1549 results about "Environmental sensor" patented technology

The invention provides a home abnormal state signal detection method and system based on multi-modal sensing, and relates to the technical field of detection, and the method comprises the steps: collecting human body motion, acoustics and environment parameters through a millimeter wave radar, an acoustic sensor and an environment sensor, inputting the parameters into a deep fusion network, a spatio-temporal attention mechanism and a multi-scale convolutional neural network are used to extract a spatio-temporal feature sequence, and joint probability features are formed in combination with an acoustic feature spectrum analysis result. And inputting the joint probability features into a time sequence knowledge graph, and obtaining scene adaptive features after processing by a graph attention network and a double-flow auto-encoder network. And finally, the multi-task learning network is combined with the risk propagation neural network and the recursive neural network to carry out anomaly prediction and risk level evaluation, and is combined with historical early warning information to output an early warning strategy. According to the invention, multi-modal data can be effectively fused, the accuracy and reliability of home abnormal state detection are improved, and a more accurate risk assessment and early warning strategy is provided.

The invention relates to the technical field of data processing, in particular to an intelligent analysis system for electric energy quality and read data, which comprises a data fusion module, a modeling module, an analysis module, a detection module, an optimization module and a control module. The data fusion module aligns data of an electric meter terminal, power grid monitoring equipment and an environment sensor through a sliding time window, and the analysis module calculates a power grid node loss transfer coefficient based on a graph neural network and fuses transformer no-load loss and line contact resistance parameters to generate a dynamic line loss evaluation matrix. And the detection module identifies the power consumption characteristic deviation degree through a random forest classifier, and generates a priority management strategy in combination with a multi-dimensional abnormal scoring model. The control module adaptively selects a communication protocol to execute a regulation and control instruction according to a network state, a heartbeat detection mechanism feeds back operation data of a governance device in real time, model parameters are driven to be iteratively updated, and dynamic cooperation of power supply quality optimization and line loss governance is achieved.

The invention discloses an LCD display screen display fault judgment method, and relates to the related technical field of LCD display screen detection. Comprising the steps of synchronous acquisition and preprocessing of multi-source signals, feature extraction, establishment of a fault judgment model, generation of a dynamic test mode, dynamic generation of a targeted test image according to a preliminary detection result to excite a potential fault, iterative optimization of a test sequence by a Q-learning algorithm, and fault diagnosis. The invention also discloses a system for judging the display fault of the LCD screen. The system comprises a multi-source data acquisition module, a central processing unit, a memory and a user interface. Various data including display data, driving voltage / current monitoring data, temperature distribution, environment parameters and the like are synchronously collected through the optical sensor, the electric signal probe, the thermal imaging module and the environment sensor, all-directional information of the LCD display screen in the operation process can be obtained, and limitation and misjudgment possibly caused by a single data source are avoided.

The invention discloses a livestock health state real-time monitoring method and system based on the Internet of Things, and the method comprises the steps: collecting livestock biological data and environment parameters through a biological chip ear tag and an environment sensor, and carrying out the preprocessing, and obtaining a standardized multi-dimensional livestock data flow; in combination with image data provided by a video monitoring system, identifying livestock individuals and analyzing behavior features, and fusing biological data and visual data to generate livestock individualized feature vectors; constructing an individualized health baseline model in combination with historical health records; comparing the deviation degree between the current state and the health baseline through an anomaly detection algorithm, and identifying a potential health problem; early warning is performed according to the abnormity severity level, intervention suggestions are generated, early warning information is pushed to the breeding personnel through multiple channels, and response measures and results are recorded to form closed-loop management. According to the invention, accurate real-time monitoring of the health state of the livestock is realized, the accuracy and timeliness of anomaly detection are improved, the breeding risk is reduced, and the production efficiency of animal husbandry is improved.

The invention relates to the technical field of optical engineering, in particular to an AI silicon carbide lens-oriented aspheric laser polishing precision online detection and feedback system. Comprising a detection module; the data processing module is electrically connected with the detection module and used for constructing a lens surface three-dimensional model and calculating polishing precision parameters; the feedback control module is electrically connected with the data processing module, is used for generating a feedback control strategy and comprises a strategy generation unit and a parameter optimization unit; an execution module; and an intelligent learning module. According to the design, a laser measuring head, an energy sensor array and an environment sensor are integrated through the detection module, synchronous acquisition of multi-dimensional data such as surface profile, laser energy distribution and environment parameters is realized, and three-dimensional point cloud data with consistent time and space are generated through multi-modal fusion processing (time domain synchronization, space registration and feature enhancement) of the data preprocessing unit; the physical essence of the polishing process is completely represented, and the detection comprehensiveness and the data relevance are improved.

