2424 results about "Multi sensor" patented technology

The invention discloses an integrated collaborative management method for construction site risk operation data, and belongs to the technical field of data management. The method comprises the following steps: constructing and calibrating a space-time risk reference database; deducing a potential risk conduction link set, and generating an associated intervention knowledge base; during operation, through multi-sensor cooperative verification, an abnormal signal is confirmed as a risk event; matching the risk event with a conduction link to calculate a risk upgrade level and dynamically adjust an early warning threshold; and when the early warning is triggered, generating and issuing a dynamic collaborative response instruction, and feeding back a processing result to correct the reference database to form a management closed loop. According to the method, the technical means of constructing the space-time risk reference, deducing the conduction link, cooperatively verifying the risk and dynamically regulating and controlling the threshold are adopted, so that the predictability of project risk management and control, the efficiency of resource cooperative scheduling and the scientificity of overall management decision are improved.

The invention discloses a panoramic image real-time splicing algorithm and system based on multi-sensor fusion, and particularly relates to the technical field of panoramic image real-time splicing, and the algorithm comprises the following steps: constructing a structured fusion sequence based on multi-source images, postures and position information, optimizing a matching effect through high-density feature extraction and repeated texture recognition, and obtaining a multi-source image fusion sequence; a dynamic foreground and a static background are distinguished by using sparse optical flow so as to improve the visual angle estimation precision, pose fusion optimization is realized in combination with a multi-mode residual error, and the continuity and stability of a spliced image are improved through edge smoothing, brightness tuning and color correction; according to the method, the structured fusion sequence is constructed through multi-source data alignment, so that the data synchronization and splicing stability is improved; identifying repeated regions based on texture direction features, and optimizing feature matching accuracy; and through edge smoothing, brightness harmonizing and color consistency processing, the visual coherence and output quality of the panoramic image are enhanced.

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 an Internet of Things intelligent gas meter leakage detection and early warning system and method, and relates to the technical field of gas leakage detection, and the method comprises the following steps: S1, collecting data through multiple sensors; s2, identifying an equipment operation state based on the data set and outputting a state confidence coefficient; s3, a stable monitoring window period is judged, a corresponding strategy is selected to compensate pressure data, and reliability is marked; s4, dynamically generating a detection threshold in combination with the historical mode, the real-time parameters and the data reliability; s5, dynamically adjusting a risk assessment weight according to the confidence coefficient and the reliability, calculating a risk score and determining an early warning level; s6, safety operation is executed according to grades; and S7, updating the model by using process data to realize self-optimization. According to the invention, by deploying a multi-sensor array and adopting a multi-modal signal fusion algorithm, the system can accurately identify the running state of the gas appliance, provides reliable preposition information for subsequent analysis, and overcomes the defect that data of a traditional single sensor is easily interfered.

The invention relates to the technical field of power transmission and transformation equipment state evaluation, in particular to a power transmission and transformation equipment state evaluation system based on multi-sensor fusion, which realizes accurate alignment of multi-modal data by acquiring temperature, current, infrared thermal images, visible light images and laser point cloud data and constructing a unified time and space coordinate system. Short-term fluctuation features, image hot spot and texture anomaly features and structure change features are extracted through time sequence modeling, image saliency extraction, point cloud residual clustering and other methods, a joint feature vector is generated through fusion, and integrity and collaboration of multi-modal information expression are enhanced. And on the basis of a fixed time period, calculating a joint feature difference value between adjacent periods to obtain a residual sequence, and carrying out normalization processing in a sliding time window to construct a multi-period state residual spectrum. And finally, the atlas is input into a pre-training classification model, dynamic identification and anomaly detection of the equipment operation state are realized, and the intelligent evaluation capability under complex working conditions is effectively improved.

The invention discloses a ship-shore cooperative tracking and positioning method based on multi-modal sensor fusion, and belongs to the technical field of target positioning. The method comprises the steps that multi-sensor layout and sensor fusion calibration are carried out on a target ship set and a shore end respectively, multi-view image sequence data and three-dimensional point cloud data are obtained, and the target ship set comprises a plurality of target ships; performing data fusion based on the multi-view image sequence data and the three-dimensional point cloud data to obtain fusion data of the target ship set, establishing an adaptive motion state model and an adaptive observation model based on the fusion data, and performing state prediction, state updating, data association and tracking management on the target ship set by adopting an unscented Kalman filtering algorithm; and establishing a space-time diagram model based on the Kalman filtering fusion observation factor and the Kalman filtering state prediction factor, and performing pose optimization on the target ship set in combination with the GPS factor. According to the method, the accuracy of ship and ship-shore cooperative positioning is improved.

