12592 results about "Monitoring methods" patented technology

To significantly improve the prediction accuracy, response speed and management efficiency of large river bank slope disasters, an integrated bank slope monitoring method based on multi-parameter collaborative recognition is proposed. The solution includes step S1 of synchronously collecting data on bank slope displacement, pore water pressure, inclination angle, vibration frequency and environmental temperature and humidity to form an original monitoring dataset and construct a multi-parameter collaborative recognition network; step S2 of using a multi-modal data fusion algorithm to generate a fusion data matrix including spatiotemporal correlation features and perform spatiotemporal data alignment and outlier cleansing; step S3 of combining a geomechanical parameter library and a past disaster case library to output a risk level map and perform dynamic risk assessment model analysis; and step S4 of triggering a multi-level early warning mechanism and generating linked control commands including treatment suggestions to perform multi-level early warning and linked control.

The invention relates to the technical field of remote sensing monitoring, in particular to a remote sensing image ocean green tide monitoring method and system fusing physical prior and spatio-temporal evolution. The method comprises the following steps: acquiring a multi-modal remote sensing monitoring image; performing multi-modal feature extraction on the acquired image, wherein the multi-modal feature extraction comprises spectral reflectivity feature extraction, ocean dynamics feature extraction and feature alignment and unified representation; establishing a physical prior of a green tide characteristic wave band by using an ocean optical radiation transmission model; constructing a dynamic space-time diagram based on the extracted multi-modal features to obtain a node global feature vector and a dynamic adjacency matrix; carrying out adaptive graph convolution feature coding based on physical prior and a dynamic space-time diagram; through fusion of multi-spectral images of multiple platforms such as satellites and unmanned aerial vehicles and ocean dynamic data and combination of atmospheric correction and wave band resampling, consistency processing and high-precision extraction of multi-source features are realized, and comprehensiveness and reliability of green tide feature recognition are remarkably improved.

The invention provides a particulate matter and ozone source monitoring method and system based on a distributed sensor, and relates to the technical field of pollution treatment. According to the invention, sensor nodes with geographic perception capability are deployed in a monitoring area in a high-density manner, pollutant concentration and meteorological parameters are collected in real time, and data are uploaded to a cloud platform for preprocessing and dynamic calibration; a machine learning model is constructed based on the combined features of the pollutants and the meteorological factors, a driving relation is mined, and pollutant influence factors are extracted; further performing joint modeling on the influence factors and regional emission source data, identifying the coupling strength between pollutants and emission sources by adopting a classification or clustering method, and judging the categories of main sources; backward trajectory simulation, source fingerprint analysis and multi-source regression decomposition are combined to realize pollution path inversion and source contribution rate quantification; and finally, constructing a geographic information visualization platform, displaying a pollution thermodynamic diagram, a contribution change diagram and an evolution path diagram, and providing support for multi-source pollution traceability and scientific management and control.

The invention relates to the technical field of industrial chain monitoring and treatment, in particular to a monitoring method and system for industrial chain breakpoint treatment. The method comprises the steps that production, logistics, finance, policy and environment dynamic information is acquired through multi-source data, industrial chain comprehensive characteristics are generated through standardization, fractal dimension embedding expression and cross-dimension fusion, and historical trend dependency is introduced to enhance prospective prediction; a dynamic coupling network is constructed, and risk propagation intensity between nodes is quantified by using a dynamic edge weight, so that cross-level breakpoint propagation analysis is realized; risk indexes are calculated by fusing node features and a network structure, breakpoint candidate nodes are screened in combination with an adaptive threshold value, and a multi-step evolution trend is predicted by adopting a nonlinear propagation function and mapped into a multi-level early warning level. And generating a governance strategy according to the risk level, and evaluating the effect in real time and dynamically adjusting parameters through a closed-loop optimization mechanism. According to the invention, closed-loop management of risk identification, prediction and adaptive treatment is realized.

