1724 results about "Thermographic imaging" patented technology

The invention discloses an intelligent photoelectric theodolite aerial target positioning and tracking system, which relates to the technical field of photoelectric detection, and comprises a multi-mode photoelectric sensor module integrating visible light, infrared thermal imaging, a laser radar and a polarized light sensor and supporting spectrum adaptive switching; the dynamic noise suppression processing module is used for eliminating environmental interference based on a time-space domain hybrid filtering algorithm; the multi-target tracking control module adopts a time-sharing partition scanning strategy and a graph neural network data association algorithm; the anti-interference servo driving module is used for realizing stable tracking under strong disturbance through inertial navigation-visual fusion compensation; and the edge computing platform is used for deploying a lightweight deep learning model to complete target recognition and trajectory prediction. According to the invention, through interdisciplinary collaboration of quantum dot materials, graph neural networks and physical equation constraints, the bottleneck of a single technology is broken through; and through closed-loop optimization of dynamic anti-interference and edge intelligence, full-link enhancement of'perception-decision-execution 'is realized.

The invention discloses a thermal imaging temperature rise trend early warning system based on time-space sequence prediction, and particularly relates to the technical field of thermal imaging data prediction and early warning. The thermal imaging temperature rise trend early warning system comprises an image conversion module, a fluctuation feature extraction module, an edge prediction module, an anomaly characterization module and a prediction decision module; a temperature dynamic change rate and gradient intensity are calculated, edge model prediction is carried out based on a fluctuation index combination, when the fluctuation index combination does not exceed a stable interval, a lightweight deep network model deployed at a thermal imaging acquisition end is called, and when the fluctuation index combination exceeds the stable interval, a prediction decision module determines whether to switch to a high-order multi-modal model; the space-time information extraction capability is improved by constructing the temperature evolution data body, the prediction path is dynamically controlled based on the fluctuation index combination, and the prediction stability and efficiency are improved; and the abnormal activation index and the prediction offset index are combined to realize adaptive switching of model calling, so that the accuracy and adaptability of the early warning system are enhanced.

The invention discloses a high-voltage equipment monitoring system and method based on thermal imaging and dynamic sensing fusion of a power plant, and belongs to the field of high-voltage equipment monitoring. The edge intelligent processing module is used for outputting a real temperature field matrix, separating equipment vibration characteristics from environment noise and generating a fused characteristic vector; the multi-modal analysis module is used for inputting the feature vectors into a constructed equipment topological graph neural network and outputting a health index and an abnormal probability distribution diagram of each monitoring point; the environment coupling fault analysis module is used for generating a fault diagnosis report; and the dynamic early warning module is used for executing graded early warning according to the health index and the fault diagnosis report. The fault detection rate and the operation and maintenance efficiency can be improved.

The invention discloses a multi-dimensional anti-bird intelligent recognition method and system based on thermal imaging, and relates to the technical field of intelligent monitoring and ecological protection. Multi-modal data is collected through thermal imaging, visible light, millimeter wave radar and a voiceprint sensor, after PTP protocol synchronization and Kalman filtering preprocessing, 3-5-second tracks of birds are predicted by using an LSTM network, and the anti-bird intelligent recognition method and system based on the thermal imaging are obtained. And the cross-modal features are fused through a Transform architecture, so that 95% of classification accuracy is realized. A bird repelling strategy is generated in real time through edge calculation, and the model is updated through cloud federal learning. The system integrates an oil-electric hybrid unmanned aerial vehicle and a ground device, supports dynamic path planning based on a thermodynamic diagram and differentiated repelling of directional sound waves, laser stroboflash and the like, has the night recognition accuracy rate of 92% and the bird repelling response time of 0.8 second, and is suitable for scenes of electric power, airports and the like. Through multi-dimensional perception, dynamic modeling and eco-friendly expelling, the problems of poor environmental adaptability, single strategy and the like of a traditional scheme are solved, and the anti-bird efficiency and the ecological safety are improved.

