2079 results about "Identification system" patented technology

The invention relates to the technical field of nondestructive testing, and discloses a steel structure weld defect ultrasonic detection and identification system. A data acquisition module of the system acquires an original ultrasonic signal of a steel structure welding seam through ultrasonic detection equipment and acquires geometric attribute data of the welding seam; the model construction module constructs a welding seam three-dimensional digital model based on the data; a feature extraction module performs feature mining on the three-dimensional digital model and extracts a weld defect feature index set; the difference analysis module carries out deviation calculation on the characteristic index set and a reference index set in a standard welding seam characteristic database, and an abnormal area is identified; the risk assessment module calculates a defect sensitivity index according to the abnormal region in combination with real-time environmental parameters, and assesses a defect risk level; and the report generation module formulates a detection scheme according to the defect risk level, generates a detection instruction, executes ultrasonic scanning, collects performance data and generates a defect detection report. The system has the advantages of high detection precision, high reliability, automatic and standardized process and the like.

The invention relates to the technical field of digital economic risk control, and discloses a digital economic risk identification system and method based on artificial intelligence. A risk data acquisition engine of the system obtains transaction behavior data streams from a plurality of digital economic transaction platforms in real time, and converts the transaction behavior data streams into a structured transaction feature matrix; an abnormal mode detection engine extracts time sequence abnormal features through a deep residual network to generate an abnormal feature vector set; the risk association analysis engine constructs a risk propagation path map through a graph neural network, and outputs a risk association degree scoring matrix; the dynamic threshold adjustment engine performs adaptive threshold calibration according to the historical risk event database to generate a dynamic risk threshold vector; and the risk decision engine compares the scoring matrix with a dynamic threshold value, marks risk transaction nodes and generates a risk early warning instruction set. The system can adapt to digital economic transaction characteristics, and the comprehensiveness and accuracy of risk identification are improved.

The invention relates to the technical field of fire safety monitoring, in particular to a firework identification system and method based on multi-modal fusion perception, and the core of the scheme is a visible light, multispectral and temperature three-modal framework: feature extraction optimization of each modal, improved YOLOv8s for visible light branches, dynamic background modeling and flame color screening, and multi-modal fusion perception. False positive is rejected by a multispectral branch depending on a waveband ratio and an index, an error compensation algorithm is introduced into a thermopile branch, and finally, a final smoke and fire area and the confidence coefficient thereof are determined by associating a three-mode area through collaborative decision. According to the scheme, the complex environment adaptability and the recognition reliability can be improved, the false alarm risk is reduced, the extremely-early smoke and fire detection capability is enhanced, good real-time performance and deployment flexibility are achieved, and the method is suitable for various types of fire safety monitoring scenes.

The invention discloses an intelligent steel pipe surface defect recognition system based on deep learning, and particularly relates to the technical field of pipe surface defect analysis. An annular polarization light source array and a high-frame-rate CMOS sensor are adopted to synchronously collect visible light and near-infrared multi-polarization images; a surface normal is calculated based on Stokes parameters, mirror surface suppression and diffuse reflection enhancement are realized, a defect candidate area is generated by fusing multi-scale Laplacian pyramid residual error and Renyi entropy segmentation threshold positioning, multi-physical quantity registration is completed through white light interference and infrared thermal imaging, a six-channel feature cube is constructed, and a three-dimensional image is obtained. According to the method, space, spectrum and thermal characteristics are jointly extracted in the multi-head attention convolutional neural network, the confidence coefficient is evaluated in combination with Jensen-Shannon divergence, and the polarization angle and the focal length are dynamically adjusted according to the confidence coefficient, so that closed-loop parameter self-optimization is realized, and the micro-scale pitting corrosion and millimeter-scale crack detection precision is remarkably improved.

