834 results about "Thermal infrared" patented technology

The invention discloses a multi-modal visual fusion complex scene small target detection tracking method and system, and relates to the technical field of unmanned aerial vehicle target tracking, and the method comprises the steps: employing a visible light camera, an infrared thermal imager and a laser radar sensor which are carried on an unmanned aerial vehicle platform, and synchronously collecting RGB images, thermal infrared images and point cloud data; the consistency of the multi-modal data is ensured through data preprocessing and space-time alignment; constructing a lightweight double-branch network to extract multi-scale features, generating a fusion feature map by adopting adaptive weighted fusion, and generating depth information by utilizing point cloud to assist in scale estimation; a small target detection head is designed based on the fusion feature map, and precise detection is realized in combination with a feature pyramid network, adaptive scale prediction and a context awareness suppression mechanism; furthermore, through multi-mode cooperative tracking, including target association, spatio-temporal context modeling, trajectory prediction and a re-detection mechanism, tracking continuity is ensured.

The invention discloses an egg surface microcrack detection system and method based on image analysis, and relates to the field of surface defect nondestructive detection.The method comprises the steps that a process identification interface module obtains a processing process identifier of an egg in real time; the multi-angle annular light source module is provided with a multi-band annular light source array comprising visible light and near-infrared LED lamp beads which are independently controlled, a polaroid group and a beam splitter prism are integrated, and visible light polarization images and near-infrared polarization images on the surfaces of the eggs are synchronously collected; the thermal excitation enhancement unit obtains a thermal infrared image; the process filtering module extracts the axial elongation of the mechanical stress microcrack after graded transportation, and calculates the mesh fractal dimension of the thermal stress microcrack after UV disinfection; the multi-modal feature fusion module generates a dual-channel crack probability graph; and the dynamic feedback control module outputs a micro-crack risk grade and adjusts the rotating speed of the objective table and the light source intensity. The method has the advantages that accurate judgment of mechanical and thermal stress cracks is realized, and curved surface reflection interference is broken through.

The invention discloses a multi-source image data fusion-based fine decoration air crack seepage quality problem detection method and device, and solves the technical problem of how to carry out comprehensive, high-precision and intelligent detection on the fine decoration surface air crack seepage quality problem. Comprising the following steps: 1) receiving visible light image data, thermal infrared image data and three-dimensional laser point cloud data of a target refined decoration surface acquired from visible light acquisition equipment, thermal infrared imaging equipment and three-dimensional laser scanning equipment respectively; 2) carrying out feature extraction on the visible light image data, the thermal infrared image data and the three-dimensional laser point cloud data; 3) obtaining point cloud projection coordinates, and then performing association fusion on the first two-dimensional feature and the second two-dimensional feature with corresponding three-dimensional features to generate fusion point cloud data containing multi-source features; and 4) based on the fused point cloud data, carrying out defect classification identification and spatial positioning to obtain a detection result. And comprehensive and high-precision detection of the quality problem of air crack seepage of the finely-decorated surface is realized.

The invention discloses a power distribution equipment on-line monitoring system based on multi-modal data fusion, and relates to the field of power distribution equipment management, and the system comprises a sensing module which is used for collecting an electrical signal, a thermal infrared signal, a mechanical vibration signal and an acoustic signal generated in the operation process of power distribution equipment through a multi-type sensing channel, converting the collected various signals into processable equipment state original data to form an equipment operation state original data set; according to the invention, through accurate acquisition of multi-dimensional signals, combination of space mapping and time delay compensation, data quality is optimized, the reliability of original information is ensured, key features are extracted according to working conditions, subtle state changes are captured by means of temperature field analysis and a variable-resolution spectrum technology, the comprehensiveness, accuracy and response timeliness of equipment operation monitoring are effectively improved, and the real-time performance of equipment operation monitoring is improved. Misjudgment and missed judgment are reduced, and fault risks are avoided in advance.

