313 results about "Home control" patented technology

The invention relates to the technical field of intelligent control, in particular to a multi-mode-based intelligent home control method and system, and the method comprises the following steps: S1, synchronously collecting a voice stream and an action video stream of a user, and respectively extracting a semantic anchor point of a voice instruction and a spatial anchor point of an action track; s2, constructing a virtual time axis, mapping the semantic anchor points and the spatial anchor points to a unified space-time coordinate system, and performing anchor point deviation correction on a mapping result based on an environment semantic field to generate an alignment instruction set; and S3, performing intention fusion on the alignment instruction set according to the spatial topological relation of the equipment, generating an equipment control instruction, and triggering execution. According to the method, the fault tolerance and execution rationality of fuzzy instructions or wrong finger behaviors are improved, the method is particularly suitable for family scenes with dense multiple devices and frequent environment dynamic changes, and the practicability and the intelligent level of the system are remarkably improved.

The invention belongs to the technical field of intelligent home control, and particularly relates to an electric blanket intelligent temperature control system, which analyzes the matching relationship between the heating power and the temperature response in the historical operation cycle of a plurality of independent subareas of an electric blanket, fuses the thermal coupling effect between the subareas, and independently quantifies the dynamic thermal inertia parameter of each subarea. Based on the independent temperature change trend and the real-time temperature deviation of each partition, a control state including a steady state, a transient state or a strong compensation state is decided and maintained for each partition, the dynamic thermal inertia parameter, the real-time temperature deviation and the current control state are integrated, differentiated feed-forward compensation amounts are generated and superposed to the output of a PID controller of the corresponding partition, and the control state of the corresponding partition is calculated. A final power control instruction is generated after amplitude limiting and smooth filtering, temperature response lag caused by thermal inertia is actively predicted and counteracted, precise control is achieved in combination with partition state recognition, and the system response speed and control stability are remarkably improved.

The invention provides a smart home control method, a readable storage medium and a smart home system. The method comprises the steps that character characteristics of target home equipment are set at least according to equipment functions of the target home equipment, the character characteristics comprise character dimensions, and the character dimensions comprise at least one of gentle, cold, humor and fine; according to at least part of the voice data, the behavior data and the indoor environment data of the user, the emotional state of the user is determined, and the behavior data comprises use condition data of the user for the multiple intelligent devices; analyzing the emotional state and the character traits by using a large model, and at least determining an interaction strategy of the target home device, the interaction strategy comprising a response style and response content; and under the condition that the emotional interaction function of the target home equipment is started, at least controlling the target home equipment to interact with the user according to the interaction strategy. According to the invention, the personification and intelligence level of the smart home equipment is improved.

The invention discloses a smart home control method, device and equipment and a medium, which can be applied to the fields of finance and medical treatment. According to the method, a heterogeneous sensor array is applied, multi-modal data are collected from an environment side and a user side, and the diversity of the environment and the actual requirements of users are captured. And based on the joint probability model, performing space-time fusion processing on the acquired multi-modal data to generate a joint state vector of the environment and the user, so that the system can understand the specific scene and situation of the user in real time. In combination with a user scene instruction and a joint state vector, an equipment control instruction set with priority is generated through a reinforcement learning decision engine, so that more intelligent operation logic is realized. According to the method, the use experience of the user in the smart home control process is remarkably improved, so that the smart home system can meet the personalized requirements of the user with higher flexibility and intelligence level, and humanized smart home management is realized.

Methods and systems for automatically mitigating risks of water damage to a property caused by insurance-related events are provided. A smart home controller and / or insurance provider remote processor may analyze data received from a smart device disposed on, within, or proximate to a property associated with monitoring water flow, as well as data received from an insurance provider. If it is determined that an actual or potential risk of water leakage exists, the smart home controller or remote processor may automatically issue commands to the smart device to take actions that mitigate the risk of water damage. The smart home controller may transmit information about the actual or potential water leakage risks and any mitigative actions to an insurance provider. The insurance provider remote processor may interpret the transmitted data and perform insurance activities, such as providing a discount or premium, and / or adjusting an insurance policy associated with the property.

