KNX-based household equipment action execution method and device

Abstract

The invention discloses a KNX-based household equipment action execution method and device. The method comprises the following steps: acquiring household equipment control information through an intelligent terminal; analyzing and processing the home equipment control information to obtain target home equipment instruction information; and performing transmission processing on the target home equipment instruction information to obtain target action execution instruction information.

Description

Technical Field

[0001] The present invention relates to the field of control technology, and in particular to a method and device for executing actions of household appliances based on KNX. Background Art

[0002] The home automation system is based on the residential foundation. By integrating wiring, communication, security, automatic control and multimedia technology, it integrates the equipment related to family life to form an efficient home facility and daily affairs management platform. Such a system aims to improve the safety, convenience, comfort and aesthetics of the residence, while promoting environmental protection and energy saving. With the advancement of network technology, a variety of intelligent gateway devices have appeared on the market, which can control smart home devices, monitor indoor air quality and realize remote operation. However, the existing intelligent gateway usually needs to be set up using traditional coding configuration tools when it is first installed in order to realize the control of home appliances. This process is quite cumbersome for users. Therefore, a method and device for executing home appliance actions based on KNX are provided to realize intelligent home appliance control and improve the user experience. Summary of the invention

[0003] The technical problem to be solved by the present invention is to provide a method and device for executing home appliance actions based on KNX, which is conducive to realizing intelligent home appliance control and improving user experience.

[0004] In order to solve the above technical problems, a first aspect of an embodiment of the present invention discloses a method for executing an action of a household device, the method comprising: Obtain home device control information through smart terminals; Analyzing and processing the home appliance control information to obtain target home appliance instruction information; The target home appliance device command information is transmitted and processed to obtain target action execution command information.

[0005] A second aspect of an embodiment of the present invention discloses a device for executing an action of a household appliance, the device comprising: An acquisition module is used to acquire home device control information through a smart terminal; A first processing module, configured to analyze and process the home appliance control information to obtain target home appliance instruction information; The second processing module is used to transmit and process the target home appliance instruction information to obtain target action execution instruction information.

[0006] The third aspect of the present invention discloses another device for executing actions of household appliances, the device comprising: A memory storing executable program code; a processor coupled to the memory; The processor calls the executable program code stored in the memory to execute part or all of the steps in the home appliance action execution method disclosed in the first aspect of the embodiment of the present invention.

[0007] The fourth aspect of the present invention discloses a computer-readable storage medium, which stores computer instructions. When the computer instructions are called, they are used to execute part or all of the steps in the home appliance action execution method disclosed in the first aspect of an embodiment of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0008] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0009] Figure 1 is a schematic diagram of a scenario of a household device action execution system provided by an embodiment of the present invention; Figure 2 It is a flowchart of a method for executing a household device action disclosed in an embodiment of the present invention; Figure 3 It is a structural schematic diagram of a household appliance action execution device disclosed in an embodiment of the present invention; Figure 4 It is a structural schematic diagram of another household appliance action execution device disclosed in an embodiment of the present invention; Figure 5 It is a structural schematic diagram of a vectorization model disclosed in an embodiment of the present invention. DETAILED DESCRIPTION

[0010] In order to enable those skilled in the art to better understand the scheme of the present invention, the technical scheme in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0011] The terms "first", "second", etc. in the specification and claims of the present invention and the above-mentioned drawings are used to distinguish different objects, rather than to describe a specific order. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions. For example, a process, method, device, product or equipment that includes a series of steps or units is not limited to the listed steps or units, but may optionally include steps or units that are not listed, or may optionally include other steps or units that are inherent to these processes, methods, products or equipment.

[0012] Reference to "embodiments" herein means that a particular feature, structure, or characteristic described in conjunction with the embodiments may be included in at least one embodiment of the present invention. The appearance of the phrase in various places in the specification does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment that is mutually exclusive with other embodiments. It is explicitly and implicitly understood by those skilled in the art that the embodiments described herein may be combined with other embodiments.

