791 results about "Severe weather" patented technology

The invention relates to the technical field of infrared target detection, and discloses a YOL0-based adaptive full-scale infrared target detection network, which comprises a trunk feature extraction network, a neck feature fusion network, a detection head network and a training optimization module, and is characterized in that all the modules are sequentially connected in series to form a complete detection link; the infrared image multi-dimensional feature extraction system is used for infrared image multi-dimensional feature extraction and comprises a convolution layer, an SPPF module and a C2MFE module which are connected in sequence, and the C2MFE module replaces a standard convolution layer in a traditional C2f module through multi-kernel feature extraction convolution (MFEEConv) to achieve multi-direction and full-scale feature capture; and the neck feature fusion network is connected with the output end of the trunk feature extraction network, comprises a multi-scale feature fusion module (MFFM) and a feature pyramid structure, and is used for enhancing the feature correlation of different levels. The adaptive full-scale infrared target detection network based on YOL0 can efficiently adapt to complex scenes such as low illumination and severe weather, and realizes cross-scene full-scale infrared target accurate detection.

The invention relates to the field of power system operation and maintenance, in particular to a power transformation equipment operation and maintenance risk online assessment method and system. A power transformation equipment operation and maintenance risk online evaluation system comprises a data acquisition module, a weight configuration module, a sequence risk evaluation module, a collaborative decision game module and a disposal scheme output module. According to the method, a multi-source data fusion and dynamic threshold mechanism is introduced, real-time state quantity, historical maintenance records, operation modes and external weather information are uniformly mapped to a convolution-long and short-term memory network, key features are adaptively amplified in a feature weighting layer, redundant features are weakened, and collaborative recognition of short-term fluctuation and long-term degradation is achieved; compared with a traditional fixed threshold value or single monitoring quantity model, the method can keep sensitive and steady risk early warning capacity under the complex working conditions of severe weather, heavy load operation and the like, the false alarm rate and the missing report rate are greatly reduced, potential faults are locked in advance, and sudden power failure events are avoided.

According to the severe weather adaptive road violation real-time monitoring method and system provided by the invention, the image quality is dynamically judged by using the visibility index, and accurate traffic target identification and violation behavior detection are realized by combining image enhancement and a gating feature fusion mechanism. The system comprises a video acquisition module, a visibility evaluation module, an image enhancement module, a feature extraction and fusion module, a target detection module and a violation recognition module, can maintain high-accuracy recognition performance in various severe weathers (such as rain and fog), and is suitable for the field of intelligent traffic and urban safety management.

According to the multi-source information fusion robot positioning method and system for the unstructured environment provided by the invention, fusion processing is carried out on multi-source information data such as laser point cloud, environment image information and acceleration which are acquired in real time, so that the multi-source information fusion robot positioning method and system for the unstructured environment can be realized in a severe weather state and a dynamic unstructured environment. Carrying out high-robustness real-time positioning under the condition of violent illumination change; in a geometrically degraded roadway area, registration positioning of laser point cloud emission fails due to lack of enough external characteristics, and at the moment, the visual positioning module can still work normally and complete robot repositioning by detecting matched image information; in an environment lacking enough illumination, the laser point cloud and acceleration information can still provide enough positioning fusion data input for the computing unit.

The invention discloses a multi-degraded image restoration method based on semantic guidance. According to the method, for degraded images captured by a vehicle-mounted camera under severe weather conditions, potential features of the images are extracted by adopting a trunk network based on Transform, multi-modal semantic information is extracted in combination with a CLIP visual language model, and semantic guidance is provided for different degradation types through a dynamic text prompt generation mechanism. A self-adaptive feature fusion module is designed, channel attention and space attention mechanisms are combined to realize effective integration of multi-modal features, and a degradation feature extraction and fusion module is introduced to enhance the generalization ability of the model. According to the semantic guidance multi-type image recovery network SGIRN provided by the invention, the global modeling capability of the Transform and the cross-modal representation capability of the CLIP visual language model are combined, so that high-quality recovery of various weather degradation types is realized.