The invention discloses a soil water and salt dynamic monitoring and quality evaluation method based on multispectral remote sensing, and relates to the field of geological monitoring, and the method comprises the steps: carrying out the scattering deviation correction and roughness correction of an observation spectrum through a satellite remote sensing image, an unmanned aerial vehicle multispectral image, a ground sample and meteorological driving data; the soil intrinsic reflectivity, the vegetation coverage component and the salt crusting component are obtained; a water-salt inversion model is established by combining thermal infrared and red edge wave band information, and multi-temporal soil volumetric moisture content and surface salinity index are inverted; by introducing meteorological conditions and vegetation dynamic characteristics, a water-salt coupling partial differential equation and a graph space-time constraint model are constructed, and continuous space-time distribution of soil water content and salinity is obtained; extracting salinity, moisture, vegetation response and soil health indexes, establishing a soil quality comprehensive evaluation model, and outputting high-risk plaques and a treatment priority sequence. The method realizes dynamic monitoring and quality grading evaluation of soil water and salt, and is suitable for saline-alkali soil treatment and ecological restoration.

The invention is suitable for the technical field of earth surface monitoring, and provides an earth surface deformation monitoring method and system based on a time sequence InSAR, and the method comprises the following steps: carrying out the SAR image screening and interferogram generation, and collecting corresponding meteorological data and thermal infrared data; performing space-time adaptive atmospheric phase correction, taking the air pressure vertical gradient and the temperature anomaly as driving factors of atmospheric delay, and separating an atmospheric phase through a space-time weighted model; dynamic deformation modeling is carried out, and deformation is decomposed into linear deformation and nonlinear deformation; the method comprises the following steps: taking a mining area road network as a geometric constraint, unwrapping a coherent region by adopting a minimum cost flow algorithm, converting an unwrapping phase into sight-line-direction deformation, and calculating horizontal and vertical deformation components in combination with InSAR sight-line-direction deformation and a digital elevation model. The method adapts to a complex deformation mechanism of a mining area by capturing linear and nonlinear deformation. Through sight line deformation and DEM geometric projection, vertical and horizontal deformation separation is realized, and the deformation direction is determined.

The invention relates to the technical field of geological disaster detection, in particular to an unmanned aerial vehicle multi-sensor image fusion slope geological disaster detection system which comprises a data acquisition module, a manifold registration module, a feature fusion module, a weight optimization module, a fusion execution module, a disaster detection module and the like. RGB images, thermal infrared images and laser point cloud data of a slope are collected through an unmanned aerial vehicle, the surface of the slope is modeled as a Riemannian manifold, and high-precision space registration of heterogeneous data is achieved; constructing a feature manifold based on a manifold learning method, and extracting and fusing multi-scale features; evaluating the information amount of different areas by adopting a differential entropy theory, and generating a self-adaptive weight distribution diagram; performing weighted fusion on the registered multi-source data to generate a fused image; geological disaster features such as cracks, abnormal vegetation and water seepage points on the surface of the slope are recognized based on the fused image, and high-precision recognition and early warning of the geological disaster of the slope are achieved.

The invention discloses a target identification method for multi-sensor data fusion, particularly relates to the technical field of data fusion, and comprises a dynamic weight fusion module, a double-branch neural network and an abnormal sensing compensation system. Data are synchronously collected through a visible light camera, a thermal infrared imager and a millimeter wave radar, and a standardized feature map is generated through sensor specificity preprocessing; the dynamic weight fusion module generates an adaptive weight matrix based on the real-time confidence score and the environmental parameters, emphasizes infrared data when illumination suddenly changes, and improves intelligent distribution of radar weights in a rain and fog environment; the combined feature map after weight fusion is input into a double-branch neural network, a channel self-calibration branch suppresses interference noise, and a target category and coordinates are output after residual connection optimization; when the recognition confidence is insufficient, the abnormal compensation system starts visible light Wiener filtering restoration, generative adversarial network infrared compensation and radar multi-frame accumulation algorithms, and the system reliability is ensured when a single sensor fails.

