405 results about "Home control" patented technology

The invention belongs to the technical field of smart home control, and particularly relates to a smart home generation strategy optimization system based on adaptive learning, and the system comprises the steps: collecting the operation history, sensor data and equipment use modes of a user in a smart home system, and determining an evaluation standard and an optimization direction; extracting features according to user behaviors, environment changes and equipment states, and constructing a feature matrix; selecting a reinforcement learning method for modeling according to an optimization problem, designing an adaptive learning mechanism, and enabling the model to be continuously adjusted according to real-time data in an operation process; according to the method, historical data is used for training the model, a reward mechanism in reinforcement learning is used for guiding the implementation of an optimization target, a control strategy adapting to the current environment and demand is generated based on model prediction, and the method has the effects of better meeting the use demand of a user for the smart home and improving the intelligent level of the system and the user experience.

The invention discloses a smart home control system and method based on a graph attention network, and belongs to the technical field of smart home. The method comprises the steps that S10, multi-source data from a smart home system are collected, and real-time data are collected through a plurality of sensors and a user interaction interface; s20, constructing a graph structure model by using the multi-source data; s30, calculating a correlation weight between the nodes by using an attention mechanism, and generating an optimized equipment control strategy; s40, generating an equipment control instruction according to an output result of the graph attention network; s50, based on user operation feedback, continuously adjusting and optimizing the graph attention network model through a self-supervised learning mechanism; and S60, continuously monitoring the environment parameters and the equipment state, and updating the equipment control strategy in real time. The method has the beneficial effects that efficient fusion of multi-source data, intelligent coordination of user preferences, function coordination of equipment and self-adaptive adjustment of users and environments can be realized.

The invention relates to the technical field of intelligent home control, and discloses a control method and a control system of an intelligent sound box. The control method of the intelligent sound box is applied to a control device, and specifically comprises the following steps: S101, collecting a voice instruction of a user, carrying out noise reduction processing and voiceprint feature extraction on the voice instruction, transmitting the processed voice instruction to a semantic analysis engine, carrying out intention recognition on voice content based on a deep learning model, and sending the intention recognition result to a semantic analysis engine; generating a structured operation instruction set; and S102, according to the device identifier contained in the structured operation instruction set. Through dual authentication of voiceprint feature extraction and dynamic verification phrase, the illegal access false recognition rate is reduced to below 0.12%, the voiceprint database is combined with the Mel frequency cepstrum coefficient and the dynamic time warping algorithm, accurate matching of user identities is achieved, the dynamically generated verification phrase comprises random initial and final combinations, and recording playback attacks are effectively resisted.

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 scene control method and system based on edge calculation, and relates to the technical field of Internet of Things. Multi-modal data of a camera, an audio collector, an environment sensor and the like are obtained and converted into tensor representation, a first data set is generated in combination with a priori rule, clustering analysis is executed for second text data to screen related information, and finally multi-round reasoning is performed in a large language model deployed on the edge side in a partitioned cache mode. And precise control of the household equipment is realized. The first partition reserves multi-mode core information, and the second partition can dynamically update user instructions or logs, so that key information is continuously concerned in a long-sequence context, and newly-added data is flexibly processed. According to the method, a real-time and efficient reasoning effect can still be maintained in a computing power limited environment by fully utilizing lightweight technologies such as pruning and quantification, and the accuracy and response speed of multi-modal scene understanding and home control are 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 an STM32F407-based intelligent walking stick system and a control method, which are used for solving the problems of single function, high false alarm rate and insufficient data security of the traditional walking stick. The system integrates a posture detection module, a heart rate and blood oxygen monitoring module and an environment sensing module, constructs a user health risk pre-judgment model through multi-modal data fusion, realizes a three-level adaptive alarm mechanism in combination with a dynamic decision algorithm, and triggers acousto-optic warning, cloud communication and emergency contact linkage according to risk levels. The payment and data transmission reliability is guaranteed by adopting a physically isolated security architecture and a national secret encryption technology; the dynamic power management system optimizes energy consumption distribution according to scene requirements, and the endurance of equipment is remarkably prolonged. According to the system, the monitoring accuracy is improved through a closed-loop protection system (risk identification-adaptive response-energy optimization), bus payment and smart home control are supported, the system has the characteristics of low false alarm rate, high environmental adaptability and low power consumption, and a safe and convenient life auxiliary solution is provided for old users.

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.