The invention discloses a quality management and control system for fabricated decoration construction, and the system comprises a sensing layer which constructs a multi-modal data collection network, carries out the three-dimensional real-time data synchronous collection through combining logistics API docking and an OCR recognition system, and constructs a construction process digital twin bottom plate; in the edge calculation layer, an edge node carries out lightweight processing on the original data; the cognitive layer is used for calling a Prolog rule through a process knowledge graph engine to reasone the feature snapshots, carrying out defect instant diagnosis and dynamic constraint propagation, outputting a root cause path with probability weight through three-stage verification, and quantifying intervention influence; the decision-making layer is used for constructing a dynamic prediction model based on a bidirectional LSTM and an attention mechanism, automatically activating a compensation mode when key interference is detected in combination with an anti-fact memory bank and a case-based reasoning compensator, and generating an alternative scheme of optimal cost / optimal construction period / comprehensive balance through a multi-target optimizer; and in the application layer, a Unity engine is utilized to develop the digital twinborn billboard.

A system for material detection and identification includes an interface configured to access a. material database associating each of a plurality of materials with one or more corresponding resonance frequencies: an RF transmitter configured to, for each material of at least a subset of the plurality of materials in the material database, transmit into an environment an RF signal at a first resonance frequency for the material; an RF receiver configured, to receive a response signal from the environment for each RF signal; and a processor configured to analyze each response signal for resonance characteristics that indicate a presence of the material and identifying the material to a user if the presence of the material is indicated by the resonance characteristics.

The invention discloses a multi-scale and mixed attention high-robustness motor imagery recognition method, which comprises the following steps: S1, acquiring motor imagery electroencephalogram signals, preprocessing the motor imagery electroencephalogram signals, dividing a training set and a test set, segmenting the training set, recombining the training set and expanding a training data set; s2, multi-scale feature extraction is conducted on the motor imagery electroencephalogram signals through a multi-scale convolution embedding module, and time dynamic and space cooperation features of different frequency bands are captured; s3, inputting the multi-scale features into LG-KAT, and respectively modeling a local fine-grained feature and a global time sequence dependency relationship through a local attention branch and a global attention branch; s4, features output by LG-KAT and low-layer embedded features are fused and flattened, a classification layer based on GR-KAN is input for nonlinear transformation and category mapping, model parameters are trained and optimized, and motor imagery task classification is achieved. The invention further discloses a multi-scale and mixed attention high-robustness motor imagery recognition system.

The invention discloses an extreme sea condition parameter identification system based on deep learning, and relates to the technical field of ship navigation auxiliary equipment, in particular to a self-adaptive sea condition identification device which is used for acquiring image data and inertial measurement data of a current sea condition; the wave field visual depth estimation module is used for extracting visible light image features and infrared image features of a wave area from image data of the current sea condition, fusing the extracted visible light image features and infrared image features, using an encoder-decoder architecture and fusing an energy function to obtain a pixel-level wave height field, and outputting the pixel-level wave height field. The three-dimensional reconstruction of the wave surface is realized; the multi-modal data fusion module uses a filter dynamic model and a cost function to eliminate space-time asynchronous errors between inertial measurement data and visual perception data, performs multi-modal data fusion, and outputs wave field real-time parameterization information. According to the invention, the sea condition parameter real-time high-precision identification capability of the autonomous unmanned ship or the offshore carrying platform can be improved.

The invention provides a shaft power generation and energy storage hybrid power efficiency optimization system for a container ship under multiple working conditions, which is applied to the field of ship energy management, and comprises a working condition strategy module, a fluctuation suppression module and a power distribution module, the working condition strategy module is electrically connected with ship radar navigation equipment and a ship identification system receiver, and the fluctuation suppression module is electrically connected with the ship identification system receiver. The power distribution module is electrically connected with a ship power grid load detector, a main engine rotating speed sensor and an energy storage charge state sensor; according to the invention, by cooperatively regulating and controlling the output power of the main engine shaft power generation equipment, the auxiliary power generation equipment and the composite energy storage equipment, a high-efficiency power calling mechanism for a ship power grid is constructed, so that under the complex ship working condition information, the safety redundancy and reliability of power supply are improved, the utilization efficiency of electric energy is improved, and the energy consumption is reduced. And the fuel consumption is reduced, so that the comprehensive target of energy conservation and emission reduction is achieved.