The invention discloses an intelligent pest and disease damage identification and targeted spraying method and system based on machine vision, and the method comprises the steps: inputting a multispectral image sequence and environment sensing data into a processing system, and obtaining a high-quality fusion image through spectrum-space joint correction and environment adaptive compensation; constructing a region growing algorithm based on a topological flow theory, and realizing accurate extraction of a target region in combination with curvature flow boundary optimization; integrating the spatial features, the time sequence features and the context information by adopting a multi-modal feature fusion method, and establishing a pest state evaluation and behavior prediction model; and according to an evaluation result, a spraying track is optimized through an environment constraint field, a self-adaptive parameter control system is adopted to adjust spraying parameters, and closed-loop optimization is realized through real-time monitoring. The technical problems of low pest and disease identification precision, poor spraying effect and the like in a complex environment are solved, and the accuracy and efficiency of prevention and treatment are improved.

The invention relates to a construction site environment dynamic regulation and control method and system fused with AIoT. According to the method, a multi-source environment sensor network is deployed to collect original data of a construction site environment in real time, a data set with aligned timestamps is generated, an abnormal event is detected by using an isolated forest algorithm after standardized denoising processing, and a specific parameter type and a time window are marked; a causal intensity matrix between parameters is constructed through Granger causal test on the basis of environment data intercepted in an abnormal time period, a directed weighted network adjacency matrix representing a pollution propagation path is generated accordingly, and an abnormal source is accurately positioned through node influence propagation calculation and timestamp verification; and finally, a regulation and control instruction sequence is dynamically generated according to the shortest influence path of the source node in the propagation network, so that autonomous analysis of implicit association among construction site environment parameters, accurate positioning of a pollution source and dynamic distribution and instant deviation correction of a multi-stage regulation and control strategy are realized.

The invention discloses a humanoid robot somatosensory control system and method based on deep learning. The method comprises the steps that physiological data and environment information of a user are collected through a wearable device and an environment sensor, and full-modal synchronous collection is achieved in combination with audio and visual data. The system carries out noise reduction processing on the collected sound, extracts a voice instruction and recognizes non-language sound features to judge the physiological state of the user; and analyzing the posture and gesture intention of the user through a human body detection and posture recognition algorithm. And multi-modal data is further fused, the intention of a user is accurately judged in combination with scene context and a security verification loop, a multi-dimensional state space and an atomic action library are constructed, and a composite reward function is designed to realize intelligent decision and execution of robot actions. The system comprises a multi-modal data acquisition module, a user intention understanding module, a dynamic action planning module, a safety monitoring and exception handling module and a continuous learning and collaborative optimization module, and can effectively improve the intention recognition precision, environmental adaptability and task execution efficiency of the robot in a complex home scene. And intelligent and personalized family service support is provided for the elderly.

The invention relates to the technical field of civil engineering, and discloses a foundation pit slope protection pile concrete strength evaluation and intelligent reinforcement integrated method which comprises the following steps: S1, data acquisition and transmission: acquiring a slope protection pile structure state and environmental parameters in real time through a multi-source sensor network, the sensor network comprises a mechanical sensor, an environment sensor and a hydrological sensor which are deployed in a spatial distribution manner; s2, data preprocessing and feature extraction: performing space-time fusion processing, including noise suppression, space-time reference unification and multi-dimensional feature extraction, on the acquired multi-source sensing data, and generating a structured feature matrix; according to the technical scheme, the multi-source sensor network and the deep learning model are combined, the strength of the foundation pit slope protection pile concrete is monitored and evaluated in real time, the accurate and lossless strength prediction effect is achieved, the problems of destructive testing and precision limitation are avoided, and the test efficiency is improved. The defects of low precision and poor real-time performance under complex geological conditions in the prior art are overcome.

The invention discloses an intelligent lighting management system and method based on multi-source data, and relates to the technical field of intelligent lighting management, and the system comprises a multi-source data collection module, an environment feature anchoring module, a dynamic error modeling and calibration module, an edge-cloud collaborative decision module and a lighting strategy execution module. The multi-source data acquisition module fuses environment sensor, visual perception and laser radar data to construct a'sensor original data + environment characteristic data 'dual-input system; the environment feature anchoring module extracts stable features from the visual / laser radar data to serve as a calibration reference; the dynamic error modeling and calibration module quantifies the error in real time based on the reference and generates calibration parameters; the edge-cloud collaborative decision-making module and cloud long-term optimization form a hierarchical collaborative architecture through edge node real-time control; and the lighting strategy execution module dynamically adjusts lighting parameters according to the calibration data and feeds back an effect to form a closed loop.