The invention discloses a smart home decision-making system based on cognitive perception and intention prediction, and relates to the field of smart home control, and the system comprises an instruction collection module which is used for collecting control instructions sent by a plurality of users to smart home equipment in the same time period; the conflict identification module is used for detecting whether a control conflict between two or more control instructions exists or not, and constructing conflict state input when the control conflict exists; the control strategy generation module is used for inputting the conflict state into a control strategy generation model obtained based on historical control data training; and the execution control module is used for receiving the overall control scheme output by the control strategy generation model and controlling a plurality of target devices to operate according to the control scheme. According to the invention, the problem of equipment control under the condition that multiple users simultaneously send out control instructions and control conflicts exist is solved.

In an apparatus, a recognizing unit recognizes at least one road marking line and at least a closer edge of a road located around an own vehicle. A detecting unit detects, based on the at least one road marking line and the closer edge of the road, an evacuation space at a location of the road where the own vehicle is parkable in an extending direction of the road. A first determining unit determines whether a detection result of the evacuation space is reliable. A second determining unit determines whether to perform limp-home control that causes the own vehicle to travel from a current location of the own vehicle to the evacuation space in response to determination of whether the detection result of the evacuation space is reliable.

The invention discloses a multifunctional AI robot which comprises a robot body, a sensing module, a voice interaction module, an environment monitoring module, a smart home control module, a mobile chassis, a display screen, a data processing module, a control module, an execution module and a power management module. The beneficial effects of the invention are that 1, a multi-sensor fusion sensing system is arranged, a detection blind area of 0.1-5 meters is effectively covered, and 92% of speech recognition rate is still maintained in a 60dB noise environment; a self-adaptive motion control system is arranged, so that the attitude stability of + / -0.5 degrees during omni-directional movement is realized, and the system is suitable for real-time path planning in a dynamic environment; 2, a safety protection system is arranged, so that the collision accident rate can be reduced to 0.01 times per thousand hours, and uninterrupted power supply within 300ms during hot plug replacement is supported; 3, man-machine interaction upgrading is realized, and the system is more intelligent and multifunctional; and thirdly, a modularized quick-release structure is realized.

The invention relates to the technical field of smart home control, and discloses a smart home equipment control system, which comprises a sensor unit used for collecting environment data and outputting sensor feedback data; the equipment control unit is used for controlling the operation state of the intelligent household equipment according to the feedback data of the sensor, and the equipment control unit comprises an air conditioner controller, a humidifier controller and an illumination controller; and the fuzzy control and adaptive adjustment module is used for receiving feedback data of the sensor. By adopting the technical means of game theory optimization, multi-module cooperation, predictive control, self-adaptive adjustment, fuzzy control and the like, the cooperation efficiency and the resource utilization rate between smart home devices are remarkably improved, environment changes and user requirements can be adapted in real time, the device state can be accurately adjusted, energy waste is reduced, the comfort level and energy efficiency of the home environment are improved, and the user experience is improved. The problems of fixed rules, insufficient energy efficiency optimization, conflicts among devices and the like in the prior art are solved.

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 system, and the system comprises a user behavior data collection module which is used for collecting and processing the daily behavior data of a user, including a position, an activity track, and the frequency and time of using home equipment; the shadow intelligent modeling module is used for creating a digital twin model of the user based on the user behavior data, analyzing user habits through a machine learning algorithm and generating a prediction rule; the home control module is used for executing home operation in advance based on the shadow intelligence prediction result, and the home operation comprises light adjustment, temperature control, home appliance starting and stopping and curtain opening and closing; and the augmented reality interaction module is used for generating a virtual image of a user, displaying a home decision of the shadow intelligence through AR equipment or a terminal, and allowing the user to modify or optimize the home decision. The method has the following beneficial effects: intelligent prediction: the shadow intelligence can predict user behaviors in advance and execute corresponding operation at a proper time point; the air conditioner is turned on in advance, water is boiled and tea is made, and light is adjusted.

The invention relates to the technical field of smart home, in particular to a simulation method and system suitable for a multi-level home energy management system. The method comprises the following steps: acquiring smart home data, and extracting communication protocol data; detecting an equipment abnormal state based on the communication protocol data to obtain the equipment abnormal state; constructing a smart home model based on the smart home data, and performing smart home control simulation by using a preset control instruction to generate smart home control data; performing energy consumption analysis according to the smart home control data to obtain energy consumption data; circuit board soldering tin thermal state fault decoding is carried out according to the equipment abnormal state, and circuit board fault data are obtained; and performing antenna impedance alteration diagnosis according to the abnormal state of the equipment to obtain antenna damage data, and performing signal decay spectrum deduction to generate signal attenuation data. According to the invention, based on the smart home technology, the equipment fault detection, the self-adaptive capability of the system and the overall energy efficiency management rate are improved.