The invention discloses a concrete crack depth detection method and system based on multi-modal data fusion. The method comprises the following steps: synchronously obtaining a visible light image sequence and a thermal imaging image sequence of a concrete crack; the thermal imaging image sequence is obtained based on adjustable thermal excitation; and through a preset multi-modal data registration algorithm, according to the visible light image, predicting a registration displacement vector field to generate a pseudo-infrared image corresponding to the enhanced visible light image, and migrating a temperature field of the thermal imaging image at the same moment and under the same picture to the pseudo-infrared image to generate a fusion modal image, obtaining a fusion modal image sequence; and through a preset heat conduction inversion model and a temperature attenuation characteristic curve generated based on the fusion modal image sequence, obtaining crack depth data and generating a three-dimensional crack map so as to visually present concrete crack depth detection data. According to the invention, the universality and accuracy of concrete crack depth detection can be improved.

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 nondestructive testing method for infrared thermal imaging spatio-temporal information fusion, which relates to the technical field of nondestructive testing and comprises the following steps: establishing a linear laser heat source scanning infrared thermal imaging nondestructive testing system; collecting a dynamic infrared thermal image sequence; and calculating and analyzing dynamic infrared thermal image sequence data, and carrying out data processing on the acquired dynamic infrared thermal image sequence by using an infrared thermal imaging spatio-temporal information fusion post-processing method to obtain a full-field thermal response image with surface crack defects. According to the method, spatio-temporal information can be fused to obtain data with higher resolution, more comprehensive and accurate full-field thermal response characteristics are obtained while the calculation efficiency is improved, the method has better flexibility and wider applicability, and the purpose of nondestructive detection of metal surface defects is achieved.

The invention discloses a quality detection method and system for vehicle safety belt production and a storage medium, and relates to the field of manufacturing quality control of automobile passive safety parts. The method aims at solving the problems that an existing detection means is weak in tiny defect recognition capacity, insufficient in defect evolution modeling and lack of intelligent feedback regulation and control. According to the method, multiple detection means such as medium-wave infrared thermal imaging, laser confocal microscopy, structured light three-dimensional reconstruction and capacitance sensing are fused, and multi-mode defect sensing of key parts such as a braid, an insertion area and an adhesive layer is achieved; deducing a defect evolution process and generating a high-credibility pseudo label by constructing a thermal-stress-rheology coupling finite element model; the graph neural network and the AI model are combined to complete defect type identification, risk quantification and grading, and automatic optimization of process parameters and production line control response are realized based on the quality grade, so that the detection precision, the response efficiency and the product yield are remarkably improved.

The invention discloses a multi-sensor fusion-based people flow dynamic identification method and system, relates to the technical field of intelligent traffic, and is used for solving the problems of people flow perception distortion and shielding tracking interruption in a complex environment. The method comprises the following steps: firstly, constructing an environment interference matrix, and dynamically distributing sensor weights according to a radar electromagnetic attenuation rate, thermal imaging illumination abrupt change and a piezoelectric mechanical noise factor; when the thermal imaging density weight is too low and the radar speed weight is greater than a threshold value, triggering piezoelectric gait verification and outputting calibration fusion data; mapping the fusion data to a dynamic grid, calculating three indexes of density entropy, velocity mutation and flow direction consistency, generating a congestion vector, and marking a risk boundary; for a shielding target, generating a virtual track by combining the final known position, speed and density distribution, and optimizing model parameters when a re-identification error exceeds a limit; and finally, a hierarchical dredging instruction is judged and triggered through the virtual track and the risk area space overlapping, and the gate is dynamically adjusted based on the congestion vector to release.

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 relates to the technical field of pipeline detection, in particular to an infrared thermal imaging full-dimensional monitoring method for corrosion of a pipeline under a thermal insulation layer, which comprises the following steps of: acquiring temperature distribution under the thermal insulation layer and screening measuring points with consistent directions, extracting a boundary continuous region to calculate thermal probability, identifying abnormal measuring points and extracting time sequence characteristics, and predicting and marking response delay measuring points based on a model. And generating a CUI risk region division result. According to the method, a temperature difference direction consistency identification mechanism is constructed through space sequence subdivision and heat flow direction trend judgment of pipeline surface temperature distribution data, a measurement point set with clear space heat conduction characteristics is obtained through continuous screening, and a gradient change rate local extreme value is further taken as an identification basis; the method comprises the following steps: constructing a thermal anomaly probability evaluation parameter by combining spatial density and change intensity to realize preliminary identification of an abnormal point, then introducing a time sequence temperature parameter to construct a multi-dimensional dynamic feature set, and completing multi-step temperature prediction through normalized combination calculation after model training.