[0013] In this application, the word "exemplary" is used to mean "used as an example, illustration, or description." Any embodiment described in this application as "exemplary" is not necessarily to be construed as being preferred or advantageous over other embodiments. The following description is given to enable any technician in the field to implement and use the present application. In the following description, details are listed for the purpose of explanation. It should be understood that a person of ordinary skill in the art can recognize that the present application can be implemented without using these specific details. In other instances, well-known structures and processes will not be elaborated in detail to avoid obscuring the description of the present application with unnecessary details. Therefore, the present application is not intended to be limited to the embodiments shown, but is consistent with the widest scope consistent with the principles and features disclosed in the present application.

[0014] It should be noted that since the method of the embodiment of the present application is executed in a computer device, the processing objects of each computer device exist in the form of data or information. For example, time is actually time information. It can be understood that if size, quantity, position, etc. are mentioned in subsequent embodiments, they are all corresponding data for processing by the computer device. The details will not be repeated here.

[0015] It should be noted that the artificial intelligence related technologies that may be involved in this application are briefly described. Artificial Intelligence (AI) is the theory, method, technology and application system that uses digital computers or machines controlled by digital computers to simulate, extend and expand human intelligence, perceive the environment, acquire knowledge and use knowledge to obtain the best results. In other words, artificial intelligence is a comprehensive technology in computer science that attempts to understand the essence of intelligence and produce a new intelligent machine that can respond in a similar way to human intelligence. Artificial intelligence is to study the design principles and implementation methods of various intelligent machines so that machines have the functions of perception, reasoning and decision-making.

[0016] Artificial intelligence technology is a comprehensive discipline that covers a wide range of fields, including both hardware-level and software-level technologies. The basic technologies of artificial intelligence generally include sensors, dedicated artificial intelligence chips, cloud computing, distributed storage, big data processing technology, operation / interaction systems, mechatronics and other technologies. Artificial intelligence software technology mainly includes computer vision technology, speech processing technology, natural language processing technology, and machine learning / deep learning.

[0017] Computer Vision (CV) is a science that studies how to make machines "see". To put it more specifically, it refers to machine vision such as using cameras and computers to replace human eyes to identify and measure targets, and further perform graphic processing so that computer processing becomes an image that is more suitable for human eye observation or transmission to instrument detection. As a scientific discipline, computer vision studies related theories and technologies, and attempts to establish an artificial intelligence system that can obtain information from images or multi-dimensional data. Computer vision technology usually includes image processing, image recognition, image semantic understanding, image retrieval, OCR, video processing, video semantic understanding, video content / behavior recognition, three-dimensional object reconstruction, 3D technology, virtual reality, augmented reality, simultaneous positioning and map construction, and other technologies, as well as common biometric recognition technologies such as face recognition and fingerprint recognition.

[0018] Unimodal information is data of only one type, such as text, image, audio, video, electromagnetic signal, etc. Multimodal information is data that includes at least two types of unimodal information. Furthermore, multimodal information is suitable for complex tasks that require the integration of multiple information sources, such as sentiment analysis, robot interaction, autonomous driving, etc. By integrating information from multiple modalities, higher performance and accuracy can usually be achieved on the task.

[0019] A large model refers to an artificial neural network model with a very large number of parameters. In the field of artificial intelligence, a large model generally refers to a model with hundreds of millions to trillions of parameters. Models usually need to be trained on large-scale data sets and require a large amount of computing resources to be optimized and adjusted. Large models are often used to solve complex tasks such as natural language processing, computer vision, and speech recognition. Generative AI is an AI that can create new content and ideas, including conversations, stories, images, videos, and music. In an embodiment of the present application, the large model may be ChatGPT, BERT, XLNet, Zhipu model, Claude, Moonshot AI model, ChatGLM model, Qianyi Tongwen model, MiniMax model, Spark model, Llama model, 360GPT model, Qwen model, Baichuan model, Skylark model, vivoLM model, and Wenxin Yiyan scale language models, which are not limited in the embodiments of the present application.

[0020] The embodiments of the present application provide a method, apparatus, computer device, and computer-readable storage medium for executing actions of a home appliance, which are described in detail below.