The invention relates to the technical field of pipeline safety monitoring, in particular to a pipeline water leakage detection platform and method.The pipeline water leakage detection platform comprises a sensor layer, an edge calculation module and a pipeline water leakage detection system of a cloud analysis platform, the sensor layer comprises a pressure sensor, a sonic sensor, a flow sensor and an environment sensor, and a distributed installation structure is adopted; the edge calculation module is used for collecting pipeline state data in real time, the edge calculation module comprises an equipment box, a built-in power supply module, a data processor (ARM Cortex-M series + FPGA acceleration unit) and a communication module (NB-IoT or LoRa), and sensor data is subjected to localization preprocessing and then uploaded to a cloud analysis platform. A cascade neural network model (CNN + LSTM) and a leakage positioning engine are deployed on the cloud platform, leakage judgment and accurate positioning are achieved through multi-source data fusion and a deep learning algorithm, and efficient and accurate pipeline leakage detection and positioning are achieved through data collection of a sensor layer, preprocessing of an edge calculation module and intelligent analysis of the cloud platform.

The invention discloses a multi-sensor monitoring landslide early warning method, device and equipment and a medium, and the method comprises the steps: collecting three-dimensional displacement data and multi-source environmental factor data of a to-be-monitored slope region through a plurality of environmental sensors, and carrying out the preprocessing of the collected data; constructing an initial landslide probability prediction model, and training based on a preset algorithm to obtain a target landslide probability prediction model; inputting the current monitoring data into the trained prediction model, and outputting a landslide probability prediction result; a preset feature contribution interpretation mechanism is introduced to quantify the influence of each environment feature in the prediction of the landslide probability; and dynamically adjusting a landslide probability index based on the predicted landslide probability and the feature contribution degree, and setting a multi-stage early warning threshold to realize graded early warning of the landslide risk. According to the invention, a landslide early warning technical framework with adaptive updating and interpretable analysis capabilities is constructed, and the accuracy, stability and engineering adaptability of landslide early warning are improved.

The invention relates to the technical field of highway tunnel safety monitoring, and discloses a highway tunnel intelligent fire safety early warning system, which comprises a data acquisition and preprocessing module used for acquiring video monitoring data, environment sensor data and vehicle positioning data in a tunnel in real time; the dynamic risk assessment module is used for constructing a risk field model based on vehicle trajectory and smoke diffusion space-time coupling; the emergency resource elastic adaptation module is used for automatically adjusting the induction screen and the fire fighting equipment according to the risk intensity; according to the method, the limitation of traditional threshold alarm is avoided, fire early warning nodes are greatly advanced through multi-source data dynamic fusion, long-term stability of a monitoring benchmark is guaranteed by utilizing an optical flow compensation and pollution self-repairing mechanism, and the self-adaptive migration of a calculation task is realized by utilizing the optical flow compensation and pollution self-repairing mechanism. And the initiative and reliability of tunnel safety prevention and control are obviously improved.

The invention discloses a small target recognition method and system for a multi-modal fusion image in a complex environment, and belongs to the technical field of computer vision and image recognition, and the method comprises the steps: obtaining a visible light image, an infrared image and environment sensor data; image registration is carried out on visible light and infrared images, and a multi-scale image feature pyramid is constructed. And respectively extracting visible light and infrared image features to obtain visible light and infrared imaging feature data. And performing multi-modal data fusion on the visible light and infrared imaging feature data based on a cross-modal attention mechanism, and adaptively adjusting a fusion weight based on environmental sensor data to generate fusion features. And performing space-time enhancement processing on the fusion feature to obtain an enhanced fusion feature. And performing target tracking detection on the small target, and outputting position and category information of the small target. According to the method, the small target recognition capability in a severe environment is remarkably improved, and high precision and robustness can still be kept in a foggy, low-visibility and dark scene.