The invention provides a dam safety perception fusion association method based on a multi-modal space-time diagram neural network. The method comprises the following steps: dividing a dam into a plurality of structural units, and mapping various data into a three-dimensional coordinate system; a heterogeneous graph structure is defined, and a dynamic adjacency matrix is calculated based on the real-time stress gradient so as to reflect physical connection, mechanical conduction and geological association relationships among nodes; carrying out fusion modeling on multi-source data in the heterogeneous graph structure by utilizing a multi-modal space-time diagram neural network, constructing a causal inference engine based on an output result of the multi-modal space-time diagram neural network, and updating a three-level modeling system through structural equation modeling, anti-factual inference and dynamic weight to obtain the heterogeneous graph structure. According to the method, the dynamic coupling rule among the dam structure, geology and material states is excavated, cross-modal space-time fusion of manual inspection and sensor monitoring data can be realized, the early recognition capability and early warning accuracy of dam potential safety hazards are improved, and the problems of data islands and insufficient relevance in a traditional monitoring method are effectively solved.

The invention provides a water conservancy project safety monitoring method and system based on data processing, and relates to the technical field of monitoring, and the method comprises the steps: obtaining and carrying out the multi-dimensional preprocessing of water conservancy project multi-source heterogeneous monitoring data through the deployment of a sensor network, and extracting multi-scale space-time fusion features from the data; performing structural state modeling, anomaly prediction, risk assessment and early warning by using a long-short-term memory neural network model integrated with a multi-head attention mechanism; and intelligent suggestions oriented to maintenance decisions are generated, so that comprehensive, accurate and prospective evaluation and early warning of the structural state of the water conservancy project are finally realized, the exception identification and risk prediction capabilities are effectively improved, the false alarm rate is reduced, refined and initiative intelligent maintenance decisions are provided, resource allocation is optimized, and the service life of the project is prolonged.

The invention discloses a concrete strength remote monitoring method and system suitable for a complex environment, and belongs to the technical field of civil engineering structure health monitoring. The remote monitoring method comprises the following steps: step 1, acquiring performance data of a concrete structure and related environmental factor data, and transmitting multi-source heterogeneous data to a data processing center in real time through a preset wireless communication protocol; step 2, constructing a five-dimensional tensor data structure, and realizing accurate mathematical expression of a complex coupling relationship between environmental factors and material characteristics through tensor decomposition; step 3, capturing nonlinear time-varying characteristics of concrete strength evolution; 4, quantifying the age effect through an intensity development rate index; step 5, based on the intensity development rate change trend, adaptively adjusting the data sampling frequency and monitoring the environmental condition fluctuation; and step 6, evaluating the safety state of the concrete structure in real time, and ensuring safe and reliable operation of the concrete structure in a complex environment.

The invention discloses a wetland ecological restoration dynamic monitoring method based on deep learning, and relates to the technical field of ecological restoration, and the method comprises the following steps: obtaining multi-source wetland ecological sensor data and remote sensing image flow in real time, constructing a space-time fusion data cube, and extracting an ecological feature tensor; performing degradation mode analysis on the ecological characteristic tensor, generating an ecological state dynamic topological graph, and calculating an ecological connectivity index; carrying out restoration demand identification based on the ecological connectivity index, positioning a degradation hot spot region through a multi-modal graph convolutional network, and generating a restoration priority region coordinate set; through multi-source data space-time fusion and deep crossing of deep learning and landscape ecology, a whole-process technical system from ecological state dynamic perception to restoration scheme intelligent optimization is constructed. The problems that in traditional wetland restoration, data scales are not matched, degradation area positioning is fuzzy, restoration path ecological adaptability is poor, and multi-target cooperation is difficult are effectively solved.