[0021] See also Figure 1 , Figure 1 Schematic diagram of a scenario of a household appliance action execution system provided in an embodiment of the present application. The household appliance action execution system may include a computer device 100, in which a household appliance action execution device is integrated, such as Figure 1 Computer equipment in.

[0022] In the embodiment of the present application, the computer device 100 is mainly used to obtain home appliance control information through a smart terminal; Analyze and process the home appliance control information to obtain the target home appliance command information; The target home appliance instruction information is transmitted and processed to obtain the target action execution instruction information.

[0023] It can realize intelligent control of home appliances and improve user experience.

[0024] In the embodiment of the present application, the computer device 100 may be an independent server, or a server network or server cluster composed of servers. For example, the computer device 100 described in the embodiment of the present application includes but is not limited to a computer, a network host, a single network server, a plurality of network server sets or a cloud server composed of a plurality of servers. The cloud server is composed of a large number of computers or network servers based on cloud computing.

[0025] It is understandable that the computer device 100 used in the embodiments of the present application may be a device including both receiving and transmitting hardware, that is, a device having receiving and transmitting hardware capable of performing two-way communication on a two-way communication link. Such a device may include: a cellular or other communication device having a single-line display or a multi-line display or a cellular or other communication device without a multi-line display. The specific computer device 100 may be a desktop terminal or a mobile terminal, and the computer device 100 may also be one of a mobile phone, a tablet computer, a laptop computer, etc.

[0026] Those skilled in the art will understand that Figure 1 The application environment shown in the figure is only one application scenario of the present application solution and does not constitute a limitation on the application scenario of the present application solution. Other application environments may also include Figure 1 More or less computer equipment as shown in Figure 1 Only one computer device is shown. It can be understood that the home device action execution system can also include one or more other services, which are not specifically limited here.

[0027] In addition, if Figure 1 As shown, the home appliance action execution system may further include a memory 200 for storing data, such as image data, location information, and the like.

[0028] It should be noted that Figure 1 The scenario diagram of the home appliance action execution system shown is merely an example. The home appliance action execution system and scenario described in the embodiment of the present application are intended to more clearly illustrate the technical solution of the embodiment of the present application, and do not constitute a limitation on the technical solution provided in the embodiment of the present application. A person of ordinary skill in the art can appreciate that with the evolution of the home appliance action execution system and the emergence of new business scenarios, the technical solution provided in the embodiment of the present application is equally applicable to similar technical problems.

[0029] The present invention discloses a method and device for executing actions of household appliances based on KNX, which are conducive to realizing intelligent control of household appliances and improving user experience. The following are detailed descriptions.

[0030] Embodiment 1 See also Figure 2 , Figure 2 1 is a flow chart of a method for executing a household device action disclosed in an embodiment of the present invention. Figure 2 The described household device action execution method is applied to a management system, such as a local server or a cloud server for management, and the embodiments of the present invention do not limit this. Figure 2 As shown, the home device action execution method may include the following operations: 101. Obtain home device control information through smart terminals.

[0031] 102. Analyze and process the home appliance control information to obtain target home appliance instruction information.

[0032] 103. Transmit and process the target home appliance device command information to obtain target action execution command information.

[0033] It should be noted that after obtaining the above-mentioned target action execution instruction information, the target home device instruction information can be sent to the smart home device through the KNX bus according to the device ID (such as device name, device number, etc.) corresponding to the target home device instruction information, so that the smart home device can execute the user's action instructions, such as turning on the TV, turning down the volume, etc., which is not limited in the embodiment of the present invention.

[0034] It should be noted that the above-mentioned KNX bus connects and groups various scattered devices through a bus and assigns different functions; the system uses serial data communication for control, monitoring and status reporting. KNX is a distributed bus system based on event control, and messages are sent to the bus only when an event occurs on the bus and when information needs to be transmitted. Furthermore, the local area network of the present application is a communication network with KNX communication control function that includes device resources such as smart home, KNX bus and communication controller, which is not limited in the embodiments of the present invention.

[0035] It should be noted that the above-mentioned smart terminal represents a mobile smart device, such as a smart phone, a PAD, etc., which is not limited in the embodiment of the present invention.

