1030 results about "Thermographic imaging" patented technology

A pest control system is disclosed comprising an optical, computational, and monitoring subsystem, optionally mounted on a mobile platform. The optical system may include a neutralizing laser or multi-wavelength light source, discovery and detail cameras (optionally stereo), a beam-steering mechanism, tunable focus, and optional thermal or depth sensors. The processor, such as a GPU or FPGA, identifies insect or biological targets, adjusts laser focus by depth, and controls beam activation. A monitoring system verifies safety by detecting humans or other non-target entities using environmental and thermal cameras; if detected, laser firing is inhibited. The mobile platform may use wheels, propellers, tracks, or cables, with GPS and data links for remote control. A visible light pre-flash may induce a blink reflex before firing. In some embodiments, a scouting drone transmits target coordinates to the neutralization unit, enabling coordinated, efficient, and safe laser-based pest control.

The invention discloses a nondestructive testing method for internal defects of a concrete structure fusing big data feature extraction and deep learning. According to the method, through multi-source data collaboration and dynamic feature fusion, the accuracy and robustness of concrete structure defect detection are remarkably improved. In a data processing link, ultrasonic electromagnetic induction infrared thermal imaging data and the like acquired by a multi-source nondestructive testing technology are subjected to collaborative preprocessing, so that the influence of noise interference and environmental fluctuation is eliminated, and standardized input is provided for feature extraction. The dynamic weight distribution network further combines the relevance of each modal feature in a historical defect sample, adjusts fusion weights of different modals in real time, reinforces ultrasonic features with great contribution to cavity recognition or infrared features sensitive to cracks, effectively compresses redundant information, and improves the accuracy of cavity recognition. According to the method, features and data-driven deep features of the fused feature vectors are manually designed at the same time, so that the limitation of single-modal data is avoided, and a model can more accurately capture multi-dimensional features of defects.

The invention discloses a single-frame infrared thermal imaging building outer wall hollowing detection method and system based on heat flow evaluation. The method comprises the following steps: acquiring a single-frame infrared thermal image and a visible light image which are synchronously acquired; generating an infrared thermal imaging sequence based on the single-frame infrared thermal image and the Green function of the heat conduction control equation; performing time-space frequency domain transformation and column harmonic function inversion on the infrared thermal imaging sequence, and inversely transforming back to a time-space domain to generate a virtual heat flow image sequence; sequentially carrying out binarization processing and discrimination function enhancement processing on the single-frame virtual heat flow image to obtain an outer wall enhanced binarization image; identifying an interferent from the visible light image by adopting a target detection network; and carrying out registration and mapping on an identification result in the visible light image and the infrared thermal image, and removing interferents in the outer wall enhanced binarization image to obtain a pure outer wall binarization image. The method has the advantages of high environmental adaptability, high recognition precision and the like, and is suitable for safety detection and maintenance of building exterior walls.

The invention relates to the technical field of target detection, in particular to a search and rescue task-oriented unmanned aerial vehicle thermal imaging visual target detection method, which comprises the following steps of: acquiring multiple frames of thermal imaging images of an unmanned aerial vehicle, extracting regional thermal difference characteristics according to a window, marking a non-background region to generate a candidate set, and fitting and reconstructing a suspected thermal target contour. And analyzing the track and the thermal change rate, screening background interference, identifying jump abnormity, positioning the gravity center, and generating a target repositioning signal. According to the method, the heat value range and variance index sequence in the image area is constructed, the thermal anomaly area is judged in combination with the temperature baseline difference, background disturbance comparison is executed in combination with the direction vector of the coordinate trajectory in the multi-frame image and the thermal change parameter, the false detection probability caused by background noise is reduced, and the detection accuracy is improved. The target jump identification is carried out according to the inter-frame heat value and area change rate in linkage with the thermal isoline closure degree, the target discrimination accuracy in a shielding scene is improved, and the robustness of thermal target extraction in a complex search and rescue environment is integrally improved.

