500 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 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 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 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 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 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 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 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 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.

The invention discloses an integrated system and method with body movement control in a home environment, and the system comprises a home task construction module which is used for constructing a simulation environment, designing home tasks, labeling task data, defining task attributes, and obtaining an original home task data set; the data enhancement module is used for performing enhancement processing on the data set by adopting a supervised fine tuning SFT method and a direct preference optimization DPO method; the evaluation index module is used for setting indexes for evaluating system performance; the HLP module is responsible for decision making and planning adaptation of a tool, and generates an output O comprising a subtask and a selected bottom layer model m based on the input instruction I; the LLE module is used for converting the subtasks output by the HLP, the selected underlying model m and the inventory inv into model calling instructions, the selected underlying model m starts to execute work, error detection is carried out in real time in the execution process, the execution result is fed back to the HLP, and the execution process is recorded. The system has excellent performance.

The invention discloses a household energy centralized management method and device, electronic equipment and a storage medium. The method comprises the following steps: collecting real-time observation data of a family environment to construct a multi-dimensional context feature vector; generating a dynamic state transition probability matrix and emission parameters of the hidden Markov model through a dynamic parameter mapping mechanism, and calculating initial posterior probability distribution of a family activity state by using a forward algorithm; when the information entropy of the probability distribution exceeds a threshold value, executing an active state exploration strategy, issuing an optimal perturbation instruction and correcting a posterior probability based on an active response observation evidence so as to determine a final family activity state; and based on the state loading control constraint set, solving a real-time optimization objective function containing energy efficiency and comfort penalty, and generating an equipment control instruction. According to the method, the uncertainty of state judgment is reduced by combining a dynamic model and an active exploration mechanism, and household energy cost optimization is realized on the premise of ensuring comfort.

In one aspect, an example method includes: (a) capturing a plurality of images of a home environment; (b) receiving a user input, wherein the user input is associated with the captured plurality of images of the home environment; (c) generating a home recommendation model using one or more machine learning models, wherein the one or more machine learning models are configured to generate the home recommendation model using the captured plurality of images of the home environment and the received user input; (d) identifying one or more home recommendations, wherein the one or more home recommendations are based on at least the generated home recommendation model; and (e) transmitting instructions that cause a computing device to display, via a user interface of the computing device, a graphical indication of the one or more home recommendations.

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

The utility model discloses a drainer capable of treating kitchen garbage, which comprises a shell and a bottom cover connected with the shell, a water inlet connector is arranged on one side of the bottom cover, and the water inlet connector is communicated with the inside of the bottom cover; a turbine assembly is arranged in the bottom cover, a cutter shaft is arranged above the turbine assembly, penetrates through the distance piece and extends into the water filtering basket of the shell, a plurality of blades are fixed to the cutter shaft, high-pressure water flow entering from the water inlet connector drives the turbine assembly to rotate, then the cutter shaft is driven to rotate, garbage entering the water filtering basket is smashed through the blades, and therefore the garbage can be recycled. The crushed garbage is discharged through a water outlet in one side of the shell, so that the kitchen garbage is prevented from breeding germs, mosquitoes and peculiar smell due to storage, the home environment is effectively optimized, the production cost is low, the safety is high, noise is avoided, and a series of problems caused by driving blades by a motor in the prior art are solved.

The invention discloses a nervous system function evaluation method and device, computer equipment and a medium, and relates to the technical field of intelligent medical treatment. The specific implementation mode comprises the following steps: acquiring sensor data of a plurality of body parts of daily activities of a user, wherein the sensor data is acquired through a sensor arranged in a home environment; obtaining a plurality of motion function evaluation parameters according to the sensor data; obtaining a nervous system function evaluation result based on the whole-age group normal population data model according to the multiple motion function evaluation parameters; and outputting a neurological function risk index and a risk index of the user according to a nervous system function evaluation result. According to the scheme, the nervous system function is evaluated by obtaining multiple evaluation parameters in daily life, and the accuracy and reliability of the evaluation result and the use flexibility of the evaluation method are improved.