[0036] It should be noted that the above-mentioned smart home devices can be smart home devices of the television type, air conditioner type, speaker type, smart assistant type, such as smart TV, Tmall Genie, etc., and the embodiments of the present invention are not limited thereto.

[0037] It can be seen that implementing the home appliance action execution method described in the embodiment of the present invention is conducive to realizing intelligent home appliance control and improving user experience.

[0038] In an optional embodiment, the above-mentioned analyzing and processing the home appliance control information to obtain the target home appliance instruction information includes: Identify the type of home device control information to obtain the target information type; The home appliance control information is analyzed and processed based on the target information type to obtain the target home appliance instruction information.

[0039] It should be noted that the above-mentioned type identification of the home appliance control information is to identify whether the information obtained from the smart terminal is voice information or text input information, so as to obtain the target information type, which is not limited in the embodiment of the present invention.

[0040] It can be seen that implementing the home appliance action execution method described in the embodiment of the present invention is conducive to realizing intelligent home appliance control and improving user experience.

[0041] In another optional embodiment, the home appliance control information is analyzed and processed based on the target information type to obtain the target home appliance instruction information, including: Determine whether the target information type is a voice type, and obtain a type determination result; When the type determination result is yes, the home appliance control information is converted to obtain standby control information; When the type determination result is no, determining the home appliance control information as standby control information; The backup control information is vectorized using a vectorization model to obtain target home appliance instruction information.

[0042] It should be noted that the above conversion process of the home appliance control information is to convert the voice signal into text information, so as to facilitate the subsequent vectorization model to perform vectorization conversion of the signal, which is not limited in the embodiment of the present invention.

[0043] It can be seen that implementing the home appliance action execution method described in the embodiment of the present invention is conducive to realizing intelligent home appliance control and improving user experience.

[0044] In yet another optional embodiment, the home appliance control information is converted to obtain standby control information, including: Extracting features of the home appliance control information to obtain first control feature information; Analyzing and processing the first characteristic control information to obtain second control characteristic information; Performing information analysis and processing on the second control characteristic information to obtain third control characteristic information; The third control characteristic information is decoded to obtain standby control information.

[0045] It should be noted that the above-mentioned analysis and processing of the first feature control information can be processed through an artificial intelligence model to convert the feature matrix into a feature vector. Furthermore, the above-mentioned artificial intelligence model can be a bidirectional LSTM network model or a BERT model, which is not limited in the embodiment of the present invention.

[0046] It should be noted that the above-mentioned information analysis and processing of the second control feature information is a key part of using the attention mechanism to understand the content to obtain the third control feature information, and the embodiment of the present invention does not limit this.

[0047] It should be noted that the decoding process of the third control feature information may be performed using a Transformer decoder, which is not limited in the embodiment of the present invention.

[0048] It can be seen that implementing the home appliance action execution method described in the embodiment of the present invention is conducive to realizing intelligent home appliance control and improving user experience.

[0049] In yet another optional embodiment, feature extraction is performed on the home appliance control information to obtain first control feature information, including: Preprocessing the home appliance control information to obtain first processing feature information; Performing Fourier transform processing on the first processed feature information to obtain second processed feature information; Performing filtering processing on the second processing characteristic information to obtain third processing characteristic information; Performing logarithmic operation on the third processing characteristic information to obtain fourth processing characteristic information; Performing inverse change processing on the fourth processed characteristic information to obtain fifth processed characteristic information; Feature extraction is performed on the first processing feature information and the fifth processing feature information to obtain first control feature information.

[0050] It should be noted that the above-mentioned feature extraction of the home appliance control information to obtain the first control feature information is to extract the acoustic features of the home appliance control information and output a feature matrix (first control feature information), which is not limited in the embodiment of the present invention.

[0051] It should be noted that the above-mentioned preprocessing of the home appliance control information is to perform pre-emphasis, framing and windowing processing on the home appliance control information in sequence, so as to obtain static acoustic feature information (first processed feature information), which is not limited in the embodiment of the present invention.

