1456 results about "Severe weather" patented technology

The invention discloses a traffic transportation operation monitoring early warning and decision analysis method based on vehicle infrastructure cooperation, and relates to the technical field of intelligent traffic. According to the method, real-time collection of dynamic traffic elements is realized through a multi-dimensional sensing network of a vehicle end, a road side and an environment and V2X communication, time-space reference unification of multi-source data is ensured, a road-vehicle-environment-event semantic network is constructed, multi-dimensional recognition of abnormal events such as accidents, congestion and severe weather is realized in combination with hierarchical feature extraction, and the method has the advantages of being high in practicability and high in practicability. The method is advantaged in that identification accuracy is improved, abnormal event propagation paths can be predicted, global road network situation prediction capability is realized, differential early warning is generated based on a comprehensive risk index, multi-level responses such as traffic signal adjustment and path planning are triggered, response time is greatly shortened, emergency response efficiency is optimized, a decision execution effect real-time feedback mechanism is established, and the method is suitable for popularization and application. The system performance is continuously optimized along with data accumulation, and the defect that a big data platform lacks an intelligent decision closed loop is avoided.

The invention relates to the technical field of remote sensing monitoring, in particular to a remote sensing target detection method and system for a low-visibility image. The method comprises the following steps: acquiring multi-modal remote sensing image data; carrying out defogging enhancement processing on the low-visibility input image; normalizing the defogged RGB image and the defogged IR image, and then splicing and fusing the RGB image and the IR image; carrying out layer-by-layer coding on the multi-modal fusion image by adopting a mixed trunk structure fusing Transform, Mamba and CNN (Convolutional Neural Network); performing frequency domain decomposition on the trunk output features based on two-dimensional wavelet transform; generating an HR feature map by adaptively selecting a key region; and carrying out cross-scale aggregation on the HR feature map to obtain a detection target frame. Through the multi-modal image defogging enhancement and feature distillation mechanism, the definition and contrast of the remote sensing image in severe weather such as haze and rainy days are effectively enhanced, the shielding interference of environmental degradation on small target detection is weakened, and the stability and adaptability of the model in complex weather scenes are enhanced.

The invention provides a method for reducing the multipath effect of a take-off and landing guide radar based on adaptive off-axis tracking, and the method comprises the steps: enabling the take-off and landing guide radar to transmit a signal, and transmitting the signal to a radar direction after receiving the signal; the radar receives the direct signal and the multi-path signal at the same time, and separates and establishes a spatial-temporal characteristic database of the multi-path signal; when the influence of the signal on the angle error exceeds a dynamic threshold value, starting an off-axis tracking mode; dynamically calculating the optimal off-axis angle, locking the elevation angle of the antenna, and performing closed-loop tracking of the azimuth; carrying out conjoint analysis, and identifying and eliminating false targets in real time; intelligent suppression is carried out on angle random errors; a real-time multi-path propagation model is constructed, and phase and amplitude compensation is carried out on a received signal. The method has excellent real-time performance and engineering realizability, and can meet strict requirements of aircraft precision approach guidance. The method has good expandability and adaptability, and provides reliable technical support for guaranteeing safe take-off and landing of the aircraft in severe weather and complex environments.

The invention belongs to the technical field of image processing, and discloses a multi-modal image fusion method and model based on global and local text perception, which are used for respectively processing global and local information of images by combining two visual language models, namely CLIP and BLIP, and realizing effective fusion of the images under complex scenes and severe weather conditions. The global text sensing module utilizes CLIP features to enhance the understanding of the model for the whole scene, and the local text sensing module utilizes BLIP features to improve the processing capability for local details. According to the double text perception mechanism, the model can more comprehensively utilize the advantages of the visual language model, and the problem that only simple text prompt is relied on or local details are excessively emphasized is avoided. The advantages of a visual language model can be fully utilized, global and local information processing is considered, and the adaptability and generalization performance of the model under complex scenes and severe weather conditions are improved.