The invention provides a smart home control method, system and device and electronic equipment. The method comprises the steps that historical interaction data of a user and the smart home are acquired, the historical interaction data comprise user behavior data, use scene perception data, voice interaction data and control operation data of the user for each smart home, and the use scene perception data represent environment data in different scenes; based on the historical interaction data, at least an interaction recognition model is adopted to construct a knowledge graph of the user for each smart home, and the interaction recognition model is a learning model used for analyzing the historical interaction data and learning use habits of the user; and obtaining real-time voice data of the user, determining a control instruction corresponding to the real-time voice data according to the real-time voice data and the knowledge graph, and controlling the corresponding smart home to work according to the control instruction. The problems that an existing smart home interaction scheme is low in interaction efficiency and difficult to meet user requirements are solved.

The invention relates to the technical field of smart home, and discloses a smart home control method and device for realizing user interaction based on a smart mirror, and the method comprises the steps: confirming a plurality of user registration instructions, obtaining a registration voice stream based on the user registration instructions, carrying out the feature extraction of the registration voice stream, obtaining reference voiceprint features, summarizing the reference voiceprint features, and carrying out the collection of the reference voiceprint features. The method comprises the following steps: obtaining a plurality of reference voiceprint features, confirming to receive a pre-confirmed voice wake-up word, carrying out voice collection based on the voice wake-up word to obtain a user voice stream, obtaining a voiceprint label by using the user voice stream and the plurality of reference voiceprint features, carrying out voice analysis on the user voice stream to obtain a control instruction, and sending the control instruction to a server. And performing permission verification by using the voiceprint tag and the control instruction to obtain a permission verification result, and obtaining response feedback based on the permission verification result. According to the invention, the problem that the personalized intelligent service cannot be provided because the current user identity cannot be distinguished and the control authority cannot be verified according to the user identity in the smart home interaction scene can be solved.

The invention relates to the technical field of smart home, in particular to a smart home control method and system based on multi-modal fusion and deep learning. The method comprises the steps of triggering a data acquisition instruction, and performing human body data acquisition; acquiring human body data, and generating a global human body motion track sequence according to the preprocessed human body data; predicting a human body track target point and a space region; acquiring a human body track target point and spatial region data, and predicting a target electric appliance and a use probability thereof; the use probability of the target electric appliances is obtained, the target electric appliances are sorted, and electric appliance starting and standby operation is carried out; the system comprises a data acquisition module, a motion trail sequence generation module, a prediction module, a target electric appliance prediction module and an electric appliance operation module. Based on the human body track generated by sensor data, the corresponding electric appliances are pre-started by predicting the room where the user is about to arrive and the electric appliances possibly used by the user, so that multi-scene and multi-space coverage is realized, and a complex indoor environment can be met.

The invention relates to a smart home equipment control method and system based on QUIC point-to-point communication, and belongs to the technical field of smart home control, and the control method comprises the steps: receiving a natural language instruction of a user terminal, and carrying out the analysis to obtain a target equipment identifier and an operation intention description text; retrieving a pre-constructed equipment registry according to the target equipment identifier, and obtaining a corresponding cue word template and a QUIC connection address; calling an artificial intelligence model deployed locally, and outputting a structured equipment control instruction in combination with the operation intention description text and the cue word template; performing signature encryption on the structured equipment control instruction, establishing a QUIC end-to-end encryption transmission channel between the user terminal and the target equipment, and transmitting the QUIC end-to-end encryption transmission channel to the target equipment; receiving operation response data fed back by the target equipment after the target equipment verifies and executes the structured equipment control instruction; the device registry is dynamically optimized based on the operational response data. According to the invention, the risk of cloud service privacy leakage can be reduced, and the data security of the smart home system is improved.

The invention discloses an intelligent home control system, which relates to the technical field of intelligent home and comprises an illumination sensor, an infrared human body sensor, an auxiliary sensor, an edge analysis module, an illumination controller and an illumination strategy optimization module. The multi-source sensing data is subjected to fusion analysis through an edge analysis module, so that accurate light control based on multi-condition judgment is realized; and the lighting strategy optimization module dynamically adjusts a logic weight or a threshold value based on the abnormal behavior characteristics of the user, so that the control strategy has an adaptive optimization capability, and the response accuracy, stability and individuation level of the system are remarkably improved.