The invention discloses a fan blade state monitoring method based on multi-sensor fusion, relates to the technical field of wind power, and is suitable for wind energy prime mover equipment manufacturing and blade state monitoring technologies of onshore and offshore wind generating sets. The method comprises the following steps: acquiring operation data, a vibration signal, an acoustic signal and a pulse signal of a fan; the current working condition state of the fan is recognized, common-mode fault verification, local damage positioning and transient stress damage analysis are carried out on the vibration signals and the acoustic signals, and a fault analysis result and a first damage analysis result are obtained; performing phase-locked amplification analysis on the vibration signal and the acoustic signal through active excitation to obtain a second damage analysis result; and finally, a comprehensive state monitoring report of the fan blade is generated, so that the problems of difficulty in identification of weak damage and high false alarm rate of blades of land and offshore wind generating sets in wind energy prime mover equipment manufacturing under a non-stable working condition are solved, and the equipment operation and maintenance intelligent level in the wind energy prime mover equipment manufacturing industry is effectively improved.

In various examples, a framework is or provides an end-to-end solution that includes multi-sensor capture, data processing, inferencing, synchronization, alignment, and 3D rendering for multi-modal perception fusion pipelines. A multi-modal perception fusion pipeline may include a mixer, an aligner, an inference environment, and a multi-view renderer. The mixer may merge sensor data from different data sources into a single HashMap frame. The aligner may use calibration data for sensor-to-sensor coordinate transformations. The inference environment may receive multi-modality data and use custom preprocessing and custom postprocessing to generate inference results. The renderer may generate different sensor data renderings. The framework may include an application that uses configuration data to generate or configure a custom multi-modal perception fusion pipeline. The inference environment may access inference models using a uniform inference interface and support remote inference, allowing the pipeline to become an API client of the inference models.

The embodiment of the invention discloses a multi-sensor fusion anti-degradation SLAM mapping method and system. The method can effectively solve the problem of pose drift of a robot in a mapping process in structure degradation environments such as an indoor long corridor, constructs a globally consistent three-dimensional point cloud map and a robot trajectory, and comprises the following steps: realizing depth coupling of an IMU and a wheel speedometer based on extended Kalman filtering, and generating high-frequency pose prediction; denoising, down-sampling and motion distortion correction are carried out on the 4D laser radar point cloud, and the normal vector and intensity characteristics of the point cloud are extracted; a normal vector and intensity feature enhanced scanning matching algorithm is adopted, and a target function is optimized through a multi-feature weight, so that the matching precision in a degradation scene is improved; loopback detection is realized through candidate key frame screening and geometric registration verification, and a closed-loop constraint is incorporated into a factor graph for global correction; and finally, incrementally updating the global point cloud map and carrying out consistency optimization, and outputting a robust three-dimensional point cloud map and a high-precision robot track.

The invention relates to the technical field of motor control, and discloses a permanent magnet synchronous motor control system and method for an electric drive hydraulic source, and the system comprises a data collection module which is used for collecting the state parameters of the electric drive hydraulic source and the operation data of a permanent magnet synchronous motor in real time through multiple sensors; the sensor redundancy and fault-tolerant module is used for performing parallel sampling through hardware dual-redundancy configuration and dual sensors; the self-adaptive fuzzy logic control module is used for dynamically adjusting a rotating speed instruction and a torque instruction of the permanent magnet synchronous motor based on the collected state parameters; a data driving optimization module; and a digital twinning prediction maintenance module. A self-adaptive fuzzy logic control module is used for dynamically generating a motor rotating speed and torque instruction based on parameters such as real-time pressure, flow deviation and change rate, a fuzzy logic algorithm is used for processing a nonlinear time-varying load and quick response, a self-adaptive fuzzy logic control rule weight and a power conversion circuit parameter are optimized through a reinforcement learning algorithm, and the control precision is improved. And dynamic self-optimization of permanent magnet synchronous motor control is realized.

