89 results about "Fog" patented technology

The application discloses a kind of visual enhancement methods for urban road all-weather environment perception, it is related to computer vision and intelligent transportation technical field, steps include: acquisition includes the data set of multiple typical weather and illumination condition;Build physical real cross-domain visual simulation environment, based on training set data, carry out night simulation, dynamic simulation, rain fog simulation, generate the synthetic image with target domain feature;Optimization is carried out to feature, including: the feature fusion based on CutMix and the consistent feature representation based on CORAL Loss;Model adaptation is carried out, from three levels of basic model reconstruction, backbone network light weight, feature fusion structure optimization, systematic adaptation is carried out, improve the perception ability under rain, fog, night complex weather conditions.The method of the application can provide technical support for intelligent perception of urban road under complex weather through real-time deployment and application on edge device.

The application belongs to the technical field of sea fog monitoring, and particularly relates to a sea fog monitoring method and system based on multi-source satellite remote sensing data, which comprises the following steps: collecting satellite radiation and microwave observation data, combining the response difference of infrared and microwave channels to distinguish the cloud top phase state, dividing clear sky and low cloud blocking scenes, and outputting identification data; combining single-time infrared brightness temperature difference and air pixel parameters to detect sea fog and retrieve optical parameters, forming a scene-specific sea fog physical parameter set; normalizing and coupling the parameter set for reconstruction, constructing a sea fog-specific feature space, separating the fog area and the non-fog area through phase state constraint to obtain an initial judgment feature field, correcting the fog area edge through neighborhood correlation and brightness temperature gradient constraint, strengthening the spatial continuity to generate a spatial distribution field, and locking a reliable area to output the final sea fog detection result. Through the progressive design of scene division, feature optimization and multi-source verification, the accuracy and integrity of sea fog identification are significantly improved.

Disclosed are mist and vapor elimination filters, devices, methods of filtering aircraft air using the devices, and systems including the devices.

This utility model relates to the field of vehicle manufacturing technology, specifically an intelligent control system for a fog cannon, comprising: a control unit with a built-in RS485 communication module and multiple I / O interfaces; a communication hub connected to the RS485 communication module of the control unit via the MODBUS RTU protocol; a data acquisition module that communicates bidirectionally with the communication hub via an RS485 branch line, including a dust concentration sensor, a multi-parameter meteorological sensor, a high-definition camera, and an angle encoder; an execution module that communicates bidirectionally with the communication hub via an RS485 branch line, including a high-pressure atomizing device, a servo gimbal, and a centrifugal fan; and an auxiliary module that communicates bidirectionally with the communication hub via an RS485 branch line, including a hypochlorous acid generator, a liquid level sensor, and a solenoid valve assembly. This utility model, through its modular architecture design, enables plug-and-play expansion of each functional module, significantly reducing maintenance costs.

The application discloses a kind of fog environment-oriented adaptive dual-path unmanned aerial vehicle detection method and system, belong to computer vision, image processing and low-altitude safety monitoring cross technical field.The present application aims to solve the problem of high false negative rate, waste of computing resources, dehazing and detection task mismatch in the fog environment of prior art.The method comprises: receiving input image and calculating global fog concentration coefficient;With preset threshold 0.52 comparison, adaptive selection dehazing enhancement path or direct detection path;Moderate fog through ASC image enhancement network processing image;Finally, through improved YOLO P2 target detection network execution detection.The application realizes computing resource on-demand allocation, improves the fog micro-target detection precision and system real-time, applicable to low-altitude safety monitoring scene.

This invention discloses an adaptive end-to-end network learning-driven polarization descattering depth estimation method. It proposes a physics-driven end-to-end network to simultaneously acquire clear reconstructed images and high-precision depth images in scattering environments. The method first acquires linearly polarized images of the scene in four directions, extracting light intensity and linear polarization feature maps through decoupling calculations. Then, a dual-task network including a polarization-guided fusion module is constructed. Utilizing the physical sensitivity of DOLP to scattering particle concentration and optical path length, it transforms these into a spatial attention mask, physically constraining and enhancing the shallow texture of the intensity map in the feature space. Finally, dehazing and depth estimation branches are encoded and decoded in parallel. This invention overcomes the intensity-depth ambiguity of traditional RGB images, effectively suppressing the interference of non-uniform fog on geometric perception and visual texture, and achieving high-precision scene depth estimation and high-fidelity dehazing imaging under adverse weather conditions.