The invention relates to the field of target detection, in particular to a UMFNet-YOLO-based joint detection algorithm under a low-light condition, which adopts a multi-scale decomposition and high-frequency-low-frequency feature collaborative enhancement strategy, separates image details from global features through high / low-pass filtering, realizes multi-resolution feature complementation in combination with dynamic weight distribution, and improves the detection accuracy. Target contour and texture information degraded in severe weather can be effectively recovered; a parallel channel compression and spatial semantic enhancement module is designed, rain and fog noise interference is suppressed through cross-dimensional adaptive feature screening, and significance expression of a key target is enhanced; an image enhancement network UMFNet is cascaded with YOLOv8, and strong correlation between feature expression and a detection task is ensured by synchronously optimizing an enhancement module and detecting network parameters through back propagation. Compared with a baseline YOLOv8 detector, the method has the advantages that the average precision (mAP50) is obviously improved, and the method is obviously superior to a traditional enhancement-detection separation scheme. The method can be applied to robust target detection scenes in complex environments such as automatic driving and security monitoring.

The invention provides a multi-dimensional visual analysis method for meteorological observation and detection data, and relates to the field of visualization. According to the method, an unmanned aerial vehicle platform carries a meteorological sensor to collect data, and primary processing, abnormal value detection and intelligent data compression are carried out; transmitting the processed data to a ground measurement and control terminal; the ground terminal fuses the multi-source data to generate a three-dimensional meteorological data set; constructing an interactive three-dimensional visual scene by utilizing self-adaptive interaction logic and a dynamic data display technology; a user can check and perform multi-view linkage operation through the interactive operation interface; a severe weather and weather raster data set can be constructed based on the three-dimensional weather data set and historical severe weather records, and simulation and display are carried out; and finally exporting the data, verifying the precision and integrity, and generating a visual analysis report.

The invention provides a multispectral fusion-based valve optical image enhancement identification method and system in severe weather, and the method comprises the steps: obtaining multispectral optical image data and mechanical dynamic response characteristics of an industrial pipeline valve in severe weather; based on the mechanical dynamic response characteristics, separating the multispectral optical image data to extract multiband texture characteristics associated with the reflection characteristics of the valve material; double amplitude correction of a spatial domain and a frequency domain is carried out on the multiband texture features; distributing a spectrum fusion weight for the multispectral optical image data according to the corrected multiband texture features; and generating an enhanced optical image matched with the actual mechanical state of the valve based on the spectrum fusion weight and the corrected multiband texture features. According to the method, the recognition accuracy of defects such as valve surface cracks and deformation in severe weather is improved.

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 discloses a battery charging control method for a battery replacement cabinet based on the Internet of Things, relates to the technical field of charging control, and has higher adaptability in the aspects of battery replacement demand prediction, charging mode switching and charging safety management compared with a traditional fixed charging mode. The problems of sudden increase of battery replacement demands, changes of environmental factors and the like can be effectively solved, and the operation stability of the battery replacement cabinet is improved; the method comprises the following steps: acquiring a battery state, a battery replacement demand and meteorological data, predicting a future battery replacement demand curve in combination with a long-short term memory (LSTM) network and a multiple linear regression model, introducing a meteorological influence factor, adjusting a charging plan of a battery replacement cabinet 24 hours before severe weather early warning, and matching the battery stock with a peak demand; the charging modes comprise a fast charging mode, a general charging mode and a slow charging mode, dynamic switching is carried out according to the battery replacement demand peak value and the daily average battery replacement demand, the fast charging mode is preferentially adopted in the battery replacement peak period, the battery supply rate is increased, and meanwhile part of charging resources are reserved.