The invention discloses a non-aligned spatio-temporal data driven multi-sensor fusion method and system, and the method comprises the steps: building a matching measurement function through collecting radar point cloud density and visual image texture, and constructing a synchronous gradient field through employing a space depth change parameter and a visual motion contour, and determining a data synchronization standard; detecting a time sequence abnormal accumulation area based on a synchronization standard, and identifying an abnormal starting point and propagation time delay through a backtracking track to form an asynchronous sensor coordinate table; iMU angle increment and sensor response delay are collected, and sampling imbalance information is generated through time domain matching of an attitude switching signal and a data collection inflection point; performing association mapping on the asynchronous coordinate table and the sampling imbalance information to generate a feature offset, determining a matching core area, and creating a hierarchical calibration sequence; performing internal and external source difference identification analysis on the calibration sequence to obtain an offset transfer speed to form an offset coupling factor; and a fusion enhancement region is determined based on the offset coupling factor, and high-precision multi-source data fusion is realized through cascade analysis and gain coefficient coding.

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 discloses an automatic spraying device for inkjet printing equipment, belongs to the technical field of spraying, solves the problem that existing equipment can only process planes and cannot process curved surfaces and irregular surfaces, and comprises a workbench with a rotary clamping piece, a multi-dimensional adjustable spraying assembly and a processing unit. During working, the clamping piece fixes a workpiece and is driven by the motor to rotate, the laser scanner scans the workpiece to generate a three-dimensional model, and dynamic digital twin bodies are constructed in combination with equipment parameters; and the processing unit drives the nozzle to dynamically adjust the position and attitude under the cooperation of the motor through surface parameterization, trajectory planning and model prediction control, so that the optimal inkjet distance is ensured, and a multi-sensor fusion and error monitoring mechanism can correct the deviation in real time and automatically update the model and the trajectory when exceeding the limit. According to the invention, accurate spray painting of special-shaped workpieces with curved surfaces, concave surfaces and the like is realized, the spray painting precision and stability are improved, the application scene is widened, and the manual intervention requirement is reduced.

The invention relates to the technical field of unmanned aerial vehicles, and discloses an unmanned aerial vehicle low-altitude complex obstacle detection and avoidance system based on multi-sensor fusion, and the system comprises a multi-mode sensing module which is used for synchronously collecting heterogeneous sensing data of the surrounding environment of an unmanned aerial vehicle; and the sensing fusion processing module is in communication connection with the multi-mode sensing module and is used for carrying out timestamp alignment, space coordinate system unification and deep fusion processing on the heterogeneous sensing data, and all-weather and high-reliability environment sensing is realized through multi-sensor fusion and hardware synchronization. The system has dynamic obstacle prediction and three-dimensional risk map construction capabilities, and realizes crossing from passive obstacle avoidance to active foresight obstacle avoidance. And a safe and smooth flight path is generated in combination with hierarchical planning and model prediction control, so that the autonomous flight safety, reliability and intelligent level of the unmanned aerial vehicle in a low-altitude complex environment are fundamentally improved.

The invention discloses a refrigerated cabinet multi-sensor heterogeneous data fusion method based on artificial intelligence, and the method comprises the following steps: collecting multi-source heterogeneous data in the operation process of a refrigerated cabinet, and constructing a unified event time axis; preprocessing the multi-modal data, and generating a physical enhanced multi-modal tensor in combination with a heat-airflow-electricity model; constructing a disturbance activation vector and a modal mask pattern, and embedding the disturbance activation vector and the modal mask pattern into a multi-modal tensor; inputting the disturbance enhanced input data into an improved Tide model to perform long sequence modeling, and outputting a state prediction sequence; performing prediction residual analysis, constructing a dynamic anomaly scoring function, and fusing a disturbance activation vector and a modal mask graph to generate an anomaly type mark and confidence fusion state representation; sparse gradient uploading, parameter aggregation and structure synchronization are carried out among multiple devices by adopting a federal training framework. According to the method, the multi-modal data fusion precision and the anomaly recognition reliability of the refrigerated cabinet can be improved, and cross-equipment collaborative intelligent optimization is realized.