The invention discloses a control method of a cleaning robot, a storage medium and equipment, and relates to the technical field of smart home / smart home, the control method of the cleaning robot comprises the following steps: determining working parameters of the cleaning robot according to a cleaning instruction, and controlling the cleaning robot to operate according to the working parameters; in the operation process of the cleaning robot, the environment sound of the environment where the cleaning robot is located and / or the emotion information of a user in the environment are / is obtained, and the working parameters of the cleaning robot are adjusted according to the environment sound and / or the emotion information. According to the method, the cleaning strategy is dynamically adjusted based on the environment change, so that the cleaning robot can better adapt to different family environments and user emotions, individual requirements of different users are met, interference to daily activities of the users is reduced, and the use experience of the users is improved.

The utility model discloses an intelligent plant maintenance tea table which comprises a main frame, and a water storage disc is installed on the bottom face of the main frame. A damp-proof lamp panel is installed on the side face of the main frame, a temperature sensor and a humidity sensor are installed on the damp-proof lamp panel, an air opening is formed in the damp-proof lamp panel, and a lamp strip is arranged on the back of the damp-proof lamp panel; a fan is installed on the main frame and located behind the air opening, and a choke plate capable of blowing is arranged in front of the fan and the air opening. A heating wire is mounted on the outer bottom surface of the water storage disc; a control panel is installed on the main frame and electrically connected with the temperature sensor, the humidity sensor, the fan and the heating wire. The intelligent plant maintenance device has the advantages that the intelligent plant maintenance device is combined with traditional furniture, the problem that indoor plants are difficult to maintain is solved, and meanwhile the function of beautifying the home environment is achieved. The environment needed by plant growth is artificially created, and the problem that household indoor plants are not easy to maintain is solved.

The invention discloses an intelligent home environment monitoring and linkage system and method based on multi-parameter fusion, and relates to the technical field of home environment monitoring, and the method comprises the following steps: S1, collecting environment data in different directions through a monitor; s2, judging the type of a pollution scene according to the difference value of the pollution indexes in all directions; s3, in combination with pre-stored indoor environment layout information, corresponding pollutant traceability analysis is executed; s4, generating and executing an intelligent linkage instruction according to the pollution scene type and the pollutant traceability judgment result; and S5, verifying a linkage effect and adaptively adjusting a subsequent monitoring strategy or a linkage instruction. Through fusion calculation of directional sampling and a multi-parameter sensor, directional traceability of the pollution source is realized, so that a user can intuitively and quickly position the pollution source, the limitation that in the prior art, only alarming can be performed, and positioning cannot be performed is fundamentally changed, and a key basis is provided for taking efficient and accurate treatment measures.

The invention discloses a multi-mode home-based care health monitoring method and system based on voice interaction suitable for aging, and belongs to the technical field of smart care and health monitoring. According to the system, physiological data of an intelligent ring (M1), behavior data of a home environment sensor (M2) and equipment state monitoring information are fused, wherein the behavior data comprise a water / electricity / gas meter and an emergency pager which are wirelessly transmitted by zigbee, a button type noctilucent coating is larger than or equal to 0.5 mm, and buzzing is 60-70 dB after triggering. And a health management closed loop without APP operation is realized through aging-suitable voice interaction (supporting more than eight dialects and wake-up word custom). The core method comprises multi-source data acquisition, six-dimensional health assessment, multi-dimensional risk grading and grading service response, the problems of single data, insufficient aging adaptability, service disjunction and the like of existing equipment are solved, the high-risk identification accuracy rate reaches 93%, the emergency call response time is shorter than 90 seconds, and the method is suitable for home-based care scenes of old people living alone or both, and has a wide application prospect. And the monitoring safety and convenience are obviously improved.