The invention discloses an intelligent chassis preview control system and method based on a roadside sensor, and the system comprises a roadside sensing unit which is used for obtaining the road condition information of a line road, and transmitting the road condition information to a roadside communication base station; the roadside communication base station is used for preprocessing the road condition information and packaging the road condition information into a set format; the vehicle-mounted communication module is used for sending the road condition information with the set format to the vehicle-mounted controller; the vehicle-mounted controller is used for performing fusion processing on the road condition information and the vehicle state data, and analyzing the fused data by adopting a preview control algorithm model to generate a control instruction of the intelligent chassis executing mechanism; and the intelligent chassis executing mechanism is used for performing preview control on the vehicle chassis based on the control instruction. The method has the advantages that pre-judgment adjustment of the vehicle chassis is achieved, the safety and stability of vehicle driving are effectively improved, and the system reliability is enhanced especially under complex road conditions and severe weather conditions.

The invention relates to the related technical field of intelligent scene labeling, in particular to an intelligent driving scene collaborative labeling method and system based on multi-modal fusion, and the method comprises the steps: constructing a dynamic feature fusion model, carrying out the cross-source correlation analysis of a multi-modal scene data set, setting organization structure elements, and setting a multi-dimensional scene labeling model. And configuring a potential danger interaction path and a scene risk index, and generating an annotation instruction set to carry out collaborative annotation operation. The technical problems that under interference of severe weather such as sensor noise, rain and fog and strong light and abnormal traffic behavior scenes, the labeling accuracy is insufficient, and complex scenes and multi-modal data requirements are difficult to adapt are solved, labeling noise data are introduced into an adversarial sample configuration assembly, a multi-dimensional scene labeling model is constructed in combination with organizational structure elements, and the multi-dimensional scene labeling accuracy is improved. The technical effects of improving the training data reliability, adapting to complex environment labeling requirements, configuring potential risk interaction paths and scene risk indexes, carrying out risk-oriented priority labeling and improving the risk scene labeling precision are achieved.

The utility model relates to a windproof and anti-icing convenient insulating device capable of live working, which comprises a lower-end metal accessory, an insulating body is arranged on the upper part of the lower-end metal accessory, and an upper-end fitting is arranged on the upper part of the insulating body; the upper part of the upper-end fitting is connected with a gland through a rotating shaft; the buckling part of the upper end fitting and the gland is provided with an arc-shaped groove for installing a high-voltage wire, and the arc-shaped groove is a silicone rubber cushion layer. An operation screw rod penetrating through the gland is mounted at the end, far away from the silicone rubber cushion layer, of the upper-end fitting; and a compression spring is sleeved on the operation screw rod between the upper end fitting and the gland. According to the insulator, the diameter of the upper umbrella cover is increased, so that the influence of dust, foreign matters or icing in severe weather on the insulator is avoided; lines can be maintained by live-line operation, and safe operation and maintenance of the lines are facilitated; the wire fixing structure can effectively avoid the problems of wire loosening and wire insulating layer abrasion caused by strong wind.

The invention discloses an automobile laser radar point cloud semantic intelligent completion method in rainy and foggy weather, and particularly relates to the technical field of automatic driving perception. The method comprises the following steps: firstly, collecting time sequence point cloud data and inertial navigation information of a vehicle under rain and fog conditions, and constructing a sparse point cloud sequence; then constructing a sparse-semantic tensor graph based on point cloud density drift, echo energy anomaly and a semantic graph, extracting context structure features through a time sequence graph neural network, and generating an affine complement containing affine transformation parameters; further realizing consistent alignment of boundaries by using a semantic drift compensation mechanism, reconstructing a false point cloud complementation frame and fusing the false point cloud complementation frame with the original point cloud, and outputting a semantic complementation enhancement frame; according to the method, the point cloud integrity and semantic precision in severe weather can be improved, the stability of obstacle detection and path planning is enhanced, and the method is suitable for a complex environment sensing scene in an intelligent driving system.

The invention discloses an intelligent heating method and system for a rear air window and a rearview mirror, a controller and a storage medium. The method comprises the following steps: acquiring real-time environment data at least comprising environment temperature and environment humidity; determining a basic heating duration based on the environment temperature through a first control logic; compensating and correcting the basic heating duration based on the environment humidity through a second control logic so as to generate a target heating duration; and finally, controlling the heating device to operate according to the target heating duration. The method further comprises a cooperative control scheme of switching to a degradation control mode based on preset configuration parameters when sensor data are invalid and adjusting heating duration based on data acquired from the ADAS in severe weather. Through multi-parameter fusion and hierarchical cooperative control, accurate matching of heating requirements is achieved, and the defrosting efficiency, the energy saving performance and the system reliability are remarkably improved.