The invention discloses a multi-modal data collaborative intelligent detection and three-dimensional positioning method for marine ships. The method comprises the following steps: performing refined preprocessing on collected multi-source heterogeneous data, and performing preliminary target detection; performing dynamic weighted fusion on the preprocessed multi-source heterogeneous data, and realizing accurate coordinate recovery of the ship target in a three-dimensional space by combining depth data of the laser radar; and predicting and updating the motion state of the identified target based on a multi-tracking mechanism collaborative strategy. Through multi-modal data perception and preliminary processing, heterogeneous data deep fusion and three-dimensional positioning, and advanced multi-target continuous tracking and state estimation, abundant texture information of a visible light image, anti-interference capability of thermal imaging and accurate space depth data of a laser radar are fully utilized, and a multi-tracking mechanism cooperation strategy is integrated. The motion state of the recognized target is predicted and updated, and the problem of tracking interruption caused by nonlinear motion or transient shielding of the marine target is effectively solved.

The invention discloses an unmanned aerial vehicle infrared thermal imaging method and device for cold leakage detection of a low-temperature storage tank, and relates to the field of automatic inspection and detection of storage tanks, and the method comprises the steps: determining the inspection range and detection distance of an unmanned aerial vehicle based on geometric parameters, a cold leakage frequency region and environment parameters of a site; calculating the distance between the shooting points and the vertical coverage height of single-circle flight, further dividing flight elevation layering, and calculating the number of the shooting points of each circle of flight; establishing a three-dimensional model; collecting a visible light image and an infrared thermal imaging image of each shooting point; the method comprises the following steps: performing multi-dimensional correction and temperature image conversion on an acquired infrared thermal imaging image, performing image registration on a temperature image with a temperature scale and a visible light image, performing cold leakage area identification to obtain a cold leakage area, performing quantitative calculation to obtain three-dimensional positioning of a cold leakage position and a cold leakage area, and performing cold leakage area identification on the cold leakage area. And generating a visual detection result, a detection report and a maintenance suggestion. According to the invention, automatic detection and accurate analysis of the cold leakage area of the low-temperature storage tank can be realized.

The invention belongs to the field of target identification, and relates to a multi-modal target identification method and device for an unmanned aerial vehicle, electronic equipment and a storage medium, and the method comprises the steps: collecting multi-modal data, and carrying out the preprocessing of the multi-modal data, the multi-modal data comprises visible light image data, infrared thermal imaging data, laser radar point cloud data and synthetic aperture radar data; carrying out hierarchical cross-modal feature extraction on the basis of the preprocessed multi-modal data; multi-scale cross-modal feature fusion based on attention guidance is carried out; predicting the position and category of each target in the image based on the fused multi-scale features; assigning a unique ID to each target by associating detection results in continuous frames to form a motion track, and correcting an identification result of the current frame by using spatio-temporal context information; and lightweight model optimization and embedded real-time deployment are carried out. The problems of target shielding and losing can be solved, current frame identification can be further optimized, and tracking continuity and accuracy are ensured.

The invention provides a textile cloth defect real-time detection method and system based on multi-modal feature fusion, and the method comprises the steps: collecting visual image data, infrared thermal imaging data and ultrasonic acoustic data of textile cloth through a multi-sensor array, and forming multi-modal input; performing time sequence alignment and noise filtering preprocessing on the multi-modal data to eliminate motion artifacts and environmental interference; a parallel feature extraction module is used for extracting texture features from the visual data, extracting temperature distribution features from the thermal imaging data and extracting acoustic impedance features from the acoustic data. By deeply fusing complementary information of three modes of vision, thermal imaging and ultrasonic wave, the detection capability is improved, the vision mode captures surface texture details, the thermal imaging mode reveals thermodynamic anomalies related to friction and materials, the ultrasonic wave mode perceives subcutaneous structure defects, hidden flaws which cannot be recognized by a single mode can be found, and the detection efficiency is improved. Therefore, the omission ratio is greatly reduced, and flaw types are distinguished more accurately.

