496 results about "Visibility" patented technology

The invention relates to the technical field of unmanned aerial vehicle inspection, and discloses a power transmission line unmanned aerial vehicle inspection task intelligent allocation collaborative planning method based on a genetic algorithm. The method comprises the following steps: firstly, collecting and analyzing basic information of a power transmission line, an unmanned aerial vehicle, a surrounding environment and weather, and establishing a space model of unmanned aerial vehicle inspection by using a grid method; based on the collected information, establishing a mathematical model to allocate inspection tasks of the unmanned aerial vehicle; a genetic algorithm is used for coding and optimizing a routing inspection path, meanwhile, a fitness function is designed to comprehensively consider multiple factors of a search effect, and quantization, weight distribution and optimization selection are carried out; and an optimal solution is found through iterative optimization, and intelligent distribution and collaborative planning of unmanned aerial vehicle inspection tasks are realized, so that the requirements of regional inspection are met. According to the technical scheme, the flight threat degree under different meteorological conditions can be quantitatively evaluated by considering the wind speed, rainfall and visibility, and the flight plan is adjusted according to the flight threat degree.

The invention relates to the field of channel management, in particular to an intelligent channel management method and system based on multi-source data fusion. The invention discloses an intelligent navigation channel management system based on multi-source data fusion. The system comprises a combined ship identification module, a positioning frequency updating module, a collision risk assessment module and an intervention management module. According to the invention, the system can sense multi-dimensional environmental parameters such as illumination intensity, visibility, AIS signal quality, radar echo signal-to-noise ratio and the like in real time by constructing a combined ship identification model containing AIS, radar, vision and infrared, and dynamically adjust the fusion weight of each sensor to compensate for the deficiency of a single sensor; according to the method, the robustness of ship dynamic monitoring is improved, high-precision tracking can be kept in severe weather, the data redundancy contradiction of a traditional multi-sensor system is avoided, and therefore continuous and stable target state input is provided for follow-up risk analysis.

The invention provides a low-visibility environment pedestrian detection method based on an improved YOLOv8n model. A double-branch fusion attention network is adopted in a backbone network of an original YOLOv8n model, and a CBFuse module and a CBLinear module are introduced for feature fusion between different branches; a feature aggregation and calibration pyramid network is introduced, multi-scale feature fusion is carried out through an up-sampling module, a down-sampling module and a feature aggregation and calibration module, and the feature aggregation and calibration module carries out feature calibration and enhancement through a local attention mechanism, a global attention mechanism and a pixel attention mechanism; an adaptive task alignment detection head is introduced to execute a dynamic convolution mechanism, a task decomposition mechanism and a dynamic feature alignment mechanism; an improved YOLOv8n model is formed based on the improvement and serves as a foggy day pedestrian detection network model; according to the method, the detection accuracy and stability of the network in processing shielded and background complex images can be enhanced, and the boundary and detail features of a fuzzy target can be extracted more accurately in low-visibility environments such as foggy days and the like.

The invention relates to a sea fog quantitative forecasting method based on a period enhanced Transform, and the method comprises the steps: firstly constructing a Transform model as a forecasting model, enabling the Transform model and a self-attention mechanism to better capture the complex nonlinear relation and long-distance time dependence related to the formation of sea fog, and being superior to a conventional statistical method and an early-stage RNN / LSTM model, a data set for model training is constructed based on a time period enhancement technology and visibility standardization mapping, and the explicit time period enhancement technology enables the model to more accurately learn and forecast seasonal and daily change rules of sea fog; and a sample amplification and training set balancing strategy is executed on the data set, so that the problem of sparse fog samples is effectively solved, and the forecasting capability of the model on key low-visibility events is improved. According to the sea fog quantitative forecasting method based on the period enhanced Transform, the precision, timeliness (forecasting per hour) and spatial resolution of sea fog forecasting of a target area can be improved, and sea fog data characteristics can be effectively processed.