This application provides a method, apparatus, storage medium, and vehicle for displaying vehicle environmental images, applied in the field of vehicle technology. The method includes: acquiring driving data and external environment detection data, including precipitation level, ambient light intensity, and environmental image data; determining the target driving scenario currently in which the vehicle is located based on the driving data and external environment detection data; determining a target image enhancement strategy corresponding to the target driving scenario from a preset set of image enhancement strategies; processing the environmental image data according to the target image enhancement strategy; and displaying the processed environmental image data. This method can perform scene-adaptive image enhancement processing, taking into account both weather complexity and differences in vehicle operating states, significantly improving the detail recognition and penetration enhancement effect of snow and fog environment images.

This invention discloses a drying device. The design of the base and detachable drying baskets allows for convenient loading and unloading of drying items, improving the ease of daily drying operations. The collaborative work of the rainproof mechanism, detection mechanism, and electronic control module changes the traditional passive situation of relying on manual supervision and maintenance for drying. Addressing the unpredictable weather conditions of summer, frequent typhoons, and the foggy and windy autumn, the device can sense weather changes in real time and automatically trigger protective actions. It can quickly unfold a tarpaulin to cover the drying baskets without manual intervention, preventing the dried items from getting wet from rain, being blown away by strong winds, or being damaged by fog and moisture, thus ensuring the quality of the dried goods. Simultaneously, the infrared detector and camera in the detection mechanism, along with a buzzer, can promptly detect and drive away small animals that try to steal the dried goods, reducing food waste and further minimizing losses for the public.

This invention discloses a navigation device for tourists who get lost in mountainous scenic areas, comprising: a rest station, a lost-aware warning mechanism, a geomagnetic signal transmitting component, a signal tracking device, and a first control system. The rest station is located on the main road of the scenic area. The lost-aware warning mechanism is located at the rest station and includes a first driving mechanism and a thermal imager. The first driving mechanism drives the thermal imager to swing up and down along the direction of gravity to identify whether tourists have left the main road of the scenic area. The first control system sends a reminder signal to the signal tracking device carried by the tourist based on the information from the thermal imager to avoid getting lost. After getting lost, the tourist uses the signal tracking device to send a distress signal. The first control system receives the distress signal and drives the geomagnetic signal transmitting component to send an electromagnetic signal. The signal tracking device receives the electromagnetic signal and, through changes in the strength of the electromagnetic signal, helps the tourist find the nearest rest station, thereby helping tourists to save themselves in extreme weather conditions such as dense fog and heavy rain.

The application discloses an atmospheric visibility prediction method considering cloud droplet concentration, wherein when a physical relation model in fog is constructed, the influence of cloud droplet concentration on visibility under different pollution conditions is additionally considered, and a diagnostic calculation formula of the visibility is derived; after reasonable cloud droplet concentration values are set, the formula is applied to main numerical prediction products such as GFS, ECMWF, CMA-MESO, etc., and is substituted into basic meteorological quantities, so that the diagnostic prediction result of the visibility can be obtained. The method is suitable for the visibility prediction under different pollution conditions, has very fast calculation speed, does not additionally occupy storage, does not need a large number of sample training, and has strong expansibility, thereby providing theoretical and technical support for the development of fine prediction and early warning technology of fog.

This invention provides a multimodal incremental railway fastener defect detection method and system under severe weather conditions, belonging to the field of target recognition technology. Step 1: Sequentially construct training and validation sets for multimodal railway fasteners under rain, snow, fog, strong light, low light, and normal weather conditions; construct a global test set containing multimodal railway fastener data for all six weather conditions. Step 2: Construct a full-cycle closed-loop mechanism for multimodal target detection to achieve continuous learning in continuously evolving scenarios. Step 3: Propose a dual-space replay screening strategy for meta-features to extract challenging and representative high-value samples for inclusion in the replay pool. Step 4: Propose a wavelet-guided non-adversarial detail injection module to resolve the adversarial conflict between noise removal and style preservation in incremental learning. This invention can continuously adapt to various severe weather changes in railway scenarios, effectively alleviate the problem of catastrophic forgetting, and achieve accurate detection of fastener defects under various severe weather conditions.