[0052] It should be noted that the above-mentioned Fourier transform processing of the first processing feature information is to convert the first processing feature information in the time domain into a signal spectrum (second processing feature information), and then filter it through a Mel filter group (the number is between 20-25) to smooth the spectrum and eliminate the effect of harmonics, highlighting the resonance peak of the original speech. Further, the filtered third processing feature information is logarithmically processed, that is, the envelope and details are respectively processed, the envelope represents the timbre, and the details are packaged with pitch. Obviously, speech recognition is to recognize the timbre. Further, the fourth processing feature information is inversely Fourier transformed using discrete cosine transform to obtain dynamic acoustic feature information (fifth processing feature information). Finally, the static first processing feature information and the dynamic fifth processing feature information are differentially processed to combine the dynamic and static features to effectively improve the recognition performance of the sound features, thereby obtaining deeply extracted speech feature information (first control feature information). The embodiment of the present invention is not limited.

[0053] It can be seen that implementing the home appliance action execution method described in the embodiment of the present invention is conducive to realizing intelligent home appliance control and improving user experience.

[0054] In an optional embodiment, the vectorized model includes a first feature analysis module, a second feature analysis module, a sampling module, a first fusion module, a second fusion module, a third fusion module, and a large model module; wherein, The input end of the first feature analysis module and the input end of the second feature analysis module are configured to receive input information of the vectorized model; the output end of the first feature analysis model is connected to the input end of the sampling module; the output end of the sampling module is respectively connected to the input end of the multi-head attention module and the input end of the second fusion module; the output end of the second feature analysis module is connected to the input end of the multi-head attention module; the output end of the multi-head attention module is respectively connected to the input end of the first fusion module and the input end of the second fusion module; the output end of the first fusion module is connected to the input end of the third fusion module; the output end of the third fusion module is connected to the large model module; the output end of the large model module is configured to output the output information of the vectorized model.

[0055] It should be noted that the above-mentioned first feature analysis module can be a convolutional neural network, which is not limited in the embodiment of the present invention. Furthermore, the convolution kernel size of the above-mentioned convolutional neural network can be 3×3, 3×1, and the number of channels can be 68, 128, 256, which is not limited in the embodiment of the present invention. Furthermore, the above-mentioned first feature analysis module extracts features from the input information of the input vectorization model from the spatial dimension, which is not limited in the embodiment of the present invention.

[0056] It should be noted that the second feature analysis module may be a long short-term memory network, which is not limited in the embodiment of the present invention. Further, the second feature analysis module extracts features from the input information of the input vectorization model from the time dimension, which is not limited in the embodiment of the present invention.

[0057] It should be noted that the above-mentioned sampling module is constructed based on the downsampling operation, which is not limited in the embodiment of the present invention. Furthermore, the above-mentioned sampling module performs a sampling operation on the feature information of the spatial dimension extracted by the first feature analysis module, so that the feature information of the spatial dimension is consistent in size with the feature information of the time dimension extracted by the second feature analysis module, so as to facilitate the subsequent fusion operation, which is not limited in the embodiment of the present invention.

[0058] It should be noted that the above multi-head attention module is constructed based on the multi-head attention mechanism, which is not limited in the embodiment of the present invention. Furthermore, the output information of the above sampling module can be input into the K head (Key (K)) and V head (Value (V)) in the multi-head attention module, and the output information of the second feature analysis module can be input into the Q head Query (Q) of the multi-head attention module, which is not limited in the embodiment of the present invention.

[0059] It should be noted that the above-mentioned first fusion module and the second fusion module are constructed based on element-by-element multiplication, which is not limited in the embodiment of the present invention. Furthermore, the above-mentioned third fusion module is constructed based on element-by-element addition, which is not limited in the embodiment of the present invention. Furthermore, through the data fusion of the above-mentioned three fusion modules, the feature information of the spatial dimension and the time dimension can be deeply analyzed and extracted, so that the subsequent large model module can perform more accurate vector conversion, which is not limited in the embodiment of the present invention.

