865 results about "Home environment" patented technology

The invention belongs to the technical field of intelligent health monitoring, and discloses a retired old people home safety monitoring system based on Internet of Things, which comprises the following steps: deploying multiple types of sensors for the home environment of old people to carry out all-weather data acquisition and analysis to obtain behavior data with activity feature tags; performing correlation analysis to obtain a personalized security risk assessment model; performing real-time monitoring and mode recognition to obtain a behavior track analysis report; performing security level division and intelligent configuration to obtain a virtual security guard area and a self-adaptive reminding rule; performing grade classification processing and early warning signal generation to obtain graded early warning information and a response authority strategy; performing integration analysis to obtain a dynamic intervention strategy, and updating the personalized security risk assessment model according to the dynamic intervention strategy to obtain a target security monitoring model; blind spots and defects of traditional manual monitoring are effectively overcome, and a safe home environment is provided for the old.

The invention discloses a smart home environment adaptive adjustment system and adjustment method, and relates to the technical field of smart home, the smart home environment adaptive adjustment system comprises an environment data acquisition module, a user behavior data analysis module, an intelligent decision module, a control module, a user interaction module, an optimization module and a security module, the method has the advantages that capturing of multi-dimensional parameters of a home environment and description of a user behavior mode are achieved by fusing an environment data collection module and a user behavior data analysis module, a neural network algorithm is adopted, a complex nonlinear relation between environment variables and user behaviors is automatically mined and modeled, and the user behavior is more accurate. Therefore, a customized home equipment control strategy is generated, and a living environment conforming to user preferences is created, so that the intelligent adjustment precision of the home environment is improved, and the satisfaction degree of personalized demands of the user is enhanced.

The invention discloses an intelligent household electrical appliance interaction control method and system, and the method comprises the following steps: collecting the operation state parameters, environment perception data and user behavior characteristics of each household electrical appliance in a living space in real time, and generating an environment perception data packet through a multi-modal data fusion model; constructing a user habit preference model library, generating a household appliance control strategy decision tree in combination with historical interaction records, and matching an optimal control strategy set according to a real-time scene type; and when a user active intervention signal or an environment sudden change event is detected, a dynamic strategy reconstruction mechanism is triggered, an instruction updating request carrying a priority identifier is broadcasted to the associated household appliance group, and a redistribution control instruction set is generated. The method has the following advantages and effects: multi-source environment data can be deeply fused, the user habit model is constructed in real time, and the method has a dynamic strategy reconstruction capability so as to solve the problems of response lag, strategy stiffness and energy efficiency imbalance in a complex home environment in the prior art.

According to the intelligent home environment intelligent regulation and control system and the control method thereof, various intelligent sensors are arranged, various environment data such as indoor temperature, humidity and illumination are monitored in real time, the data are preprocessed and then sent to a data analysis layer, data features are extracted according to the monitored data and historical data based on the deep learning technology, and user preferences are deduced; the control parameters are adjusted and sent to the equipment control layer, a specific instruction is sent to an indoor smart home, an air conditioner, a humidifier or a curtain and the like are controlled to accurately control the indoor environment, the living preference of a user is met, and in the process, the effect monitoring and dynamic adjusting layer monitors the adjusting condition in real time, and the user experience is improved. If the ideal effect is not achieved after adjustment, the equipment control layer is controlled to continue adjustment, in the process, the abnormal condition of the system is detected, the power consumption of the equipment and other data are monitored at the same time, normal work of the system is guaranteed, possible faults are predicted, a user is reminded in time, and safe and energy-saving operation of the whole system is guaranteed.

A smart lighting device, and a system including one or more smart lighting devices, that are well-suited for use in at-home setting. The devices may be configured as night lights, and can provide continuous monitoring of a person in the at-home setting and provide alerts to the person of potential fall risk areas in the home. Further, the devices may provide alerts to others (other designated persons / organizations), e.g., in the event of a fall or fall risk. Further, the devices may recognize unknown persons in the home, and provide alerts to others. The system may be configured to route these alerts to according to user preference and / or a severity of the detected situation / alert.