The invention discloses an intelligent safety management method for a gas operating enterprise. According to the invention, through deep fusion of Internet of Things perception and intelligent decision technologies, the full-period management capability of safety risk prevention and control is significantly improved. On the aspect of beforehand prevention, the global sensing network and the dynamic risk assessment system form double defense lines, hidden danger signals such as pipe network pressure abnormity and equipment aging are captured in real time, the risk evolution trend is pre-judged in combination with meteorological environment changes, and accurate deployment of protection resources is guided. The autonomous inspection robot cluster breaks through the space-time limitation of manual inspection, continuously obtains high-precision detection data under complex terrains and severe weather, and ensures that hidden danger recognition is free of dead angles. The user side safety enabling system converts professional protection knowledge into visual interaction guidance, so that residents can check hidden dangers of gas appliances by themselves, and an enterprise-user linkage basic protection net is constructed.

The invention discloses a power transmission and transformation equipment fault risk assessment method and system in extremely severe weather, and the method and system improve the fault detection effect through the cloud-side intelligent cooperation technology, a self-learning federated learning mechanism and the construction of a risk map. Power transmission line fault detection based on cloud edge cooperation is composed of an edge end and a cloud end. The edge end comprises a hidden danger identification module, a data acquisition module and an algorithm iteration module. And the cloud is responsible for integrating and globally updating the model and the map. A self-learning federated learning method is used, the edge ends use own data to train a model, and model parameter data uploaded to the cloud end by each edge end are updated globally at the cloud end and deployed to each edge end. The phenomenon that the recognition capability is low due to unbalanced data collection at the edge end, missing of sample categories and overfitting is avoided, and the problem that the transmission pressure of a network channel is too large is solved.

The invention provides a road inspection method and device in severe weather, electronic equipment and a storage medium, and the method comprises the steps: carrying out the recognition processing of a scene image through a road inspection model, and obtaining a road inspection result of a target road; the training process of the road inspection model comprises the following steps: acquiring a data set, wherein the data set comprises a severe weather image; performing feature extraction on the data set by adopting a pyramid network to obtain a plurality of levels of first feature extraction results; when information fusion is carried out on the first feature extraction results of the multiple levels, feature adaptation processing is carried out according to the first feature extraction results, and a second feature extraction result is obtained; performing feature enhancement modulation on the second feature extraction result by adopting double branches, and merging feature enhancement modulation results to obtain a modulation feature map; and predicting the modulation feature map to obtain a prediction result. The method has the beneficial effect that the vehicle type identification accuracy during road inspection in severe weather is improved.

The invention provides a vehicle safety early warning method and system for a highway network, relates to the field of traffic safety management and control, and solves the technical problem of inaccurate early warning in the prior art. The method comprises the following steps: acquiring environment data of a highway; dividing the expressway into a plurality of areas, and calculating an initial risk value of each area according to the environment data; calculating a risk propagation value of each region based on a dynamic risk propagation equation according to the initial risk value, and determining a risk level of each region according to the risk propagation value; and triggering early warning measures of each area according to the risk level. The method is used in the real-time safety management and control process of the highway network in severe weather, accurate prediction and graded early warning of risks can be realized through multi-dimensional data fusion and dynamic risk propagation modeling, and the safety and traffic efficiency of the highway network under complex meteorological conditions are improved.

The invention discloses a severe weather image sharpening method based on continuous learning, and the method comprises the steps: 1, carrying out the preprocessing of an image data set under various severe weather conditions, including image pairing and multi-task division; 2, dividing the image restoration network into two parts of feature projection and output projection, designing a degradation sensing module for extracting degradation specific information output by the feature projection under different weather conditions, and establishing background supervision and degradation supervision by adopting a multi-teacher model for guiding learning of a student model; and 3, establishing respective optimization targets for the student model and the teacher model, inputting pairing data of different weather removal tasks, and optimizing the student network by a multilateral distillation normal form, thereby obtaining an optimal model with integrated image severe weather removal capability. According to the method, different types of severe weather images continuously acquired in a real scene can be dealt with, and the implementation effect of an image restoration network in a resource-limited scene is improved.