This invention discloses a smart construction site management method and system based on the Internet of Things (IoT), relating to the field of smart construction site environmental control technology. The method includes performing vector projection on the point cloud of the material storage area's surface to obtain the tangential wind speed components of the surface points, calculating the voxel center elevation difference of the three-dimensional point cloud, screening wind erosion active areas, uniformly dividing the wind erosion active areas into sub-zones, calculating the horizontal drift distance, predicting the landing center coordinates, and generating a predicted landing circle; calculating the fog cannon coverage circle, determining the spatial intersection of the predicted landing circle and the fog cannon coverage circle, calculating the fog cannon activation time in the control area, and sending the activation time to the fog cannon for execution. This invention improves the dynamic simulation capability of dust particle falling behavior by combining particle size distribution quantification with a settling velocity estimation model, and improves the accuracy of dust fall range prediction by calculating the horizontal drift of dust particles combined with a landing circle prediction model.

This invention discloses a pixel-level visibility and fog structure joint modeling method and system based on binocular depth estimation and pixel-by-pixel transmittance fusion. The method utilizes binocular vision to obtain accurate scene depth and constructs a two-stage neural network: the first stage estimates depth through stereo matching; the second stage fuses depth and fog map texture features, jointly regresses pixel-by-pixel transmittance and visibility maps, and detects localized patchy fog by analyzing their spatial distribution. This invention overcomes the limitations of monocular methods due to the lack of geometric information, achieving physically interpretable high-precision visibility perception and effectively identifying non-uniform fog conditions that endanger traffic safety. The system can run in real-time on an embedded platform, combined with a gimbal for panoramic monitoring, and is applicable to fields such as intelligent transportation, autonomous driving, and meteorological observation.

The application discloses a kind of vehicle-mounted millimeter wave radar error correction methods for complex traffic scene, it is related to radar data processing technical field, and stable weak scattering point set Sca is extracted by candidate scattering point set Csp, and the weak point trace in the sheltered area is not ignored by the aid of boundary corresponding set Bdr, so that track keeps continuity and credibility on time series and spatial coordinates;Combined with the track compensation set Com generated by time synchronization error Ets and calibration residual Ecl and applied to track fitting set Fit, the corrected track set Cor obtained can effectively eliminate the drift and misplacement caused by radar sampling delay and installation angle deviation, to correct track set Cor as output result is used for scene reconstruction and target identification, so that vehicles can still obtain time alignment and spatially accurate error correction final track results in complex traffic environment such as night multi-lane highway, rain and fog weather intersection.

This invention discloses a fog identification method that automatically generates prompts based on ground observation data and satellite images. The method includes: data acquisition, acquiring satellite remote sensing images and ground meteorological observation station data; prompt generation, generating initial prompts based on ground observation data through logical judgment, and filtering and optimizing the prompts using satellite image features; feature extraction, extracting high-frequency features from satellite images through frequency domain transformation, and fusing them with deep semantic features to obtain enhanced features; and fog area identification, inputting the optimized guidance prompts and enhanced features into a segmentation model and outputting fog area identification results. This invention solves the problems of relying on manual annotation and the difficulty of multi-source data fusion in fog identification by automatically generating guidance prompts from ground observation data and combining them with frequency domain enhanced features, achieving high-precision automatic monitoring of sea fog without human intervention.