The invention discloses a multi-modal fusion defect detection method and system, and belongs to the technical field of intelligent detection and machine vision, and the system comprises a visible light sensor, a thermal infrared sensor, a hyperspectral sensor, a multi-band light source trigger control system, a modal preprocessing and alignment module, a cross-modal feature fusion module, and a defect detection and output module. According to the invention, three sensors are used to construct a multi-modal visual perception system, and a multi-band light source triggers a control system to complete image acquisition; after multi-modal information is subjected to preprocessing and cross-modal alignment through the modal preprocessing and alignment module, multi-modal fusion is achieved through the cross-modal attention module and the multi-scale feature fusion pyramid structure, and finally defect recognition and output are conducted through the defect detection and output module. According to the method, the defect identification precision can be obviously improved, and the method has obvious advantages especially for low-contrast and early-stage hidden crack defects, and has good expandability and deployment suitability at the same time.

The invention discloses a wildlife detection method fusing an unmanned aerial vehicle thermal infrared image and a visible light image, and belongs to the field of small target wildlife identification, and the method comprises the following steps: S1, obtaining a preprocessed TIR-RGB image pair set; s2, an FDM-YOLO double-source target detection model improved based on YOLOv81 is constructed, and the improved FDM-YOLO double-source target detection model is trained based on the preprocessed TIR-RGB image pair set obtained in the step S1; and S3, inputting an image acquired in real time into the improved FDM-YOLO double-source target detection model trained in the step S2, and outputting a wild animal detection result. By adopting the wild animal detection method fusing the thermal infrared image and the visible light image of the unmanned aerial vehicle, high-precision, real-time and robust detection of a small target of a wild animal in a complex field environment is realized by improving the FDM-YOLO model.

The invention relates to the technical field of vehicle image recognition, and discloses a multi-modal data enhanced vehicle recognition method and system based on a generative adversarial network, and the method comprises the steps: collecting vehicle multi-modal data (a visible light image, a thermal infrared image and three-dimensional laser point cloud data), and constructing a vehicle multi-modal data set; performing preprocessing and feature alignment on the data set to obtain standardized multi-modal data; constructing a cross-modal generator based on an adaptive attention mechanism, learning inter-modal feature association through a dynamic weight distribution module, and generating vehicle feature fusion data; designing a dual discriminator structure consisting of a perception consistency discriminator and a semantic fidelity discriminator, and optimizing the generator by adopting an alternate adversarial training strategy; generating a supplementary data sample for the complex scene by using the optimized generator, and constructing an enhanced data set; and constructing a multi-mode cooperative vehicle identification model based on the enhanced data set, and realizing high-precision vehicle attribute identification.

The invention relates to the technical field of soil detection and multi-source information fusion, in particular to a soil moisture content cooperative detection method and system.The method comprises the steps that a target area is determined, and a target thermal infrared image of surface soil of the target area is obtained; extracting target characteristic parameters related to the moisture content from the target thermal infrared image, inputting the target characteristic parameters into the trained BP neural network, and predicting to obtain a surface soil moisture content distribution diagram; based on the surface soil moisture content distribution diagram, determining a target range region with abnormal moisture content through threshold comparison; after the air coupling stepping radar is driven to be aligned with the thermal infrared imaging system in a space-time mode, scanning is conducted in a target range area, and a radar reflection coefficient extracted from an obtained radar image and phase difference information serve as radar characteristic parameters; and performing modeling analysis on the radar characteristic parameters based on a support vector regression (SVR) model, and obtaining soil profile moisture content distribution of the moisture content abnormal region by constructing a nonlinear mapping relation and optimizing model hyper-parameter inversion.