The invention relates to the technical field of geological disaster monitoring and early warning, in particular to a complex high-position landslide risk dynamic identification system based on multi-source InSAR cooperation, and the system comprises a data collection module which is used for constructing a normalized feature vector representing the state of each monitoring node; the correlation modeling module is used for quantifying the physical influence degree among the heterogeneous nodes; the state prediction module is used for iteratively updating the hidden state of each heterogeneous node by adopting a space-time diagram neural network model and interpreting the hidden state into incremental displacement; the risk assessment module is used for calculating a risk precursor index of each heterogeneous node deviating from a normal evolution mode based on the hidden state, and judging and issuing graded early warning according to a preset threshold system; according to the method, the overfitting problem of a pure data driving model during data sparsity is effectively relieved, and the generalization ability and the physical interpretability of a prediction result are remarkably improved.

The invention discloses a cable tunnel fire risk feature identification system and identification method, and belongs to the technical field of cable tunnel safety monitoring. The whole stage of a fire is covered through multi-class cooperative detection, the target identification precision and scene adaptability are greatly improved, and the safety of the cable tunnel is improved. According to the whole system, a flame and smoke detection unit and a flame / smoke special detection unit are innovatively added to a detection engine module, an original heat source detection unit and an original human body detection unit are combined, a heat source-flame-smoke-human body four-category collaborative detection framework is formed, high-precision recognition is achieved on the basis of a YOLO model framework, and the detection efficiency is improved. Compared with the problems that a traditional system is high in single target detection omission ratio and cannot cover the whole stage of smoldering-initial open fire-violent combustion of a fire, the system has the advantages that the recognition rate of early flame and weak smoke is increased, the false alarm rate is greatly reduced, meanwhile, a heat source of operation and maintenance personnel is prevented from being misjudged as a fire hazard through human body detection, and the safety of the fire hazard is improved. And cable monitoring and personnel safety protection are both considered.

The invention relates to the technical field of power equipment monitoring, and discloses a hydropower station high-altitude equipment fault intelligent identification system based on multi-sensor fusion, which collects multi-modal data in real time and evaluates data quality by deploying multi-source sensors at key parts of high-altitude equipment. Extracting multi-scale features of each modal, performing normalization processing, calculating a fusion weight based on feature saliency and data credibility, and performing weighted fusion and dimension reduction on the multi-modal features; based on the fusion feature vector, intelligent matching analysis of fault features and intelligent identification of fault types are carried out; a fault identification result is obtained; in addition, the system also comprises safety monitoring of overhead working personnel, and realizes closed-loop management from fault identification to safety maintenance. According to the invention, early weak faults can be accurately identified, and the safe operation level of equipment and the intelligent degree of operation safety management are improved.

The invention relates to the technical field of data management and information, and discloses a data asset identification system and method based on multi-dimensional rule engine blood relationship analysis, and the method comprises the steps: obtaining static code information and dynamic operation tracking information of to-be-analyzed data; constructing a theoretical data blood relationship map based on the static code information; constructing a real-time data blood relationship map based on the dynamic operation tracking information; generating a fusion data consanguinity map by adopting a time window alignment strategy; constructing a five-dimensional data asset identification rule model; starting a blood relationship driven multi-dimensional rule engine; executing the multi-dimensional rule engine, and evaluating and determining the nature of the to-be-analyzed data; in the evaluation process, a conflict resolution mechanism is started. According to the method, the multi-dimensional rule engine is driven through dynamic and static combined blood relationship analysis, and conflict judgment is performed by using blood relationship information.