The invention relates to the technical field of wind power generation, discloses an intelligent safety early warning method for wind power hoisting operation, and aims to solve the technical problems of insufficient perception and lack of data fusion and intelligent analysis decision in existing operation safety management. The method is characterized by comprising the following steps: constructing an intelligent terminal integrated with a UWB / IMU / safety belt / environment sensor; deploying a positioning base station network and a signal relay system; a multi-source data fusion intelligent analysis platform is established, and high-precision positioning, behavior recognition and deep learning risk prediction are achieved; and a closed-loop intelligent decision-making and execution system is constructed, and graded early warning, electronic fence, environment linkage and one-key help calling are realized. According to the method, a traditional experience driving mode is innovated into a data driving mode, the safety level and the operation efficiency of wind power hoisting operation are remarkably improved, and the defects of insufficient perception, information isolation and the like are overcome.

The invention relates to a shipbuilding safety monitoring system and a monitoring method thereof, and belongs to the technical field of shipbuilding monitoring, and the system comprises a multi-modal data collection module, an edge computing node, a digital twin platform, a digital twin platform, an analysis module, a block chain traceability module, and a feedback control module. The multi-modal data acquisition module comprises a visual sensor, an acoustic sensor, a thermal imager, a strain sensor, a laser displacement sensor and an environment sensor. The visual sensor, the acoustic sensor, the thermal imager, the strain sensor and the environment sensor are installed in a shipbuilding workshop. According to the invention, by fusing visual, acoustics, thermodynamics and other multi-sensor data and combining edge calculation real-time processing and digital twinning dynamic simulation, process quality prediction, defect detection and carbon emission tracking are realized, and the problems of insufficient real-time performance, difficulty in cross-system collaboration and lack of green manufacturing monitoring in the prior art are solved.

The invention relates to the technical field of gas monitoring, and discloses an air quality monitoring method based on an olfactory chip, which comprises the following steps: step 1, preparing a multi-modal sensor system which comprises an olfactory chip module, a surface enhanced Raman scattering module and a microfluidic time division multiplexing module, the sensor surface of the olfaction chip module is modified with a metal organic framework material, and the substrate of the surface enhanced Raman scattering module is made of a core-shell structure nano material; and 2, synchronously collecting resistance response data of the olfaction chip module, spectral data of the surface enhanced Raman scattering module and temperature, humidity and wind speed data of the environment sensor. According to the invention, a multi-modal sensor cooperative detection technical scheme is adopted, the high-sensitivity detection effect on low-concentration harmful gas in air is achieved, and compared with a technical scheme of a single sensor and a fixed fusion algorithm in the prior art, the defect that accurate recognition of trace gas cannot be realized under ppb-level concentration is overcome.

The invention relates to the technical field of power equipment monitoring, and discloses a power equipment insulation state online monitoring system. The system comprises a multi-source data fusion module which collects operation parameters, environment sensing and historical insulation degradation record data streams in real time; and the space insulation field intensity distribution module performs field intensity dynamic mapping on the operation parameter data flow in combination with an equipment structure topological graph and an insulation degradation feature library to generate space insulation field intensity distribution feature information. The reference state topology module constructs an insulation state reference topology network through dynamic weight distribution according to element service duration distribution and operation modes; the insulation degradation dynamic detection module analyzes the field intensity deviation degree of the two regions and generates a primary insulation degradation index. When the primary index exceeds the threshold value, the delay response verification module executes continuous time sequence degradation trajectory tracking verification; the insulation state trend prediction module performs multi-step state evolution simulation to obtain predicted field intensity distribution characteristics, and the monitoring compensation optimization module corrects the primary indexes according to the predicted field intensity distribution characteristics and generates optimized insulation degradation indexes.

The invention relates to the field of wind power plants, in particular to a wind power cluster preventive maintenance strategy intelligent generation method and system. The method comprises the following steps: collecting multi-source heterogeneous data of a wind power cluster in real time, including SCADA system operation data, CMS monitoring data and environmental sensor data; performing standardization processing and abnormal value elimination on the collected data, and constructing a wind power equipment operation state database; a wind power equipment health state evaluation model is established based on a deep learning algorithm, the equipment operation state is analyzed in real time, and the potential fault risk is predicted; generating a preventive maintenance strategy by adopting a multi-objective optimization algorithm according to a fault risk prediction result in combination with historical maintenance records of the equipment and operation and maintenance resource scheduling information; and issuing the generated maintenance strategy to a wind power cluster operation and maintenance system to realize intelligent execution of preventive maintenance. The problems of high data fusion difficulty and low processing efficiency in the prior art are solved.