The invention discloses a civil aviation wind shear and visibility early warning method, which comprises the following steps of: acquiring an echo signal by using a high-precision and high-temporal-spatial-resolution coherent laser radar, and simultaneously acquiring civil aviation meteorological conventional observation data; calculating wind field distribution through a wind field inversion algorithm, and calculating visibility distribution through a visibility inversion algorithm; positioning a wind shear region by using a wind shear recognition algorithm, analyzing a visibility space-time evolution trend by combining a space-time deduction algorithm, and generating a visibility profile; inputting the wind shear identification data, the visibility profile data and the meteorological routine observation data into a machine learning model for modeling training; generating a wind shear and visibility early warning scheme over the airport based on the trained prediction model; and generating a wind shear alarm signal and visibility early warning information based on the early warning scheme, and outputting the signals to a civil aviation meteorological early warning system. According to the invention, high-precision wind shear and visibility combined early warning can be realized, and the early warning accuracy under complex weather conditions is improved.

A haze visibility detection method and device based on depth and brightness information and a medium are used for foggy weather visibility detection, a multi-mode visibility detection model comprehensively utilizing image data and meteorological data is provided, the model takes the image data and the meteorological data as inputs at the same time, and the visibility detection accuracy is improved through a parallel network structure. The data of the two modes are respectively sent to different channels to extract respective features, and then the features of the two modes are fused to jointly invert the visibility value. According to the method, the visual information and the environmental parameters are combined by constructing the multi-mode visibility detection model, and the depth information and the brightness information are introduced to optimize the model design, so that the accuracy and the robustness of the model are improved, and the visibility can be detected more accurately and reliably.

The invention relates to the technical field of intelligent traffic, in particular to an integrated fog measurement type highway safe driving guidance device and method, and provides the following scheme that local visibility data is acquired based on a plurality of highway safe driving guidance devices and uploaded to a region controller to generate guidance information. And the regional controller performs research and judgment calibration and gradient calibration on the data, corrects measurement errors and generates road visibility space distribution information by screening calibration anchor points and adopting an exponential attenuation function and iterative gradient calculation. Based on the information, a connectivity algorithm is used to identify a fog area, and in combination with road environment and historical accident data, an optimal induction strategy is matched, and induction information is dynamically adjusted. The fog area detection accuracy is improved, the induction information matching is optimized, and the driving safety and the passing efficiency under the low-visibility condition are improved.

The invention discloses a fog boundary layer parameterization scheme correction method based on a multi-scale physical coupling network, relates to the crossing field of numerical weather forecast and artificial intelligence, and aims to improve the performance of different boundary layer parameterization schemes in a WRF mode, construct a fusion framework of a physical mode and deep learning, adopt a double-branch heterogeneous network structure, and improve the performance of a fog boundary layer parameterization scheme. Establishing a nonlinear mapping relation between different parameterization scheme deviations and large eddy simulation turbulence characteristics by combining a space-time attention mechanism; by correcting systematic deviation existing in a parameterization scheme, the analysis capability of a turbulence structure is improved, and physical description of a boundary layer process is optimized; compared with a traditional boundary layer scheme, the method has the advantages that the fog zone simulation precision is remarkably improved, the fog zone visibility, the liquid water content and the root-mean-square error of a liquid water path are reduced by 66.7%, 50% and 58.3% respectively, the problem that turbulence intermittent characterization is insufficient under a stable boundary layer in a traditional method is effectively solved, and a new normal form is provided for refined forecasting of the fog generation and elimination process.

The invention provides an automatic driving test scene generation method based on real traffic data, and solves the problems of low accident data utilization rate, SIL / HIL test splitting and insufficient boundary coverage in the prior art. Comprising the following steps: acquiring multi-source heterogeneous traffic accident data; cleaning data by adopting a joint interpolation-anomaly detection mechanism; vehicle dynamic sudden change characteristics within 0.5 second before braking are extracted through LSTM and DTW algorithms; constructing a three-dimensional scene pipeline driven by a physical engine, and dynamically associating the pavement slippery coefficient with the rainfall intensity; analyzing the accident text into simulation parameters by using a semantic-physical parameter converter; performing SIL-HIL cooperative verification: performing extreme illumination perception test and narrow road planning verification in an SIL environment, and realizing 1ms step length fault injection test in an HIL environment; positioning failure parameters based on Bayesian optimization; a GAN is adopted to generate a long-tail scene, and a test boundary is expanded by coupling extreme conditions such as rainstorm / low visibility; and outputting a standard scene library containing the collision probability thermodynamic diagram. The safety verification efficiency under the extreme working condition is remarkably improved.