The invention discloses a machine learning-based weld defect intelligent identification system, relates to the technical field of weld defect intelligent identification, solves the technical problems of multi-modal data fusion precision and robustness optimization and defect shielding or overlapping feature deficiency, and provides a machine learning-based weld defect intelligent identification method based on PSNR dynamic parameter adjustment and gradient weight optimization. The limitation of existing fixed parameter denoising is solved, the edge feature retention rate of cracks, air holes and other defects is improved, the omission ratio is reduced, improved DeepLabv3 + segmentation semantic masks are introduced and mapped to point cloud voxels, geometric + semantic double-attribute enhanced point clouds are formed, the defect area positioning accuracy is improved, and through a cross-modal attention module, the defect area positioning accuracy is improved. Weights are dynamically distributed according to illumination intensity and workpiece materials, feature waste caused by fixed weights is avoided, depth mutation and a shielding area with semantic defects are positioned by utilizing depth information of enhanced point cloud, real overlapping and projection overlapping can be effectively distinguished by combining an improved Poisson fusion algorithm, and the overlapping defect recognition accuracy is improved.

The invention discloses a laser radar and multispectral polarization imaging fused target identification system and method. The system comprises a sensor configuration and data preprocessing module, a cross-modal feature extraction and fusion module and a multi-task output and optimization module. The sensor configuration and data preprocessing module performs time-space synchronization processing on the collected original optical signals and laser signals in the environment to obtain multispectral image data and laser radar point cloud data; the cross-modal feature extraction and fusion module performs feature extraction and fusion enhancement processing on the multispectral image data and the laser radar point cloud data, and outputs high-dimensional semantic enhancement point cloud representation containing image semantics and point cloud geometry; and the multi-task output and optimization module processes the high-dimensional semantic enhanced point cloud representation and outputs a three-dimensional target recognition result, target speed information and a pixel-level depth map. Through module design and data processing, the defects in the prior art are overcome, and the accuracy of target recognition is improved.

The invention relates to a tunnel three-dimensional disease intelligent identification system and method based on a large model, the system comprises a point cloud data acquisition module and a processor, and the processor comprises a data processing module, a three-dimensional tile optimization module and a disease identification module. The data processing module carries out standardization, noise reduction and registration processing on the received point cloud data; the three-dimensional tile optimization module constructs a multi-level tile pyramid structure based on the registered point cloud data, establishes a mapping relation between a space coordinate and a tile index, compresses tile data based on a curvature point cloud simplification algorithm and adjusts texture quality to form a three-dimensional tile image; calculating a comprehensive score of the tile quality to verify the quality of the three-dimensional tile image; a disease identification module extracts multi-modal fusion features of the preprocessed three-dimensional tile image and geometric features corresponding to disease types; fusing the multi-modal fusion feature and the geometric feature to obtain a joint fusion feature; performing field fine tuning on the joint fusion features; and obtaining a disease identification result.

The invention relates to the technical field of optical cable line external force damage monitoring, and discloses an optical cable external force damage intelligent identification system. The system comprises an optical fiber vibration sensing module, a multi-source feature fusion analysis module, an abnormal event association positioning module and an intelligent checking strategy generation module. The optical fiber vibration sensing module constructs a model based on historical data and outputs a reference vibration characteristic value; the multi-source feature fusion analysis module performs three-dimensional fusion analysis of time domain, frequency domain and phase on the reference value and the measured value to generate a feature difference matrix; the abnormal event association positioning module is used for generating an abnormal probability distribution cloud picture and positioning an abnormal area in combination with the topological parameters and the environment information; and the intelligent checking strategy generation module configures checking parameters according to the cloud picture, starts high-frequency acquisition for a high-probability region, and applies an environment disturbance test to adjacent lines. The system can accurately identify and position optical cable external force damage, and the monitoring efficiency is improved.