The invention relates to the technical field of smart home cooperative control, and discloses a cooperative operation method of a smart home system, and the method comprises the steps: firstly constructing a smart home equipment state prediction model and an equipment cooperative control decision model, and predicting the operation state of each piece of intelligent equipment in the future through collecting user habit data and home environment data; and generating a preliminary collaborative operation instruction. Thirdly, a difference fusion model is constructed, the model optimizes the preliminary instruction according to the difference between the predicted operation state and the actual operation state of the equipment, and an optimal cooperative operation instruction is generated; and the system continuously monitors the actual operation state of the equipment, compares the actual operation state with a prediction result, and immediately triggers the difference fusion model to recalculate an instruction once a significant deviation is found, thereby ensuring that the smart home system is always kept in an optimal operation state. Through combination of prediction and real-time optimization, the cooperation efficiency and response speed of the smart home system are improved, and more comfortable and convenient smart home experience is provided for users.

The invention relates to the technical field of smart home environment control, in particular to a temperature and humidity control method and system for smart home, and the method comprises the following steps: S1, monitoring the indoor temperature and humidity and the VOC concentration in the air in real time; s2, on the basis of the VOC concentration data collected in the S1, identifying and analyzing peculiar smell components; s3, constructing a temperature and humidity and peculiar smell interaction relation model; s4, generating an adjusting scheme for the current environment according to the temperature and humidity and peculiar smell relation model constructed in the S3; s5, executing corresponding adjustment operation according to the adjustment scheme generated in the step S4; and S6, dynamically updating and optimizing the temperature and humidity and peculiar smell interaction model. According to the method, the interactive relation model of the temperature, the humidity and the VOC concentration is constructed, the adaptive optimization technology is combined, the temperature, the humidity and the air quality are dynamically adjusted, and the accuracy of indoor peculiar smell control and the intelligent level of environment adjustment are improved.

The invention relates to the technical field of intelligent control, in particular to an air conditioner control system and method based on a smart home. The air conditioning module is used for refrigerating or heating air; the image acquisition module is used for acquiring an image of a user smart home environment and defining the image as a first image; the analysis module is used for judging whether a user exists in the first image or not, identifying the identity, position and behavior of the user, retrieving a personal data packet of the user based on an analysis result, and outputting a control signal; the temperature adjusting module is used for adjusting the air supply temperature and speed of each air supply outlet; the wind direction adjusting module is used for adjusting wind directions of the air supply outlets. The operation parameters of each air supply outlet of the air conditioner are automatically adjusted based on the corresponding personal information database by recognizing the feature information such as the identity, the position and the behavior of the user, so that corresponding operation control is performed when different users are in different areas and different states, and the user experience is improved. And the air supply parameters of the air conditioner blown to the user are more suitable for the personalized requirements of the user in the current state.

The invention discloses a smart home energy management method and system based on personalized federal reinforcement learning, and belongs to the field of smart home energy management, and the method comprises the following steps: modeling a plurality of heterogeneous smart home energy cost minimization problems, and designing an environment state, an action and a reward function corresponding to a Markov decision process; the edge end intelligent agents of the N heterogeneous smart home environments locally utilize a deep reinforcement learning algorithm optimization strategy and perform federated learning by means of the cloud center server to obtain a pre-training global model with stable training performance; performing post-training fine tuning on the edge end intelligent agent of each heterogeneous smart home environment to obtain N personalized energy management strategies suitable for the N heterogeneous smart home environments; and the personalized energy management strategy obtained through fine adjustment is deployed in an actual environment for operation, and the energy cost of the system can be effectively reduced on the premise that the thermal comfort of residents is fully guaranteed.

The invention relates to the field of smart home, and discloses a smart home environment sensing and energy-saving control system based on the Internet of Things, and the system comprises a sensing module which comprises an environment sensor unit and a human body sensor unit, and the environment sensor unit is used for collecting the indoor temperature, humidity, illumination intensity and air quality in real time; the human body sensor unit is used for detecting existence and activity conditions of indoor personnel; the Internet of Things communication module transmits data collected by the sensing module to the control module through Internet of Things communication; the energy-saving analysis module receives the data acquired by the sensing module, analyzes and processes the data, and judges whether to adjust the energy-saving strategy of each smart home according to the analysis result; the control module controls the operation state of each smart home according to the energy-saving strategy; the energy-saving strategy comprises the steps of automatically switching on and off light and air conditioning equipment according to the personnel activity condition, automatically adjusting the indoor illumination brightness according to the illumination intensity and adjusting the air conditioner operation parameters according to the indoor and outdoor temperature difference.