The invention belongs to the technical field of aviation meteorological monitoring, and relates to an airport dominant visibility automatic measurement method and system based on a three-dimensional laser radar, and the method comprises the steps: carrying out the full-space scanning of an airport through the laser radar, so as to collect echo signals; preprocessing and calibrating the echo signal; performing atmospheric extinction coefficient inversion on the preprocessed echo signal based on an inversion algorithm to obtain an atmospheric extinction coefficient, and then converting the atmospheric extinction coefficient into meteorological visibility; carrying out quantile statistics on all meteorological visibility in a period, judging a dominant visibility value, and carrying out adaptive weighted fusion on the dominant visibility value, the visibility value measured by the point-type visibility meter and the visibility value observed manually to obtain a fused dominant visibility value. The system can break through the limitation of traditional manual and point type visibility meter observation, and realizes accurate and automatic measurement of all-region, all-weather and unattended dominant visibility of an airport.

The invention discloses an automatic driving evaluation system based on artificial intelligence, and relates to the technical field of automatic driving, and the system comprises the following components: a meteorological model coupling module, a sensor parameter adjustment module, an automatic driving simulation module and an evaluation analysis module. According to the invention, data connection is established between the meteorological model coupling module and an external meteorological model, comprehensive meteorological data including temperature, humidity, wind speed, rainfall, snowfall, visibility and the like are acquired in real time, and a simulated extreme weather scene is constructed based on the data. The sensor parameter adjusting module automatically adjusts performance parameters of a vehicle sensor according to different weather types to ensure the accuracy of sensor data under extreme weather conditions, and the automatic driving simulation module operates an automatic driving system model in a simulated extreme weather scene to simulate the vehicle driving process.

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.

The invention discloses a weather early warning method and device based on radar data and meteorological data and storage equipment, and the method comprises the following steps: S1, obtaining high-temporal-spatial-resolution wind field data, an inversion wind shear field and a visibility field through a high-precision high-temporal-spatial-resolution coherent laser radar; s2, detecting atmospheric parameters of an overhead area of the airport in real time to form airport auxiliary observation data; s3, based on the wind shear field, the visibility field and the airport auxiliary observation data, artificial intelligence machine learning is used for data processing, and a forecasting scheme is formed. Compared with the prior art, the method is based on the high-precision and high-temporal-spatial-resolution coherent laser radar, atmospheric parameters of the area above the airport are detected in real time, high-temporal-spatial-resolution wind field data are obtained, and the prediction accuracy is improved. And inverting a wind shear field and a visibility field, and processing by using methods such as artificial intelligence machine learning and the like to form a forecasting scheme.

The invention discloses a traffic meteorological data fusion method based on inverse distance weight interpolation, and relates to the technical field of traffic meteorological data fusion. The traffic meteorological data fusion method based on inverse distance weight interpolation comprises the following steps: acquiring longitude values, latitude values and visibility measurement values of a plurality of time points of each traffic meteorological station in a set area; each traffic weather station in the set area is screened and analyzed based on the visibility measurement values of the plurality of time points to obtain a plurality of effective traffic weather stations, and the data from different time points and places are integrated and analyzed, so that the data utilization rate and accuracy are remarkably improved; the time sequence analysis of the visibility helps to understand the behavior pattern of each station changing with time, the spatial analysis ensures the continuity and consistency of data in geography, and the integration method optimizes the data processing flow, so that the visibility prediction is more accurate, and the complex traffic and meteorological condition analysis is effectively supported.

The invention discloses a regional unmanned aerial vehicle safety risk assessment method, which comprises the following steps: acquiring parameters such as unmanned aerial vehicle body length, wingspan width and body height, and preset task scene data such as regional tall building area ratio, unmanned aerial vehicle density, specific parameters and quantity of aircrafts; based on the data, regional ground and airspace safety risk values are calculated. And determining visibility, rainfall and wind speed as meteorological risk assessment indexes, performing weighting by using an analytic hierarchy process, and calculating a meteorological risk assessment coefficient. And constructing a regional unmanned aerial vehicle safety risk assessment model by integrating assessment data obtained under three environmental conditions of weather, ground and airspace. And comprehensively evaluating the safety risk of the unmanned aerial vehicle in the region by using the model. According to the technical scheme of the invention, three environmental factors of meteorological conditions, ground conditions and airspace conditions are integrated, and various environmental risk types possibly experienced by the unmanned aerial vehicle in the operation process can be comprehensively evaluated.