The invention relates to the technical field of perimeter intrusion monitoring, in particular to a perimeter intrusion monitoring system based on cooperation of infrared thermal imaging and an intelligent algorithm. The perimeter intrusion monitoring system based on infrared thermal imaging and intelligent algorithm cooperation comprises a multi-source sensing layer, an edge computing node, a cloud analysis platform and a response execution layer. According to the perimeter intrusion monitoring system based on cooperation of infrared thermal imaging and an intelligent algorithm, environmental noise interference is effectively suppressed through a dynamic threshold compensation mechanism, and accurate classification of personnel, vehicles, unmanned aerial vehicles and animals is realized by combining multi-modal feature fusion of infrared thermal imaging, visible light vision and millimeter wave radar; according to the method, intention modes of passing, observation, invasion and the like can be identified, threat level dynamic adaptation is realized in combination with a hierarchical response system, the technical bottlenecks of poor environmental adaptability, high target misjudgment rate, behavior analysis deficiency and the like of a traditional system are solved, and the intelligent level and all-weather protection capability of perimeter security and protection are remarkably improved.

The invention discloses a motor coil visual detection system and method based on multi-modal data fusion, and relates to the technical field of image recognition. The system solves the problems of single detection dimension, spatial structure information loss and the like in the existing motor coil visual detection, and comprises an image acquisition module, a data processing module, a defect identification module and a result output module. The image acquisition module comprises an RGB camera, a depth camera and a thermal imaging camera, and is used for respectively acquiring an appearance image, three-dimensional structure information and a temperature distribution diagram of the motor coil; the data processing module is used for performing registration, denoising and feature extraction on the multi-source image, and constructing uniform feature representation through a multi-modal fusion algorithm; the defect identification module analyzes the fusion features based on a deep learning model to realize appearance defect identification, three-dimensional deformation detection and thermal anomaly positioning of the coil; and the result output module classifies and evaluates the identification result and uploads the identification result to an upper system. The method is suitable for quality control and intelligent judgment of motor coil assembly.

The invention discloses a terminal crimping quality detection method and system, and relates to the technical field of terminal crimping image detection. Comprising the steps that space coordinates of a standard datum point in a crimping image are determined, and the standard datum point is used for representing the space coordinate position, meeting the standard terminal crimping inspection standard, between a corresponding wire and a terminal notch. According to the method, the crimping samples which do not meet the standard terminal crimping inspection standard are rapidly removed in a spatial positioning mode, and then the crimping sample thermal imaging images which do not meet the standard terminal crimping inspection standard are obtained on the crimping surfaces of the crimping samples by means of trigger logic. According to the method, the crimping samples are detected, the invisible faults of the crimping samples are judged based on the set standard terminal crimping inspection standard, and finally different commands are input to the crimping equipment according to the types and severity levels of the invisible faults, so that the rapid detection of the crimping samples and the dynamic adjustment of the crimping equipment are realized, and the stability of the monitoring dimension of the crimping quality is ensured.

The invention relates to the technical field of battery detection, in particular to a battery multi-physical-quantity performance test and intelligent diagnosis method, system and device and a storage medium, and the method comprises the steps: building a virtual battery model containing an electrode, electrolyte and diaphragm structure, and marking initial mechanical strength parameters; synchronously acquiring real-time current and surface infrared thermal imaging temperature data of a real battery; inputting the current data into the virtual model to execute electrochemical and thermal coupling simulation, and comparing differences to locate a physical abnormal area; disassembling an abnormal area, measuring structural characteristic parameters of the material, recording space coordinates, and extracting electrochemical state variables at corresponding positions of the virtual model through space calibration; and establishing a mapping relationship between the material structure parameters and the electrochemical variables, outputting a dominant failure mechanism, and updating the failure criterion of the virtual model. Abnormality is accurately positioned by combining infrared thermal imaging and simulation difference analysis, multiple physical quantities are fused to reveal correlation between structural degradation and performance attenuation, and data are dynamically updated to improve diagnosis reliability.