The invention relates to the technical field of kitchen safety monitoring, and discloses a kitchen gas stove abnormity early warning system based on infrared thermal imaging multispectrum. The system comprises an infrared thermal imaging acquisition module, a dynamic feature extraction module and an abnormal behavior identification module. The infrared thermal imaging acquisition module synchronously captures the surface temperature field distribution of the gas cooking range through a multispectral infrared sensor array, obtains flame form data in combination with a visible light image, and generates a multi-channel fused infrared thermal imaging characteristic spectrum through processing. The dynamic feature extraction module adopts a three-dimensional convolution kernel to scan continuous time sequence frames, identifies flame morphological feature differences, calculates thermal radiation intensity energy distribution offset, and outputs dynamic feature vectors. The abnormal behavior recognition module extracts a correlation mode through a deep residual network, compares current features with a historical normal working condition feature library, and generates a preliminary early warning signal containing an abnormal type code and a risk level when the attenuation rate of flame spectral energy in a specific infrared band exceeds a first threshold value.

The invention belongs to the technical field of intelligent operation and maintenance and automatic inspection of power equipment, and particularly discloses a power transformation and distribution station room inspection method and device based on multi-mode visual perception. The method comprises the following steps: controlling the intelligent inspection robot to move and synchronously acquiring visible light, infrared thermal imaging, partial discharge ultrasound and other multi-mode sensing data streams; generating an ultrasonic-guided enhanced infrared image, a power equipment structure map and an ambient light field distribution map through deep fusion in combination with ambient illumination parameters; and on the basis of the deeply fused information, generating a refined health state level of the power equipment, and finally automatically generating an inspection report conforming to the regulation. According to the invention, through deep cooperation and mutual verification of the multi-modal data, the perception robustness and diagnosis accuracy of early faults and abnormal states of equipment in a complex environment are improved, intelligent inspection from later judgment to beforehand prediction is realized, and the power supply safety of a power transformation and distribution station room is guaranteed.

The invention provides a manufacturing method of an ultra-low dielectric loss high-speed copper-clad plate, which comprises the following steps of: constructing a physical coupling model of a multi-zone independent temperature control module and an electro-thermal conductivity adjusting layer based on the thickness of the copper-clad plate and a thermal response parameter of a resin system; space and time dynamic distribution of heating area power and heat conductivity is achieved; a dynamic thermal boundary regulation and control algorithm, finite element simulation and self-adaptive PID feedback are fused, real-time monitoring and deviation compensation of a temperature field are carried out through infrared thermal imaging and edge calculation, heating and heat dissipation strategies are cooperatively adjusted, a heat flow path can be actively guided, the uniformity of thermal stress distribution and the structural stability are achieved, and the method is suitable for large-scale popularization and application. And the thermal management efficiency of the copper-clad plate in a complex manufacturing environment is improved.

The invention discloses a boiler tube group diagnosis and maintenance method, and relates to the technical field of boiler monitoring, and the method comprises the following steps: building a thermodynamic simulation model, and carrying out reference simulation to obtain a reference system response parameter and a reference thermodynamic parameter; based on the simulation model, multiple times of simulation are carried out by adjusting the operation condition and the fault state, and a tube panel group health evaluation model is constructed by utilizing machine learning; introducing a thermal medium into the target tube group system, collecting actual measurement data, and screening out a problem tube panel group through the health evaluation model; and performing three-dimensional laser scanning and thermal imaging image acquisition on the defective tube panel group, fusing temperature information and space coordinates through coordinate mapping to construct a three-dimensional temperature field data set, positioning an abnormal tube section needing to be maintained according to the three-dimensional temperature field data set, and performing local maintenance. According to the method, step-by-step positioning and targeted maintenance from system-level abnormity to pipe section-level defects can be realized, and the diagnosis accuracy and maintenance efficiency of the boiler pipe group are remarkably improved.