The invention discloses a battery abnormity identification monitoring method for battery operation and maintenance, and particularly relates to the technical field of battery detection. After a battery enters a station, a temperature difference array, shell micro-deformation, cavity gas indication and end plate near-end temperature are synchronously acquired to form a skin observation vector; applying slight current disturbance and recording voltage response and temperature rise differential to generate a disturbance differential vector; performing event alignment, same-window shadow trajectory depolarization and quantile projection normalization under a unified time reference to obtain a stable and reliable equivalent short window representation vector; performing weighted matching on the vector and historical reference fingerprint quality to obtain a consistency score, calculating a multi-physical disturbance coupling index and a historical trajectory deviation index, outputting a short window credibility coefficient through a discrimination model, generating a risk tag by combining a consistency threshold coefficient after zooming the consistency score, and performing risk identification on the risk tag. And linkage isolation recheck, power limitation and temperature rise limitation or release and archiving are carried out, so that the problem that abnormity in a short time window is easily covered by an environment common mode and artifacts is solved.

The invention belongs to the technical field of dam safety early warning, provides a dam safety monitoring method based on digital twinning, and aims at solving the problems that an existing monitoring method is weak in prediction capability, lags in early warning and the like. The method comprises the following steps: deploying multiple types of sensors to collect multi-dimensional physical state data; establishing a digital twinborn model containing a multi-physics field coupling simulation sub-model and data driving correction based on the BIM, the Internet of Things and finite elements; inputting a historical data driving model to predict an output state trend sequence and a state data value at a certain moment; performing difference analysis to construct an error vector, and calculating and correcting a deviation index; and calculating a structure health score, and if a threshold value is exceeded, early warning. The method has the advantages of improving prediction capability, enhancing adaptability, realizing comprehensive evaluation and providing systematic advanced decision basis for safety management.

The invention provides a production data monitoring method and system based on artificial intelligence, and belongs to the technical field of data processing, and the method comprises the steps: generating an initial monitoring strategy according to a quality influence factor evaluation model; based on the multi-modal data acquisition parameters, controlling multi-modal data acquisition equipment to acquire multi-source data in the charging pile assembling process; performing fusion processing on the multi-source data to generate a quality feature vector representing an assembly state; comparing the quality feature vector with a quality judgment threshold value, and outputting a state monitoring result; inputting the quality feature vector into a preset quality prediction model to obtain an assembly quality prediction result; determining a quality defect type and a risk level of the charging pile through a predefined quality defect mapping rule based on the state monitoring result and the assembly quality prediction result; according to the quality defect type and the risk level, at least one of the initial monitoring strategies is adjusted, and an updated monitoring strategy is generated; and the production quality and the production efficiency of the charging pile are improved.

The invention discloses an intelligent forest fire monitoring method and device, electronic equipment and a medium, and the monitoring method employs a multi-mode deep learning model based on a double-attention mechanism to enhance the capturing capability of early flame thermal radiation characteristics and smoke form characteristics, and greatly improves the recognition sensitivity. In combination with triple verification and a confidence coefficient decision-making mechanism, errors caused by environmental interference are effectively avoided, and the recognition reliability is comprehensively improved; meanwhile, based on intelligent gridding three-dimensional monitoring of terrain complexity and vegetation types, the blind area coverage rate is greatly reduced, normalized inspection and post-disaster quick response are performed through the unmanned aerial vehicle platform, the monitoring coverage range is enlarged, and the response efficiency is improved.

The invention discloses a rock multi-field coupling test system and a damage evaluation and prediction method thereof, belongs to the technical field of geotechnical engineering and geomechanics experiments, and solves the problem that the rock state under the multi-field coupling condition cannot be monitored in real time in the prior art. The system comprises a multi-physics field coupling loading system, a multi-field data monitoring system and a multi-field data acquisition system. The system can simulate and apply axial pressure, confining pressure, pore pressure and temperature of an in-situ environment to a rock sample, integrates an acoustic monitoring system, a deformation monitoring system and a CT scanning system to realize multi-dimensional real-time monitoring of rock sample damage, overcomes the limitation of a single monitoring method, realizes multi-system electrical connection by taking an upper computer as a data acquisition center, and realizes multi-dimensional monitoring of rock sample damage. And a multi-parameter model is constructed by fusing sound waves, deformation and CT data, so that the whole-process quantitative evaluation from microcrack initiation to macroscopic fracture is realized, and the damage evaluation is comprehensively performed.