The invention relates to the technical field of voice recognition intelligent home control, and discloses a non-specific person voice recognition intelligent switch control method and system based on deep learning. The non-specific person voice recognition intelligent switch control method is applied to intelligent switch control equipment, and specifically comprises the following steps of S101, receiving original audio signals continuously collected in a to-be-controlled environment, and preprocessing the collected original audio signals, and then a starting point and an ending point of an effective voice segment are positioned by adopting endpoint detection based on a double-threshold method and combining the characteristic parameters of the short-time energy and the short-time zero-crossing rate. A multi-layer hidden layer structure with Dropout regularization is adopted in a neural network model, the generalization ability of the model is enhanced, a context sensing mechanism is introduced into a semantic understanding module, a composite instruction containing azimuth information can be intelligently analyzed, crossing from recognition to understanding is achieved, and the method has the advantages of being high in practicability and easy to popularize. The system is ensured to maintain a high recognition rate for voice instructions of different users under different environment conditions.

The invention relates to the technical field of smart home control, and particularly discloses a smart home central control system and method based on multi-mode perception, and the system comprises the steps: synchronously collecting a voice audio signal, a gesture image signal, an infrared thermal imaging signal and a millimeter wave radar signal through a plurality of groups of sensors; performing blind source separation processing on the voice and gesture signals, extracting a voice command component and a gesture action component which are independent in statistics, and performing space-time alignment and Kalman filtering fusion on the infrared and radar signals to generate a dynamic environment sensing graph; voice intention features, gesture track features and environment anomaly features are extracted through Mel frequency cepstrum coefficient analysis, skeleton key point tracking and multi-level convolution processing; constructing a three-dimensional decision matrix based on the features, performing weighted evaluation through a fuzzy logic rule base to generate a control instruction priority sequence, and dynamically adjusting an equipment operation mode according to the priority; according to the invention, the problems of control conflict and response delay caused by multi-mode signal coupling are solved.

The invention discloses a brain-computer interface interaction control device and method based on multi-mode brain signal fusion. The device comprises a perception acquisition layer, an edge processing layer, a fusion decoding layer, a control application layer and a closed-loop optimization cloud platform. The sensing acquisition layer synchronously acquires multi-modal brain signals; the edge processing layer adopts a condition alignment time sequence diffusion model to carry out signal enhancement and completion; the fusion decoding layer realizes multi-modal feature fusion and intention decoding through a lightweight graph neural network; the control application layer provides adaptive control mapping and multi-mode feedback; and the closed-loop optimization cloud platform realizes continuous performance optimization through federated learning and incremental learning. According to the method, the problems of single signal, insufficient precision and poor practicability of a traditional brain-computer interface are effectively solved, the decoding precision, the real-time performance and the individuation degree of the system are remarkably improved, and the method can be widely applied to the fields of intelligent home control, medical rehabilitation training, industrial control and the like.

The invention discloses a smart home control method and system based on graph retrieval enhanced generation and multi-agent gaming, and the method comprises the steps: constructing a basic knowledge graph based on a current smart home environment, carrying out the semantic enrichment of service entities in the basic knowledge graph, generating an enhanced knowledge graph, and storing the enhanced knowledge graph in a vector database; constructing an example library containing a plurality of formalized observation-logic hypothesis pairs; acquiring a situation snapshot of the user, and outputting a structured scene analysis report based on the situation snapshot and the vectorization database through a front-end LLM; collaborative review is carried out by combining a factor-tracing reasoning agent, a user preference agent and a conflict detection agent with a scene analysis report and an example library, and a final decision is output by a core decision LLM and mapped into an equipment control instruction to call corresponding equipment for execution. According to the method, the fuzzy instruction of the user is combined with the knowledge graph, so that the intention recognition accuracy is improved; the multi-agent parallel auditing mechanism improves the robustness and safety of decision making.