The invention relates to the technical field of battery thermal management, in particular to an energy storage battery module multi-mode heat flow field coupling early warning cooling decision system and method, which comprises a multi-sensor acquisition module, an infrared thermal imaging preprocessing module, a battery pack thermal diffusion mechanism model module, a data fusion model module and a cooling decision module, the core innovation lies in that a battery pack thermal diffusion mechanism model is constructed based on a differential geometry theory, a battery pack surface temperature field is regarded as a Riemannian manifold embedded in a three-dimensional space, early prediction of thermal runaway is realized by analyzing topological structure change of a heat flow field, and a system acquires temperature, pressure, vibration and other multi-modal data of an energy storage battery module and performs thermal runaway prediction on the energy storage battery module. The method comprises the following steps: preprocessing infrared thermal imaging data, generating three-dimensional thermal diffusion data, constructing an electric-thermal-flow multi-physical field dynamic model, identifying a critical point and a topological structure in a thermal flow field, generating thermal field characteristic data, fusing multi-source data to generate a thermal diffusion imbalance criterion, and optimizing cooling parameters by adopting a reinforcement learning algorithm.

The invention discloses a photovoltaic power station defect detection method based on thermal imaging data, and relates to the technical field of artificial intelligence, and the method comprises the steps: S1, collecting the thermal imaging data of a photovoltaic module under different working conditions, and carrying out the preprocessing of the thermal imaging data, and obtaining a thermal imaging data set; s2, based on the thermal imaging data set, dynamically dividing a temperature interval and calculating an adaptive bandwidth, and performing weight distribution and color band center weighted fusion by using a Sigmoid function to generate an adaptive pseudo-color image capable of enhancing local contrast; s3, constructing a double-branch defect detection model based on the original temperature data and the pseudo-color image; and S4, performing defect detection on the real-time thermal imaging data based on the double-branch defect detection model to obtain a defect detection result of the current photovoltaic power station. The method can be effectively applied to large-scale inspection operation and maintenance of the photovoltaic power station.

The invention discloses a three-mode saliency target detection method and system based on frequency domain decomposition and reconstruction. The method comprises the following steps: firstly, respectively preprocessing a training set and a test set in a three-mode saliency target detection data set; secondly, constructing a three-mode saliency target detection network based on frequency domain decomposition and reconstruction; and finally, sending the preprocessed training set image into a three-mode saliency target detection network for processing, outputting a prediction map consistent with the input image in size, completing target detection, and performing training and testing. According to the invention, through designing the interaction, fusion and enhancement network, the information complementation advantages of three modes of visible light, depth and thermal imaging are fully utilized, the synergistic interaction and global perception efficiency among multi-mode information are further enhanced, and accurate salient target detection is realized.

The invention provides a thermal imaging automatic calibration and key region segmentation method. The method comprises the steps of obtaining a thermal imaging image, and then performing preliminary denoising processing on the image to obtain a denoised image; constructing a high-density grid in an image pixel dot matrix by adopting a dense sampling method according to the de-noised image so as to determine an initial pixel coordinate of each grid point; obtaining neighborhood feature vectors through an abnormal point set, and grouping the vectors by adopting K-means clustering so as to determine a temperature change mode, and fusing a temperature gradient to extract a boundary extraction basis as grid optimization input; and extracting a boundary extraction basis of the key part from the temperature change mode, adjusting the high-density grid according to the boundary extraction basis to obtain an optimized grid, and starting relocation iteration adjustment through coordinate deviation calculation. According to the method, a closed-loop optimization process of high-density grid construction, abnormal point identification, clustering analysis, grid dynamic optimization and iterative relocation is constructed, so that refinement and adaptive segmentation of a thermal anomaly region are realized.

This invention relates to a system and method for non-invasive detection of gravesites and archaeological features using multimodal remote sensing and machine learning. Remotely sensed datasets, including RGB, multispectral, hyperspectral, LiDAR, and thermal imagery, are orthorectified, mosaicked, and subdivided into tiled image segments. Features are labeled through manual annotation of visible markers and environmental signatures and expanded via iterative augmentation. A supervised pipeline trains computer vision models, such as YOLO-based detectors, in parallel with tabular models derived from spectral indices (NDVI, NDRE), LiDAR elevation derivatives, and thermal anomalies. Inference outputs are cross-validated against thresholded evidence layers to reject false positives and upgraded when spectral, spatial, and thermal evidence align. Validated detections are exported as GIS-compatible layers with confidence scores and metadata. The system provides a scalable, replicable tool supporting archaeologists, Indigenous communities, and planners in cemetery investigations, cultural resource management, and humanitarian searches for unmarked or clandestine graves.