The invention relates to the technical field of remote sensing, and discloses a forest land health state analysis method and system based on multi-source remote sensing image analysis. The system comprises a multi-source remote sensing acquisition module, a feature extraction and fusion module, a health assessment module, a traceability analysis module, a strategy matching module, a visual reconstruction module, an early warning decision module and an execution feedback module, and constructs a multi-modal forest land observation data set by fusing multi-source remote sensing data of multispectrum, hyperspectrum, radar and thermal infrared. The comprehensive extraction of multi-dimensional information such as vegetation coverage, canopy biochemical characteristics, under-forest structures and surface thermal environments is realized, the limitation of single data source analysis is overcome, and the comprehensiveness and accuracy of forest land health condition evaluation are improved; through dynamic comparison of multi-stage remote sensing images and health risk level mapping, early identification and early warning of forest growth abnormity, degeneration trend and pest and disease risk are realized.

The invention relates to the technical field of water environment monitoring, and discloses a sewage detection and traceability system based on multi-source data fusion. The system comprises an unmanned aerial vehicle data acquisition module, an edge end preprocessing module, a cloud data fusion and modeling module, a traceability decision module and a closed-loop optimization module, and all the modules work cooperatively to realize sewage detection and traceability full-process automation. Hyperspectral, thermal infrared, LiDAR and meteorological data are collected in an integrated mode through a multi-source sensor, pollution area identification, pollutant concentration quantification, sewage draining exit positioning and pollution diffusion path tracing are accurately completed through edge end preprocessing and cloud deep fusion modeling, a detection strategy is dynamically adjusted through a closed-loop optimization mechanism, and the detection accuracy is improved. And high precision, high efficiency and real-time response of sewage detection under complex terrains are realized.

The invention discloses a house structure safety edge visual monitoring method and device, equipment and a storage medium, and relates to the technical field of building safety monitoring, and the method comprises the steps: collecting multi-modal data which comprises a visible light video stream, a thermal infrared video stream and point cloud data; performing parameter synchronous extraction processing on the multi-modal data to obtain a first displacement sequence, a first inclination angle, a first crack width and a first hollowing mask; packaging the first displacement sequence, the first inclination angle, the first crack width and the first hollowing mask to obtain a first feature data packet; and carrying out structured numerical compression on the first feature data packet to obtain a compression vector, and sending the compression vector to a cloud platform, so that the cloud platform updates the digital twin model in real time according to the first feature data packet and triggers an early warning mechanism to complete safety monitoring of the house structure. High-precision, non-contact and real-time monitoring of multiple parameters such as inclination, settlement, vibration, cracks and hollowing of a building structure is realized.

The invention provides a YOLOv7-based photovoltaic module fault detection method and a YOLOv7-based photovoltaic module fault detection device, and relates to the technical field of target detection. According to the method, a thermal infrared and temperature information fusion module is introduced at the front end of a network, a thermal infrared image and a pixel-level temperature matrix thereof are fused through alignment, coding and attention mechanisms, and the characterization capability of multi-modal features is enhanced. A shallow layer-deep layer information aggregation module is introduced into the neck network, and the context perception and feature discrimination ability of the model to a multi-scale small target is improved. A global context sensing module is introduced in front of a detection head, a directional channel context branch and a query-key value branch of the global context sensing module capture directional global dependence and spatial context relations respectively, and global modulation of features is achieved. According to the method, the detection precision and robustness of various faults such as faults, fragmentation, hot spots and shielding of the photovoltaic module junction box are remarkably improved, the weak and small target positioning capability is optimized, the method is suitable for being deployed on mobile or embedded equipment, and efficient and intelligent inspection of a photovoltaic power station is achieved.

The invention relates to the technical field of ceramic intelligent manufacturing, in particular to a ceramic firing process thermal deformation intelligent prediction and self-adaptive temperature precise regulation and control system, which comprises a multi-source sensing module (a thermal infrared camera array, a temperature sensor, a laser displacement meter and a powder characteristic analyzer); a hybrid prediction model (a space-time convolutional neural network constrained by a physical mechanism); and the self-adaptive temperature control actuator comprises a partitioned burner group, a quenching / slow cooling pipeline and a gas-air volume decoupling controller. According to the scheme, through a multi-mode perception-hybrid modeling-dynamic optimization full-process closed-loop architecture, the single-point technical limitation of existing patents is broken through, and the zero-defect precise manufacturing target of ceramic firing is achieved.