The invention discloses an auxiliary driving method and device and intelligent driving equipment, and the method comprises the steps: obtaining weather information which indicates the weather type and / or road surface type of the current environment of the intelligent driving equipment; wherein the first weather type comprises one or more of a weather type for limiting visibility of a driver, a weather type for limiting sensing capability of a sensing system, or a weather type for increasing a braking distance of the intelligent driving equipment, and the first road surface type comprises a road surface type for increasing the braking distance of the intelligent driving equipment; according to the first environment information, a prompt device of the intelligent driving equipment is controlled to prompt a driving suggestion, and the driving suggestion comprises a driving strategy corresponding to the first environment information. The method can be applied to vehicles such as intelligent vehicles and new energy vehicles, and when severe weather influencing driving safety is detected, the vehicle user can be prompted about related information in time, so that the influence of the severe weather on driving is reduced.

The invention provides a target detector construction method based on transfer learning in a foggy scene, and belongs to the technical field of deep learning. According to the method, two lightweight neural network modules, namely an image adaptive module and a feature attention enhancement module, are designed according to the requirements of an unmanned aerial vehicle target detector in a foggy scene, and the two lightweight neural network modules are integrated into a plug-and-play image defogging and enhancement network; and then designing a combined target detector network according to the characteristics of the image defogging and enhancing network and various widely used target detectors. According to the invention, the target detector can improve the target detection performance in a foggy scene under the condition that model parameters and computing resources are hardly increased, technical support is provided for reliable visual perception under a severe weather condition, and the method has a wide application prospect.

The invention relates to the technical field of information processing, in particular to an anti-interference fusion sensing system for a multi-source heterogeneous sensor, and aims to solve the problem that the sensing performance of the multi-source heterogeneous sensor is reduced in severe weather such as rain and fog. The system comprises a data acquisition and synchronization module, a multi-modal feature extraction and weather interference suppression module, an adaptive multi-modal fusion module, a target detection and tracking module and a sensing result output module. According to the method, robust features are extracted from interfered laser radar and camera data through resistance robust feature learning, and millimeter wave radar information is combined; dynamically adjusting the weight of each modal according to weather through a self-adaptive fusion module; the sensing precision and reliability of the vehicle in complex environments such as rain and fog are effectively improved, and driving safety is guaranteed.

The invention discloses a multi-source heterogeneous data assimilation-driven high-altitude atmospheric motion numerical simulation method, which comprises the following steps of: obtaining satellite microwave radiation brightness temperature data and ground dual-polarization radar differential reflectivity data, inputting the data into a quantum annealing collaborative inversion model, and solving three-dimensional humidity field distribution data; humidity gradient tensor field data are calculated, and the space coordinate range of the frontal area is judged based on a preset humidity gradient modulus length threshold value; carrying out vector included angle calculation on humidity gradient tensor field data of a frontal area and background wind field data, generating a cloud micro-physical parameterization regulation coefficient, and dynamically selecting a cloud phase change dominant mode; and inputting the three-dimensional humidity field distribution data and the cloud microphysical parameterized regulation and control coefficient into a four-dimensional variational assimilation system to solve optimal analysis field data, and outputting high-altitude wind field prediction data in a driving numerical mode. According to the method, the response sensitivity and the physical consistency of the cloud microphysical process in numerical simulation are enhanced, and the cloud phase evolution simulation precision in the severe weather process is improved.

The invention discloses an intelligent optical sensing system based on multispectral fusion, and relates to the technical field of optical sensing, and the system comprises a multispectral image collection module which is used for synchronously collecting original image data of a target scene under three spectral channels of visible light, near-infrared and short-wave infrared; the space-time registration and preprocessing module is used for carrying out high-precision space-time registration and radiation correction on the images of different spectrum channels; the self-adaptive feature extraction and fusion module is used for extracting multi-scale spectral features from the registered multi-spectral image and dynamically selecting and weighting a fusion strategy according to scene content; and the lightweight decision network module outputs a final target identification and state discrimination result. According to the technical scheme, the time-space consistency of multispectral data acquisition can be realized, the cross-band registration precision is improved, the robustness under the conditions of low contrast, shielding and severe weather is enhanced, meanwhile, the calculation complexity and memory occupation are greatly reduced, and the method is suitable for edge calculation equipment with limited resources.

