according to emotional changes, so that the home appliances in the home system can adjust the lights and music.

[0102] In this embodiment, by responding to the bioelectric signals and voice signals sent by the wearable device, fusing these signals to obtain emotional state information, and then generating a home management strategy, and controlling the operation of home appliances according to the strategy, the comfort and convenience of the home system can be effectively improved.

[0103] See also Figure 6 , Figure 6 is an exemplary structural block diagram of an electronic device of the smart home management method of the present application. Figure 6 As shown, the electronic device 600 of the present application may include a processor 601 and a memory 602, wherein the processor 601 and the memory 602 communicate with each other via a bus. The memory 602 stores program instructions for smart home management, and when the program instructions are executed by the processor 601, the method steps described in the above text in conjunction with the accompanying drawings are implemented:

[0104] In response to a bioelectric signal and a voice signal of a user acquired by the wearable device, the bioelectric signal and the voice signal are fused to obtain emotional state information;

[0105] generating a home management strategy based on the emotional state information;

[0106] According to the home management strategy, the working mode and operating parameters of at least one home appliance are adjusted.

[0107] Optionally, the processor 601 performs the step of fusing the bioelectric signal and the voice signal to obtain the emotion state information, including:

[0108] Extracting features from the bioelectric signal to obtain bioelectric features, and extracting features from the speech signal to obtain speech features;

[0109] Using a multimodal data fusion method to fuse the bioelectric feature and the speech feature to obtain fused data;

[0110] Classify the fused data to obtain the emotional state information.

[0111] Optionally, after the processor 601 executes the step of the home system further interacting with the server, fusing the bioelectric signal and the voice signal to obtain the emotion state information, the step further includes:

[0112] Sending the emotional state information to the server, so that the server analyzes the emotional state information and generates emotional suggestions;

[0113] The emotional suggestion is returned to the wearable device so that the wearable device provides the emotional suggestion to the user.

[0114] Optionally, the processor 601 executes the step of generating a home management strategy according to the emotional state information, including:

[0115] Analyzing and predicting the emotional state information to obtain the emotional change trend;

[0116] The emotion change trend is mapped with the preset device function to determine the home management strategy.

[0117] Optionally, the processor 601 performs the step of analyzing and predicting the emotional state information to obtain the emotional change trend, including:

[0118] Inputting the emotional state information into a pre-created prediction model for analysis and prediction to obtain the emotional change trend;

[0119] The method further comprises:

[0120] Get historical sentiment feedback data;

[0121] Reinforcement learning and optimization algorithms are used to combine the emotional state information and historical emotional feedback data to update the parameters and structure of the prediction model.

[0122] Optionally, the processor 601 executes the step of adjusting the working mode and operating parameters of at least one home appliance according to the home management strategy, including:

[0123] Determine the target home device according to the home management strategy to generate an operation instruction corresponding to the target home device;

[0124] The operation instruction is sent to the target home device to adjust the working mode and the operation parameters of the target home device.

[0125] Optionally, the processor 601 executes the method including:

[0126] Acquire the user's bioelectric signal and voice signal, and send them to the home system;

[0127] The home system responds to the bioelectric signal and the voice signal, fuses the bioelectric signal and the voice signal, and obtains emotional state information; generates a home management strategy based on the emotional state information; and adjusts the working mode and operating parameters of at least one home appliance based on the home management strategy.

[0128] Optionally, the processor 601 executes at least one of the displaying of the emotional state information, the home management strategy, the working mode and operating parameters of the home equipment.

[0129] See also Figure 7 , Figure 7 is an exemplary structural block diagram of a computer-readable storage medium of the smart home management method of the present application. Figure 7 As shown, the computer-readable storage medium 700 stores a computer program 701. When the computer program 701 runs on a computer, the computer executes the above-mentioned related method steps to implement a smart home management method in the above-mentioned embodiment.

[0130] The above scheme responds to the bioelectric signals and voice signals of the user obtained by the wearable device, fuses the bioelectric signals and voice signals to obtain emotional state information; generates a home management strategy based on the emotional state information; and adjusts the working mode and operating parameters of at least one home appliance according to the home management strategy. By responding to the bioelectric signals and voice signals sent by the wearable device, fusing these signals to obtain emotional state information, and then generating a home management strategy, and controlling the operation of the home appliances according to the strategy, the comfort and convenience of the home system can be effectively improved.