The invention discloses a visibility inversion method based on millimeter wave cloud radar and laser radar data fusion, and relates to the technical field of meteorology, and the method comprises the steps: guiding a millimeter wave cloud radar and a laser radar to carry out data correction and equivalent relation construction through employing a fog drop particle size distribution function measured by a fog drop spectrometer in a sample scene in advance; and then applying the correction coefficient and the equivalent function relationship to a target scene to perform multi-source data correction and fusion on the millimeter wave cloud radar and the laser radar and invert visibility. The method can fuse complementary advantages of the laser radar and the millimeter wave cloud radar; the introduction of the fogdrop particle size distribution function effectively avoids the distribution hypothesis of fogdrop shapes in the traditional model, can weaken the seasonal drift of the traditional empirical coefficient, significantly improves the accuracy and reliability of visibility inversion, and achieves the high-precision visibility monitoring under various weather conditions from clear sky to dense fog.

According to the expressway self-adaptive agglomerate fog early warning and response control method and system, meteorological data and traffic flow data of an expressway are collected in real time, and a reinforcement learning algorithm is utilized to predict a space-time mode of occurrence of agglomerate fog. By calculating the agglomerate fog risk index, if the risk exceeds the standard, the system triggers agglomerate fog early warning and dynamically adjusts the traffic management strategy, so that the driving safety is improved to the maximum extent and the traffic jam is reduced to the maximum extent. The system continuously optimizes decisions through traffic flow and visibility information fed back in real time, realizes adaptive adjustment through reinforcement learning, and adapts to changes under different weather and traffic conditions. The method can accurately predict the occurrence of agglomerate fog, optimizes the traffic flow, reduces the occurrence rate of traffic accidents, improves the passing efficiency and safety of the expressway, and has a wide application prospect.

The invention relates to sensing and tracking of intelligent driving in rain, snow, fog, night and large shielding scenes. In order to solve the problems of target invisibility, track interruption and misconnection caused by low visibility, a shielded dynamic target identification and tracking method based on deep learning is provided; according to the method, under a unified spatial index, sparse point cloud and road topology, passable and traverse areas, static shielding volume coding, ray marking visibility boundary and shielding entrance and exit are carried out; generating a motion voting field with consistent visibility through multi-agent situation reasoning, performing normal directional enhancement according to a passable boundary, and extracting a risk corridor; voxelization is carried out on the multi-frame point cloud in the corridor, a dynamic sparse voxel map is constructed, a voting field is used for gating cross-frame edge connection, occupation changes and infinitesimal displacement are aggregated, and three-dimensional verification candidates, limited state estimation and a time continuous track are obtained; outputting the target and the corridor to which the target belongs, and forming a potential conflict zone according to the intersection of the target and the own vehicle path; the reproduction rate and the advance are improved, and cross-lane misconnection is reduced.

The invention discloses a highway abnormal parking detection method, which comprises the following steps of: firstly, calculating a moving speed vector of an unmanned aerial vehicle by combining feature point extraction and an optical flow method, then extracting a vehicle detection frame through a YOLO target detection algorithm, and further calculating to obtain a ratio of an average diagonal length of the detection frame to an actual vehicle length; the method comprises the following steps: firstly, obtaining a vehicle, then calculating a moving speed vector of the vehicle relative to the unmanned aerial vehicle by combining a Bytrace multi-target tracking algorithm with an optical flow method, finally obtaining an actual speed vector of the vehicle based on the moving speed vector of the vehicle relative to the unmanned aerial vehicle and the moving speed vector of the unmanned aerial vehicle, and judging whether the vehicle is abnormally parked or not according to the actual speed vector. According to the method, the abnormal parking event on the expressway can be automatically monitored in real time and quickly responded, the workload of workers is greatly reduced, the monitoring range coverage is wide, the influence of severe weather, low visibility at night and the like is low, the omission factor is low, and the detection accuracy is higher.

