Smart home scene generation and setting method and device, equipment and storage medium

Abstract

Embodiments of the present application relate to the field of smart home, and disclose a smart home scene generation and setting method, device, equipment and storage medium, comprising: forming a comprehensive state vector for describing comprehensive states of each device based on acquired device state information of each device in a home environment at a first moment; performing scene classification on the comprehensive state vector by using a pre-trained scene classifier to obtain a scene type to which the home environment at the first moment belongs; and storing the device state information of each device in the home environment at the first moment and a scene name of the scene type to which the home environment at the first moment belongs in association, so as to generate a scene mode corresponding to the scene type. The application can automatically generate a scene mode for reproducing the home environment at the first moment.

Description

Technical Field

[0001] This invention relates to the field of smart homes, and particularly to a method, apparatus, device, and storage medium for generating and setting up smart home scenes. Background Technology

[0002] With the increasing popularity of smart home products, more and more smart appliances, lights, and other devices in users' homes support intelligent control. Controlling a single device can no longer meet users' needs for convenient control, so scenario-based control is becoming increasingly important. However, before setting up a scenario, users need to complete complex scenario settings through mini-programs or apps, or through complex voice interactions. This is difficult for ordinary users to operate, and there is also the problem that the set scenario deviates significantly from the user's expectations. Summary of the Invention

[0003] The purpose of this invention is to provide at least one method, device, equipment and storage medium for generating smart home scenes, which can at least solve the problem that users need to complete complex scene settings before scene control, making it difficult for many ordinary people to operate. This application can automatically generate a scene mode corresponding to the home environment at the current moment that the user wants to generate.

[0004] To address the aforementioned technical problems, at least one embodiment of this application provides a method for generating smart home scenes, including:

[0005] Based on the device status information of each device in the home environment at the first moment, a comprehensive state vector is formed to describe the overall state of each device.

[0006] The comprehensive state vector is used to classify the scene using a pre-trained scene classifier to obtain the scene type of the home environment at the first moment;

[0007] The device status information of each device in the home environment at the first moment is associated with the scene name of the scene type to which the home environment belongs at the first moment and stored to generate the scene mode corresponding to the scene type.

[0008] In some optional embodiments, the scene classifier includes multiple preset output scene types;

[0009] The comprehensive state vector is classified using a pre-trained scene classifier to obtain the scene type of the home environment at the first moment, including:

[0010] The comprehensive state vector is classified using a pre-trained scene classifier to obtain the classification probability value of each preset scene type to which the home environment belongs at the first moment;

[0011] The scenario type with the highest probability value among the various classification probability values ​​is selected as the scenario type to which the home environment belongs at the first moment.

[0012] In some optional embodiments, the step of forming a comprehensive state vector to describe the overall state of each device based on the acquired device state information of each device in the home environment at a first moment includes:

[0013] Encode the device status information of each device in the home environment at the first moment to obtain the status value of each device in multiple states;

[0014] Write the state values ​​of each device under the multiple states into a pre-set data model to generate a device state matrix. Each row or column in the device state matrix represents a device state vector of a device.

[0015] The overall state vector is obtained by concatenating the first and last device state vectors in the device state matrix.

[0016] In some optional embodiments, the step of classifying the integrated state vector using a pre-trained scene classifier to obtain the scene type of the home environment at the first moment includes:

[0017] Feature extraction is performed on the integrated state vector to obtain a high-dimensional feature vector of the integrated device state vector;

[0018] The high-dimensional feature vector is input into a scene classifier to classify the scene and obtain the scene type of the home environment at the first moment.

[0019] In some optional embodiments, the step of extracting features from the integrated state vector to obtain a high-dimensional feature vector of the integrated device state vector includes:

[0020] The high-dimensional feature vector is obtained by extracting features from the integrated device state vector using a deep learning network.