The invention relates to the technical field of snow melting disaster early warning, in particular to a snow surface rain disaster identification method based on a high and cold mountainous area and a related product, and the method comprises the steps: constructing a snow surface rain disaster single-point identification rule capable of being physically explained by using meteorological observation data and remote sensing inversion data of a small amount of weather stations in the high and cold mountainous area; the method is advantaged in that classification is carried out without depending on a black box model, precision and interpretability of single-point snow surface rain event discrimination are improved, regional popularization is carried out through dynamic downscaling simulation of a regional climate mode WRF coupling land surface process model Noah-MP, a point-surface fusion snow surface rain disaster discrimination and identification system is constructed, and the method is advantaged in that the method is simple and convenient to operate. The method is suitable for identifying snow rain disasters in complex terrain areas with high and cold areas, high altitude, large gradient, lack of data, even no data and the like, and solves the technical problems of insufficient snow rain disaster space identification capability and low precision caused by sparse observation data in the high and cold mountainous areas in the prior art.

Apparatus and associated methods relate to an artwork authenticity owner validation system. In an illustrative example, an artwork remote identification system (ARIS) may include an artwork companion chip (ACC) and a certificate of authenticity (COA). The ACC, for example, may be physically attached to an artwork. The ACC and the COA, for example, may each include a unique identifier. For example, a centralized server may be configured to automatically authenticate an ownership of the artwork attached to the ACC by validating the ACC and the COA. For example, the user may scan the COA and then the ACC within a predetermined time limit to gain access to modify an ownership record in an artwork profile of the artwork. Various embodiments may advantageously allow a modification to the ownership information only when the centralized authentication server validates the COA and the ACC within the predetermined time period.

The invention provides an intelligent gas meter remote meter reading method and system based on the Internet of Things, and relates to the technical field of intelligent gas meters. The method comprises the following steps: collecting local metering data and Internet of Things transmission data; constructing a dual-source data multi-dimensional conflict identification system, and screening conflict data groups; performing accuracy verification on the conflict data group; an encrypted transmission channel is established, and the traceability of the data transmission process is realized; triggering a grading early warning mechanism for the abnormal usage data deviating from the baseline; and carrying out iterative updating on the meter reading data knowledge base, and carrying out remote calibration. According to the method, double-source data conflicts are identified through multiple dimensions, conflict data are corrected in combination with a hybrid model, metering accuracy is guaranteed, and disputes are reduced; through constructing a baseline model and a grading early warning mechanism, abnormal usage is accurately identified and disposed, hidden dangers are timely prevented, and service is excellent. Through dynamic on-demand remote calibration of equipment, the metering precision is guaranteed, the operation and maintenance cost is reduced, the user data transparency is improved, and intelligent gas management is assisted.

The invention discloses a self-encoder-based water pump operation abnormal working condition identification system and method. The system comprises a water pump monitoring data acquisition module, a data preprocessing feature extraction module, a self-encoder training module and an abnormal data monitoring analysis module. The water pump monitoring data acquisition module is connected with the data preprocessing feature extraction module, the data preprocessing feature extraction module is connected with the auto-encoder training module, and the auto-encoder training module is connected with the abnormal data monitoring analysis module; the problems that comprehensive state prediction evaluation is not considered, the maintenance efficiency is low and the stability is limited in the prior art are solved.

The invention discloses a light field surface defect identification system and method based on multi-angle scattering, and relates to the field of optical image analysis. The system comprises a multi-angle scattered light field acquisition module and a defect intelligent identification module. The acquisition module obtains a hemispherical space scattered light field image of an optical element to be detected through one-time exposure of a collimation laser light source, a hemispherical dome and the like. The recognition module integrates a data preprocessing unit and an SFD-YOLO network, and on the basis of YOLOv11, the network replaces CBS in a shallow layer with a PSiam attention module to strengthen local significant feature perception, and replaces C3K2 in a deep layer with an OfficitMambaCSP module to strengthen global semantic modeling. The method comprises the steps of image acquisition, data set generation preprocessing, network training and defect identification. The problems that in the prior art, single-view-angle information is limited, and scattering data is insufficient in utilization are solved, Top1ACC reaches 95.6%, the parameter quantity is only 1.25 M, and the method is suitable for high-precision real-time detection of optical element defects.