The invention is suitable for the technical field of smart home control, and provides a skill generation method, control equipment and a control system for an intelligent agent with a body. The method comprises the following steps: acquiring a first behavior sequence formed by interaction events of a user and smart home equipment in a first time period; performing multi-dimensional mining on the first behavior sequence, and mining a plurality of behavior modes; for any behavior mode, generating a corresponding skill package based on the behavior mode; and when a trigger source is detected, the intelligent agent controls the corresponding smart home equipment to execute corresponding actions according to the generated skill packages. According to the invention, behavior modes can be mined from multiple dimensions, complex behaviors can be understood, and self-adaptive smart home personalized services can be realized.

The invention discloses a smart home control method and system based on a multi-modal large model, and the method comprises the steps: carrying out the cross-modal feature extraction and semantic alignment processing of voice, image and sensor data through an improved multi-modal large model, and achieving the semantic space mapping of multi-source data based on a context sharing mechanism and reasoning logic; the method comprises the following steps: abstracting local smart home equipment into a standardized resource entity by using an MCP technology, defining an equipment function feature set through a protocol interface, and packaging the equipment function feature set into a callable tool chain; constructing a real-time data fusion model, and generating a control instruction sequence through the multi-modal large model based on the real-time data fusion model; and calling a corresponding tool in the tool chain according to the control instruction sequence to execute equipment control. The problems that an existing system depends on single-mode data and is easily interfered by the environment, the access cost of new equipment is high due to the fact that a unified data fusion framework does not exist, a control strategy is difficult to adapt to user behavior changes, equipment communication protocols are different, and the user requirement matching degree is low are solved.

A second control unit performs a switching control task of repeatedly switching between an on state and an off state of an upper-arm switching device of a voltage converter in response to determination that a monitored terminal voltage across a smoothing capacitor is not lower than or equal to an anomaly determination threshold during failure of a first control unit. The second control unit determines whether the switching control task causes the monitored terminal voltage across the smoothing capacitor to be lower than or equal to the anomaly determination threshold. The second control unit controls, in response to determination that the switching control task causes the monitored terminal voltage across the smoothing capacitor to be lower than or equal to the anomaly determination threshold, at least one motor-generator to perform a limp-home control task of the electric vehicle.

The invention relates to the technical field of smart home control, and discloses a smart home control method and system based on an end-side large model, and the method comprises the steps: deploying a lightweight end-side large model in a terminal device, collecting the voice information of a target user corresponding to a home control scene, extracting a Mel-frequency cepstral coefficient and a zero-crossing rate of the voice information by using a lightweight end-side large model so as to extract key intention words of the voice information; generating a first executable control instruction of the home control scene; identifying linkage intention features of the key intention words, and generating a second executable control instruction of the home control scene; the method comprises the following steps: acquiring a first executable control instruction and a second executable control instruction, analyzing a user behavior of a target user and an abnormal environment of a home control scene, and generating a target execution control instruction of the home control scene under the condition of the first executable control instruction and the second executable control instruction. And privacy protection, localization, individuation and humanization household intelligent control are realized.

The utility model discloses an intelligent household control switch and socket integrated structure, which comprises a socket shell and an intelligent household control switch shell, the socket shell is provided with a fixing assembly and an ejection assembly, the top of the socket shell is provided with a connecting groove, the inner wall of the connecting groove is provided with a rotating groove, and the rotating groove is provided with a rotating shaft. A connecting rod is fixedly mounted at the top of the smart home control switch shell, rotating shafts are fixedly mounted on the outer walls of the two ends of the connecting rod, the rotating shafts are rotatably mounted on the inner walls of the rotating grooves, the fixing assembly comprises a plug pin, and a pull rod and a first spring rod are fixedly mounted on the lower surface of the plug pin; the pull rod penetrates through a through hole formed in the bottom of the socket shell, a groove is formed in the bottom of the socket shell, and a spring groove is formed in the inner wall of the groove. Through the above structure, the integrated structural design of the intelligent household control switch and the socket is effectively realized, the waterproof effect on the socket is also realized, and the use effect is good.