The invention discloses a natural resource engineering monitoring method and system based on cloud computing, and relates to the technical field of engineering monitoring. A cloud access layer is constructed, a standardized monitoring data set is formed, a monitoring quality abnormity label set is established, and an engineering state vector set is generated based on a space-time alignment monitoring data set; according to the method, standardization and unified management of multi-source monitoring data are achieved through cloud access, the data accuracy is guaranteed in combination with time synchronization, space mapping and a quality label mechanism, the method constructs an engineering state profile and generates consistent snapshots, and the method has the advantages of being simple in structure, convenient to operate and high in practicability. Historical comparison and tracing are supported, a risk event set and a risk evidence chain are generated through causal atlas inference, risk full-chain tracing is achieved, a scheduling instruction is further generated and combined with an execution receipt to form closed-loop management, differential evaluation and dynamic adjustment are supported, and therefore linkage and continuous optimization of monitoring, risk identification and engineering scheduling are achieved.

The invention discloses a territorial space planning dynamic monitoring method based on multi-source data fusion, and belongs to the technical field of territorial space planning intelligent monitoring, and the method comprises the steps: obtaining data, and generating a multi-source heterogeneous data set; performing space-time alignment processing by using a preset regional association rule of a planning knowledge base to generate a space-time unified data set; constructing a dynamic knowledge graph taking planning elements as a core based on the data set, and updating node relation weights in real time; guiding a multi-source data fusion direction through the map relation weight to generate a fusion feature vector; incremental learning monitoring processing is carried out on the feature vectors, parameters are optimized, and a planning implementation state monitoring result is output; and updating the knowledge graph node relation weight in a closed loop manner according to a monitoring result, and synchronously optimizing incremental learning monitoring processing. According to the method, a dynamic knowledge graph is adopted to guide data fusion and an incremental learning closed-loop optimization mechanism in real time, and accurate perception and adaptive decision support of a planning implementation state can be realized.

The invention discloses a multi-modal collaborative security monitoring method, device and equipment and a storage medium, and relates to the technical field of computer vision and sensor fusion, the method comprises the following steps: collecting multi-modal data of a monitoring area, the multi-modal data comprising image data, infrared temperature data and environmental parameter data; based on the target detection model, detecting personnel security features and environment security features in the image data, and outputting a visual identification result and visual identification confidence; and when the visual identification confidence coefficient is lower than a preset threshold value and the consistency of the multi-modal data meets a synchronization condition, performing association verification of the multi-modal data by adopting weighted fusion in combination with the infrared temperature data and the environmental parameter data to perform secondary identification of a target so as to output the multi-modal fusion confidence coefficient. Through combination of visual identification and a multi-sensor fusion technology, accuracy and robustness of personnel and environment safety identification in a complex industrial environment are improved.

The invention discloses a low-altitude unmanned aerial vehicle trajectory tracking and monitoring method based on a 5G-A communication sensing integrated base station, and relates to the technical field of low-altitude traffic management and communication sensing fusion, and the method comprises the steps: firstly collecting multi-source data such as a communication sensing fusion signal, environment interference and unmanned aerial vehicle attributes, and carrying out the alignment and packaging of a unified timestamp and a coordinate system into a synchronous data frame; then, deep fusion and anti-interference processing are carried out on the data frames, noise is filtered out, and pure fusion data is generated; and furthermore, real-time track calculation and motion trend prediction are carried out on pure data by utilizing multi-base-station cooperative calculation and prediction. Based on this, through a multi-target feature recognition and clustering separation mechanism, independent individual trajectories are accurately stripped from a complex mixed data stream, and compliance verification and anomaly judgment are performed on the trajectories in combination with an airspace rule base. In this way, the problems of signal interference and multi-target aliasing in a complex environment can be effectively solved, and therefore high-precision global tracking of the low-altitude unmanned aerial vehicle and real-time monitoring of abnormal behaviors are achieved.