The invention provides an acoustic array adaptive calibration correction system applied to an underwater moving target, which relates to the technical field of marine equipment and comprises a multi-source excitation and environment perception module, an intelligent array perception and diagnosis module, an adaptive position inversion and uncertainty quantification module and a closed-loop calibration execution and self-learning optimization module. The multi-source excitation and environment perception module is responsible for providing reference signals required by calibration and establishing a correlation model of environment and formation distortion, and the intelligent array perception and diagnosis module realizes multi-modal data acquisition, array element health state monitoring and formation geometry self-perception. The adaptive position inversion and uncertainty quantization module completes signal processing, position estimation and error quantization propagation, and the closed-loop calibration execution and self-learning optimization module executes a compensation strategy, verifies a calibration effect and continuously optimizes system performance through online learning; the system solves the problem that a traditional method cannot process environment time varying, multi-sensor conflicts and uncertainty quantization.

The smoke detection method based on multi-sensor fusion analysis provided by the invention comprises the following steps: when a smoke sensor detects that the smoke concentration in a detection range reaches a first threshold value, acquiring a temperature rising speed in the detection range detected by a temperature sensor, acquiring the environmental heat radiation uniformity in the detection range detected by the human body sensor; generating a corresponding correction coefficient to correct the smoke concentration and the first threshold value to obtain a corresponding corrected smoke concentration and a corrected threshold value; if the corrected smoke concentration reaches a corrected threshold value, a comprehensive risk value is calculated based on the corrected smoke concentration and the temperature rising speed; and if the comprehensive risk value reaches a risk threshold, determining that the fire is a real fire. According to the invention, through the fusion processing of the smoke sensor, the temperature sensor and the human body sensor, the defect that fire detection through smoke is easily interfered by the environment and misinformation is generated at present is overcome.

The invention provides a water network monitoring method and system based on intelligent water supply and drainage, and the method comprises the steps: obtaining multi-source heterogeneous monitoring data of a water network in real time, carrying out the unified format conversion and time-space synchronization processing of the multi-source heterogeneous monitoring data based on a protocol adaptive fusion mechanism, and obtaining a standardized multi-source time series data set; performing exception identification processing on the standardized multi-source time sequence data set based on the time-varying statistical characteristics and the dynamic control limit to obtain a candidate exception event set; performing multi-sensor cross validation processing on the candidate abnormal event set based on a pipe network topological space association knowledge graph to obtain a high-confidence abnormal event set; performing reverse scene matching analysis on the high-confidence abnormal event set based on a hydraulic numerical simulation model to obtain a preliminary positioning area; performing positioning optimization processing on the preliminary positioning area based on a graph theory pressure wave propagation algorithm to obtain an abnormal position; and performing multi-dimensional fusion display processing on the abnormal position to generate a monitoring report. According to the method, accurate positioning of the abnormal position is realized.

The invention discloses a mobile robot control device giving consideration to field navigation patrol and transportation load, and relates to the field of robot control, and the device comprises a sensor data collection module which forms multi-source sensor data; the environment understanding module is used for predicting terrain risks and weather influences based on the feature vectors and outputting environment feature data and risk prediction; the navigation control module is used for navigating a path based on the environment characteristic data and generating a robot motion control instruction; the load management module adjusts parameters of the navigation control module based on the load state; the feedback adjusting module is used for dynamically adjusting control parameters according to the load state and feeding back the control parameters to the navigation control module to optimize and modify a robot motion control instruction; and the intelligent detection module is used for detecting environment abnormity and dangerous case events and outputting an alarm signal. The method has the advantages that accurate prediction of environmental risks is realized through multi-sensor layered feature extraction and attention weighted fusion, and motion parameters are dynamically adjusted in combination with a moment balance model.

A method performed by an apparatus for controlling autonomous driving of a vehicle is introduced. The method may comprise generating, based on a segmentation model processing point cloud data, a first semantic grid map, generating, based on an object detection model, a second semantic grid map, adjusting a probability regarding whether occupancy exists for an element included in each grid of the first semantic grid map and the second semantic grid map, and generating a fused grid map by determining, as a representative label, at least one label corresponding to a highest value among final probabilities of the at least one label, wherein the final probabilities are determined based on whether the at least one label matches the element, outputting, based on the fused grid map, a signal, and controlling, based on the signal, autonomous driving of the vehicle.