The invention relates to the technical field of computer vision and fire monitoring, and provides a fire image detection and segmentation method based on an end-to-end unified framework and physical knowledge embedding. Aiming at the problems of computation redundancy, feature segmentation and strong dependence on visible light caused by traditional staged processing, the invention provides the following technical scheme: constructing an end-to-end network comprising an Officient Hybrid Ender encoder and a mask-dino decoder, and realizing global modeling and cross-scale feature fusion through a single-layer Transform; thermal imaging physical knowledge embedding is innovatively introduced, and three fusion modes of pixel-level addition, feature-level Embedding and interactive learning are adopted; a lightweight single-layer Transform architecture and a multi-task loss function are designed, and GIOU, Dice and a joint detection segmentation hybrid matching strategy are combined. According to the method, a bounding box and mask prediction are synchronously generated through a unified query mechanism, the adaptability of a low-illumination scene is enhanced by using thermal imaging data, and the training efficiency is improved by decoupling bounding box loss. According to the invention, the real-time performance, robustness and precision of fire monitoring are significantly improved in a complex scene.

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 relates to an abnormal battery positioning method and system, equipment, a medium and a product. The abnormal battery positioning method comprises the steps of determining operation data of a battery pack; performing abnormal value detection and normalization processing on the operation data to obtain target operation data; performing time sequence analysis on the target operation data, determining time characteristics, and obtaining spatial characteristics according to the position information of each battery and the target operation data; inputting the spatial-temporal characteristics into a thermal imaging model to generate a thermal image of the battery pack according to the spatial-temporal characteristics; and determining that the battery corresponding to the target area is abnormal in response to the fact that the target characteristic value of the target area in the thermal image exceeds a preset threshold value. By adopting the method, the problems that the traditional infrared thermal imaging technology is greatly influenced by the environment, the installation space is limited, the cost is high and the weak signal space-time resolution capability is insufficient can be solved, the thermal state of the battery pack can be accurately reflected, and the abnormal positioning and accurate early warning of the thermal runaway early stage can be realized.

The invention relates to the field of electric power systems, in particular to an electric power facility remote intelligent maintenance method and system. The method comprises the following steps: collecting three kinds of heterogeneous monitoring data of vibration spectrum, infrared thermal imaging and partial discharge signals; mapping the data to a three-dimensional feature fusion space, and calculating a mahalanobis distance to generate a fusion feature matrix; extracting spatio-temporal features by using a convolutional long-short-term memory network, and outputting a triple diagnosis result including a fault type, a severity level and an evolution trend; searching a dynamic maintenance scheme in a maintenance strategy knowledge graph based on the diagnosis result; solving an optimal resource scheduling scheme by adopting an improved Hungary algorithm in combination with the geographic topology and the resource state; the maintenance operation is remotely guided through the augmented reality terminal, and real-time verification is carried out; and collecting the maintained data, carrying out residual analysis, and reversely optimizing the knowledge graph. The method realizes full-process intelligent management, improves timeliness and reliability of operation and maintenance of electric power facilities, and is suitable for a remote intelligent maintenance scene of an electric power system.

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 relates to the technical field of packaging quality detection, and discloses an aluminum foil sealing quality detection system, which comprises a transmission module, a detection module and a control module, the thermal imaging acquisition module is used for acquiring temperature distribution data of a sealing area on the to-be-detected container; the image processing module is used for analyzing the temperature distribution data based on an image recognition algorithm and extracting defect characteristics of the aluminum foil seal; the temperature calibration module is used for dynamically calibrating the thermal imaging acquisition module according to preset parameters; the sorting module is used for removing defective products from the conveying module according to the extracted defect features; and the intelligent decision module is used for integrating the detection data and the production parameters, generating process optimization suggestions and outputting regulation and control instructions. According to the system and the method, high-precision real-time detection and classification of aluminum foil sealing defects are realized, and meanwhile, the automation level, the detection stability and the quality control efficiency of a production line are improved through dynamic sorting and process optimization suggestion output.