The present disclosure provides an apparatus, method and kit to provide a safe traversal of stairs in a home environment. The system has subsystems for mounting and trolley apparatus. The system also has subsystems for de-slacking a tether along with harness configurations. The kit provides a retrofittable de-slacking, tether, and harness configuration for safety systems of other configurations. The present disclosure further provides interchangeable features of the various subsystems. The mounting and trolley subsystem may be mounted to a wall or a ceiling depending on the needs of the user.

The invention relates to the technical field of smart home, and discloses an identity recognition method, which comprises the following steps: preprocessing original data acquired by a low-resolution radar and an inertial measurement unit, and respectively generating a radar state vector and an inertial measurement state vector; performing matching degree calculation on the radar state vector and the inertial measurement state vector with a pre-constructed identity vector library; when the matching degree calculation result meets a preset cross-modal feature fusion condition, carrying out fusion processing on the radar data features and the inertial measurement data features; and performing user identity recognition based on fusion features obtained through fusion processing. According to the scheme, the accuracy of identity recognition is guaranteed, the strict requirements of a family scene for light weight and low cost are met, and stable and accurate multi-target identity recognition can be achieved in a dynamic family environment by fully utilizing low-resolution radar characteristics. The invention further discloses an identity recognition device and a computer readable storage medium.

The invention discloses an intelligent wheeled humanoid robot as a home-based care appliance, and relates to the technical field of robots. The robot specifically comprises a wheel type chassis walking mechanism and a robot upper body structure arranged on the wheel type chassis walking mechanism, and the robot upper body structure comprises a robot trunk structure and at least one robot arm mechanism arranged on the robot trunk structure; the robot further comprises a height adjusting mechanism, and the height adjusting mechanism is arranged between the wheel type chassis walking mechanism and the robot trunk structure and used for adjusting the vertical height of the robot trunk structure. A clamping jaw is installed at the tail end of the robot arm mechanism, a multi-mode touch sensor is integrated on the clamping jaw, and the multi-mode touch sensor is used for detecting physical characteristics of an object when the clamping jaw makes contact with the object. The invention aims to enable the wheeled robot to keep efficient moving ability and to adapt to multi-height operation requirements in a family environment.

The invention relates to the technical field of intelligent old-age care monitoring, and discloses a method for identifying abnormal behaviors of home elderly based on millimeter-wave radar and vision fusion, which comprises the following steps: S1, multi-modal data acquisition and privacy protection configuration: deploying a configurable sensing suite in a home environment of the elderly, comprising a millimeter wave radar, a visual sensor (infrared and visible light), environment sensors (doors and windows, water, illumination and ground vibration) and wearable equipment (such as a smart bracelet / chest card). According to the method and the system for identifying the abnormal behaviors of the home elderly based on millimeter-wave radar and vision fusion, all-around behavior monitoring is realized on the premise of guaranteeing the privacy of the elderly through multi-modal sensor fusion and a hierarchical privacy protection mechanism, and the millimeter-wave radar and the vision sensor are complementarily fused, so that the privacy worry of a pure vision technology is overcome, and the safety of the home elderly is improved. And the limitation of a single radar on complex behavior identification is solved, and meanwhile, a configurable sensing suite and a hardware-level privacy switch are adopted.

The invention discloses a chronic disease family oxygen therapy intelligent management method and system based on an IKAP theory, and relates to the field of chronic disease family oxygen therapy. Patient baseline data is collected through a structured questionnaire and comprises oxygen therapy knowledge reserve data, caregiver adaptability data and family environment adaptability data; and obtaining an initial oxygen therapy knowledge mastery score, a caregiver adaptability score and a home environment risk label based on structured questionnaire analysis. According to the invention, through the fusion of the IKAP theory and the intelligent technology, the upgrading of chronic disease family oxygen therapy from extensive management to precision, individuation and closed loop is realized, the health income of patients is guaranteed, the medical resource allocation is optimized, and the method has significant clinical value and social significance. The core logic can be migrated to family management of other chronic diseases, rapid adaptation can be achieved by adjusting a knowledge graph and monitoring indexes, and the method has wide popularization value.