The invention provides avionic equipment based on high-precision inertial navigation and a signal processing method, relates to the technical field of aviation navigation control, and aims to evaluate the selection of a driving sensor by using environmental complexity, optimize the acquisition quality of inertial data such as angular velocity and acceleration, pixel coordinates and specific optical data in real time, reduce redundancy and improve the precision of navigation. Spectral decomposition and adaptive filtering are utilized, signals are dynamically cleaned according to environment noise of different frequency bands, key features are reserved, high-dimensional feature vectors are generated through spatial-temporal feature reconstruction, the expression ability of low-quality data is enhanced, fusion weights are dynamically adjusted based on the signal-to-noise ratio and consistency indexes, predictive error correction is combined, and long-term drift is eliminated. Hardware support ensures that processing delay is less than 10ms, anti-interference shielding maintains data stability, dependence of traditional navigation on external signals is broken through, long-time high-precision navigation is realized, the method is particularly suitable for aviation secret tasks and civil aviation flight in severe weather, and task success rate and safety are improved.

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.

The invention discloses a one-stage severe weather fruit detection method based on dynamic coding, and the method comprises the steps: collecting fruit image data on site in an agricultural scene, carrying out the data preprocessing of the collected image data, setting the hyper-parameters of a to-be-constructed one-stage severe weather fruit detection model based on dynamic coding, and carrying out the calculation of the hyper-parameters of the to-be-constructed one-stage severe weather fruit detection model. Constructing and training a first-stage severe weather fruit detection model based on dynamic coding; detecting fruits in the test set image, outputting a detection result mask, and comprehensively evaluating the model; the method solves the problems of poor generalization and applicability, tedious steps of a monitoring method and low real-time performance in the prior art, and has the characteristics that accurate detection can be carried out without pre-processing the image, and the training time and tedious steps of image pre-processing are reduced.

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 provides a photovoltaic module support amplitude stabilizing device, which relates to the technical field of photovoltaic equipment and comprises a support amplitude stabilizing execution module, a data acquisition module, a windproof control type determination module and a windproof control module. By constructing a three-level windproof control strategy system, accurate response to different wind levels is achieved, a first-level windproof control strategy is adapted to the vibration frequency and amplitude of a support in real time by dynamically adjusting the damping coefficient of a damping shock absorber, and a second-level windproof control strategy is adapted to the length of a windproof hydraulic cylinder based on wind speed and wind direction data. According to the three-level windproof control strategy, when strong wind exceeds a mechanical counteracting range, the dip angle of the photovoltaic panel is adjusted to a safe and efficient conversion angle, the wind resistance and the light energy utilization rate of the structure are balanced, and the wind energy utilization rate of the photovoltaic panel is increased. And the reliability and the economical efficiency of the photovoltaic system under the severe weather condition are improved.

The invention is suitable for the technical field of smart civil aviation, and provides a flight plan dynamic generation method and system in severe weather, and the method comprises the following steps: obtaining data, including origin-destination and plan time of a flight, an air route network, and aircraft performance of the flight; on the basis of meteorological radar data, according to the risk threshold value, the future communication condition of the air route network is predicted; constructing a flight plan dynamic generation model library based on different scheduling priorities; and according to control conditions, a solver is used for carrying out serialization solution on each model, and the most adaptive model and the corresponding optimal strategy are determined by comparing the closeness of positive and negative ideal distances between a model result and the best worst scheme. According to the invention, flight plan dynamic generation model libraries with different scheduling priorities are established, a multi-scale scheme evaluation recommendation technology is discussed, and technical support is provided for daily dispatchers to carry out flight plan intelligent management.

The invention, which relates to the field of high-speed traffic operation situation prediction, discloses a travel-service-oriented operation situation prediction system for high-speed traffic emergencies, comprising a severe weather traffic influence prediction module, a traffic time duration prediction module, and a traffic flow operation situation prediction module. The system is characterized in that the severe weather traffic influence prediction module carries out quantitative processing on the influence of the meteorological data and the traffic data on the highway running speed and the accident rate based on the collected meteorological data and the traffic data. According to the travel service-oriented running situation prediction system for the high-speed traffic emergencies, through integration of multi-source data, comprehensive consideration of various influence factors and application of a data analysis method and an advanced model, accurate prediction of the running situation of the high-speed traffic emergencies can be effectively realized, and at the same time, the running situation of the high-speed traffic emergencies is guided by public behaviors, so that the running situation of the high-speed traffic emergencies is predicted. Personalized travel information and suggestions are provided, the public is helped to reasonably plan the travel, congested and dangerous road sections are avoided, and the travel safety and efficiency are improved.