The invention relates to the technical field of smart home, and discloses a smart home environment regulation and control method and device based on multi-modal behavior perception, and the method and device can understand the home environment parameter preference of a user in different scenes through a home habit reference value. The current physiological state, behavior mode and environment condition of the user can be comprehensively captured by collecting multi-mode sensing data. And calculating a multi-modal data deviation value to quantify the difference between the current home environment and the user habits, and positioning environment factors needing to be adjusted. The multi-modal data deviation value is subjected to weighted fusion calculation according to the weight matrix driven by the current health state of the user, and the comfort degree deviation index is generated, so that the recognition accuracy of user requirements is improved, and personalized and dynamic comfort degree evaluation is realized. An intelligent regulation and control strategy is determined according to the comfort degree deviation index, precise regulation and control of the home environment in the fields of medical science and technology and home-based care for the aged are achieved, and the accuracy and adaptability of home environment parameter regulation and control are effectively improved.

The invention provides a photoelectric sensing smart home linkage control method and system, and relates to the technical field of data processing, and the method comprises the steps: receiving first electric data and second electric data which represent the illumination intensity of a first data point and the illumination intensity of a second data point respectively; calculating a cross data value of the illumination intensity of the first data point and the second data point; comparing the cross data value with a preset illumination demand threshold value to judge the overall illumination demand of the current home environment; generating a corresponding adjustment instruction based on the overall illumination demand of the current home environment; sending the adjusting instruction to an actuator connected with the household equipment, wherein the actuator comprises an intelligent bulb and an intelligent curtain motor; and after receiving the adjusting instruction, the actuator automatically executes corresponding operations, including turning on light, turning off light or adjusting a curtain, so as to realize automatic adjustment of the household equipment. According to the invention, the intelligent level of home illumination adjustment is improved.

The invention discloses a talkback method and system based on label door lock cooperation, and the method comprises the steps: broadcasting door lock binding information: obtaining a broadcasting result of the door lock binding information according to the fact that a door lock A detects that a Bluetooth label Tag-DID carried by an owner a leaves a communication range; neighbor door lock state detection: sending an empty query request according to a call button of a Bluetooth tag pressed by an owner a, and obtaining a response result of an idle door lock; door lock selection and call establishment: selecting a door lock ID displayed on a display screen of a door lock Z according to the owner a, extracting the cached IP by the door lock Z and initiating a call request to the door lock A by the door lock Z to obtain an establishment result of call connection between the door locks; and voice communication and switching management: realizing voice communication between the door lock Z and the door lock A according to the established call connection, and obtaining a cross-door-lock voice communication switching result. By utilizing the embodiment of the invention, the cooperative work capability between the door locks can be improved, and the communication experience of a user in an intelligent home environment is optimized.

The invention discloses a multi-user-oriented smart home resource conflict negotiation and allocation method, which comprises the following steps of: detecting equipment use conflicts caused by at least two users by constructing a structural causal model for representing a causal relationship of a plurality of variables in a smart home environment, generating a candidate decision strategy set comprising resource isolation and alternative compensation, and allocating the candidate decision strategy set to the smart home environment; carrying out anti-fact inference by utilizing a causal model, quantifying the causal effect of each strategy on the user state, and selecting an optimal strategy for execution based on a minimum negative effect and a Pareto optimal principle; besides, the method ensures that the system dynamically adapts to user habit changes through online monitoring of prediction errors and correction of the causal model, and compared with the prior art, the method improves the decision accuracy through causal reasoning, realizes fair and personalized user resource allocation, and improves the user experience and long-term effectiveness of the smart home system.