Described herein are systems and methods for reducing damage to solar tracker systems during severe weather, such as hailstorms. In one example, a solar module assembly, with edge protection against hail damage, includes a solar module that holds a plurality of photovoltaic cells, the solar module having a front surface and a sidewall extending from the front surface. A frame having a frame wall, which includes a first wall, is disposed about a perimeter of the solar module sidewall and supports the solar module. Further, a hail absorption wall extends along and is spaced from the first wall. The hail absorption wall is attached to the frame and resilient and deflectable towards the first wall. The hail absorption wall absorbs impact energy from hail falling in a direction towards the first wall.

The invention relates to the technical field of expressway safety monitoring, in particular to an expressway safety monitoring method and system facing severe weather, and the method comprises the steps: collecting an expressway monitoring video image in severe weather, and carrying out the graying of the image; calculating weather severity; screening feature directions and vehicle pixel points in all the images; performing connected domain analysis on the vehicle pixel points, and judging whether two nearest neighbor connected domains are merged or not; and the monitoring video image combined by the connected domains is used for realizing vehicle target identification and monitoring in severe weather. The objective of the invention is to improve the integrity of the vehicle detection target and further improve the reliability of the highway safety monitoring system.

The invention discloses a target detection method and device based on domain self-adaption, equipment and a medium. The target detection method based on domain adaptation is realized through a target detection model, the target detection model comprises a dynamic domain adaptation module, and the dynamic domain adaptation module comprises an image level domain classifier with a first dynamic confrontation gradient inversion layer and an object level domain classifier with a second dynamic confrontation gradient inversion layer. In the back propagation of the training process of the target detection model, the gradient inversion intensity of the first dynamic confrontation gradient inversion layer is dynamically adjusted according to the image level domain classification loss, and the gradient inversion intensity of the second dynamic confrontation gradient inversion layer in the back propagation is dynamically adjusted according to the object level domain classification loss; according to the method, the target detection precision under the severe weather condition can be improved, the target missing detection rate and the target false detection rate under the severe weather condition are reduced, and then the safety and the reliability of the automatic driving system are improved.

The invention relates to the technical field of unmanned aerial vehicle control, in particular to an unmanned aerial vehicle adaptive shooting method and system based on multiple sensors and AI, and the method comprises the steps: multi-sensor data collection, deep learning fusion processing, multi-modal exception processing, decision-making layer path planning reinforcement, adaptive shooting precision improvement, and a re-planning mechanism. Compared with the defects that environmental perception is incomplete and the shooting quality is suddenly reduced under complex illumination or severe weather due to dependence on a single sensor in the prior art, according to the scheme of the invention, a multi-mode sensor array (laser radar / millimeter wave radar / illumination sensor and the like) and Transform-GNN deep learning fusion architecture is adopted; joint modeling of obstacle positions, dynamic tracks and illumination distribution is realized; therefore, the dimensionality and precision of environmental perception are remarkably improved, and the unmanned aerial vehicle can still output clear and stable high-quality pictures under extreme conditions of strong light, rain and fog and the like.

The invention relates to the technical field of expressway early warning, and discloses an expressway multi-source risk factor dynamic fusion early warning system under severe weather, which comprises a multi-source risk factor acquisition module, a patrol vehicle and a mobile monitoring subunit integrated with a vehicle-mounted terminal through an along-line fixed monitoring subunit, collecting parameter data of severe weather, road conditions and traffic flow; the data preprocessing module adopts a machine learning algorithm to eliminate abnormal values, complement missing values and standardize the missing values to generate standardized risk data; the machine learning dynamic fusion module constructs a three-layer evaluation index system, a fusion weight is generated through an improved AHP model, nonlinear fusion is realized through a fuzzy integral fusion model, and a comprehensive risk value is output; and the early warning grade judgment module generates an early warning grade through threshold comparison, and finally the early warning grade is converted into standardized early warning information containing early warning types, influence road sections and duration by the early warning output module. The method provides powerful guarantee for road traffic safety.