[0021] At least one embodiment of this application also provides a method for setting up a smart home scene, including:

[0022] Based on a given scene name, a target scene mode corresponding to the scene name is determined from a plurality of preset scene modes, and the target device status information of each device contained in the target scene mode is extracted; the plurality of preset scene modes are generated by the smart home scene generation method described above.

[0023] Control the device status of each device in the current home environment to be consistent with the status information of the target device.

[0024] At least one embodiment of this application also provides a smart home scene generation device, comprising:

[0025] The processing module is used to form a comprehensive state vector describing the overall state of each device based on the device status information of each device in the home environment at the first moment.

[0026] The scene classification module is used to classify the comprehensive state vector using a pre-trained scene classifier to obtain the scene type of the home environment at the first moment.

[0027] The scene generation module is used to associate and store the device status information of each device in the home environment at the first moment with the scene name of the scene type to which the home environment belongs at the first moment, so as to generate the scene mode corresponding to the scene type.

[0028] At least one embodiment of this application also provides a smart home scene setting device, including:

[0029] The extraction module is used to determine the target scene mode corresponding to the given scene name from a set of preset scene modes, and extract the target device status information of each device contained in the target scene mode; the set of preset scene modes are generated by the smart home scene generation method described above.

[0030] The control module is used to ensure that the device status of each device in the current home environment is consistent with the status information of the target device.

[0031] At least one embodiment of this application also provides an electronic device, including: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, the instructions being executed by the at least one processor to enable the at least one processor to perform the above-described smart home scene generation method, or to implement the above-described smart home scene setting method.

[0032] At least one embodiment of this application also provides a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the above-described smart home scene generation method or the above-described smart home scene setting method.

[0033] This application provides a method, apparatus, device, and storage medium for generating and setting up smart home scenes. It involves forming a comprehensive state vector describing the overall state of each device based on the device state information of each device in a home environment at a first moment; then, classifying the comprehensive state vector using a pre-trained scene classifier to obtain the scene type to which the home environment at the first moment belongs; finally, associating and storing the device state information of each device in the home environment at the first moment with the scene name of the scene type to which the home environment at the first moment belongs, thereby generating a scene pattern corresponding to the scene type. This achieves automatic generation of scene patterns for reproducing the home environment at the first moment. Attached Figure Description

[0034] One or more embodiments are illustrated by way of example with reference to the accompanying drawings, and these illustrative descriptions do not constitute a limitation on the embodiments.

[0035] Figure 1 This is a flowchart of a smart home scene generation method provided in one embodiment of this application;

[0036] Figure 2 This is a schematic diagram of a data model provided in one embodiment of this application;

[0037] Figure 3 This is a schematic diagram of the multi-layer neural network structure used in a scene classifier provided in one embodiment of this application;

[0038] Figure 4 This is a flowchart of a smart home scene setting method provided in another embodiment of this application;

[0039] Figure 5 This is a schematic diagram of the structure of a smart home scene generation device provided in another embodiment of this application;

[0040] Figure 6 This is a schematic diagram of the structure of a smart home scene setting device provided in another embodiment of this application. Detailed Implementation

[0041] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the various embodiments of this application will be described in detail below with reference to the accompanying drawings. However, those skilled in the art will understand that many technical details have been provided in the various embodiments of this application to help readers better understand this application. However, the technical solutions claimed in this application can be implemented even without these technical details and various changes and modifications based on the following embodiments. The division of the various embodiments below is for the convenience of description and should not constitute any limitation on the specific implementation of this application. The various embodiments can be combined with and referenced by each other without contradiction.

[0042] To facilitate understanding of the embodiments of this application, relevant content regarding smart home scenario-based applications will be introduced first.

[0043] With the increasing popularity of smart home products, more and more smart appliances, lights, and other devices in users' homes support intelligent control. Controlling a single device can no longer meet users' needs for convenient control, so scenario-based control is becoming increasingly important. However, before setting up a scenario, users need to complete complex scenario settings through mini-programs or apps, or through complex voice interactions. This is difficult for ordinary users to operate, and there is also the problem that the set scenario deviates significantly from the user's expectations.