The invention relates to the technical field of image quality optimization, and discloses a hunting camera imaging quality optimization method based on multimode data fusion. The method comprises the following steps: acquiring a visible light image, a thermal infrared image, a laser ranging point cloud and inertial measurement unit data, and carrying out space-time alignment and registration to form synchronous multimode data. A controllable imaging parameter set and scene context description information are separated by performing joint feature extraction on synchronous data. And for each parameter to be optimized, the system retrieves a plurality of candidate strategies from the imaging optimization knowledge base by taking the current value and the scene context as query conditions, selects an optimal strategy through fusion decision calculation, and finally generates and executes a global imaging parameter optimization instruction set to control a camera to complete image capture. According to the method, intelligent optimization of imaging parameters based on depth scene understanding is realized, and the image quality and adaptability of the hunting camera in a complex environment are improved.

The invention provides a multi-platform satellite thermal infrared hyperspectral atmospheric ammonia monitoring method and system, and belongs to the technical field of satellite remote sensing. The method comprises the following steps: collecting thermal infrared hyperspectral data, atmospheric state parameters, earth surface parameters and instrument characteristic parameters of a stationary satellite or a polar orbit satellite; based on a thermal infrared radiation transmission physical mechanism, inputting the preprocessed standardized parameters into a fast radiation transmission forward model to obtain a simulated spectrum consistent with the actually measured data format of a stationary satellite or a polar orbit satellite; constructing a cost function containing observation error constraint and NH3 prior information constraint on the basis of the simulated spectrum and the actually measured spectrum in combination with an optimization estimation theory, solving a minimum value of the cost function by adopting a Levenberg-Marquardt iterative algorithm, and performing inversion to obtain an atmospheric ammonia concentration profile; and performing quality control and column concentration conversion on an inversion result, and verifying the precision by combining multi-source observation data to obtain an atmospheric ammonia concentration data set.

The invention belongs to the technical field of computer vision and fire detection, and particularly relates to a lightweight fire detection method based on bimodal image fusion, and the method comprises the steps: collecting an RGB visible light image and a Thermal thermal infrared image in the same scene, and forming a group of samples; inputting the sample into a feature extraction module to obtain an RGB exclusive feature map and a Thermal exclusive feature map; the feature extraction module comprises a backbone network, an RGB feature branch and a Thermal feature branch; inputting the RGB exclusive feature map and the Thermal exclusive feature map into a feature fusion enhancement module to obtain a fused feature map; the feature fusion enhancement module comprises an MSR-CSSA module; inputting the fused feature map into an adaptive detection output module to obtain a prediction result; the self-adaptive detection output module comprises a three-branch detection head and a dynamic weight generation module; according to the invention, the detection precision and the real-time performance can be effectively improved.

The invention relates to the technical field of production environment monitoring, in particular to a high-temperature and high-brightness production environment monitoring system based on deep learning, which is characterized in that visible light and thermal infrared images in a high-temperature and high-brightness environment are acquired through cooperation of an industrial camera and an infrared camera, space registration and time synchronization are carried out, and accurate alignment of multi-modal data is ensured. Extracting a highlight overflow mask, a dark part texture guide map and color cast estimation, fusing to generate a joint perception feature tensor, inputting the joint perception feature tensor into a model with a cross-modal attention mechanism and time sequence feature modeling, recognizing a potential distortion region in advance, improving the system robustness, outputting an imaging state evaluation result and an imaging parameter reference value, and improving the imaging quality. And furthermore, a camera adjustment parameter is generated through a strategy network, an image acquisition process is dynamically optimized, a prediction control closed loop is constructed, and adaptive maintenance and optimization of image quality in a complex environment are realized.