This invention discloses a spatiotemporal reconstruction method and system for coastal vegetation indices based on geographic logic gating and multi-agent systems, belonging to the fields of remote sensing ecological monitoring and deep learning. The method acquires and preprocesses multi-temporal high- and low-resolution remote sensing images to construct NDVI spatiotemporal feature sequences. Spatiotemporal features are obtained through deep encoding, and a multi-agent system incorporating information on vegetation, cloud cover, tides, and topography is established. Coastal geographic logic constraints are introduced to construct multi-scale spatiotemporal differences and generate geographic logic gating, which weights and modulates the spatiotemporal features to enhance reliable information and suppress interference noise. Multi-agent collaborative reconstruction is achieved under multi-objective constraints, outputting high-resolution vegetation indices and supporting edge deployment and online learning. This invention effectively overcomes problems such as cloud and fog obstruction, tidal inundation, and data gaps, significantly improving reconstruction accuracy, geographic rationality, and spatiotemporal continuity. It is more adaptable to the complex ecological environment of coastal zones, providing a stable and reliable technical means for long-term dynamic monitoring of vegetation.

The utility model relates to vehicle sensor technical field, and disclose a kind of vehicle external environment sensing device. Including vehicle body, still include: warning light, protective cover, millimeter wave radar, three camera systems, protective fence, the vehicle body upside is fixedly installed with warning light, the vehicle body bottom corner is installed with millimeter wave radar, the millimeter wave radar outside is fixedly installed with protective cover, the vehicle body downside is fixedly installed with protective fence, the protective fence upside is fixedly installed with three camera systems, the three camera systems include camera main part, the camera main part one side is fixedly installed with three evenly distributed camera lens, it is favorable to pass through synchronous integration visible light, near infrared and thermal imaging three big spectral channel, has constructed all-weather environmental perception ability, both retain visible light rich color and detail information, significantly improve rain and fog weather and backlight environment under target identification reliability.

The utility model belongs to the technical field of fog weather driving induction lamp, and disclose a kind of multi-angle adjustment fog weather driving induction lamp frame, to solve the problem of poor induction effect caused by the fixed angle of induction lamp under complex road conditions, it includes the base of highway guardrail, the installation platform for installing induction lamp, and the rotating component and the adjustment locking component connecting both, the rotating component realizes the up-down inclination angle adjustment of installation platform by rotation cooperation, the angle after adjustment can be locked by hand screw bolt of adjustment locking component, avoid natural loosening, and adjustment does not need tool, it is convenient to install and debug, wire channel system ensures that wire is not damaged when angle is adjusted, guarantee equipment normal operation. The lamp frame can quickly adjust the induction lamp to the optimal angle, greatly improve the safety of fog weather driving, easy and practical to operate.

Container wharf crane cable splicing composite optimization clean monitoring protective cover, fixed frame (1), top plate (2) and the door (3) are multi-layer composite structure; the top surface of fixed frame (1) is fixed with top plate (2), and the door (3) is installed on the local hollowed-out loading port (201) of top plate (2); monitoring module (10) is installed on fixed frame (1), top plate (2) or the door (3); the damping, mechanical strength and weather resistance of the protective cover are greatly improved, the temperature, humidity and whether there is damage of the protective cover are monitored in real time, and the data are transmitted to the monitoring center, remote monitoring and early warning are realized. The multi-section splicing design is convenient for installation and disassembly, can effectively resist sea wind, salt fog, mechanical collision and sun and rain, and greatly prolongs the service life of the cable. When mechanical collision occurs, the impact force can be effectively absorbed, and the cable is protected from damage. The protective cover is firmly installed on the cable, the loosening and falling during the operation of the crane are prevented, and the installation time and labor cost are reduced.

This invention belongs to the field of rock mass engineering testing devices, and provides a physical model test device and method for flood discharge fog and rain simulation, including: a model box, a water supply system, a fog and rain simulation system, and a groundwater simulation system. The model box consists of a sedimentation tank and a test chamber. The water supply system mainly consists of an inlet pipe, a storage tank, a delivery pipe, a variable frequency self-priming booster pump, a flow meter, a check valve, a bend joint, and a main water pipe. The fog and rain simulation system mainly consists of a frame, a servo motor, steel strands, fixed supports, anchors, a top water pipe, a tee joint, a bend joint, and nozzles. The groundwater simulation system includes a front water level regulating tank and a rear water level regulating tank. This invention realizes the influence of fog and rain zoning and the coupling effect of fog and rain with groundwater level on fractured rock slopes, which is of great significance for the study of the stability of high dam fractured rock slopes under flood discharge fog during the construction and operation of hydropower projects.