The invention discloses an automatic driving vehicle speed control method based on environmental complexity, which sequentially executes the following four steps in a cycle period less than 50ms: step 1, synchronously acquiring road geometric morphology, traffic flow density, visual distance, road surface attachment condition and meteorological data, and acquiring the road geometric morphology, the traffic flow density, the visual distance, the road surface attachment condition and the meteorological data; performing fusion by using a Kalman filtering algorithm to obtain an environmental parameter vector; step 2, inputting the environment complexity label into a double-layer evaluation network, and outputting four levels of environment complexity labels; step 3, inputting the vehicle speed into a control model constructed based on a hierarchical adaptive complexity vehicle speed control algorithm, and generating a target vehicle speed; 4, if the target vehicle speed is larger than or equal to the collision risk index, the environmental complexity level is increased in time, the step 3 is returned to recalculate the target vehicle speed, and a vehicle speed execution instruction is output until the target vehicle speed is smaller than the collision risk index. According to the invention, the safety and the driving efficiency of the vehicle are obviously improved, and the stability and the reliability of the automatic driving system are guaranteed.

The invention belongs to the technical field of mine lighting, and particularly relates to an adaptive intelligent mining anti-explosion lighting system which comprises a positioning base station, a lighting network, a personnel identification card, a three-dimensional GIS module, an environment monitoring module and a central processing module. According to the system, a visibility compensation component is constructed according to empirical formulas of visibility and smoke, so that the change of the visibility is compensated when the luminous power is adjusted, and additional light sensors and motion sensors are not needed. In addition, the invention also provides a simulation K shortest path algorithm which organically combines a Yen algorithm, fire simulation simulation, a particle swarm algorithm and risk assessment, so that dynamic and comprehensive intelligent escape path planning is realized. And finally, the central processing module controls the light emitting units on the way of the escape path to indicate the escape path in a stroboscopic mode in sequence from far to near according to the distance relative to the end point of the escape path.

The invention discloses an automobile light adaptive control method and system based on machine learning and a medium, and the method comprises the steps: obtaining vehicle state data, external environment information and GPS positioning information of a target vehicle, and the external environment information comprises illumination intensity information, road condition information and weather condition information; determining an illumination intensity change rate, a road surface reflection coefficient and a visibility index according to the external environment information and the GPS positioning information, and performing feature fusion on the illumination intensity change rate, the road surface reflection coefficient and the visibility index to obtain external environment feature data; inputting the external environment characteristic data and the vehicle state data into a pre-trained vehicle external lamp brightness prediction model to obtain a target external lamp brightness level; and adjusting and controlling the outer lamp brightness of the target vehicle according to the target outer lamp brightness level. The driving experience and the driving safety of the user are improved, and the method can be widely applied to the technical field of vehicle control.

The invention discloses a visibility prediction method and system, and the method employs a dynamic window division and abnormal value processing technology based on data variability, solves a problem that meteorological data abnormal values are difficult to accurately recognize, achieves the precise recognition and processing of the abnormal values, and improves the data quality. By adopting the missing value interpolation method based on the weighted distance, the problem that the missing value of the meteorological data is difficult to fill is solved, the accurate interpolation of the missing value is realized, and the integrity and the reliability of the data are enhanced. According to the method, a dynamic normalization processing method is adopted, the problems of different scales of meteorological factor data and local characteristic differences are solved, adaptive normalization of the meteorological factor data is achieved, important fluctuation information is reserved, and the sensitivity of a model to data details is improved.

The invention relates to the field of beacon light control, in particular to a beacon light intelligent control method and system based on visible light communication. The method comprises the following steps: acquiring a weather type, a weather type grade, ambient light brightness, visibility and a wind speed of a target area; monitoring the target area based on the ambient light brightness to obtain a target ship; constructing a beacon light power prediction model, and predicting the beacon light power by using the beacon light power prediction model according to the weather type, the weather type grade, the ambient light brightness, the visibility and the wind speed; and obtaining a visible light communication protocol, and controlling the beacon light to flicker at the beacon light power based on the visible light communication protocol and the target ship. According to the invention, the problem that the beacon light is difficult to automatically adjust according to the environmental information in the prior art is solved, the real-time performance, the reliability and the self-adaptability of the working of the beacon light in a complex environment are improved, and the safety of ship navigation is further improved.