The invention discloses an adaptive fine-grained ocean ship rescue positioning method based on severe weather, and belongs to the technical field of ocean ship rescue positioning, and the method comprises the steps: collecting photo data, carrying out the feature extraction of pollution elements, and carrying out the fitting with the photo data to form a data set; based on a multi-scale linear attention decoupling head, the fine-grained classification features and the fine-grained regression features are decoupled; using wavelet transform features to capture bottom texture information, and aligning the bottom texture information with the features of the decoupling head; a loss function and a training model are provided, a total loss function value is calculated, back propagation is carried out, optimization is carried out through a selected optimizer and corresponding parameters, and a final ship positioning model is obtained. According to the method, a multi-scale linear attention decoupling head, a wavelet transform feature decomposition space semantic alignment module and a progressive reinforcement learning loss function fine-grained module are introduced, the fine-grained learning ability of the method is improved, the detection ability and robustness of the model are improved, and finally the ship positioning accuracy and efficiency in severe weather are improved.

The invention provides a train operation control method and device under severe weather, and the method comprises the steps: obtaining train idling slip information sent by a train automatic protection subsystem or a train control and management system, and determining a rail slippery state according to the train idling slip information; determining a severe weather mode instruction of the target train based on the rail slippery state and the weather state; and sending a severe weather mode instruction to the train automatic protection subsystem or the train control and management system, wherein the severe weather mode instruction is used for indicating a target train in the target operation area to enter a severe weather mode. According to the train operation control method and device in the severe weather, the severe weather mode instruction of the target interval is determined according to the wet and slippery state of the rail and the weather state, safety protection can be carried out on the guaranteed emergency braking rate in the severe weather, and the situation that due to the fact that the train braking adhesion coefficient is reduced to break through the existing design boundary, the train operation efficiency is improved is avoided. Therefore, the safety of train operation is improved.

The invention relates to an outdoor protective box-type substation, which relates to the technical field of box-type substations and comprises a box body, a box door hinged to the box body and a box cover covering the top of the box body. A plurality of heat dissipation openings are formed in the opposite vertical side faces of the box body, protective covers with downward openings are fixedly arranged on the outer side opening edges of the heat dissipation openings, and drainage covers with upward openings are fixedly arranged on the inner side opening edges of the heat dissipation openings. According to the invention, the protective cover can shield most of rainwater falling from the side surface and prevent the rainwater from directly entering the heat dissipation opening, so that the risk of rainwater permeation is greatly reduced. Meanwhile, due to the design of the drainage cover, when rainwater infiltrates accidentally, water flow can be guided to flow out upwards along the cover body, and the water flow is prevented from making contact with electrical elements in the box body. The dual protection mechanism not only guarantees the heat dissipation requirement of the box-type substation, but also remarkably improves the rainproof performance of the box-type substation, and ensures the stable operation of equipment under severe weather conditions.

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 discloses a bad weather degraded image restoration method and device based on dynamic degradation generation, and relates to the field of image processing, and the method comprises the steps: employing an E step of an expectation maximization algorithm to optimize a hidden variable in a training process of a noise predictor and a dynamic degradation generator, and obtaining an optimized hidden variable; in M steps of an expectation maximization algorithm, inputting the optimized hidden variables and state variables into a dynamic degradation generator, generating a degradation layer to perform semi-supervised training on a noise predictor and the dynamic degradation generator, and obtaining a trained noise predictor; in an image restoration stage, an image degraded in severe weather and random Gaussian noise are input into a trained noise predictor, an iterative denoising step obeying a Markov chain is executed, and a clean restored image is obtained. The problems that an existing severe weather image restoration model is limited in generalization ability and unstable in restoration effect are solved.

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 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.