[0044] To address the aforementioned technical problem that requires users to complete complex scene settings before scene control, making it difficult for many ordinary people to operate, this invention proposes a smart home scene generation method. The implementation details of the smart home scene generation method in this embodiment are described below. The following content is only for ease of understanding and is not necessary for implementing this solution.

[0045] Example 1:

[0046] The smart home scene generation method of this embodiment can be applied to electronic devices with communication, computing, and data storage capabilities. Its specific process can be as follows: Figure 1 As shown, it includes:

[0047] Step S11: Based on the device status information of each device in the home environment at the first moment, a comprehensive state vector is formed to describe the comprehensive state of each device.

[0048] Specifically, when a user wants to set a scene mode based on their home environment at a given moment, they can trigger "scene generation" through methods such as mini-programs, apps, the central control panel screen, and voice interaction. At this time, the device status information of each device in the home environment at that moment is obtained to form a comprehensive state vector describing the overall state of each device.

[0049] In some embodiments, step S11 includes:

[0050] Step S111: Encode the device status information of each device in the home environment at the first moment to obtain the status value of each device in multiple states.

[0051] For example, the home environment at the first moment includes an air conditioner and a fan, and the device status of the air conditioner and the fan at the first moment is as follows:

[0052] Air conditioner: On, cooling mode, 16 degrees Celsius, medium fan speed.

[0053] Fan: On, high speed.

[0054] The coded device status of the air conditioner is: 1, 0, 16, 1.

[0055] The device status of the coded fan is: 1, 2.

[0056] Step S112: Write the state values ​​of each device under the multiple states into a pre-set data model to generate a device state matrix. Each row or column in the device state matrix represents a device state vector of a device.

[0057] The data model is as follows Figure 2 As shown, the vertical axis represents various devices, such as air conditioners, fans, and dehumidifiers, while the horizontal axis represents the various states of the devices, such as on / off, mode, and fan speed.

[0058] Here, we will still take the aforementioned first-moment home environment, which includes air conditioning and fans, as an example. In the device state matrix, the air conditioner state vector is I1 = [1,0,16,1], and the fan state vector is I2 = [1,2].

[0059] Step S113: The device state vectors in the device state matrix are concatenated end to end to obtain the comprehensive state vector.

[0060] Taking the aforementioned first-moment home environment, which includes air conditioning and fans, as an example, the air conditioning state vector I1 = [1,0,16,1] and the fan state vector I2 = [1,2] in the device state matrix are sequentially concatenated to obtain the comprehensive state vector X = [1,0,16,1,1,2,...].

[0061] Step S12: For the comprehensive state vector, a pre-trained scene classifier is used to classify the scene to obtain the scene type of the home environment at the first moment.

[0062] Specifically, the scene classifier can be trained using a multi-layer neural network, such as a softmax network. Taking the scene classifier trained using a softmax network as an example, the multi-layer neural network structure used in this embodiment is as follows: Figure 3 As shown. The calculation formula for the scene classifier is:

[0063]

[0064] Among them, y i h is the calculated classification probability value for the i-th scene type; i It is the i-th scalar element in the vector input to the scene classifier. It is the h of ei Exponentiation.

[0065] In some embodiments, the scene classifier includes multiple preset output scene types. Step S12 includes: classifying the comprehensive state vector using a pre-trained scene classifier to obtain a classification probability value for each preset scene type to which the home environment belongs at the first time; and selecting the scene type with the highest probability value from among the classification probability values ​​as the scene type to which the home environment belongs at the first time.

[0066] Taking the aforementioned first-moment home environment, including air conditioning and fans, as an example, the scene classifier includes a preset output scene type. The scene classifier is used to classify the comprehensive state vector X = [1, 0, 16, 1, 1, 2, ...], resulting in y1 = 0.0.5, y2 = 0.65, ..., where y2 ≥ y i , i∈[1,n], y i Let y2 be the classification probability value of the i-th scene type. That is, if y2 has the highest classification probability value, and y2 is the "homecoming scene", then the scene type of the home environment at the first moment is "homecoming mode".