The invention relates to the technical field of mine equipment monitoring and maintenance, and discloses a mine equipment state monitoring and maintenance method based on the Internet of Things, which comprises the following steps: S1, multi-source data acquisition: synchronously acquiring equipment operation parameters and environmental parameters through vibration, temperature and pressure sensors and environmental sensors deployed on mine equipment; s2, edge calculation processing: performing filtering and anomaly detection on the sensor data at a mine edge gateway to generate a compressed data packet; s3, online learning and model dynamic updating; s4, new equipment is accessed; s5, maintenance decision and resource scheduling; and S6, performing closed-loop feedback optimization. High-error historical sub-trees are dynamically eliminated, new sub-trees are generated, model parameters are adjusted in combination with maintenance feedback data, on the basis that historical effective knowledge is reserved, equipment dynamic features are extracted, accurate matching of prediction results and maintenance strategies is ensured, and the self-learning ability and maintenance decision reliability of the model are remarkably improved.

The invention provides an intelligent inspection risk assessment method and system based on multi-sensor fusion, and the method comprises the steps: obtaining original multi-source data of a transformer substation, the original multi-source data comprises a binocular vision image, infrared thermal image data and environment sensor data, and carrying out the time-space calibration and preprocessing of the original multi-source data, obtaining a multi-source sensor data stream; carrying out feature extraction on the multi-source sensor data stream to obtain a multi-modal feature set, generating a refined semantic mask based on the multi-modal feature set, constructing an initial scene relation graph, calculating a risk level based on a multi-layer perceptron classifier, and generating a risk level evaluation result and a risk distribution graph; outputting a safety distance violation warning and a risk area identifier; and generating comprehensive risk early warning information based on the risk level assessment result, the risk distribution diagram, the safety distance violation warning and the risk area identifier.

The invention relates to the technical field of power load decomposition, and discloses a non-intrusive power load decomposition method and system based on multi-modal feature learning. The method comprises the following steps: synchronously acquiring electric power parameter data of an intelligent electric meter, environmental parameter data of an environmental sensor and use behavior data of user equipment to obtain multi-modal load monitoring data; performing cross-modal feature extraction through a non-negative matrix factorization layer of the first equipment state recognition model to obtain a multi-modal fusion feature vector; carrying out load mode recognition through a first decomposition layer of the first equipment state recognition model to obtain a first decomposition load matrix; performing clustering optimization through a second decomposition layer of the first equipment state recognition model to obtain a second load decomposition matrix; and executing a dynamic fuzzy decision based on the second load decomposition matrix to obtain an equipment operation state identification result. According to the method, the limitation that a traditional method only depends on a single power signal is broken through, and high-precision and high-robustness non-intrusive power load decomposition is achieved.

The invention relates to the technical field of data integration, and particularly discloses a perfluoro / polyfluoroalkyl substance multi-source exposure data integration risk assessment system, which is characterized in that environmental exposure data of perfluoro / polyfluoroalkyl substances is acquired through a multi-source environmental sensor array, and a PFAS multi-mode exposure feature database is established; carrying out pollution source isotope fingerprint analysis, and obtaining source contribution rate distribution maps of three pollution sources of industrial emission, surface runoff and atmospheric settlement through a nonlinear source analysis algorithm; constructing a three-dimensional geographic information dynamic migration model according to the source contribution rate distribution map, and generating a multi-medium dynamic migration flux matrix; a composite risk assessment model is established based on the multi-medium dynamic migration flux matrix, probability risk assessment is executed in combination with an ecological toxicity threshold database, and a space gridding risk grade map is output; the method not only fills the blank of the prior art in the aspects of multi-medium dynamic modeling and nonlinear source analysis, but also provides powerful technical support for environmental pollution control and ecological risk prevention and control.

The invention particularly relates to an intelligent security and protection dynamic risk assessment and prevention and control system based on multi-source data fusion, and the system comprises a data collection module which collects corresponding data and carries out the preprocessing of the data; the data processing module is used for analyzing the data to obtain a risk assessment coefficient; the risk assessment module is used for obtaining a risk level based on the risk assessment coefficient and making a corresponding prevention and control strategy; and a prevention and control decision optimization module. According to the invention, the data acquisition module integrates sensor data, service system data and external data, and realizes omnibearing data coverage of a security scene; real-time data collected by a visual sensor, an environment sensor and the like are combined with business system data of access control, vehicle management and the like, then external information of meteorology, public security, public opinions and the like is fused, feature extraction and weight distribution are carried out by using technologies of deep learning, an entropy weight method and the like through a data processing module, and finally an accurate risk assessment coefficient is obtained. More risk factors are comprehensively considered, and potential safety hazards are effectively identified.