[0060] It should be noted that the above-mentioned large model module is a fine-tuned large model (such as ChatGPT, Kimi, etc.), which is not limited in the embodiment of the present invention. Further, the output information form of the above-mentioned large model module is a vector, which is not limited in the embodiment of the present invention.

[0061] Furthermore, each module in the vectorized model in the present application may be obtained by training based on a cross entropy loss function, which is not limited in the embodiments of the present invention.

[0062] It can be seen that implementing the home appliance action execution method described in the embodiment of the present invention is conducive to realizing intelligent home appliance control and improving user experience.

[0063] In another optional embodiment, the target home appliance instruction information is transmitted and processed to obtain the target action execution instruction information, including: The target home appliance instruction information is transmitted from the smart terminal to the local area network gateway to obtain the first action execution instruction information; The first action execution instruction information is parsed and processed to obtain target action execution instruction information.

[0064] It should be noted that the above-mentioned LAN gateway is a signal transfer device for controlling a smart home, which may be a KNX smart gateway, and the embodiment of the present invention does not limit this.

[0065] It should be noted that the above-mentioned parsing and processing of the first action execution instruction information can be to send the first action execution instruction information from the LAN gateway to the local server corresponding to the home LAN, and then perform signal analysis on the local server (for example, by calling the eval function for analysis), and then send the parsed data from the local server back to the LAN gateway to obtain the target action execution instruction information, which is not limited to the embodiments of the present invention.

[0066] It can be seen that implementing the home appliance action execution method described in the embodiment of the present invention is conducive to realizing intelligent home appliance control and improving user experience.

[0067] Embodiment 2 See also Figure 3 , Figure 3 Schematic diagram of a structure of a household appliance action execution device disclosed in an embodiment of the present invention. Figure 3 The described device can be applied to a management system, such as a local server or a cloud server for management, etc., which is not limited in the embodiments of the present invention. Figure 3 As shown, the device may include: An acquisition module 201 is used to acquire home appliance control information through a smart terminal; The first processing module 202 is used to analyze and process the home appliance control information to obtain target home appliance command information; The second processing module 203 is used to transmit and process the target home appliance instruction information to obtain target action execution instruction information.

[0068] It can be seen that the implementation Figure 3 The described home appliance action execution device is conducive to realizing intelligent home appliance control and improving user experience.

[0069] In another optional embodiment, Figure 3 As shown, the first processing module 202 analyzes and processes the home appliance control information to obtain target home appliance instruction information, including: Identify the type of home device control information to obtain the target information type; The home appliance control information is analyzed and processed based on the target information type to obtain the target home appliance instruction information.

[0070] It can be seen that the implementation Figure 3 The described home appliance action execution device is conducive to realizing intelligent home appliance control and improving user experience.

[0071] In yet another optional embodiment, Figure 3 As shown, the first processing module 202 analyzes and processes the home appliance control information based on the target information type to obtain the target home appliance instruction information, including: Determine whether the target information type is a voice type, and obtain a type determination result; When the type determination result is yes, the home appliance control information is converted to obtain standby control information; When the type determination result is no, determining the home appliance control information as standby control information; The backup control information is vectorized using a vectorization model to obtain target home appliance instruction information.

[0072] It can be seen that the implementation Figure 3 The described home appliance action execution device is conducive to realizing intelligent home appliance control and improving user experience.

[0073] In yet another optional embodiment, Figure 3 As shown, the first processing module 202 converts the home appliance control information to obtain standby control information, including: Extracting features of the home appliance control information to obtain first control feature information; Analyzing and processing the first characteristic control information to obtain second control characteristic information; Performing information analysis and processing on the second control characteristic information to obtain third control characteristic information; The third control characteristic information is decoded to obtain standby control information.

[0074] It can be seen that the implementation Figure 3 The described home appliance action execution device is conducive to realizing intelligent home appliance control and improving user experience.