[0067] In some embodiments, the integrated state vector typically contains a large number of low-level features, which may be insufficient to describe the high-level characteristics of the data. Therefore, in order to capture the high-dimensional features in the integrated state vector to more accurately describe the high-level characteristics of the data, step S12 includes: extracting features from the integrated state vector to obtain a high-dimensional feature vector of the integrated device state vector; inputting the high-dimensional feature vector into a scene classifier for scene classification to obtain the scene type to which the home environment belongs at the first moment.

[0068] In one example, the integrated device state vector is subjected to feature extraction using a deep learning network to obtain the high-dimensional feature vector.

[0069] Step S13: Associate and store the device status information of each device in the home environment at the first moment with the scene name of the scene type to which the home environment belongs at the first moment, so as to generate the scene mode corresponding to the scene type.

[0070] This embodiment generates a comprehensive state vector describing the overall state of each device based on the device status information of each device in the home environment at a first moment. Then, a pre-trained scene classifier is used to classify the comprehensive state vector to obtain the scene type to which the home environment at the first moment belongs. Finally, the device status information of each device in the home environment at the first moment is associated with the scene name of the scene type to which the home environment at the first moment belongs, and stored to generate a scene pattern corresponding to the scene type. This achieves automatic generation of scene patterns to reproduce the home environment at the first moment.

[0071] Example 2:

[0072] The smart home scene setting method of this embodiment can be applied to electronic devices with communication, computing, and data storage capabilities. Its specific process can be as follows: Figure 4 As shown, it includes:

[0073] Step S21: Based on the given scene name, determine the target scene mode corresponding to the scene name from a plurality of preset scene modes. The plurality of preset scene modes are generated by the smart home scene generation method as described in Embodiment 1.

[0074] Step S22: Extract the target device status information of each device included in the target scene mode.

[0075] Step S23: Control the device status of each device in the current home environment to be consistent with the target device status information.

[0076] For example, when a user returns home from get off work and wants to set a "homecoming mode," they can provide the desired scene name as "homecoming mode" through any interaction method, such as a mini-program, app, central control panel screen, or voice interaction. Based on the user-provided scene name "homecoming mode," a target scene mode corresponding to this name is determined, and the target device status information of each device included in the target scene mode is extracted. Then, based on the extracted target device status information of each device included in the target scene mode, the device status of each device in the current home environment is controlled to be consistent with the target device status information.

[0077] Example 3:

[0078] Another embodiment of this application relates to a smart home scene generation device. The implementation details of this smart home scene generation device are described below. The following details are for ease of understanding and are not essential for implementing this solution. A schematic diagram of the smart home scene generation device in this embodiment can be seen as follows: Figure 5As shown, it includes a processing module 301, a scene classification module 302, and a scene generation module 303.

[0079] The processing module 301 is used to form a comprehensive state vector describing the overall state of each device based on the device state information of each device in the home environment at the first moment.

[0080] Specifically, when a user wants to set a scene mode based on their home environment at a given moment, they can trigger "scene generation" through methods such as mini-programs, apps, the central control panel screen, and voice interaction. At this time, the device status information of each device in the home environment at that moment is obtained to form a comprehensive state vector describing the overall state of each device.

[0081] In some embodiments, the processing module 301 includes an encoding unit, a generation unit, and a splicing unit.

[0082] The encoding unit is used to encode the device status information of each device in the home environment at the first moment to obtain the status value of each device in multiple states.

[0083] For example, the home environment at the first moment includes an air conditioner and a fan, and the device status of the air conditioner and the fan at the first moment is as follows:

[0084] Air conditioner: On, cooling mode, 16 degrees Celsius, medium fan speed.

[0085] Fan: On, high speed.