The invention discloses a comprehensive online monitoring method and system based on GIS partial discharge, and relates to the technical field of power monitoring, and the method comprises the steps: obtaining the operation state information of GIS equipment, and constructing a partial discharge monitoring model; acquiring a multi-modal sensing signal through the partial discharge monitoring model to obtain fused monitoring data; calculating a filter coefficient based on environment characteristic parameters and equipment operation characteristics, performing interference suppression processing on the fused monitoring data, and outputting filtered monitoring data; performing hierarchical processing on the filtered monitoring data by adopting a cloud edge collaborative architecture, extracting basic feature parameters through edge nodes, and transmitting the basic feature parameters to cloud nodes for deep learning analysis; and a deep learning algorithm is combined to identify a partial discharge fault feature mode, and a GIS equipment comprehensive online monitoring result is generated based on multi-dimensional feature fusion and a consistency verification mechanism. According to the invention, intelligent identification and credibility evaluation of the fault mode are realized, the misjudgment rate is obviously reduced, and the accuracy of maintenance decision is improved.

The invention relates to the technical field of vision, in particular to an object monitoring method based on a multi-camera joint calibration technology and a monitoring camera thereof. The method comprises the following steps: driving all cameras to synchronously shoot a calibration reference object with known geometric characteristics, and solving internal and external parameters of each camera and an accurate space pose relationship between the internal and external parameters by utilizing shot images to form a joint space relationship chain; controlling the camera array to synchronously shoot a target object, and correspondingly converting the two-dimensional image points acquired by the cameras into spatial point coordinates in a unified three-dimensional coordinate system by using the joint spatial relation chain to generate three-dimensional point cloud data of the target object; and processing the generated three-dimensional point cloud data, extracting key geometric features of the target object in real time, performing comparative analysis on the extracted features and a preset standard or a historical state, and outputting a state monitoring result of the target object. According to the invention, the influence of temperature drift and mechanical vibration on the measurement precision is effectively suppressed, and the stability of long-term monitoring of an industrial field is guaranteed.

The invention discloses a composite structure damage form monitoring method and system based on deep learning, and the method comprises the following steps: collecting multi-source monitoring data of a composite structure in a loaded state, and carrying out the preprocessing; reconstructing a damage evolution trajectory in a high-dimensional phase space by adopting a delay coordinate embedding method, and executing dimension reduction to generate a chaotic dynamics low-dimensional trajectory; extracting singular attractor features, and generating a singular attractor feature set; carrying out sequence modeling through an improved Linformer damage identification network, and generating a prediction vector; training an improved Linformer damage identification network based on the prediction vector, and introducing nonlinear dynamic constraints to generate a damage identification network of the nonlinear dynamic constraints; and performing damage form classification and damage evolution prediction. According to the method, dynamics and deep learning are fused, composite structure damage monitoring is achieved, and the method has the advantages of being high in accuracy, high in stability and reliable in early warning.

The invention provides a data flow monitoring method and system based on a large model, and the method comprises the steps: obtaining a data flow record set generated by a to-be-monitored system in a continuous operation period, carrying out the correlation path construction of the data flow record set, generating a data flow topological graph containing a node interaction relation and a time sequence dependency relation, and carrying out the correlation path construction of the data flow record set; calling a pre-trained circulation behavior analysis large model to perform node sequence pattern recognition on the data circulation topological graph, and generating behavior abnormal confidence and abnormal pattern labels of each node in the data circulation topological graph; and according to the abnormal behavior confidence and the abnormal mode label, screening an abnormal interaction node cluster in the data flow topological graph. According to the method, relevance between abnormal nodes and time sequence relevance are considered, missing detection or false detection is avoided, and the reliability of the monitoring effect is improved.

The invention discloses a transformer equipment oil leakage monitoring method and system based on image recognition, relates to the technical field of image processing, and effectively eliminates static background interference by constructing a standardized image sequence S (t), performing gray gradient analysis and pixel feature extraction, recognizing candidate oil spot areas and constructing a disturbance active score Sx. The edge structure and the morphological stability coefficient of the dynamic oil stain suspected area are further extracted, and the credible level quantitative judgment of the dynamic oil stain suspected area is realized by integrating the characteristic indexes and the morphological stability coefficient, so that the dynamic identification capability and the anti-interference capability are realized; the oil leakage identification accuracy and the system automation risk response capability in a complex environment are improved, false alarm and missing alarm are effectively avoided, and the operation safety of equipment is guaranteed.