The invention discloses a home monitoring method and system based on a multi-agent large model, the multi-agent large model comprises a central large model and agents, and the central large model is used for scheduling at least one agent to execute monitoring, analysis and alarm tasks; the method comprises the following steps: reading video stream data of a household camera in real time, and preprocessing source data to obtain unified video format data; and inputting the processed video data into the multi-agent large model, distributing a task to at least one agent by the central large model, and executing corresponding monitoring, analysis and alarm operations by the at least one agent according to the task. By performing uniform format processing on real-time read camera video streams, the accuracy of system data is ensured, and monitoring identification and intelligent alarm of abnormal conditions in a home environment are realized through a multi-agent collaborative decision-making mechanism in combination with intelligent analysis and reasoning capabilities of a large language model. And the reliability, the intelligence, the user experience and the like of the home safety monitoring system are remarkably improved.

The invention relates to the technical field of intelligent medical treatment, in particular to a household intelligent health management system and a method thereof.The system collects household environment parameters and user physiological indexes through a data collection subsystem, constructs a medical knowledge graph of a three-layer structure through a knowledge graph driving subsystem, and dynamically updates the medical knowledge graph; the multi-target health intervention subsystem generates a candidate intervention scheme set based on a knowledge graph, the digital twinborn verification subsystem predicts and selects a scheme with a health improvement index higher than a target by establishing a user, environment and interactive twinborn model, and the adaptive interaction subsystem pushes the verified scheme to a user in a personalized manner and collects feedback information. Meanwhile, the digital twinborn verification subsystem transmits the feedback information to the knowledge graph driving subsystem to trigger dynamic updating of the knowledge graph, the system comprehensively collects health influence factors, comprehensive data support is provided for health risk assessment, and personalized health management is achieved.

The invention discloses an elderly user risk perception interaction method and system in a smart home environment, and relates to the technical field of data processing, and the method comprises the steps: connecting smart homes, analyzing the data features of the connected smart homes, and constructing a perception database; obtaining risk perception demands of the elderly users, constructing a risk perception demand library, carrying out risk demand analysis, and obtaining a risk analysis link according to an identification causal chain of the risk demands; and performing correlation analysis on the risk analysis link and the perception database, identifying a mapping relationship between the perception data and the risk analysis link, projecting the mapping relationship to the risk analysis link for risk identification, and obtaining a risk identification result. The technical problem that the prior art lacks accurate and real-time monitoring and personalized early warning of the health risk of the elderly user is solved, and the technical effects of real-time accurate recognition and personalized early warning of the health risk of the elderly user are achieved through multi-device data analysis and association analysis.

The invention discloses an intelligent temperature control energy-saving system and method based on personnel position detection and prediction. The system monitors indoor personnel distribution and temperature and humidity in real time through the human body and environment sensing module; the behavior prediction module utilizes an LSTM deep learning model to analyze the historical moving track of the user and predict the next target room and the arrival time of the user in combination with the environment context; the environment regulation and control module starts equipment such as an air conditioner in the target room in advance to adjust to a comfortable state; and the energy efficiency optimization module is used for implementing energy-saving control on the unmanned room. According to the method, the crossing from passive reaction to active prediction is realized, and the energy utilization efficiency of the home environment is remarkably improved on the premise of ensuring the comfort.

Systems and methods for providing curated neurostimulation where users are enabled to provide improved therapy in a home environment. Stimulation protocols can be assessed to provide improved targeted stimulation of a nerve and less unwanted side effects. Sets of stimulation montages and associated weights are defined and are selected or adjusted in pre-defined using pre-defined operations that simplify adjustment of characteristics of the stimulation field such as the geometry and location of the provided stimulation. Stimulation matrix pads are arranged and activated to provide advantages. Strategies for adjusting the intensity of the stimulation field incorporate weighting values in non-primary channels and are disclosed. Curated neurostimulation can provide defined schedules for events and activities related to the therapy such as providing schedules for stimulation treatment, surveying a user, providing education and remote user support. Features related to decreasing and managing risk of skin events are included as part of the therapy.