The invention discloses a smart home control method and device, electronic equipment and a storage medium, and relates to the technical field of transmission and bearing, and the method recognizes feature information based on a radar module, and controls a smart home system to execute a target program according to the feature information. Wherein the feature information comprises at least one of action information, quantity information, movement track information, breathing and heartbeat information and posture information of the target feature, and the target program and the feature information have an association relationship. Through application of the technical scheme of the invention, perception without external terminal or voice dependence is realized through the radar module, multifunctional control of the smart home can be realized, the control difficulty of the smart home is reduced, sensitive information such as a human face does not need to be collected, convenience, functional diversity and user privacy are considered, and thus the use experience is improved.

The invention discloses a smart home multi-device linkage power utilization control method based on MOS tubes, and belongs to the technical field of smart home control. The digital processor is used as a core, the built-in numerical control module and the data processing unit are matched, the on-off state of the MOS tube is accurately regulated and controlled in combination with a program control algorithm, fine management of power utilization parameters of intelligent household equipment is achieved, the limitation that traditional control response is lagged and poor in adaptability is broken through, rated parameters and real-time requirements of the equipment are dynamically matched, and the power utilization efficiency is improved. The optimal operation of equipment is guaranteed to prolong the service life, invalid energy consumption is reduced, a high-precision foundation is laid for multi-equipment cooperative linkage, the stability and energy efficiency of a system are improved, multi-scene linkage logic is preset by means of a logic state machine, and rapid switching and reliable execution of multi-scene linkage are realized by matching with an application selection switch of an MOS tube array structure. Scene requirements are automatically adapted, and through independent loop control, fault detection and standby loop design, the problems that linkage switching is tedious and the fault influence is large are solved.

The invention belongs to the technical field of intelligent home systems, and provides a home intelligent control method and system based on computer vision, and the method comprises a temperature and humidity data clustering and alternative clustering set generation module which obtains an alternative temperature and humidity clustering set through K-Means clustering, hardware capability effectiveness analysis and hotspot map screening after removing a timestamp; the alternative temperature and humidity cluster set reliability verification and reference cluster set determination module introduces a timestamp to verify the reliability of the cluster set and screens out the reference cluster set in combination with Min-Max normalization and Euclidean distance; the user temperature and humidity preference prediction and pre-regulation and control parameter generation module is used for predicting a user preference interval through an LSTM + Attention model, generating pre-regulation and control parameters through double-time matching and driving hardware to carry out regulation and control in advance, precision, intelligence and perspectiveness of temperature and humidity regulation and control are achieved, and user experience is improved.

The invention discloses a smart home control method based on voice recognition and context awareness. The method comprises the following steps: S1, collecting voice signals and environment state information; s2, performing preprocessing on the voice signal; s3, inputting the speech feature sequence into a speech recognition model based on a recurrent neural network transduction structure for recognition processing, and outputting a semantic text sequence; s4, constructing a two-stage trigger matching strategy, and if a matching result exists, determining that the request is a voice wake-up request; s5, executing situation classification, and outputting a current situation type label; and S6, according to the current situation type label, generating different instructions. According to the invention, by fusing voice recognition and environment perception, high-accuracy smart home control suitable for an aged scene is realized, and the system has timely response and personalized interaction capabilities.

The invention discloses a smart home screen localization control system and method based on edge computing, and belongs to the technical field of home equipment control. According to the method, a control network is formed by building a home scene edge computing node and a local control link of an intelligent home screen, the link transmission state is recorded, instruction behaviors are marked, a bimodal control feedback information set is generated, a pointing characteristic value is calculated based on the feedback set, instruction priority is evaluated, and a control sequence is output in sequence. The system correspondingly comprises a control link establishment module, a state recording module, a feedback set generation module, a priority evaluation module and a main control module. According to the invention, cloud dependence is reduced through localized link establishment, efficient instruction scheduling is realized through bimodal feedback and priority evaluation, the problems of response lag and unreasonable resource allocation of existing smart home control are solved, and the real-time performance and reliability of smart device control in a home scene are improved.