[0086] The coded device status of the air conditioner is: 1, 0, 16, 1.

[0087] The device status of the coded fan is: 1, 2.

[0088] The generation unit is used to write the state values ​​of each device under the multiple states into a pre-set data model to generate a device state matrix, wherein each row or column of the device state matrix represents a device state vector of a device.

[0089] The data model is as follows Figure 2 As shown, the vertical axis represents various devices, such as air conditioners, fans, and dehumidifiers, while the horizontal axis represents the various states of the devices, such as on / off, mode, and fan speed.

[0090] Here, we still take the aforementioned first-moment home environment, which includes air conditioning and fans, as an example. In the device state matrix, the air conditioner state vector is I1 = [1,0,16,1], and the fan state vector is O2 = [1,2].

[0091] The splicing unit is used to sequentially splice the beginning and end of each device state vector in the device state matrix to obtain the comprehensive state vector.

[0092] Taking the aforementioned first-moment home environment, which includes air conditioning and fans, as an example, the air conditioning state vector I1 = [1,0,16,1] and the fan state vector I2 = [1,2] in the device state matrix are sequentially concatenated to obtain the comprehensive state vector X = [1,0,16,1,1,2,...].

[0093] The scene classification module 302 is used to classify the comprehensive state vector using a pre-trained scene classifier to obtain the scene type of the home environment at the first moment.

[0094] Specifically, the scene classifier can be trained using a multi-layer neural network, such as a softmax network.

[0095] In some embodiments, the scene classifier includes multiple preset output scene types. The scene classification module 302 includes a classification unit and a selection unit.

[0096] The classification unit is used to classify the comprehensive state vector using a pre-trained scene classifier to obtain the classification probability value of each preset scene type to which the home environment belongs at the first moment.

[0097] The selection unit is used to select the scene type with the highest probability value from each of the classification probability values ​​as the scene type to which the home environment belongs at the first moment.

[0098] Taking the aforementioned first-moment home environment, including air conditioning and fans, as an example, the scene classifier includes a preset output scene type. The scene classifier is used to classify the comprehensive state vector X = [1, 0, 16, 1, 1, 2, ...], resulting in y1 = 0.0.5, y2 = 0.65, ..., where y2 ≥ y i , i∈[1,n], y i Let y2 be the classification probability value of the i-th scene type. That is, if y2 has the highest classification probability value, and y2 is the "homecoming scene", then the scene type of the home environment at the first moment is "homecoming mode".

[0099] In some embodiments, the comprehensive state vector typically contains a large number of low-level features, which may be insufficient to describe the high-level characteristics of the data. Therefore, in order to capture the high-dimensional features in the comprehensive state vector to more accurately describe the high-level features of the data, the scene classification module 302 includes a feature extraction unit and a classification extraction unit.

[0100] The feature extraction unit is used to extract features from the integrated state vector to obtain a high-dimensional feature vector of the integrated device state vector.

[0101] In one example, the integrated device state vector is subjected to feature extraction using a deep learning network to obtain the high-dimensional feature vector.

[0102] The classification extraction unit is used to input the high-dimensional feature vector into the scene classifier to classify the scene and obtain the scene type of the home environment at the first moment.

[0103] Specifically, the classification and extraction unit actually includes the classification unit and extraction unit mentioned above.

[0104] The scene generation module 303 is used to associate and store the device status information of each device in the home environment at the first moment with the scene name of the scene type to which the home environment belongs at the first moment, so as to generate a scene mode corresponding to the scene type.

[0105] In this embodiment, the processing module 301 generates a comprehensive state vector describing the overall state of each device based on the acquired device status information of each device in the home environment at the first moment. Then, the scene classification module 302 uses a pre-trained scene classifier to classify the comprehensive state vector into the scene type of the home environment at the first moment. Finally, the scene generation module 303 associates and stores the device status information of each device in the home environment at the first moment with the scene name of the scene type to which the home environment belongs, thereby generating a scene pattern corresponding to the scene type. This achieves automatic generation of scene patterns to reproduce the home environment at the first moment.