The invention provides a photovoltaic fault identification method based on unmanned aerial vehicle inspection, and belongs to the field of photovoltaic technology, and the method comprises the steps: collecting a photovoltaic panel image through the infrared thermal imaging of an unmanned aerial vehicle, building a multi-scale image pyramid, inputting a small target enhancement identification model, and carrying out the feature extraction; a space attention mechanism is utilized to adaptively enhance small target area feature response according to texture complexity, the resolution of a suspected fault area is improved through super-resolution reconstruction, optimal enhancement factor parameters are globally searched in combination with a particle swarm optimization algorithm, fault type classification recognition and confidence evaluation are achieved, and the fault classification recognition accuracy is improved. And adjusting the inspection frequency according to the confidence score, and establishing a fault distribution thermodynamic diagram to trigger region early warning. The technical problem that small-size photovoltaic fault features in the infrared inspection image of the unmanned aerial vehicle are difficult to accurately identify is solved.

The invention relates to the technical field of smart home control, and particularly discloses a smart home central control system and method based on multi-mode perception, and the system comprises the steps: synchronously collecting a voice audio signal, a gesture image signal, an infrared thermal imaging signal and a millimeter wave radar signal through a plurality of groups of sensors; performing blind source separation processing on the voice and gesture signals, extracting a voice command component and a gesture action component which are independent in statistics, and performing space-time alignment and Kalman filtering fusion on the infrared and radar signals to generate a dynamic environment sensing graph; voice intention features, gesture track features and environment anomaly features are extracted through Mel frequency cepstrum coefficient analysis, skeleton key point tracking and multi-level convolution processing; constructing a three-dimensional decision matrix based on the features, performing weighted evaluation through a fuzzy logic rule base to generate a control instruction priority sequence, and dynamically adjusting an equipment operation mode according to the priority; according to the invention, the problems of control conflict and response delay caused by multi-mode signal coupling are solved.

The utility model relates to the technical field of inspection robots, and discloses an indoor inspection robot which comprises a base, one side of the interior of the base is fixedly connected with a first supporting shell, the other side of the interior of the base is fixedly connected with a second supporting shell, and the inner wall of the base is fixedly connected with driving motors which are in bilateral symmetry. And moving wheels are fixedly connected to the output ends of the driving motors, the first supporting shell and the second supporting shell jointly store internal parts of the robot, and headlamps are arranged in the first supporting shell and the second supporting shell correspondingly. According to the utility model, through cooperation of the dual-light holder, the visible light camera and the infrared thermal imager, visible light and thermal imaging dual-mode inspection is realized, and the environmental monitoring precision is improved; the microphone module and the gas sensor module work cooperatively to collect sound information and environment gas parameters and enhance the inspection function; the two-stage lifting arm is matched with the sliding arm, so that the heights of the camera and the sensor are adjustable, and different inspection requirements are met.

The invention belongs to the technical field of laser additive manufacturing process control, and particularly relates to a temperature feedforward control method for a laser additive manufacturing molten pool. Comprising the following steps: step 1, acquiring a surface temperature field of an Nth layer after solidification through a thermal imaging system as an initial state T0; step 2, acquiring a scanning path of an (N + 1) th layer from a CAM system, calculating an equivalent heat dissipation index of each point on the path, and generating an EHDI sequence; 3, inputting the T0 and EHDI sequences into a pre-trained LSTM prediction model, and predicting the dynamic change of the molten pool temperature; and step 4, based on a model prediction path integral control algorithm, optimizing and generating an optimal laser power feed-forward control sequence of N + 1 layers by taking minimization of a cost function including a temperature tracking error, a power change and a power size as a target. According to the method, the path information is quantified into the EHDI sequence, the thermal dynamic state is accurately predicted through the LSTM model, the MPPI controller can'predict 'thermal disturbance before scanning is started, the optimal power scheme is generated, and feedforward accurate control over the temperature of the molten pool is achieved.