The invention discloses a carbon fiber composite material surface modification spraying system and a control method thereof. The system comprises a multi-axis robot, a plasma spray gun, a contact angle measuring probe, a 3D line laser scanner, a hyperspectral imager, an environment sensor and a controller. According to the method, the plasma power and the robot speed are adjusted in real time through contact angle measurement and plasma treatment feedback control, so that the CFRP surface can accurately reach a target value, and the coating adhesive force is improved; 3D line laser scanning and hyperspectral imaging are combined, and the posture, the distance, the wet film thickness and the component uniformity of the spray gun are monitored in real time; based on a prediction model fusing a physical model and a neural network, a self-adaptive fuzzy PID control algorithm is adopted, and the coating flow and the track posture of a spray gun are accurately regulated and controlled in real time. The problems of weak coating binding force, uneven thickness, orange peel, sagging and serious coating waste in traditional spraying are effectively solved, and self-adaptive, high-quality and green spraying of workpieces with complex curved surfaces is achieved.

The invention discloses a vehicle-mounted laser gas pollution detection and prediction system, which comprises a mobile laser detection platform, a machine prediction system and a dynamic pollution map generation system, and is characterized in that the mobile laser detection platform is carried on a new energy vehicle and is integrated with a laser gas detector, a high-precision positioning module, an inertial navigation system and an environment sensor group; the machine prediction system receives real-time traffic flow data, weather forecast data and topographic data and outputs a pollution diffusion prediction result; the dynamic pollution map generation system comprises a space-time synchronization module, a data fusion module and a pollution event marking module. And the space-time synchronization module binds the gas pollution data with the positioning coordinates and the millisecond timestamp through hardware clock synchronization.

The invention proposes an underwater target intelligent recognition system based on multi-sensor fusion, and relates to the technical field of underwater target recognition, and the system comprises an underwater robot which is provided with an optical sensor, an acoustic sensor, a three-dimensional laser sensor, and a water quality environment sensor; the data acquisition module is used for synchronously acquiring original multi-modal data and water quality parameters from the underwater robot; the data preprocessing module is used for generating preprocessed multi-modal data; the multi-modal feature fusion module is used for extracting each modal feature from the preprocessed multi-modal data and generating a fusion feature; the target recognition and positioning module is used for processing the fusion features by adopting a target detection network and outputting the category, the position and the confidence of the underwater target; and the intelligent analysis output module is used for comprehensively analyzing the information of the underwater target and generating an analysis result and visual data. According to the invention, the limitation of a single sensor can be overcome, and the underwater target can be accurately and stably identified by combining an intelligent algorithm.

The invention provides a smart home scene control method and system based on edge calculation, and relates to the technical field of Internet of Things. Multi-modal data of a camera, an audio collector, an environment sensor and the like are obtained and converted into tensor representation, a first data set is generated in combination with a priori rule, clustering analysis is executed for second text data to screen related information, and finally multi-round reasoning is performed in a large language model deployed on the edge side in a partitioned cache mode. And precise control of the household equipment is realized. The first partition reserves multi-mode core information, and the second partition can dynamically update user instructions or logs, so that key information is continuously concerned in a long-sequence context, and newly-added data is flexibly processed. According to the method, a real-time and efficient reasoning effect can still be maintained in a computing power limited environment by fully utilizing lightweight technologies such as pruning and quantification, and the accuracy and response speed of multi-modal scene understanding and home control are improved.

The invention 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.

A system and method for generating patient care instructions based on real-time sensor and medical data. The method includes receiving time-stamped sensor data from a sensor network comprising motion, occupancy, and environmental sensors, and receiving medical data associated with a patient, including medical conditions, treatment history, medication data, and biometric data. The sensor data is enriched with room-specific information, and activity pattern data is generated in real time using a pattern recognition model. The activity pattern data includes mobility, sleep patterns, medication adherence, statistical measures, temporal patterns, and correlations with medical data. Anomalies indicating potential health risks are detected by comparing current activity patterns with baseline data. A prediction model, trained on historical patient data, assesses the patient's health and generates care instructions accordingly. The care instructions are securely delivered to patient devices, caregiver applications, or automated medication dispensing systems, enabling timely interventions and continuous patient monitoring.