The invention relates to the technical field of solid waste treatment, in particular to an intelligent waste sorting method based on multi-sensor fusion. Carrying out multi-level fusion decision making: carrying out sewage reflection suppression on the visible light image; carrying out matching identification and rejecting a liquid pollution area based on a near infrared spectrum; fusing the visible light texture, the spectral feature and the electromagnetic signal to generate a space grid feature vector; identifying the material category through a cascade classifier, and dynamically adjusting the weight of each sensor in the decision according to the environmental parameters; self-adaptive sorting execution: triggering a corresponding sorting execution mechanism according to an identification result; based on the transmission belt speed, the execution mechanism response delay and the material physical attribute, the action advance compensation amount is calculated; the action time of an execution mechanism is adaptively prolonged for the stacked materials according to the stacking hierarchy; and online feedback optimization. Through dynamic compensation and time sequence adjustment, the time sequence consistency of multi-sensor data is ensured, and the influence of band speed fluctuation is overcome.

The invention relates to the technical field of robot obstacle avoidance methods, in particular to an obstacle avoidance method of a robot. According to the method, a dynamic environment sensing system is constructed through multi-sensor fusion, and a dynamic feature description package and a static environment sketch are generated. The system generates a global navigation route by adopting an improved path planning algorithm, and constructs an obstacle space-time probability distribution map in combination with a multi-hypothesis prediction technology. According to the method, a two-channel intention analysis mechanism is introduced, the motion intention of a target is predicted by analyzing the kinematics characteristics and the biological behavior characteristics of the target, and a four-dimensional space-time threat model is established by fusing a space-time probability graph. And according to the threat level, a hierarchical obstacle avoidance strategy is dynamically generated, wherein the hierarchical obstacle avoidance strategy comprises various response modes such as path fine adjustment, speed cooperative adjustment and emergency channel avoidance. The environment cognition and behavior model is continuously updated through a closed-loop optimization mechanism, and finally safe and smooth autonomous navigation in the high-dynamic man-machine coexistence environment is achieved.

The invention relates to the technical field of terrain reconstruction and path planning, and particularly provides a night terrain reconstruction and path planning method and system based on multi-sensor collaboration, and the method comprises the steps: obtaining the multi-source sensing data of a night environment through a data collection module; performing multi-source data fusion processing on the multi-source sensing data to generate a night environment reconstruction model fusing multi-dimensional features; performing obstacle semantic analysis on the night environment reconstruction model, extracting static obstacle distribution information and a dynamic obstacle movement track, and constructing a composite environment map; according to the composite environment map, a passable path is generated through a dynamic optimization algorithm in combination with terrain passability parameters and kinematics constraint conditions of a mobile carrier, and path parameters are dynamically adjusted based on real-time update data of a night environment reconstruction model; when it is detected that the obstacle distance or the motion state exceeds a safety threshold value, path real-time correction and graded obstacle avoidance instruction output are triggered through multi-sensor confidence evaluation and path collision risk prediction.

The invention relates to the technical field of supervision control and data acquisition, in particular to an intelligent monitoring system based on a multi-sensor fusion technology, comprising a multi-sensor acquisition and preprocessing module which acquires and self-calibrates at least two kinds of physical quantity data in real time and fuses the physical quantity data into a unified monitoring feature vector; and the fault diagnosis and prediction module is used for integrating interpretable semantic analysis in combination with a production plan and a maintenance record. And performing equipment state diagnosis, operation trend analysis and fault prediction in real time by utilizing electric inspection type monitoring, generating health assessment and early warning, and guiding optimization and adjustment of a control strategy. And the data storage module is used for storing semantic data model evaluation results, diagnosis reports and early warning information, and supporting fault root analysis and control effect evaluation based on processing batches and time sequences. And the remote man-machine interaction interface is used for displaying the equipment running state, the control parameters and the automatic process in real time, supporting voice instruction interaction and facilitating remote monitoring and intervention of an operator.

The invention discloses a complete switch equipment online detection method and system based on multi-sensor fusion, and relates to the technical field of equipment state detection.The method comprises the steps that temperature distribution, partial discharge signals, mechanical vibration waveforms and operation current data of switch equipment are collected, key fault features are extracted based on preprocessed multi-source data, and the key fault features are extracted; generating a high-dimensional feature matrix, constructing a graph convolution-long and short-term memory hybrid network model as a fault diagnosis model, and obtaining a defect detection result according to the spatial-temporal features; and constructing an equipment degradation index based on defect characteristics to calculate an equipment health index, performing probabilistic prediction of the remaining service life in combination with a Wiener degradation model, and triggering an early warning signal when defects are detected or the service life is lower than a threshold value. According to the method, the problems of large data limitation and insufficient fault diagnosis precision of a single sensor in the operation state monitoring of the whole set of switch equipment are solved, and fault evaluation and residual life prediction are further realized by combining a hidden defect evolution rule.