The invention belongs to the technical field of augmented reality, and discloses an AR scenario furniture identification and dynamic removal system based on artificial intelligence, and the system comprises the steps: carrying out the multi-view image registration, furniture entity segmentation and shielding completion analysis based on the obtained hotel scene AR image sequence data, and forming a complete furniture contour feature; the style similarity of the furniture multi-dimensional feature vectors is analyzed, and a furniture style recognition result is obtained; the method comprises the following steps: acquiring AR scanning data of a home environment, performing furniture removal priority ranking through space conflict analysis and style coordination evaluation, generating a dynamic removal strategy, establishing furniture three-dimensional model data based on the dynamic removal strategy, and performing material texture mapping and shadow fusion to obtain a realistic rendering effect; an interactive optimization adjustment scheme is generated, and scene parameter real-time adjustment and optimization and visual effect improvement are realized through user feedback; the reality sense of virtual home display and the user experience effect are remarkably improved.

The invention discloses a smart home safety monitoring system and method based on multi-modal sensing and intelligent analysis. The system comprises a multi-modal sensing module, an intelligent analysis module, a communication module, a cloud server, a user terminal and a smart home central control screen module. According to the invention, through multi-mode sensing fusion and intelligent analysis technologies, all-around and multi-level sensing of an intelligent home environment can be realized, compared with a traditional single-sensor monitoring system, various security events can be detected and identified more accurately, the reliability and the security of the system are improved, the identity and behavior actions of personnel can be judged accurately, and the safety of the personnel is improved. The multi-mode sensing module, the intelligent analysis module and the cloud server are integrated, abnormal behaviors are recognized, early warning is given out in time, potential risks are reduced, in addition, real-time data transmission among the multi-mode sensing module, the intelligent analysis module and the cloud server is achieved by adopting multiple wireless communication protocols, and safety and privacy of data transmission are guaranteed in the transmission process in combination with the data encryption unit.

The invention relates to the technical field of household intelligent alarm systems, and discloses a household intelligent alarm system, which comprises an environment sensing module, a state recognition module, a strategy generation module and an abnormity handling module. The environment sensing module integrates multi-source parameters such as temperature, smoke concentration, human body infrared signals and door and window opening and closing states through multi-mode detection, and an associated data space containing a normal state layer, an early warning state layer and an emergency state layer is constructed. The state recognition module extracts feature sets such as a parameter mean value, a change rate and a sudden change amplitude by adopting a multi-scene sampling and time sequence feature analysis model; the strategy generation module extracts a common rule from different scene and time period strategies, and constructs a rule matching engine to generate an identification strategy adapted to a new scene; the exception handling module monitors parameters in real time and triggers preset strategies such as parameter adjustment, early warning notification and emergency linkage. According to the system, comprehensive monitoring of the family environment, scene self-adaption and intelligent abnormity handling are achieved, and the intelligent level of safety protection is improved.

A method of detecting and responding to a visitor to a smart home environment via an electronic greeting system of the smart home environment, including determining that a visitor is approaching an entryway of the smart home environment; initiating a facial recognition operation while the visitor is approaching the entryway; initiating an observation window in response to the determination that a visitor is approaching the entryway; obtaining context information from one or more sensors of the smart home environment during the observation window; and at the end of the time window, initiating a response to the detected approach of the visitor based on the context information and / or an outcome of the facial recognition operation.

The invention discloses a smart home equipment cooperative control method and system based on edge computing, and relates to the technical field of smart home equipment cooperative control, and the method comprises the steps: employing multiple sensors to collect the temperature, humidity, illumination intensity and user behavior data in a home environment, and obtaining an original data set; preprocessing the original data set by adopting a data processing method to obtain a high-quality data set; constructing a multi-modal collaborative decision prediction model based on the high-quality data set in combination with an open-source large language model LLM, inputting the high-quality data set into the collaborative decision prediction model, and outputting a control strategy result; based on a control strategy result, performing instruction distribution and state synchronization on the smart home equipment by adopting a high-efficiency communication protocol between heterogeneous equipment to obtain an equipment operation instruction set; and based on the equipment operation instruction set, computing and storage resources of the edge nodes of the smart home equipment are distributed through a dynamic resource management and load balancing method, and a resource configuration scheme is obtained.