[0075] In yet another optional embodiment, Figure 3 As shown, the first processing module 202 extracts features from the home appliance control information to obtain first control feature information, including: Preprocessing the home appliance control information to obtain first processing feature information; Performing Fourier transform processing on the first processed feature information to obtain second processed feature information; Performing filtering processing on the second processing characteristic information to obtain third processing characteristic information; Performing logarithmic operation on the third processing characteristic information to obtain fourth processing characteristic information; Performing inverse change processing on the fourth processed characteristic information to obtain fifth processed characteristic information; Feature extraction is performed on the first processing feature information and the fifth processing feature information to obtain first control feature information.

[0076] It can be seen that the implementation Figure 3 The described home appliance action execution device is conducive to realizing intelligent home appliance control and improving user experience.

[0077] In yet another optional embodiment, Figure 3 As shown, the vectorized model includes a first feature analysis module, a second feature analysis module, a sampling module, a first fusion module, a second fusion module, a third fusion module, and a large model module; wherein, The input end of the first feature analysis module and the input end of the second feature analysis module are configured to receive input information of the vectorized model; the output end of the first feature analysis model is connected to the input end of the sampling module; the output end of the sampling module is respectively connected to the input end of the multi-head attention module and the input end of the second fusion module; the output end of the second feature analysis module is connected to the input end of the multi-head attention module; the output end of the multi-head attention module is respectively connected to the input end of the first fusion module and the input end of the second fusion module; the output end of the first fusion module is connected to the input end of the third fusion module; the output end of the third fusion module is connected to the large model module; the output end of the large model module is configured to output the output information of the vectorized model.

[0078] It can be seen that the implementation Figure 3 The described home appliance action execution device is conducive to realizing intelligent home appliance control and improving user experience.

[0079] In yet another optional embodiment, Figure 3 As shown, the second processing module 203 transmits and processes the target home appliance instruction information to obtain the target action execution instruction information, including: The target home appliance instruction information is transmitted from the smart terminal to the local area network gateway to obtain the first action execution instruction information; The first action execution instruction information is parsed and processed to obtain target action execution instruction information.

[0080] It can be seen that the implementation Figure 3 The described home appliance action execution device is conducive to realizing intelligent home appliance control and improving user experience.

[0081] Embodiment 3 See also Figure 4 , Figure 4 is a schematic diagram of the structure of another household appliance action execution device disclosed in an embodiment of the present invention. Figure 4 The described device can be applied to a management system, such as a local server or a cloud server for management, etc., which is not limited in the embodiments of the present invention. Figure 4 As shown, the device may include: A memory 301 storing executable program codes; a processor 302 coupled to the memory 301; The processor 302 calls the executable program code stored in the memory 301 to execute the steps in the household device action execution method described in the first embodiment.

[0082] Embodiment 4 An embodiment of the present invention discloses a computer-readable storage medium storing a computer program for electronic data exchange, wherein the computer program enables a computer to execute the steps of the household appliance action execution method described in the first embodiment.

[0083] Embodiment 5 An embodiment of the present invention discloses a computer program product, which includes a non-transitory computer-readable storage medium storing a computer program, and the computer program is operable to enable a computer to execute the steps in the household appliance action execution method described in the first embodiment.

[0084] The device embodiments described above are only illustrative, wherein the modules described as separate components may or may not be physically separated, and the components displayed as modules may or may not be physical modules, i.e., they may be located in one place, or they may be distributed on multiple network modules. Some or all of the modules may be selected according to actual needs to achieve the purpose of the scheme of this embodiment. Those of ordinary skill in the art may understand and implement it without creative work.

[0085] Through the specific description of the above embodiments, those skilled in the art can clearly understand that each implementation method can be implemented by means of software plus a necessary general hardware platform, and of course, can also be implemented by hardware. Based on such an understanding, the above technical solution can be essentially or the part that contributes to the prior art can be embodied in the form of a software product, and the computer software product can be stored in a computer-readable storage medium, and the storage medium includes a read-only memory (ROM), a random access memory (RAM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), a one-time programmable read-only memory (OTPROM), an electronically erasable rewritable read-only memory (EEPROM), a compact disc (CD-ROM) or other optical disc storage, magnetic disk storage, magnetic tape storage, or any other computer-readable medium that can be used to carry or store data.