[0106] It is worth mentioning that all modules involved in this embodiment are logical modules. In practical applications, a logical unit can be a physical unit, a part of a physical unit, or a combination of multiple physical units. Furthermore, to highlight the innovative aspects of this application, this embodiment does not introduce units that are not closely related to solving the technical problems proposed in this application; however, this does not mean that other units are absent in this embodiment.

[0107] Example 4:

[0108] Another embodiment of this application relates to a smart home scene setting device. The implementation details of this smart home scene setting device are described below. The following details are for ease of understanding and are not essential for implementing this solution. A schematic diagram of this smart home scene setting device can be seen as follows: Figure 6As shown, it includes an extraction module 401 and a control module 402.

[0109] The extraction module 401 is used to determine the target scene mode corresponding to the given scene name from a plurality of preset scene modes, and extract the target device status information of each device included in the target scene mode; the plurality of preset scene modes are generated by the smart home scene generation method as described in Embodiment 1.

[0110] The control module 402 is used to ensure that the device status of each device in the current home environment is consistent with the status information of the target device.

[0111] For example, when a user returns home from get off work and wants to set a "homecoming mode," the user can provide the desired scene name as "homecoming mode" through any interaction method such as a mini-program, app, central control panel screen, or voice interaction. Then, the extraction module 401 determines the target scene mode corresponding to the user-provided scene name "homecoming mode" and extracts the target device status information of each device included in the target scene mode. Subsequently, the control module 402, based on the extracted target device status information of each device included in the target scene mode, controls the device status of each device in the current home environment to be consistent with the target device status information.

[0112] It is worth mentioning that all modules involved in this embodiment are logical modules. In practical applications, a logical unit can be a physical unit, a part of a physical unit, or a combination of multiple physical units. Furthermore, to highlight the innovative aspects of this application, this embodiment does not introduce units that are not closely related to solving the technical problems proposed in this application; however, this does not mean that other units are absent in this embodiment.

[0113] Example 5:

[0114] Another embodiment of this application relates to an electronic device, including: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, the instructions being executed by the at least one processor to enable the at least one processor to perform the smart home scene generation method or the smart home scene setting method in the above embodiments.

[0115] The memory and processor are connected via a bus, which can include any number of interconnecting buses and bridges, connecting various circuits of one or more processors and memories. The bus can also connect various other circuits, such as peripheral devices, voltage regulators, and power management circuits, which are well known in the art and will not be described further herein. The bus interface provides an interface between the bus and the transceiver. The transceiver can be a single element or multiple elements, such as multiple receivers and transmitters, providing a unit for communicating with various other devices over a transmission medium. Data processed by the processor is transmitted over the wireless medium via an antenna, which further receives data and transmits it to the processor.

[0116] The processor manages the bus and general processing, and also provides various functions, including timing, peripheral interfaces, voltage regulation, power management, and other control functions. Memory is used to store data used by the processor during operation.

[0117] Example 6:

[0118] Another embodiment of this application relates to a computer-readable storage medium storing a computer program. When executed by a processor, the computer program implements the method embodiments described above.

[0119] That is, those skilled in the art will understand that all or part of the steps in the methods of the above embodiments can be implemented by a program instructing related hardware. This program is stored in a storage medium and includes several instructions to cause a device (which may be a microcontroller, chip, etc.) or processor to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0120] Those skilled in the art will understand that the above embodiments are specific embodiments for implementing this application, and in practical applications, various changes can be made to them in form and detail without departing from the spirit and scope of this application.