Systems and methods involving various registration and authentication workflows are disclosed herein. A user may be authenticated without the use of static usernames or passwords. In some embodiments, an authentication identifier may be generated that is associated with an authentication request for a user to access a protected resource (e.g., a web app). An authentication code may be generated based on the authentication identifier. The authentication code may be sent to a computing device to be provided to the user, who may provide the authentication code to an application on their mobile device. The mobile device may send a payload containing the authentication identifier, credentials saved on the user device from a previous registration step, and a digital signature. The digital signature may be authenticated using contents of the payload before validating the authentication identifier.

The invention provides a grape disease and pest identification method based on an LW-YOLO network, and aims to improve small target detection precision and multi-scale feature extraction capability and reduce model calculation complexity at the same time. The method comprises the following steps: step 1, acquiring a public grape disease and insect pest data set or establishing a data set according to requirements of a specific grape variety, a specific geographic area and a special disease and insect pest variety; step 2, manually marking a category label and a bounding box of each string of grapes in each picture in the grape disease and insect pest enhancement data set; 3, configuring a model training environment; the method comprises the following steps: step 1, constructing an LW-YOLO network model, step 2, constructing an LW-YOLO model, step 5, loading the constructed LW-YOLO network model to a configured model training environment, and training and verifying the LW-YOLO network model by using a preprocessed data set; and step 6, constructing a grape disease and insect pest recognition system based on an LW-YOLO network, splitting the system into a visualization subsystem and a disease and insect pest analysis subsystem, enabling the visualization subsystem to realize a real-time video stream lightweight image interception function by using an FFmpeg library in a Javacv library, and providing pictures to the disease and insect pest analysis subsystem for disease and insect pest target detection. And finally, displaying a detection result to a front-end page.

The invention discloses an autoclaved aerated concrete member surface defect intelligent identification system based on image processing, and particularly relates to the field of defect identification, comprising an image acquisition module, an image preprocessing module, a defect candidate region extraction module, a defect identification and classification module, and a result output and alarm module; according to the method, a high-definition industrial camera is used for collecting a component surface image, and adaptive median filtering and a Retinex algorithm are adopted for image denoising and enhancement, so that the influence of noise and uneven illumination is eliminated; utilizing an improved multi-threshold segmentation and Canny edge detection algorithm to accurately extract a defect candidate region; the method comprises the following steps: extracting three types of feature parameters of shape, texture and gray scale, and inputting the three types of feature parameters into a deep learning model taking ResNet50 as a basic network to realize automatic identification and classification of four types of typical defects of cracks, holes, unfilled corners and surface peeling; and finally, the system divides severity levels according to the defect size, and triggers differentiated visual alarm and linkage control.

The invention relates to the technical field of intelligent operation and maintenance of photovoltaic power stations, in particular to an AI video recognition system for photovoltaic power station equipment inspection, which comprises a data acquisition unit, a data preprocessing unit and a defect recognition unit, and is characterized in that characteristics from one mode are used as query, evidence information serving as keys and values is searched from corresponding area characteristics of other modes, and the data acquisition unit is used for acquiring data; the method is used for synergistically diagnosing compound and early defects with weak or invisible characteristics in a single mode. According to the method, cross-modal collaborative reasoning can be realized: when a suspicious feature is found in one modal, whether evidence features capable of mutually verifying exist in the same position in other modals or not can be inquired, the diagnostic logic of field experts is simulated, different physical phenomena can be associated, and the probability of mutual verification is reduced. Therefore, early-stage or composite defects which are extremely difficult to find in any single mode can be accurately identified. Therefore, the problem that the recognition performance is reduced due to environmental interference such as illumination and shadow can be fundamentally solved.