[0086] Finally, it should be noted that the KNX-based household appliance action execution method and device disclosed in the embodiment of the present invention only discloses the preferred embodiments of the present invention, which are only used to illustrate the technical solution of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, it should be understood by those skilled in the art that the technical solutions described in the aforementioned embodiments can still be modified, or some of the technical features therein can be replaced by equivalents. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for executing a household device action, characterized in that: The method comprises: Obtain home device control information through smart terminals; Analyzing and processing the home appliance control information to obtain target home appliance instruction information; The target home appliance device command information is transmitted and processed to obtain target action execution command information.

2. The method for executing a household device action according to claim 1, characterized in that: The analyzing and processing the home appliance control information to obtain target home appliance instruction information includes: Performing type identification on the home appliance control information to obtain a target information type; The home appliance control information is analyzed and processed based on the target information type to obtain target home appliance instruction information.

3. The method for executing a household device action according to claim 2, characterized in that: The analyzing and processing the home appliance control information based on the target information type to obtain the target home appliance instruction information includes: Determine whether the target information type is a voice type, and obtain a type determination result; When the type determination result is yes, converting the home appliance control information to obtain standby control information; When the type determination result is no, determining the home appliance control information as the standby control information; The standby control information is vectorized using a vectorization model to obtain target home appliance instruction information.

4. The method for executing a household device action according to claim 3, characterized in that: The converting and processing the home appliance control information to obtain the standby control information includes: Extracting features from the home appliance control information to obtain first control feature information; Analyzing and processing the first characteristic control information to obtain second control characteristic information; Performing information analysis on the second control characteristic information to obtain third control characteristic information; The third control characteristic information is decoded to obtain standby control information.

5. The method for executing a household device action according to claim 4, characterized in that: The extracting features of the home appliance control information to obtain first control feature information includes: Preprocessing the home appliance control information to obtain first processing feature information; Performing Fourier transform processing on the first processing feature information to obtain second processing feature information; performing filtering processing on the second processing characteristic information to obtain third processing characteristic information; Performing logarithmic operation on the third processing characteristic information to obtain fourth processing characteristic information; Performing inverse change processing on the fourth processing characteristic information to obtain fifth processing characteristic information; Feature extraction is performed on the first processing feature information and the fifth processing feature information to obtain first control feature information.

6. The method for executing actions of household appliances according to claim 3, characterized in that: The vectorized model includes a first feature analysis module, a second feature analysis module, a sampling module, a first fusion module, a second fusion module, a third fusion module, and a large model module; wherein, The input end of the first feature analysis module and the input end of the second feature analysis module are configured to receive input information of the vectorized model; the output end of the first feature analysis model is connected to the input end of the sampling module; the output end of the sampling module is respectively connected to the input end of the multi-head attention module and the input end of the second fusion module; the output end of the second feature analysis module is connected to the input end of the multi-head attention module; the output end of the multi-head attention module is respectively connected to the input end of the first fusion module and the input end of the second fusion module; the output end of the first fusion module is connected to the input end of the third fusion module; the output end of the second fusion module is connected to the input end of the third fusion module; the output end of the third fusion module is connected to the large model module; the output end of the large model module is configured to output the output information of the vectorized model.

7. The method for executing actions of household appliances according to claim 1, characterized in that: The transmitting and processing the target home appliance instruction information to obtain the target action execution instruction information includes: The target home device instruction information is transmitted from the smart terminal to the local area network gateway to obtain the first action execution instruction information; The first action execution instruction information is parsed and processed to obtain target action execution instruction information.

8. A household appliance action execution device, characterized in that: The device comprises: An acquisition module is used to acquire home device control information through a smart terminal; A first processing module, configured to analyze and process the home appliance control information to obtain target home appliance instruction information; The second processing module is used to transmit and process the target home appliance instruction information to obtain target action execution instruction information.

9. A household appliance action execution device, characterized in that: The device comprises: A memory storing executable program code; a processor coupled to the memory; The processor calls the executable program code stored in the memory to execute the home device action execution method as described in any one of claims 1-7.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer instructions, and when the computer instructions are called, they are used to execute the household device action execution method as described in any one of claims 1-7.

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