The invention relates to the technical field of unmanned aerial vehicle control, in particular to an unmanned aerial vehicle dynamic obstacle avoidance method based on multi-sensor fusion. Comprising the steps of receiving an image sequence, a depth point cloud set, a pose state parameter and a flight speed parameter; calculating an illumination distortion gradient value and a speckle noise entropy value based on the image sequence and the depth point cloud set; performing coordinate registration and feature extraction on the image sequence, the depth point cloud set and the pose state parameter, and outputting a visual feature set and a radar feature set; generating a weighted visual feature set and a weighted radar feature set through a weight adjustment function, and generating a joint heterogeneous feature tensor through alignment splicing; and generating a body attitude and thrust control instruction by using the combined heterogeneous feature tensor through a probability prediction model and a model prediction control algorithm device. According to the method, through cross-domain multiplexing and deep coupling of the body physical parameters in the data stream, error accumulation and decision delay caused by multi-stage series calculation are avoided, and global performability of an obstacle avoidance task is facilitated.

The invention relates to the technical field of panel display brightness adjustment, and discloses a panel display module brightness adjustment method based on multi-sensor fusion. The method comprises the following steps: collecting multi-source sensor data of a panel display module; processing multi-source sensor data by using a dynamic feature fusion algorithm, distributing dynamic weights according to the real-time confidence of each sensor data source through a weighted fusion mechanism, and generating a brightness adjustment feature set; constructing a dynamic brightness adjustment index table, representing display partition brightness parameters by nodes, representing visual relevance among display partitions by edges, and updating node attributes and edge relevance strength in real time based on an environment change triggering mechanism; according to the brightness adjustment feature set, executing hierarchical association extension calculation in a dynamic brightness adjustment index table, and dynamically determining a brightness optimization action range; and in the optimization action range, synchronously analyzing the color deviation tolerance, the power consumption constraint condition and the visual comfort index through a multi-dimensional evaluation algorithm, and generating a brightness adjustment decision parameter.

The invention belongs to the technical field of electric power metering box state monitoring, and particularly relates to an electric power metering box state monitoring method based on multi-sensor fusion. The method comprises the following steps: acquiring displacement, temperature and humidity, water immersion and infrared sensing data, performing acquisition calibration and time alignment, and generating a unified time base and a sensing credibility table; performing time sequence processing, feature construction and scene prior association to form a fusion feature set; generating an evaluation result set through fusion evaluation and anomaly recognition; carrying out graded early warning and strategy generation to generate an evidence obtaining trigger list; and finally, evidence obtaining and learning updating are executed to generate a baseline updating package. The beneficial effects are that monitoring accuracy, reliability and adaptive capability are improved, and closed-loop management and intelligent optimization are realized.

The invention provides an automatic processing method for real-time observation data of an ocean station, and belongs to the technical field of ocean observation data processing. A wavelet packet decomposition multi-scale noise separation algorithm is established to distinguish environmental noise and real signals, dual-sensor redundancy configuration is combined with Bayesian inference to identify sensor drift, and a calibration coefficient is updated in real time through a recursive least square method. A one-dimensional time sequence is mapped to a high-dimensional phase space by utilizing a phase space reconstruction algorithm to realize high-precision prediction of a chaotic signal, a hierarchical data storage architecture is established, and a data migration strategy is iteratively optimized through a hierarchical correlation degree function; the technical problem that time synchronization signals are difficult to reconstruct accurately according to asynchronous sampling data of multiple sensors of an ocean station is solved.

The invention relates to the technical field of data processing, in particular to a laboratory safety monitoring method and system based on multi-sensor data. The method comprises the following steps: acquiring standardized data of dimension data of multi-source sensors deployed at different positions in a laboratory at a plurality of sampling moments; obtaining a fused data stream at each sampling moment; constructing a dynamic heterogeneous graph in a laboratory; updating the node state of the dynamic heterogeneous graph in real time by using the fusion data stream, and processing the dynamic heterogeneous graph by using a graph neural network model to extract a graph feature vector sequence representing the overall state of the laboratory scene; and inputting the graph feature vector sequence into a time sequence prediction model, and outputting a risk vector so as to monitor the safety risk of the laboratory environment. According to the scheme of the invention, the safety monitoring of the laboratory environment can be safely and accurately carried out.