The invention provides an electromechanical equipment fault prediction method and system based on digital twinning. According to the method, vibration signals and surface temperature are collected, periodic impact components are separated, temperature gradient features are extracted, and a fusion feature sequence is generated according to time window alignment; inverting heat source distribution and constructing a mapping relation of the heat source distribution along with the dynamic change of the load based on the load variable quantity and a thermal infrared imager temperature field model; calculating an impact strength attenuation rate and a heat source abnormal fluctuation trend, capturing collaborative change strength in a time window, and constructing a bearing wear probability distribution curve; and adjusting the weight ratio of the impact component to the temperature gradient according to the curve fluctuation amplitude, and outputting a wear fault prediction result after weighted stacking. According to the invention, multi-dimensional dynamic evaluation of bearing wear is realized, and the fault prediction accuracy and the temperature and vibration parameter collaborative sensitivity are improved.

The invention relates to an unmanned aerial vehicle target tracking method and system based on multi-modal perception and end-to-end, and the method comprises the steps: predicting the future motion track of an unmanned aerial vehicle through the multi-modal fusion of an RGB image, a thermal infrared image and a laser radar point cloud, combining the historical position information of a target, and employing an end-to-end deep learning network. The network structure comprises a trajectory anchor point prediction branch and a target position regression branch, wherein the trajectory anchor point prediction branch outputs a trajectory existence probability, a trajectory offset and a trajectory cost score; and the position regression branch directly predicts the three-dimensional coordinates of the target. And generating the flight path of the unmanned aerial vehicle from the terminal to the terminal. And directly mapping the trajectory points into flight control executable low-level control instructions, including position, speed and attitude instructions, and directly executing the instructions by a flight control system to realize autonomous tracking of the target. The method shows high robustness and accuracy in weak light, shielding and complex environments, and is suitable for various application scenes such as electric power inspection, disaster rescue, security monitoring and automatic driving coordination.

The invention provides a shallow coal seam hidden fire source full-field monitoring and early warning method and system, and belongs to the technical field of coal spontaneous combustion fire monitoring and early warning, and the method comprises the steps: S1, obtaining thermal infrared image and visible light image data; s2, eliminating redundant thermal imaging frames and fuzzy thermal imaging frames; s3, obtaining a preliminary surface temperature field distribution map; s4, an open fire source and a suspicious area are distinguished, and positions are marked; s5, acquiring surface temperature gradient distribution and indication gas concentration data of the suspicious area, and acquiring surface temperature data and underground temperature data of the verification point, indication gas data escaping from the detection hole and radioactive radon content data; s6, establishing identification indexes of the dark fire source; s7, establishing a dark fire source grading risk standard; and S8, outputting an early warning result. According to the method, full-time, full-field and three-dimensional monitoring is realized, the accuracy and the reliability are improved, intelligent graded early warning and decision support are realized, and the environmental adaptability and the automation level are enhanced.

The invention relates to the technical field of building disease detection, in particular to an urban building disease detection method and device, electronic equipment and a storage medium. Multi-modal image data formed by original visible light and thermal infrared image data is obtained, and an original thermal infrared image is subjected to geometric correction; calculating a mapping relation with an original visible light image so as to complete pixel-level registration, obtaining target multi-modal image data, inputting the target multi-modal image data into a hierarchical deep learning recognition model, recognizing building disease information, then performing three-dimensional space mapping, generating a building three-dimensional mesh model containing disease three-dimensional space setting coordinates, and finally performing three-dimensional mesh modeling. And then calculating a relationship between a model surface grid vertex and a disease point cloud density, generating a disease distribution thermodynamic diagram, analyzing disease aggregation characteristics in multiple dimensions according to the thermodynamic diagram, and quantitatively analyzing spatial correlation between the disease and a building construction node in combination with building component information. According to the invention, the urban building disease detection efficiency and precision are improved.