This application provides a millimeter-wave cloud radar observation method and related apparatus based on multi-source data. A fused dataset is formed by real-time acquisition and preprocessing of at least one of the following: weather radar mosaic data, meteorological satellite data, ground visibility observation data, and co-located or adjacent aerosol lidar data. A preset rule set is used to determine that the current weather scene at the cloud radar station includes at least weather echo approaching, weather echo receding, local weather processes, fog, and non-precipitating clouds. Based on the identified weather scene type, the corresponding combined scanning strategy (composed of a basic scanning mode consisting of vertical top-view scanning, distance-height scanning, and three-dimensional volume scanning, and a fine scanning mode consisting of local planar horizontal scanning and layered planar horizontal scanning, combined according to preset logic) is called from a preset strategy mapping table and executed. During execution, the parameters of the fine scanning mode are dynamically adjusted based on the position and intensity information of the real-time radar echo or changes in the detection mode.

The application discloses a system and method for eliminating fog clusters and generating electric energy by air water extraction, and belongs to the technical field of environment and resource recycling. The system and method are used for solving the technical problems of high energy consumption, single function attribute and failure to effectively convert the gravity potential energy of fog drops, which leads to continuous power supply for remote road section monitoring equipment. The system comprises a detachable fog removal assembly, a water collecting tank and a Kelvin water drop power generation module. The application deeply couples the air water extraction assembly and the Kelvin water drop power generation module, relies on the gravity of the fog drops to complete the polarization, induction and positive feedback accumulation of electric charge, and does not need external power supply or complex power supply pipelines, so that the effect of green, low-cost and sustainable power supply for remote road section monitoring equipment is achieved.

This invention provides a method and system for testing icing under rotating conditions of supercooled large water droplets, relating to the field of aero-engine icing testing. It aims to address the problem of excessive liquid water content caused by significant settling of supercooled large water droplets and the inability of the uniform cloud zone to cover the test specimen. The method includes: obtaining the projected area S of the rotating test specimen on the test cross-section under rotating conditions. A ; Obtain the overlapping area S of the cloud and fog region between the uniform cloud and fog region and the projected area of ​​the rotating test specimen. B The liquid water content obtained from the calibration test is L. B According to the projected area S A Area S where clouds and fog overlap B and liquid water content of L B The liquid water content L corresponding to the freezing condition of the rotating test piece was obtained. A By using the relative area of ​​the uniform cloud and fog zone and the test zone, liquid water is equivalently transformed. The freezing condition conversion method of the rotating state test under the freezing condition of supercooled large water droplets is adopted, which effectively supports the rotating state test under the freezing condition of supercooled large water droplets.

This application provides a method and apparatus for generating panoramic fog scene images based on depth information. The method includes: step S1, acquiring an outdoor panoramic image and its depth map; step S2, calculating the scattering coefficient corresponding to each image region based on the regional distribution of the outdoor panoramic image; step S3, calculating a light transmittance map based on the depth map and the scattering coefficient corresponding to each image region, and performing fogging processing on the outdoor panoramic image based on the light transmittance map and a preset atmospheric light value to obtain a panoramic fog scene image. The technical solution of this application calculates the scattering coefficient corresponding to each region based on the regional distribution of the outdoor panoramic image, which can simulate the physical characteristics of fog concentration changing with spatial position in the real atmosphere, such as denser at the bottom and lighter at the top, denser near and lighter far, making the generated fog scene image more in line with the atmospheric optical laws and improving the spatial layering and volume of the fog effect.

A sky direction channel damping method for correcting accelerometer zero offset based on FOG-SINS, by establishing a new model, taking the height of the satellite and the sky direction velocity as the external reference quantity, estimating the sky direction accelerometer zero offset error, and entering the corrected sky direction specific force information into the damping feedback loop, the convergence speed of the sky direction velocity error and height error estimation in the loop is accelerated, thereby improving the sky direction channel tracking accuracy of the FOG-SINS inertial navigation equipment, solving the problem that in the original sky direction channel damping model, only the height is taken as the main reference quantity, the sky direction specific force error is not corrected, resulting in poor sky direction channel tracking accuracy and low measurement accuracy during the movement of the carrier.