The invention provides a cross-platform intelligent lamp control APP system based on an MQTT protocol, and relates to the technical field of intelligent home control, and the method comprises the steps: receiving an access request set of intelligent lamps under different operation system platforms, including equipment identification, platform types and control capability description information; performing access adaptation processing based on an MQTT protocol to obtain a cross-platform access adaptation result; generating a unified control instruction template suitable for all access lamps; converting the unified control instruction template into an MQTT format control message and sending the MQTT format control message to the lamp; the instruction execution feedback message returned by the lamp is received, efficient and unified control over the cross-platform intelligent lamp is achieved, and control convenience and compatibility are improved.

This invention discloses a unified whole-house smart control method based on StarFlash, belonging to the field of smart home control technology. By constructing a StarFlash Mesh network with lighting fixtures as relays through a central control module, it achieves automatic discovery, unified access, and collaborative scheduling of lighting fixtures, curtains, air conditioners, and sensors without a gateway. A local autonomous algorithm ensures uninterrupted connectivity even during network outages, allowing users to centrally manage all devices in the house through a single app. This invention solves the problems of incompatible protocols, poor coverage, multiple app switching, network outage failures, and difficulties in cross-device linkage in traditional smart home systems. It features high reliability, wide coverage, low latency, and easy deployment, making it suitable for smart control in various scenarios, including homes, businesses, and industries.

This application provides a method, device, electronic device, and storage medium for controlling home appliances. The method, applied in the field of smart home control technology, includes: receiving an input voice signal, converting the voice signal into text information, rewriting the text information according to N rewriting dimensions to obtain an initial text command, standardizing the initial text command to obtain a target text command, and finally controlling the target home appliance based on the target text command. This method can accurately parse user input commands and achieve precise control of home appliances.

This application belongs to the field of millimeter-wave radar perception and hand posture estimation, and discloses a multi-target hand posture estimation method based on millimeter-wave radar, including: Step 1: Create a hand area: Detect all objects in the millimeter-wave radar perception area, use a wake-up detection model to detect predefined wake-up gestures to identify the target user, evaluate the probability of the wake-up gesture, and identify the hand area; Step 2: Construct a range-azimuth-pitch three-dimensional feature cube in the created hand area, wherein the three-dimensional feature cube represents the hand posture characteristics; Step 3: Estimate the hand posture characteristics using the m2HandNet model. This application uses millimeter-wave radar as the research object, realizes hand posture estimation through millimeter-wave radar, supports multi-user and multi-hand interaction, and effectively solves occlusion problems to meet a wide range of application scenarios such as smart home control, smart terminal interaction, virtual reality equipment, and sign language recognition.

This invention provides a smart home remote control system, relating to the fields of visual detection and intelligent control, comprising: a dynamic home feature calibration module: screening and classifying smart home devices controllable by the remote control system, and acquiring initial three-dimensional coordinate values ​​of feature points through visual detection technology; a static home environment ground state acquisition module: acquiring initial environmental feature data of static home devices in real time; a control target routing module: selecting whether to call the dynamic home response verification module or the static home response verification module; a dynamic home response verification module: calculating the dynamic home change index and outputting the dynamic home control judgment result; and a static home response verification module: calculating the static home environment change index and outputting the static home control judgment result. This invention enables the system to objectively and accurately confirm whether the control has been truly executed and produced the expected effect, even when the home devices themselves do not provide status feedback or the feedback is unreliable.

The utility model provides a hotel smart home control equipment mounting base, which comprises a mounting seat, a fixing assembly is arranged on one side of the mounting seat, the fixing assembly comprises two mounting sleeves, mounting blocks are arranged in the two mounting sleeves, a U-shaped mounting frame is connected between the two mounting blocks, and the U-shaped mounting frame is connected with the mounting seat. A mounting plate is mounted on one side of the U-shaped mounting frame; and the multiple positioning assemblies are arranged at the four corners of the bottom of the mounting base correspondingly. According to the hotel smart home control equipment mounting base provided by the utility model, the two mounting sleeves and the two mounting blocks are arranged on one side of the mounting base, the U-shaped mounting frame with the mounting plate can be mounted on the surface of a wall in a fixed manner on a controller, the mounting can be performed according to the position of a wire holder, and meanwhile, the occupied desktop space is also reduced.