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 discloses a pumped storage unit fault diagnosis method based on multi-modal data fusion, and the method comprises the steps: collecting a voiceprint signal, an infrared thermal imaging image and historical operation data of a pumped storage unit, and carrying out the noise reduction of a voiceprint set through employing an improved unscented Kalman filtering algorithm, a COMRes + model is used to train the voiceprint signal after noise reduction and historical operation data, a future voiceprint signal is predicted, an improved Deeplabv3 + model is used to train an image set, and features of an infrared thermal imaging image are extracted; the voiceprint feature, the historical operation data text feature and the image feature are input into an improved CentraNet model for multi-modal data fusion, and a fault category is output through a classification recognition module, so that diagnosis of the pumped storage unit fault is completed; according to the method, through fusion of the multi-modal data and optimization of the deep learning model, the accuracy and efficiency of fault diagnosis can be effectively improved, and a powerful guarantee is provided for safe operation of the pumped storage unit.

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 relates to the technical field of electronic equipment detection, in particular to a multi-dimensional anti-candid photographing detector and a detection method thereof. The system comprises a processing unit, a thermal imaging module, an electromagnetic radiation detection module and a wireless security module, wherein the wireless security module comprises at least two wireless network interface cards, and a first wireless network card is used for connecting a specified internal network and scanning and analyzing internal network equipment; the second wireless network card works in a monitoring mode and is used for sniffing all surrounding Wi-Fi signals, Bluetooth signals and associated equipment thereof and performing data packet analysis; the user interaction module, the positioning auxiliary module, the storage unit and the power supply module are used for providing electric energy for the detector. According to the invention, four technical dimensions of thermal imaging, wireless safety detection, infrared light detection and electromagnetic radiation detection are fused, and an intelligent analysis and positioning algorithm is combined, so that the comprehensiveness, the accuracy and the efficiency of detecting candid equipment are improved, and clear positioning guidance and comprehensive safety evaluation are provided for a user.

The invention relates to the field of new energy, and discloses a photovoltaic system efficiency evaluation method and system based on unmanned aerial vehicle multi-source image fusion, and the method comprises the steps: collecting a visible light orthoimage, thermal imaging data and environment parameters of a photovoltaic system, and obtaining a registration result through employing an improved feature point matching algorithm; based on a registration result, combining spatial features of a visible light image and temperature features of thermal imaging, realizing accurate segmentation of the photovoltaic panel through a deep learning model, and identifying the type, the arrangement mode and the installation angle of the photovoltaic panel at the same time; establishing a mathematical model of the relation between the photovoltaic panel temperature distribution and the power generation efficiency, distinguishing the normal working temperature difference and the fault hot spot, and analyzing and determining the efficiency attenuation degree and the fault type of the photovoltaic system through a heat distribution mode. The method is accurate in image registration, good in data fusion effect, accurate in temperature anomaly detection and comprehensive in efficiency evaluation, and provides more efficient and accurate technical support for management and maintenance of the distributed photovoltaic system.

The invention relates to the technical field of photoelectric pod target tracking, and discloses a multi-mode target automatic identification tracking method and system based on a photoelectric pod. A multi-modal data acquisition module of the system integrates visible light, infrared thermal imaging and laser ranging sensors based on a photoelectric pod, acquires target multi-modal sensing data and completes time-space synchronous calibration; the dynamic feature extraction module adopts a multi-scale convolutional neural network to process multi-modal data, generates a target multi-level feature map and performs significance region labeling; the adaptive tracking decision module deploys a recurrent neural network to predict a target motion trajectory according to a labeling result, and generates a tracking control instruction through an optimization algorithm; the execution control module drives the photoelectric pod holder mechanism and adjusts the orientation of the sensor to realize target tracking; the feedback optimization module monitors the tracking state in real time, calculates a tracking deviation index, and generates a strategy adjustment parameter to dynamically optimize the tracking strategy.