This invention relates to a driving warning method based on water film fogging and image visibility. The method involves a rear-mounted system that captures and analyzes video of vehicles traveling in adjacent lanes ahead. This includes applying a visibility detection model to detect the forward visibility distance corresponding to the video capture location, and using a road surface water film thickness detection model to determine the water film thickness of the road segment where the vehicle is located, calculating the equivalent fogging effect distance of the vehicle behind. Finally, by combining the visibility distance and the equivalent fogging effect distance and mapping under a pre-defined classification design, a driving warning is determined for the rear video capture location. A corresponding system is further designed to achieve practical application. The modular construction and implementation of the design method determine accurate and specific warning information, enabling driving warnings during vehicle travel and significantly improving driving safety in rainy weather.

This application provides a vehicle defrosting control method, electronic device, and vehicle. The method determines the temperature distribution of relevant areas of the vehicle window through multimodal feature maps. Based on the multimodal feature maps and historical temperature sequences, a frost and fog generation area prediction map can be determined to predict the frost and fog generation area in real time. The frost and fog diffusion trend map sequence can predict the diffusion trend of frost and fog in real time. Combined with the risk score of the risk area, a defrosting path map is dynamically generated to guide the operation of the defrosting equipment. The angle, air volume, temperature, and heating power of the defrosting equipment outlet are adjusted according to the defrosting path map to achieve precise defrosting while reducing energy consumption.

The present application relates to the technical field of power transmission line safety monitoring, and provides a power transmission line ice thickness calculation method, system, medium and equipment to solve the problem of low ice thickness identification accuracy in rain, snow and fog weather or backlight scenes. The power transmission line ice thickness calculation method includes, under the mixed constraints constructed by geometric information and physical constraints, using a perspective transformation method based on sensor physical parameters and feature point coordinates to optimize the distortion-corrected power transmission line image; using a trained power transmission line segmentation model to process the optimized power transmission line image to obtain a power transmission line region image; extracting ice feature points in the power transmission line region image, calculating the power transmission line center offset based on feature point matching and correcting the power transmission line position deviation before and after icing to obtain the ice-covered power transmission line diameter and the actual diameter of the non-iced power transmission line, introducing a temperature correction coefficient to obtain the final ice thickness, which takes into account environmental adaptability and detection accuracy.

This invention relates to the field of power transmission line safety monitoring technology. To address the problem of low accuracy in identifying icing thickness under rain, snow, fog, or backlight conditions, it provides a method, system, medium, and equipment for calculating power transmission line icing thickness. The method for calculating power transmission line icing thickness includes optimizing a distortion-corrected power transmission line image using a perspective transformation method based on sensor physical parameters and feature point coordinates under a hybrid constraint constructed from geometric information and physical constraints; processing the optimized image using a trained power transmission line segmentation model to obtain a power transmission line region image; extracting icing feature points from the region image; calculating the center offset of the power transmission line based on feature point matching and correcting the positional deviation of the power transmission line before and after icing to obtain the diameter of the iced power transmission line and the actual diameter of the uniced power transmission line; introducing a temperature correction coefficient to obtain the final icing thickness, which balances environmental adaptability and detection accuracy.

This invention relates to a large-scale collaborative ANIL target recognition method and system for cameras operating in icy and foggy conditions, belonging to the fields of computer vision and road safety technology. The invention employs an edge-cloud collaborative architecture. On the edge, the dark channel defogging coefficient and gamma correction coefficient are dynamically adjusted based on visibility and icing conditions to adaptively preprocess the image. A lightweight ANIL network is used for real-time target detection, and a confidence threshold is dynamically determined based on visibility. When the confidence level is insufficient, a large-scale Grounding DINO 2.0 model is triggered in the cloud for accurate inference. Simultaneously, the output of the large-scale model is used as prior knowledge to perform online transfer learning and parameter optimization on the edge-side ANIL network, forming a continuous evolutionary closed loop. Finally, millisecond-level priority warnings are achieved for sudden situations. This invention is used in intelligent transportation systems for target recognition and safety warnings of cameras in severe weather conditions such as icing and fog, improving the recognition accuracy, real-time performance, and environmental adaptability of traffic monitoring.