The invention provides an artificial intelligence identification system GeoFusionFormer for a shale SEM image. The artificial intelligence identification system comprises a data acquisition and labeling module, an image preprocessing module, a self-supervision pre-training and fine tuning module, a convolution-TransFormer coding and decoding module, a multi-modal 3D convolution fusion module, a boundary optimization module and a result visualization statistics module. A system encoder and a decoder alternately use multi-layer convolution and Transform self-attention to realize dual modeling of local texture and global dependence; the multi-modal fusion integrates the features of different imaging modals through 3D convolution; the Dice Loss, the Focal Loss and the boundary loss are jointly used to improve the segmentation precision; the edge optimization module extracts Sobel edges and enhances boundary information through attention. According to the method, high-precision segmentation can be carried out on the geological components, and the proportion of each component is counted.

The invention discloses a comprehensive remote sensing recognition system for hidden danger of ice lake outburst. The system comprises a data acquisition and preprocessing module, an ice lake extraction module, an ice dam stability evaluation module, a risk calculation module and a visualization and early warning module. According to the system, optical images, SAR images, LiDAR point cloud and meteorological data are utilized, firstly, a glacial lake boundary is extracted through a water body index, the area and the volume are calculated, then the water level rising rate is monitored in combination with multi-temporal data, the dam body stability is evaluated by utilizing three-dimensional terrain and deformation parameters, and the dam body stability is evaluated by combining the temperature melting corrosion rate and triggering factors such as rainfall, earthquakes and inflow. And constructing a multi-factor outburst risk index model, and finally carrying out visualization and graded early warning on a result in a GIS platform. The method can achieve the quick, comprehensive and intelligent recognition of the hidden danger of ice lake outburst, has the advantages of being high in monitoring precision, high in automation degree, high in early warning timeliness and the like compared with a traditional single data or experience judgment method, and can be widely applied to the disaster prevention and control work of dense ice lake areas such as the Qinghai-Tibet Plateau and the Himalaya mountain.

The invention discloses a driver fatigue state real-time identification system and method based on multi-modal deep learning, and relates to the technical field of fatigue driving detection. Firstly, feature extraction is performed on brain wave shapes and eye movement coordinates, and respective weights are calculated by using an attention mechanism, so that dynamic distribution of different modal features is realized. And then, in-vehicle illumination data is introduced to establish a credibility mapping function so as to carry out adaptive correction on an eye movement weight, thereby effectively reducing interference of a complex illumination environment on an identification result. And carrying out weighted splicing on the corrected multi-modal features, mapping the multi-modal features into a brain-eye collaborative fatigue value, and carrying out judgment in combination with the duration, so as to finally realize stable and reliable early warning control. The method has the advantages of high fusion precision, high environmental adaptability and low false alarm rate while ensuring the real-time performance, and the driving safety guarantee capability can be remarkably improved.

The invention relates to the technical field of intelligent traffic and road maintenance, and provides a road disease intelligent identification system and method based on artificial intelligence and Beidou positioning, and the method comprises the steps: carrying out the edge calculation preprocessing of noise reduction, image enhancement and region-of-interest segmentation of collected road image data, and extracting initial image features; inputting the initial image features into a deep learning disease recognition model based on transfer learning optimization, outputting a disease type and a disease grading result, and forming multi-source fusion data; performing spatio-temporal data association analysis on the multi-source fusion data to realize disease environmental impact assessment; the method comprises the following steps: predicting future development conditions of road diseases, sending out early warning information, dynamically evaluating road health condition grades according to road disease data, making an optimal maintenance plan according to positions, types and grades of the diseases and environmental influence evaluation results, and optimizing traffic dispersion and route recommendation according to the road disease conditions.