689 results about "Home environment" patented technology

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.

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 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.

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.

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 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.

The invention provides a household fire early warning method and system, electronic equipment and a storage medium, and relates to the field of household fire early warning. The method comprises the following steps: collecting historical multi-modal data including power utilization, environment and equipment states, and constructing family environment state characterization through preprocessing; and then analyzing power consumption data under different time scales, obtaining an overall power consumption rule and a single-device power consumption mode, and establishing a household comprehensive power consumption behavior model. And designing a multi-modal abnormal scoring function based on the environment state characterization and the power consumption behavior model. For multi-modal data collected in real time, a deep learning model is used for extracting shared anomaly characterization, and meanwhile, an anomaly scoring function is used for calculating a multi-modal anomaly score. And fusing the abnormal score and the shared abnormal representation to obtain a comprehensive score, and finally determining an early warning level and an emergency measure according to the comprehensive score and a preset dynamic decision early warning level. According to the technical scheme, the reliability and practicability of household fire early warning are improved.

The disclosure describes techniques for interacting with visitors at a visitor interaction system. A method includes obtaining video data captured by the visitor interaction system, identifying an approach of a person, and analyzing the video data to determine an identity of the person. The method includes automatically and without user intervention determining that the person belongs to a visitor groups of a plurality of visitor groups. Each of the visitor groups corresponds to a response model that includes a plurality of response actions. At least one of the response actions includes an autonomous response action. The method includes identifying a response model corresponding to the visitor groups and initiating an autonomous response action associated with the response model. Initiating the autonomous response action including presenting a message to the person, monitoring a response to the message, and sending a report message to the user via a client device.

The invention provides a sound source localization rehabilitation training method, system and device and a medium. The sound source localization rehabilitation training method comprises self-adaptive training and scene simulation training, the self-adaptive training comprises the steps of providing a training task in the period, and the training task in the period comprises the step of selecting training difficulty; according to the training difficulty, sound stimulation is presented in different spatial orientations, and the training difficulty can be adjusted according to a certain logic; the training difficulty of the next period of training is adaptively adjusted according to the correct rate p of one training group of the subject until the preset training amount is reached, and the difficulty setting includes but is not limited to scene setting, analog input information amount and signal-to-noise ratio adjustment. According to the scheme, a hearing-impaired person can perform rehabilitation training in a home environment, the need of manual intervention is reduced, and the training convenience and flexibility are improved. Through adaptive training, on the basis of ensuring scientificity, accuracy and effect of a training plan, the positioning ability of the hearing-impaired person is enhanced, and the integration of the hearing-impaired person into the society is accelerated.

The invention discloses a smart home interconnection control method, system and device, and a medium. The method comprises the following steps: obtaining user commuting information and user preference information; calculating home interconnection time according to the user commuting information, and setting a home interconnection strategy according to the user preference information, the home interconnection time and the weather information; controlling the smart home to perform starting response according to the home interconnection strategy; according to the invention, intelligent regulation and control of a winter home environment can be realized by integrating user information, weather forecast information and intelligent home equipment; through intelligent calculation and execution, a more convenient and accurate window closing and air conditioner warm air opening scheme is provided for the user, the user experience of the intelligent home system is further improved, and the intelligence, the air conditioner heating efficiency and the control flexibility are improved.

The invention discloses a robot common sense reasoning method based on a graph neural network and a spatial knowledge graph, and the method specifically comprises the steps: S1, constructing an indoor common sense knowledge graph composed of entities and relationships through collecting and sorting common sense in an indoor home environment scene; s2, constructing an instance knowledge graph by using a visual relationship recognition method, and generating a triple from an article to a spatial relationship to an article in real time aiming at an actual article spatial relationship existing in a robot operation environment; s3, learning knowledge representation by using a propagation aggregation method in the graph neural network, representing entities and relationships in the knowledge graph into a continuous feature space, and realizing semantic representation of the entities and relationships in the spatial domain knowledge graph; and S4, reasoning indoor common sense by utilizing spatial domain knowledge graph representation, or designing related entity query, and reasoning similar entities by utilizing entity representation in the graph neural network model. By introducing a propagation and aggregation method of a graph neural network, the efficiency of common sense reasoning is improved.