The invention discloses a visibility prediction method based on machine learning, and belongs to the technical field of visibility prediction, and the method comprises the following steps: obtaining historical meteorological data, carrying out the preprocessing of the historical meteorological data, and carrying out the feature selection to obtain a time sequence feature and a meteorological data feature set; constructing a visibility prediction model, inputting the time sequence features and the meteorological data feature set into the visibility prediction model for training, and performing optimization by using a loss function and gradient descent to obtain a trained visibility prediction model; performing visibility prediction based on the trained visibility prediction model to obtain a prediction result; the visibility prediction model obtained through the method is used for predicting actual data, it is found that the prediction result of the model is well matched with actual observation data, and it is indicated that visibility can be effectively predicted.

Systems and techniques are provided for implementing LiDAR point cloud stitching. An example method includes determining a visibility grid that is based on a plurality of measurements from a first LiDAR device, wherein the visibility grid includes a plurality of range values corresponding to a field of view of the first LiDAR device; identifying, based on the visibility grid, at least one occluded region in the field of view; and modifying a point cloud that is based on the plurality of measurements to include additional points corresponding to one or more measurements obtained from a second LiDAR device, wherein the one or more measurements correspond to the at least one occluded region.

The invention relates to the technical field of image data processing, in particular to an interactive road sign system which comprises a data acquisition module, a determination module, an adjustment module, an adjustment module, a judgment module, a drawing module and a correction module. Through real-time monitoring and dynamic adjustment, various environmental changes can be effectively handled, and it is ensured that road sign information is always clearly visible under different weather, visibility and driving conditions. The system automatically adjusts the display contrast and definition of the road sign by comprehensively considering water mist, particle diameter, facial definition and real-time position, ensures that the target can accurately receive the road sign information under the condition of severe weather such as haze and rainwater or limited sight distance, and improves the accuracy of the target. The system also analyzes the association between the road sign display effect and the visibility by drawing a change curve, further optimizes the display setting, and effectively solves the problem that the display effect of the system is unstable in a complex environment due to strong light source dependence.

The invention provides a visibility analysis method and system, electronic equipment and a storage medium, and relates to the field of visibility analysis. The visibility analysis method comprises the following steps: extracting a moving target in a target image according to two adjacent frames of target images; extracting a target area image from the target image based on the moving target; wherein the target area image comprises an image of a moving target; extracting image visibility characteristics from the target area image; wherein the image visibility characteristic represents the visibility corresponding to the target image; and determining a target visibility grade corresponding to the target image according to the image visibility characteristics. By using the visibility analysis method provided by the embodiment of the invention, the visibility under different weather conditions can be accurately analyzed, so that a reliable guarantee is provided for traffic safety, production safety and public safety.

A vehicle including a windshield, a flexible display screen, a flexible display screen attachment component, at least one processor, and at least one memory configured to store instructions is disclosed. The instructions, when executed by the at least one processor, cause the at least one processor to (i) generate a set of images from sensor data received from one or more sensors mounted on a body of the vehicle; and (ii) display the set of images on the flexible display screen attached to the flexible display screen attachment component while covering the windshield. The set of images has effects of adverse weather conditions or poor lighting conditions removed.

The invention discloses a sea fog visibility prediction method based on an SLSTM-SVM model, and aims to overcome the defects of an existing sea fog visibility prediction method, the sea fog visibility prediction method comprises the following steps: firstly carrying out smooth stabilization processing on an LSTM network to obtain an SLSTM, and then replacing a softmax layer in the SLSTM with an SVM classifier to construct a model. Four types of hydrological data such as rainfall and water level and five types of meteorological data such as air temperature and air pressure form a sequence input model, an SVM classifier predicts and outputs a value through SLSTM layer smoothing and stabilization processing and data deep mining, parameters are updated through back propagation, and iterative training is carried out until errors reach the standard. Meanwhile, an autocorrelation function is adopted to evaluate data stability, and a Z-score method is utilized to normalize the data. The method can quickly converge the network weight to a globally optimal solution, is high in prediction accuracy, and has great application potential in long-term sea fog visibility prediction.