The invention provides a calibration method suitable for a multi-view heterogeneous imaging system, a medium and equipment. The method comprises the following steps: S1, acquiring a multi-modal calibration template image set by each camera of a multi-view heterogeneous imaging system; performing preprocessing by using guide filtering to obtain a filtering result image set of the multi-modal calibration template; s2, carrying out frame mean processing on the filtering result image set of the visible light calibration template; s3, detecting a hot spot extreme value center point of the hot spot of the filtering result image set of the thermal infrared calibration template by using a search maximum value algorithm; s4, determining an accurate center point of the hot spot by using a mean smoothing algorithm; s5, performing grid processing on the hot spot accurate center point set, and reconstructing a thermal infrared checkerboard calibration image set; and S6, calibrating internal and external parameters of each camera of the multi-view heterogeneous imaging system by using a Zhang Zhengyou calibration method. According to the method, calibration of an existing thermal infrared camera and an existing visible camera can be achieved, and then the problem of texture information alignment between heterogeneous images is solved.

The methods described herein provide improvements in composite infrared absorption imaging. The scanning approach described herein reduces erroneous measurements caused by thermal drift and optical interference that occurs between light scattering from the top surface of the sample and light scattering from the underlying substrate. Embodiments described herein relate to imaging and spectroscopy, and more particularly, to improvements in photo-thermal imaging and spectroscopy systems, and techniques for obtaining spectral information characterizing optical properties and / or materials or chemical compositions of a sample, such as information related to infrared (IR) absorption spectroscopy.

The invention discloses an automatic identification method for hot spot defects of a photovoltaic module based on thermal infrared imaging, relates to the technical field of defect identification, and aims to improve the environmental adaptation precision and the multi-frequency thermal wave penetration coverage dimension in the detection process of the hot spot defects of the photovoltaic module, realize efficient and accurate multi-frequency excitation parameter configuration and environmental thermal noise suppression and improve the detection accuracy of the hot spot defects of the photovoltaic module. The defect identification accuracy and the fault diagnosis reliability level in the operation and maintenance process of the photovoltaic module are remarkably improved, the situation that response of fixed excitation parameter setting to actual thermophysical property changes is insufficient is effectively prevented, depth deviation and defect misjudgment caused by experience judgment are avoided, the pertinence and defect identification effect of final heat source distribution field reconstruction are effectively improved, and the method is suitable for large-scale popularization and application. And determining the packaging layer to which the defect influencing the performance of the photovoltaic module belongs, and ensuring that the defect layering identification measure is highly matched with the actual heat source depth distribution risk.

The invention relates to a runway visual range prediction method based on multi-modal fusion, and the method comprises the following steps: S1, constructing a time-space matched data set which comprises Himawari-9 satellite thermal infrared channel brightness temperature image data, airport site meteorological element data and airport observation RVR / MOR data, carrying out the cleaning work of the data, and dividing the data into a training set, a verification set and a test set; s2, constructing an RVR / MOR forecasting model based on a Cross ViViT model, training model parameters by using a training set and a verification set, adjusting and optimizing the parameters, and performing precision evaluation on the model by using a test set; s3, mapping the high-dimensional semantic representation presented by the constructed RVR / MOR prediction model into a specific RVR prediction value for utilization; the problems that at present, only a traditional statistical model or a depth model based on a single mode is relied on, a complex RVR generation mechanism is often difficult to describe accurately, and particularly, response lag or prediction distortion phenomena easily occur in sudden weather events, so that the prediction effect is affected are solved.

The invention relates to the technical field of forest fire prevention monitoring, in particular to a forest fire prevention monitoring method and system based on multi-modal data and intelligent collaboration, and the method comprises the steps: obtaining thermal infrared images of a forest region at all moments, and the temperatures and wind speeds of different monitoring points in the forest region at all moments; calculating the temperature anomaly degree of each pixel point, and extracting all abnormal areas; determining a distribution characteristic value, a heat source evaluation value and a suspected degree of the abnormal region, and screening out a suspected region; acquiring a matching point corresponding to each pixel point in each frame of grayscale image; calculating the change confusion degree and the flicker evaluation value of each pixel point, and screening flicker points; and determining a flame edge value of each flicker point, and extracting a flame region in each frame of grayscale image. According to the method, the accuracy and reliability of forest fire identification are improved, and meanwhile, the false alarm rate caused by complex environment interference is remarkably reduced.