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 belongs to the field of millimeter-wave radar sensing and hand posture estimation, and discloses a millimeter-wave radar-based multi-target hand posture estimation method, which comprises the following steps of: 1, creating a hand region: detecting all objects in a millimeter-wave radar sensing region, detecting a predefined wake-up gesture through a wake-up detection model to recognize a target user, the method comprises the following steps: step 1, evaluating the occurrence probability of a wake-up gesture, and recognizing a hand region, step 2, in the created hand region, constructing a distance-azimuth-pitching three-dimensional feature cube which represents hand posture features; and step 3, estimating hand posture features through an m < 2 > HandNet model. The millimeter wave radar is used as a research object, hand posture estimation is achieved through the millimeter wave radar, multi-user and multi-hand interaction is supported, the shielding problem is effectively solved, and wide application scenes such as intelligent home control, intelligent terminal interaction, virtual reality equipment and sign language recognition are met.

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 linkage control scene building method and system for a smart home, and relates to the technical field of smart home control, and the method comprises the steps: predicting a motion track of a user through a user behavior prediction model, and outputting a user behavior prediction result. And setting a scene template, and selecting the most conforming scene configuration from the scene template in combination with the user behavior prediction result and the environmental parameters to obtain a recommended scene configuration scheme. And designing a graphical user interface, viewing the recommended scene configuration scheme by the user through the graphical user interface, performing fine adjustment according to personal preference, and confirming a final scene configuration scheme. And according to the final scene configuration scheme, sending an instruction to the target home equipment, and outputting a home equipment state execution report. According to the invention, the scene configuration scheme is optimized by using the genetic algorithm, the balance between the energy consumption and the user comfort is considered, and the recommended scene configuration scheme is ensured to be economical and environment-friendly.

The invention discloses infrared AI smart glasses for controlling smart home based on a voice recognition technology, and belongs to the technical field of smart home control. Comprising a glasses body, and a hardware system and a software system which are arranged on the glasses body, the hardware system comprises a microphone array, an AI processing chip, a wireless communication module, a loudspeaker and a power supply module, the microphone array is used for collecting voice signals of a user, the AI processing chip processes and analyzes the voice signals in real time, the wireless communication module realizes data mutual transmission between the glasses body and the smart home equipment, and the loudspeaker is used for transmitting the voice signals to the power supply module. The loudspeaker is used for receiving and playing the voice prompt information processed by the AI processing chip, and the power supply module supplies power to the microphone array, the AI processing chip, the wireless communication module and the loudspeaker; the software system is integrated on a voice recognition algorithm, a semantic understanding module, an equipment control module and a voice prompt module on an AI processing chip, and the voice recognition algorithm carries out preprocessing, feature extraction, model training and reasoning on voice signals.

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 provides a control method and device of smart home equipment, electronic equipment and a storage medium. The method is applied to a wearable device and comprises the following steps: generating a virtual environment image according to a current environment image; the virtual environment image comprises virtual three-dimensional models of a plurality of smart home devices; in response to the first selection operation, determining a target smart home device corresponding to the target virtual three-dimensional model and displaying a virtual control interface; the first selection operation is used for selecting a target virtual three-dimensional model from the plurality of virtual three-dimensional models; in response to a trigger operation on the virtual control interface, generating a control instruction and sending the control instruction to the target smart home device, so that the target smart home device executes the control instruction; and receiving the operation parameters returned by the target smart home device, and displaying the operation state change of the target smart home device through animation. Thus, the intelligent home control system is combined with the wearable device, control operation is integrated on the virtual control interface, and the control process is simplified.

The invention provides a control method and device of cleaning equipment, the cleaning equipment and a readable storage medium, and relates to the field of home control. The method comprises the steps that in the cleaning task process, the actual moving path of the cleaning equipment is recorded, the cleaning equipment is simulated to move in a stable edge state, and a simulated moving path is obtained; and if the actual moving path is compared with the simulated moving path, judging whether the cleaning area needs to be cleaned or not. According to the embodiment of the invention, the problem of missing cleaning in the walking process of the cleaning equipment along the edge can be detected, and the cleaning equipment is controlled to perform supplementary cleaning when the problem of missing cleaning is detected, so that the cleaning area is covered in a larger range, and the cleaning effect of the cleaning equipment on the ground is improved.

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.