The invention relates to the technical field of geological monitoring, and discloses a side slope deformation monitoring method based on spaceborne synthetic aperture radar, which comprises the following steps: S1, data acquisition: acquiring multi-source spaceborne synthetic aperture radar (SAR) data of a side slope area, the multi-source spaceborne SAR data comprising C-band SAR data and L-band SAR data; s2, self-adaptive preprocessing: self-adaptive preprocessing is performed on the multi-source spaceborne SAR data according to a ground surface coverage scene of a slope area, and the ground surface coverage scene comprises a low vegetation area, a medium-high vegetation area, a flying dust interference area and a high and steep terrain area. According to the slope deformation monitoring method based on the spaceborne synthetic aperture radar, a self-adaptive wave band fusion strategy is adopted for different vegetation coverage scenes, a time sequence coherence enhancement algorithm is combined, the vegetation area monitoring precision is remarkably improved, and noise signals are effectively eliminated and interference errors are reduced through flying dust and construction interference dynamic identification and a multi-dimensional filtering algorithm.

The invention relates to the technical field of unit detection, in particular to a unit magnetic variable on-line monitoring method and system, and the method comprises the steps: carrying out the multi-source data collection through a sensor, carrying out the correction of the multi-source data through a multi-dimensional calibration mechanism, synchronizing a timestamp through a dual-synchronization system, and carrying out the frequency-band-divided conditioning and standardization processing; environmental noise in the standardized multi-source data is eliminated through an intelligent algorithm, feature vectors are extracted, and purified feature vectors are obtained; screening effective abnormal features through an isolation forest algorithm; and inputting the effective abnormal features into an LSTM prediction model to obtain a corrected prediction value, substituting the corrected prediction value into a logistic regression formula to obtain a fault prediction probability of fault occurrence, calculating a health degree score, and performing graded early warning according to the health degree score. According to the scheme, through multi-dimensional calibration, working condition adaptive feature extraction and graded early warning, the unit monitoring data precision, the fault feature recognition accuracy and the operation and maintenance decision efficiency are improved.

The invention discloses a reservoir dam safety monitoring method and system based on edge calculation, and relates to the technical field of hydraulic engineering safety monitoring, and the method comprises the steps: obtaining monitoring data by each edge calculation node, carrying out the preprocessing, and generating a standardized data matrix; establishing a reference database, executing anomaly detection, and generating a labeling time sequence data matrix; performing multi-scale decomposition on the labeled time sequence data matrix, and constructing a node response feature matrix; the central processing unit receives data of each edge computing node, analyzes a multi-parameter spatial propagation mode by constructing a parameter-spatial correlation matrix, and obtains a spatial correlation feature matrix; establishing a Bayesian risk assessment model, predicting a dam risk level and outputting a risk evolution trend; and issuing a differentiated early warning instruction according to the risk assessment result. Through a distributed architecture combining edge calculation and central processing, real-time monitoring, intelligent analysis and accurate early warning of the dam safety state are realized, and the monitoring efficiency and the risk identification accuracy are improved.

The invention relates to the technical field of power equipment state monitoring, in particular to a lightning arrester state monitoring method, system and equipment based on multi-physics field coupling and a storage medium. Acquiring current-voltage characteristic parameters, temperature distribution data and mechanical stress data of the lightning arrester, performing electro-thermal-mechanical coupling analysis based on multi-physical field monitoring data, identifying overlapping positions of an electric field distortion area, a temperature abnormal area and a stress concentration area, and determining the overlapping positions as a degradation key area; establishing a correlation response relationship between the leakage current and the temperature and a transfer response relationship between the temperature and the mechanical stress for the deteriorated key area; on the basis of the leakage current change trend of the degradation key area and the coupling influence of the superimposed temperature field and stress field, a degradation feature fusion factor is constructed, the evolution law of the degradation feature fusion factor in the time sequence is analyzed, and the degradation threshold value under the multi-field synergistic effect is determined in combination with the electric-thermal coupling acceleration effect and the thermal-mechanical coupling weakening effect.