[0131] In the several embodiments provided in the present application, it should be understood that the disclosed methods, electronic devices and storage media can be implemented in other ways. For example, the device implementation described above is only schematic. For example, the division of the modules or 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 an indirect coupling or communication connection through some interfaces, devices or units, which can be electrical, mechanical or other forms.

[0132] 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 present embodiment.

[0133] In addition, each functional unit in each embodiment of the present application 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. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0134] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application is essentially or the part that contributes to the prior art or all or part of the technical solution can be embodied in the form of a software product, and the computer software product is stored in a storage medium, including a number of instructions to enable a computer device (which can be an electronic device, a personal computer, a server, or a network device, etc.) or a processor (processor) to perform all or part of the steps of the method described in each embodiment of the present application. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk and other media that can store program code.

[0135] The above description is only an implementation method of the present application, and does not limit the patent scope of the present application. Any equivalent structure or equivalent process transformation made using the contents of the present application specification and drawings, or directly or indirectly used in other related technical fields, are also included in the patent protection scope of the present application.

Claims

1. A smart home management method, characterized in that: The method is applied to a home system, the home system includes at least one home device, the home system interacts with a wearable device, and the method includes: In response to a bioelectric signal and a voice signal of a user acquired by the wearable device, the bioelectric signal and the voice signal are fused to obtain emotional state information; generating a home management strategy based on the emotional state information; According to the home management strategy, the working mode and operating parameters of at least one home appliance are adjusted.

2. The method according to claim 1, characterized in that The step of fusing the bioelectric signal and the voice signal to obtain the emotional state information comprises: Performing feature extraction on the bioelectric signal to obtain a bioelectric feature, and performing feature extraction on the speech signal to obtain a speech feature; Using a multimodal data fusion method to fuse the bioelectric feature and the speech feature to obtain fused data; Classify the fused data to obtain the emotional state information.

3. The method according to claim 1, characterized in that The home system also interacts with the server, and after the step of fusing the bioelectric signal and the voice signal to obtain the emotional state information, it further includes: Sending the emotional state information to the server, so that the server analyzes the emotional state information and generates emotional suggestions; The emotional suggestion is returned to the wearable device so that the wearable device provides the emotional suggestion to the user.

4. The method according to claim 1, characterized in that: The step of generating a home management strategy according to the emotional state information comprises: Analyzing and predicting the emotional state information to obtain the emotional change trend; The emotion change trend is mapped with the preset device function to determine the home management strategy.

5. The method according to claim 4, characterized in that The step of analyzing and predicting the emotional state information to obtain the emotional change trend comprises: Inputting the emotional state information into a pre-created prediction model for analysis and prediction to obtain the emotional change trend; The method further comprises: Get historical sentiment feedback data; Reinforcement learning and optimization algorithms are used to combine the emotional state information and historical emotional feedback data to update the parameters and structure of the prediction model.

6. The method according to claim 1, characterized in that The step of adjusting the working mode and operating parameters of at least one home appliance according to the home management strategy comprises: Determine the target home device according to the home management strategy to generate an operation instruction corresponding to the target home device; The operation instruction is sent to the target home device to adjust the working mode and the operation parameters of the target home device.

7. A smart home management method, characterized in that: The method is applied to a wearable device, the wearable device interacts with a home system, the home system includes at least one home device, and the method includes: Acquire the user's bioelectric signal and voice signal, and send them to the home system; The home system responds to the bioelectric signal and the voice signal, fuses the bioelectric signal and the voice signal, and obtains emotional state information; generates a home management strategy based on the emotional state information; and adjusts the working mode and operating parameters of at least one home appliance based on the home management strategy.

8. The method according to claim 7, characterized in that The wearable device is used to display at least one of the emotional state information, the home management strategy, and the working mode and operating parameters of the home equipment.

9. An electronic device, characterized in that: The electronic device comprises: A memory for storing executable program codes; A processor, used to call and run the executable program code from the memory, so that the electronic device executes the smart home management method as described in any one of claims 1 to 6 or 7 to 8.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the smart home management method as described in any one of claims 1 to 6 or 7 to 8 is implemented.

Images