The invention discloses a method and system for collaborative voice transfer between door locks, and the method comprises the steps: Bluetooth tag state monitoring and broadcast request: a door lock A detects that a Bluetooth tag (Tag-DID) carried by an owner a leaves a communication range, the exit state information of the tag is cached, and a broadcast result of the transfer request is obtained; matching and reminding a neighbor door lock Bluetooth tag: receiving a broadcast message according to the door lock Z, and sensing that the Tag-DID enters a communication range of the door lock Z, so as to obtain a reminding result of a person carrying the Bluetooth tag; switching authorization and connection establishment: obtaining a switching connection result between the door locks according to the answer button of the Bluetooth tag pressed by the owner a; voice data transfer and call management: according to the established voice channel, voice data are transmitted among the door lock Z, the door lock A and the mobile phone APP of the owner b in real time, and a complete process result of voice transfer is obtained. By utilizing the embodiment of the invention, seamless voice interconnection between the intelligent door locks can be realized, and the efficient communication requirement of a user in an intelligent home environment is met.

The invention discloses a multi-device cooperative intelligent household electrical appliance control method, which belongs to the technical field of intelligent home furnishing, and specifically comprises the following steps: a household electrical appliance collects environment and self state data in real time, and when any device detects that the data exceeds a preset threshold value, a trigger message containing an event type code is broadcasted to a local area network; a device receiving the message calls a capability database, and a control parameter set containing operation instruction sequences of the device and associated devices is generated in combination with current environment parameters; the parameter sets are uploaded to a shared storage area, each device performs conflict detection on other parameter sets, and conflict marks and correction parameters are generated when output variables are monitored in a local simulation environment and a safety range is exceeded; two parameter sets with the least conflicts are selected according to the number of conflict marks to compare the difference, each device quantitatively scores according to a preset rule, finally the device with the highest score is selected as an execution scheme, and the device analyzes and executes a corresponding instruction; according to the invention, multi-device cooperative intelligent control is realized, and the efficiency and reliability of home environment regulation and control are improved.

The invention discloses environment-friendly cassava cat litter and a preparation method thereof, and belongs to the technical field of pet supplies, and the environment-friendly cassava cat litter is prepared from the following raw materials in parts by weight: 45-55 parts of cassava starch, 20-30 parts of cassava peel, 8-15 parts of corn starch, 8-12 parts of water hyacinth fiber, 5-10 parts of bamboo fiber, 2-4 parts of propolis and 1.5-3.0 parts of mussel mucin. The raw materials are subjected to step-by-step synergistic enzymolysis and step-by-step fermentation to obtain the environment-friendly cassava cat litter. According to the mode, natural raw materials are combined, and the preparation process of step-by-step enzymolysis and step-by-step fermentation is adopted, so that the antibacterial performance and water absorption performance of the cat litter are improved, the defecation comfort and cleanliness of pet cats are improved, sanitation and health of pets and family environments are guaranteed, meanwhile, the product can be completely degraded in the natural environment after being used, and the environment is protected. The cat litter does not generate permanent solid waste like bentonite and silica gel cat litter, and is very environment-friendly.

The invention discloses a household intelligent equipment integrated management and linkage system based on AI voice interaction. The system comprises a voice interaction module which collects user voice input and generates a digital voice stream signal; the semantic analysis module receives the digital voice stream signal, and generates a standardized instruction signal containing an equipment object, an execution action and a control parameter through a natural language understanding model; the intelligent decision-making module receives the standardized instruction signal and combines the standardized instruction signal with a real-time environment state signal obtained from the environment sensing module; the environment sensing module collects multi-dimensional sensor data in a home environment in real time and generates a real-time environment state signal; and the equipment linkage execution module receives the multi-equipment cooperative control signal, and generates a control instruction signal corresponding to each intelligent equipment through the heterogeneous equipment protocol adapter. The AI voice interaction-based household intelligent equipment integrated management and linkage system can solve the problems of intelligent household equipment linkage fragmentation, passive response and non-intelligent interaction.

The invention relates to the technical field of smart home, and discloses a smart home environment alarm method and device, computer equipment and a storage medium, and the method comprises the steps: obtaining initial perception data of a smart home environment, the perception data comprising at least two of environment data, security data, audio and video data, energy data and user data; determining at least one abnormal event in the smart home environment according to the initial sensing data, and performing cross verification on the abnormal event to obtain a verification result; and generating corresponding alarm information based on the verification result, and executing a pushing operation of the alarm information. The problem that an existing smart home alarm method is based on a single sensor or a static rule, lacks a cross validation mechanism of multi-modal data and is prone to causing a high false alarm rate is solved.