The present application discloses a method, system, and computer system for detecting inclement weather driving conditions. The method includes obtaining an image captured by a camera mounted to a vehicle, determining a classification for road and weather conditions using a condition prediction model to analyze the image, in response to determining that the classification for road and weather conditions matches a particular predefined road and weather classification, determining an active measure associated with the particular predefined road and weather classification, and causing the active measure to be performed.

The invention provides a highway operation and service intelligent system, which comprises a road section platform, an area platform and a road network platform, and is characterized in that the road section platform is used for collecting and summarizing various sensing data of a corresponding highway section based on an ETC system to obtain system business data; road network situation historical statistics, road network situation real-time monitoring and road network situation future prediction are carried out according to the ETC data of the corresponding expressway section and the adjacent expressway road network; and long and short time traffic flow prediction, congestion prediction, holiday and festival traffic flow prediction and traffic flow prediction in a severe weather scene aiming at the corresponding expressway section are realized from multiple angles. According to the invention, the emergency disposal response speed and the intelligent degree of expressway operation and service can be improved, the timeliness rate, the accuracy rate and the correct rate of detection of various traffic and meteorological events can be improved, and the requirements of cooperative monitoring and linkage emergency disposal of low-time-delay traffic safety events are met.

The invention provides an industrial vision-based power line repair operation safety monitoring method, which relates to the technical field of power system automation, and comprises the following steps: collecting multi-source visual data of a repair operation site, and carrying out space-time alignment and feature level fusion on the multi-source visual data; based on the fused visual features, through a personnel safety equipment detection model and an operation behavior identification model, identifying the safety equipment wearing state and the operation behavior compliance of the operator in real time; the position, the motion track and the distance between the operator and the danger source are tracked in real time, and potential risks in a few seconds in the future are predicted through a rehearsal algorithm; and generating a safety operation report based on the monitoring data of the whole process, and feeding back key data in the report to the personnel safety equipment detection model and the operation behavior recognition model for parameter optimization. According to the method, interference of severe weather, shielding, illumination change and the like is effectively overcome, and accurate and robust recognition of the safety equipment wearing standard degree and various operation behaviors is achieved.

The invention provides an outdoor cross-modal robust positioning and navigation method for extreme severe weather, and relates to the field of online positioning and navigation of mobile robots. The method comprises the following steps: acquiring and preprocessing data; generating a path point set based on a CMR network, perfecting the path point set through local trajectory fitting and error optimization, and constructing a path point relative pose measurement-topology hybrid map; motion prior estimation is carried out based on Doppler velocity, effective matching prior estimation is generated in combination with LiDAR odometer information, cross-modal pose estimation is carried out through a CMR network, and a pure tracking strategy and PD controller path point tracking are adopted; and geometric and intensity alignment is carried out on the forward 4D millimeter wave radar point cloud and the omnidirectional 3D laser radar point cloud through a CMR network, a rotation matrix and a translation vector are output, and accurate positioning is completed. According to the invention, the sensing and positioning defects in extreme weather in the prior art are overcome, and the positioning precision and robustness are improved.

The invention discloses a deviation correction method and system for load prediction during severe weather. The method comprises the following steps: acquiring historical load data and weather prediction information; generating a baseline load prediction value through a baseline prediction model based on historical data; judging whether a major weather event is triggered based on the weather information; if so, dynamically extracting deviation correlation characteristics and inputting the deviation correlation characteristics into a compensation signal generator to obtain a load deviation compensation value; and superposing the baseline prediction value and the compensation value and outputting a final load prediction correction result. By designing a two-channel architecture in which baseline prediction and dynamic compensation are parallel and performing whole-process tracking and simulation on major weather events in combination with a deviation path diagram, accurate and forward-looking compensation on load deviation is realized, the overall precision and timeliness of load prediction during major weather are improved, and the load prediction efficiency is improved. And the scheduling reliability and the operation safety level of the power grid in the extreme weather are obviously improved.