The invention relates to the technical field of mine safety, and discloses a coal mine underground gas monitoring method and system. The method comprises the following steps: acquiring methane absorption intensity data by adopting a tunable diode laser absorption spectrum, converting the methane absorption intensity data into gas concentration based on a Lambert-Beer law, performing temperature and humidity compensation, calculating a transmission priority according to concentration deviation to form a data packet, and establishing a spatial interpolation model by utilizing multipoint data to predict gas concentration distribution. Calculating a risk assessment index based on the predicted distribution, automatically adjusting the rotating speed of the ventilator, and controlling the electrical equipment to be powered off. According to the method, the technical problems of low detection precision, incomplete monitoring coverage, low data transmission efficiency and lack of prediction and early warning capabilities in the existing underground coal mine gas monitoring method are solved. And the accuracy of gas concentration detection and the intelligent level of gas monitoring are improved.

The invention discloses a sleep state monitoring and analyzing system based on multi-sensor fusion, and relates to the technical field of health monitoring, the sleep state monitoring and analyzing system comprises a multi-modal sensor module used for collecting multi-dimensional data related to a sleep state, the multi-dimensional data comprises a bio-electricity signal, a physiological parameter, body movement data and an environment parameter, and the multi-modal sensor module is used for collecting the multi-dimensional data; the multi-modal sensor module comprises a non-contact sensor, a flexible electronic skin sensor and a bio-electricity signal sensor, and the data fusion and processing module is used for carrying out preprocessing, dynamic self-adaptive fusion and federal learning modeling on collected original data. According to an existing contact type sleep state monitoring scheme, more flexible and accurate sleep state data are realized through a non-contact type monitoring sensor in cooperation with dynamic adjustment of sleep monitoring content and adjustment of weights of various sensors for monitoring the sleep state; and a more accurate and intuitive reference report is provided for the sleep state and the health state of the subsequent user.

The invention discloses a nuclear power plant radiation environment monitoring method and system, and the method comprises the steps: collecting the real-time monitoring data of a nuclear power plant radiation environment, and obtaining an environment monitoring real-time data set; performing dynamic threshold comparison on the environment monitoring real-time data set to generate an abnormal identifier or a normal feature vector; under the condition that the abnormal identifier is not generated, inputting the normal feature vector into a pre-trained environment trend prediction model, and outputting a radiation environment change trend curve and a potential abnormal probability in a future preset time period; and based on the change trend curve and the potential abnormal probability, generating graded early warning information, and finally outputting a monitoring early warning result including an early warning grade, prediction time efficiency and a coping strategy. According to the embodiment of the invention, the method can achieve the intelligent monitoring of the whole process of the radiation environment from the abnormal recognition to the prediction intervention, and improves the early warning accuracy, timeliness and decision support capability.

The invention relates to the technical field of power transformer state monitoring, and discloses a main transformer winding temperature rise on-line monitoring method and system, and the method comprises the steps: obtaining the calibration flight time of ultrasonic waves through an acoustic sensor array disposed on the outer wall of an oil tank in a quasi-isothermal state of a transformer; calculating a structure additional time delay which is caused by an internal fixed structure and does not change along with the temperature, and establishing an acoustic fingerprint for the transformer; during on-line monitoring, oil way net propagation time reflecting temperature change of the insulating oil is obtained from on-line flight time measured in real time; and then, based on the net propagation time of the oil way, an acoustic tomography algorithm is adopted to reconstruct internal two-dimensional or three-dimensional temperature field distribution, and the winding hot-spot temperature is finally and accurately calculated in combination with the real-time load current. According to the invention, through the acoustic fingerprint correction technology, the measurement interference of an internal fixed structure is eliminated, and non-intrusive, high-precision and visual online monitoring of the winding hot-spot temperature is realized.