The invention discloses a multi-modal depth sensing and grabbing system based on a transparent object, which relates to the field of robot operation and comprises a multispectral sensing module, a depth correction module, a grabbing posture generation module and a control module. The visual information and the thermal radiation information of a transparent object are comprehensively obtained by combining two perception modes of an RGB-D image and a thermal imaging (TIR) image, systematic error analysis is performed on a depth map, and error sources of an RGB-D camera and a TIR camera are detected. The system adopts an encoder-decoder model as a depth correction core, the model extracts complementary features of an RGB-D image and a TIR image through a modal exclusive encoder, feature alignment and integration are completed by using a feature fusion module, and the depth estimation precision on the transparent surface is effectively improved. And meanwhile, a Bayesian optimization method is adopted to carry out hyper-parameter optimization on the depth correction model, through hyper-parameter optimization and model fitting processing, the system effectively avoids the problems of over-fitting and under-fitting, and the robustness of the depth correction model and the transparent object grabbing precision are further improved.

A system and method are disclosed for advanced multimodal-sensor systems integrating various sensing characteristics for machine condition monitoring, including thermal imaging cameras, ultrasonic, vibration, temperature, magnetic sensors, high-band microphones, electro-magnetic emission detectors and others. The system's architecture comprises edge computing capabilities for real-time anomaly detection and operational feedback, utilizing edge machine learning (ML) models trained in the cloud. The system supports flexible configuration and calibration to optimize measurements based on machine requirements, while a cloud-based analysis framework enables robust data fusion, training and condition assessment. The method comprises performing automated diagnostic workflows enabled by models at the edge, reducing or eliminating human intervention, and significantly enhancing operational performance, maintenance and efficiency across multiple machines in an industrial setting.

The invention relates to the technical field of computer vision, in particular to a pipe network hidden leakage detection method based on unmanned aerial vehicle thermal imaging, which comprises the following steps: according to an input thermal imaging image, setting the size and step length of an overlapped sliding window and traversing the whole image; calculating a first-order moment, a second-order central moment, a third-order central moment and a fourth-order central moment of all pixel temperature values in each window, and generating a self-adaptive local threshold matrix; according to the method, the adaptive local threshold matrix is generated by calculating the multi-order statistical moment of all pixel temperature values in the overlapped sliding window, so that the segmentation reference can be closely attached to each local thermal distribution feature in the image, and the problem of non-uniform background temperature caused by complex environmental factors such as surface material difference and illumination shadow change is solved; and capturing of weak temperature difference abnormity is ensured.

Multispectral imaging and related techniques are provided to detect fire hazards and alerts that are false positive indicators of the fire hazards. The embodiments are directed to systems for identifying pre-fire hazards using thermal images, visual images, and different rules corresponding to temperature configuration levels. The embodiment are also directed to identifying false positive indicators of fire hazards using regions of interest in the thermal and visual images, identifying objects that have hot components which may generate a false positive indicator for a fire hazard, and identifying sun reflections which may generate a false positive indicator for the fire hazard.

The invention discloses a damage detection method and system based on machine vision, and relates to the technical field of machine vision detection. A polarization structured light-thermal imaging three-mode acquisition framework of a customized machine vision array is adopted, the problems of composite noise superposition and unstable internal thermal signal diffusion caused by dynamic heterogeneity of a target surface are solved, the adaptability of damage detection to a complex environment is greatly improved, and by means of a space attention mechanism and a cascade analysis network, the damage detection accuracy is improved. According to the method, geometric measurement and heat conduction simulation technologies are combined, the problems that damage space distribution is rough in description, and there is no stable corresponding relation between surface and deep abnormity are solved, accurate space mapping and multi-dimensional quantification of damage from the surface to the interior are achieved, and through linkage of a dual optimization mechanism and an edge end improved MobileViT lightweight model, the problem that damage space distribution is rough in description is solved. Pain points where real defects and interference artifacts are difficult to distinguish are broken, stable correspondence between surface signals and deep damage is enhanced, and accuracy and practicability of damage detection in an industrial scene are guaranteed.