The invention discloses an electronic cleaning equipment operation optimization method and system based on the Internet of Things, and belongs to the field of control systems.The electronic cleaning equipment operation optimization method based on the Internet of Things comprises the following steps that ground stains are sensed in real time through a multi-mode sensor and an edge computing technology, and common stains and excrement stains are distinguished; after excrement stains are recognized, the electronic cleaning equipment is controlled to stop sweeping immediately, a rolling brush / dust collection module is closed, and stain diffusion is avoided; through cooperation of the Internet of Things and family environment data, a risk area is pre-judged, and cleaning logic is adjusted; compared with the prior art, the method has the beneficial effects that the dirt is classified, and when the dirt is judged to be excrement dirt, the electronic cleaning equipment is controlled to stop cleaning, so that the excrement dirt is prevented from being diffused; meanwhile, through cooperation of the Internet of Things and family environment data, the risk area is pre-judged, the cleaning logic is adjusted, the cleaning frequency and path of the electronic cleaning equipment are dynamically changed, and the stain diffusion risk is reduced.

The invention relates to the technical field of intelligent household appliances and man-machine interaction, and discloses an electric cooker anti-noise voice interaction system based on multi-mode perception and a control method, and the system comprises a multi-mode perception and collection unit which synchronously collects millimeter wave radar echo signals and acoustic signals; the signal preprocessing and feature extraction unit is used for extracting a user physiological vibration signal and a three-dimensional space position vector from the radar signal and extracting an acoustic energy envelope and a sound source direction vector from the acoustic signal; the time-space consistency verification unit and the voice gating and recognition unit are used for carrying out time synchronization verification by calculating the correlation between the physiological vibration signal and the acoustic energy envelope, and distinguishing real human voice from an environment false trigger source; meanwhile, space consistency verification is carried out by comparing the user direction of radar positioning with the sound source direction of acoustic positioning, so that non-target human voice interference is eliminated. According to the invention, the anti-interference capability and reliability of voice interaction in a real home environment are improved through double verification on a physical level.

The invention belongs to the field of nursing robots, and particularly discloses a multi-robot collaborative operation method and system for home nursing of old people, and the method comprises the steps: building a virtual simulation platform which comprises a simulation robot, the old people and a home environment, a large motion planning model built based on the PaLM-E is deployed on the simulation robot; the multi-modal information comprises visual sense, touch sense, sound and temperature sensing information; multi-robot cooperative operation training is carried out based on a virtual simulation platform, so that the action planning large model is trained; multi-modal information and nursing task information about the old people are obtained and input into the trained motion planning large model, unit motion planning of all robots is obtained, and home nursing of the old people with multi-robot collaborative operation is achieved. According to the invention, personalized, efficient, comprehensive, safe and intelligent nursing services can be provided for old people.

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.

The invention relates to an intelligent home equipment intelligent control method based on deep learning, and the method comprises the following steps: S1, collecting environment data and the operation state information of each piece of home equipment in real time through various sensors disposed in an intelligent home environment, the environment data including temperature, humidity, illumination intensity and personnel activity conditions; the operation state information of the home equipment comprises an on-off state, a working mode and an energy consumption parameter of the equipment; all the collected data are transmitted to the central control system; s2, according to the environment data and the home equipment operation state information obtained in the step S1, the current operation mode of the smart home equipment is analyzed, and the operation mode of the smart home equipment at least comprises a basic function mode, an energy efficiency management mode and a multi-equipment interaction linkage mode; and S3, based on the current operation mode analyzed and determined in the step S2, generating an optimization control instruction for one or more smart home devices, and selecting different energy consumption driving modes.

The embodiment of the invention provides a smart home equipment control method and device, equipment and a medium. The method comprises the following steps: acquiring sensor data collected for a current home environment; determining environment state information of the current home environment and user state information of a user in the current home environment according to the sensor data; according to the environment state information and the user state information, determining to-be-adjusted target smart home equipment and generating a target control strategy for the target smart home equipment; and adjusting the target smart home device according to the target control strategy. According to the embodiment of the invention, the intelligent control of the intelligent home equipment is realized according to the environment state information and the user state information, and the comfort and convenience of home life are improved.