The invention relates to a bridge road section monitoring early warning and prevention and control system, and belongs to the technical field of traffic safety. The system comprises a data acquisition end, a data processing end, a first dual-channel power supply module and a second dual-channel power supply module, the data acquisition end comprises an image processing device, a sensor, a video sensing device and an unvarnished transmission data sending device; the data processing end comprises a core control unit, an early warning screen controller, a bridge abnormal state early warning screen and a transparent transmission data receiving device; according to the invention, real-time monitoring of the health state of the bridge in the bridge section can be realized, the method is suitable for in-service highway bridges, the traffic safety capability of the bridge section under severe weather conditions is improved, the defects of traditional bridge monitoring are overcome, and monitoring blind spots of the highway bridges are eliminated.

The application relates to the technical field of information processing, in particular to a multi-source heterogeneous sensor anti-interference fusion perception system, which aims to solve the problem of decreased perception performance of multi-source heterogeneous sensors in adverse weather such as rain and fog; the system comprises a data acquisition and synchronization module, a multi-modal feature extraction and weather interference suppression module, an adaptive multi-modal fusion module, a target detection and tracking module and a perception result output module; the system extracts robust features from the interfered laser radar and camera data in combination with millimeter wave radar information through adversarial robust feature learning; and then the adaptive fusion module is used to dynamically adjust the weights of various modes according to the weather; the application effectively improves the perception accuracy and reliability of vehicles in complex environments such as rain and fog, and guarantees driving safety.

The invention discloses a power transmission line insulator severe weather defect detection method based on a YOLOv10-MPM algorithm, and the method comprises the steps: designing different image enhancement strategies to simulate a natural scene based on an original visible light image, and constructing a synthetic data set containing three typical complex weather scenes, namely fog, rain and snow; sequentially dividing the synthetic data set into a training set, a verification set and a test set according to a proportion, and dividing data set labels into flashover, defect and insulator according to defect types; a designed multi-shape coordination attention mechanism MSSA is used for replacing an original point mode space attention module PSA in the YOLOv10n model; a C2f module is improved by using an MOCAA module; pIOU2 is adopted to replace a traditional loss function; carrying out lightweight design on the model by using LAMP pruning; therefore, a YOLOv10-MPM model is constructed; and training the YOLOv10-MPM model based on the training set, and detecting the model by the verification set. According to the method, the detection precision is improved, and the size of the algorithm model is greatly compressed, so that the algorithm model can be conveniently deployed on edge equipment such as an unmanned aerial vehicle.

The invention discloses a target image recognition and target detection method based on a video enhancement algorithm, and relates to the technical field of image processing. The method comprises the following steps: firstly, receiving a rain, snow and fog scene video stream through a visual sensor, extracting a video frame target image, performing video enhancement processing, eliminating rain and snow shielding, fog blurring and noise, and generating an effectively enhanced image which is complete in target contour, clear in details and adaptive to subsequent detection; inputting the image into an improved YOLO model of the rain, snow and fog scene, completing feature extraction and category recognition through an optimized feature extraction network, outputting a preliminary target bounding box and a category label, and judging whether a target to be detected and a specific category exist or not; and finally, if the target exists, counting the detection data and carrying out validity verification, thereby realizing high precision, low misjudgment and strong real-time performance of target detection in severe weather of rain, snow and fog, and further effectively solving the problem of high detection result misjudgment rate caused by parameter adjustment lag of adaptive filtering in the prior art.

The invention relates to a road risk early warning method and system based on radar-video fusion, and belongs to the technical field of road traffic safety, and the method comprises the steps: collecting multi-source data through a radar, a video and a meteorological sensor, carrying out the denoising of a radar point cloud through a meteorological self-adaptive dynamic density clustering algorithm, and carrying out the early warning of a road risk. Performing weather compensation on the video features by using a generative adversarial network; through a space-time diagram neural network and an attention mechanism, performing high-precision fusion and complementation on the radar and the visual trajectory to generate a unified trajectory with multi-dimensional attributes such as target type, speed, behavior characteristics and the like; and calculating an interaction risk probability of a target and an environment based on a Bayesian network, and realizing graded early warning through a dynamic threshold value of reinforcement learning optimization. According to the method, the target sensing reliability in severe weather, the track continuity in a complex scene and the self-adaptive capability of risk assessment are effectively improved, and continuous optimization of system performance is realized through a federated learning framework.