The invention discloses a radar and infrared fusion-based human body recognition system for power plant mistaken intrusion detection, which can realize all-weather and accurate recognition and positioning alarm of personnel intrusion. Comprising a sensing layer, a processing layer and an output layer. The sensing layer comprises an infrared camera and a laser radar which are fixedly installed on the same installation support, the infrared camera and the laser radar keep a fixed spatial relative position relation, the optical axes of the infrared camera and the laser radar point to the same monitoring area, thermal imaging images are collected through the infrared camera, three-dimensional point cloud data are obtained through the laser radar, and the three-dimensional point cloud data are sent to the monitoring layer. Performing time synchronization; the processing layer and a processor are internally provided with a personnel detection deep learning model, and the processing layer comprises an image and point cloud preprocessing module, a multi-modal fusion detection module and a human body detection and recognition module; the output layer comprises an alarm unit and a control center display terminal interface; and the alarm unit triggers the sound-light alarm unit according to a preset strategy, so that monitoring personnel can perform field management conveniently.

The invention discloses a power equipment fault prediction method and system based on deep learning. The method comprises the following steps: acquiring multi-modal monitoring data and carrying out standardization and normalization processing on the multi-modal monitoring data; respectively extracting preliminary features of the electrical signal, the mechanical vibration, the thermal imaging image and the sound spectrogram through a one-dimensional neural network and a two-dimensional convolutional neural network; fusing each modal feature by adopting an attention mechanism, calculating an association weight and generating a comprehensive feature vector; different long and short-term memory networks are selected according to the weight to process sequence dependence, and an association sequence is obtained; abnormal points are detected and clustered, and a high-risk abnormal mode is identified; performing multi-step prediction on the high-risk equipment to obtain a future evolution sequence; and a fault early warning is generated by comparing the similarity of the current and future features. According to the invention, multi-source information can be fused, early abnormity can be accurately captured, and intelligent prediction and early warning of power equipment faults can be realized.

The invention provides a rock-soil moisture content and permeability testing system and method based on electromagnetic waves and infrared rays, and particularly relates to the technical field of geotechnical engineering testing and multi-source signal detection. According to the test system, construction of a rock-soil permeability experiment is realized through a rock-soil sample permeability test platform, the electromagnetic wave detection module realizes perception of a water-containing state in rock-soil, and the infrared thermal imaging module realizes perception of a wet boundary and seepage expansion characteristics of a rock-soil surface. The data acquisition and control module realizes integral control of a penetration experiment and acquisition of dielectric characteristics and infrared images, the signal fusion and inversion module realizes fusion of the dielectric characteristics and the infrared images and joint inversion of dynamic moisture content and penetration performance, and the result processing and visualization module realizes visual display of an inversion result; the method has the remarkable advantages of non-destructiveness, real-time performance, high spatial resolution, high adaptability, intelligent processing capacity and the like, and comprehensive characterization of the permeability behavior and the moisture content evolution process of the rock and soil sample is achieved.

The invention relates to the technical field of image recognition, in particular to an abnormal security scene monitoring method and system for infrared thermal imaging, and the method comprises the steps: collecting a picture of a monitoring region through an infrared thermal imager, building a video frame sequence, and constructing a Gaussian mixture model for each pixel point; performing spatial analysis on the video frame sequence, establishing a foreground patch, and determining artificial environment spatial interference of each pixel point in the current frame through the foreground patch; performing time analysis on the video frame sequence in combination with the artificial environment space-time interference to obtain the artificial environment space-time interference of each pixel point in the current frame; evaluating space-time interference of an artificial environment by using a Gaussian mixture model, and judging and marking whether each pixel point in the current frame is in a candidate state or not; and presetting a decision threshold, counting the number of continuous frames with candidate state marks, comparing the number of continuous frames with the decision threshold, and distinguishing normal and abnormal environmental changes. Benign environment changes and real threats are effectively distinguished, and a large number of invalid alarms are prevented from being generated.