Claims

1. A method for generating smart home scenes, characterized in that, include: Based on the device status information of each device in the home environment at the first moment, a comprehensive state vector is formed to describe the overall state of each device. The comprehensive state vector is used to classify the scene using a pre-trained scene classifier to obtain the scene type of the home environment at the first moment; The device status information of each device in the home environment at the first moment is associated with the scene name of the scene type to which the home environment belongs at the first moment and stored to generate a scene mode corresponding to the scene type. The scene mode is used to reproduce the home environment at the first moment. Based on the device status information of each device in the home environment acquired at the first moment, a comprehensive state vector is formed to describe the overall state of each device, including: Encode the device status information of each device in the home environment at the first moment to obtain the status value of each device in multiple states; Write the state values ​​of each device under the multiple states into a pre-set data model to generate a device state matrix. Each row or column in the device state matrix represents a device state vector of a device. The overall state vector is obtained by concatenating the first and last device state vectors in the device state matrix.

2. The smart home scene generation method according to claim 1, characterized in that, The scene classifier contains multiple preset output scene types; The comprehensive state vector is classified using a pre-trained scene classifier to obtain the scene type of the home environment at the first moment, including: The comprehensive state vector is classified using a pre-trained scene classifier to obtain the classification probability value of each preset scene type to which the home environment belongs at the first moment; The scenario type with the highest probability value among the various classification probability values ​​is selected as the scenario type to which the home environment belongs at the first moment.

3. The smart home scene generation method according to claim 1, characterized in that, The comprehensive state vector is classified using a pre-trained scene classifier to obtain the scene type of the home environment at the first moment, including: Feature extraction is performed on the comprehensive state vector to obtain a high-dimensional feature vector of the comprehensive state vector; The high-dimensional feature vector is input into a scene classifier to classify the scene and obtain the scene type of the home environment at the first moment.

4. The smart home scene generation method according to claim 3, characterized in that, The step of extracting features from the comprehensive state vector to obtain a high-dimensional feature vector of the comprehensive state vector includes: The high-dimensional feature vector is obtained by extracting features from the comprehensive state vector using a deep learning network.

5. A method for setting up a smart home scene, characterized in that, include: Based on a given scene name, a target scene mode corresponding to the scene name is determined from a plurality of preset scene modes, and the target device status information of each device included in the target scene mode is extracted; the plurality of preset scene modes are generated by the smart home scene generation method as described in any one of claims 1 to 4. Control the device status of each device in the current home environment to be consistent with the status information of the target device.

6. A smart home scene generation device, characterized in that, include: The processing module is used to form a comprehensive state vector describing the overall state of each device based on the device state information of each device in the home environment at the first moment; the processing module is also used to encode the device state information of each device in the home environment at the first moment to obtain the state value of each device in multiple states; write the state values ​​of each device in multiple states into a preset data model to generate a device state matrix, where each row or column of the device state matrix represents a device state vector of a device; and concatenate the device state vectors in the device state matrix end to end to obtain the comprehensive state vector; The scene classification module is used to classify the comprehensive state vector using a pre-trained scene classifier to obtain the scene type of the home environment at the first moment. The scene generation module is used to associate and store the device status information of each device in the home environment at the first moment with the scene name of the scene type to which the home environment belongs at the first moment, so as to generate a scene mode corresponding to the scene type. The scene mode is used to reproduce the home environment at the first moment.

7. A smart home scene setting device, characterized in that, include: An extraction module is used to determine a target scene mode corresponding to a given scene name from a set of preset scene modes, and to extract the target device status information of each device included in the target scene mode; the set of preset scene modes are generated by the smart home scene generation method as described in any one of claims 1 to 4. The control module is used to ensure that the device status of each device in the current home environment is consistent with the status information of the target device.

8. An electronic device, characterized in that, include: At least one processor; as well as, A memory communicatively connected to the at least one processor; wherein, The memory stores instructions that can be executed by the at least one processor, which, when executed by the at least one processor, enables the at least one processor to perform the smart home scene generation method as described in any one of claims 1 to 4, or to perform the smart home scene setting method as described in claim 5.

9. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by the processor, it implements the smart home scene generation method according to any one of claims 1 to 4, or the smart home scene setting method according to claim 5.

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