240 results about "Data projection" patented technology

The invention belongs to the technical field of ship collision avoidance, discloses a ship collision avoidance early warning method and system based on vision and AIS data collaborative perception, and firstly provides an enhanced YOLO (E-YOLO) target detector which can detect a ship in a complex environment in a high-precision and real-time manner and perform ship tracking and visual trajectory extraction in combination with a DeepSORT algorithm. Secondly, a homography coordinate conversion method based on multiple frames of static images is provided, conversion between a pixel coordinate system and a world coordinate system can be realized without accurate camera calibration, and the fusion precision of visual data and AIS data is improved. AIS and visual information are matched through a K-D tree algorithm, AIS data are projected to visual coordinates, and accurate matching of heterogeneous data is achieved. The system is provided with ship illegal behavior judgment and collision early warning rules, behaviors such as overspeed and abnormal AIS closing can be recognized, and early warning is triggered. Experiments prove that the method provided by the invention is strong in adaptability and high in robustness, and can effectively improve the ship monitoring and traffic management level of the bridge water area.

The invention discloses a road protection multi-scene coupling simulation method and system based on digital twinning, and relates to the field of electric digital data processing, and the method comprises the steps: collecting dynamic response data (strain, acceleration and displacement) of a traffic protection facility through multiple sensors, and building a scene feature library containing vehicle collision, wind load and seismic waves; and projecting response data to a unified time axis, identifying a multi-scene overlapping interval, and determining an action sequence and duration. Primary and secondary scenes are judged according to scene action parameters, and mechanical correction values and standard response parameters are extracted. And establishing a digital twinborn model for coupling simulation to obtain a stress distribution condition, evaluating a structural damage level and generating a safety performance report. By implementing the method, the deviation between the mechanical property simulation result of the protection facility and the actual working condition can be reduced.

The invention relates to a multi-mode tumble detection method, device and equipment, and belongs to the technical field of intelligent nursing. The method comprises the steps of obtaining fall detection data; performing motion point extraction on the radar point cloud data based on time sequence analysis, screening out motion points whose positions change in continuous time, and generating motion point cloud data; projecting the moving point cloud data to a coordinate system corresponding to the thermal infrared imaging data to generate a radar point cloud projection matrix; combining the radar point cloud projection matrix with the thermal infrared imaging data, calculating the three-dimensional coordinates of the target, and generating target height change data; and when the target height change data meets a preset tumble condition, inputting the constructed time sequence multi-modal fusion data into a deep learning behavior recognition module, and outputting a classification result of tumble behaviors. According to the method, the radar sensor and the thermal infrared imaging data are fused, and time sequence analysis and deep learning behavior recognition are combined, so that the privacy of the user is protected while high-precision fall detection is realized.

The invention relates to the technical field of projection display, and discloses a projection curtain light field display gain method and system used under ambient light. According to the method, environment light data, projection curtain data and original output signals of projection equipment are collected in real time, and a dynamic light field distribution model is established after fusion processing. And based on the dynamic light field distribution model, adopting an adaptive genetic algorithm to optimize a compensation weight, generating a light field compensation parameter set, performing reverse compensation processing of brightness gain, contrast and chromaticity offset on an original output signal, and generating an enhanced projection signal. The system comprises a multi-source data acquisition module, a light field feature extraction module, a dynamic compensation calculation module, a signal enhancement processing module and a closed-loop feedback control module, ensures that the projection image keeps an optimal display effect under different ambient light conditions, and remarkably improves the watching experience of audiences.

The invention relates to the technical field of three-dimensional scanning, and discloses a binocular three-dimensional scanning system and method with a texture mapping function. The system comprises a depth data acquisition module, a depth data correction module, a point cloud topology construction module, a texture mapping engine module and a mapping optimization module. The depth data acquisition module synchronously acquires a left view and a right view of a target object through a binocular camera array, and generates an original depth image data set; the depth data correction module performs abnormal value detection on the original depth data, extracts discrete distribution through aggregation boundary analysis and replaces abnormal values; the point cloud topology construction module generates a three-dimensional point cloud spatial topology structure, and associates point cloud coordinates with left view pixel positions; the texture mapping engine module projects left view RGB data to the point cloud to generate an initial texture map; and the mapping optimization module verifies the local color consistency through the generative adversarial network, and outputs an optimized three-dimensional texture model. The system improves the texture precision and authenticity of the three-dimensional model.

The invention belongs to the field of CT image processing, and particularly relates to a CT image reconstruction method based on a three-dimensional diffusion model and a related device.The method comprises the steps that a medical CT scanner is adopted to obtain three-dimensional CT voxel data, and the three-dimensional CT voxel data is preprocessed; extracting a medical object surface point cloud from the preprocessed three-dimensional CT voxel data by adopting a voxel-level reconstruction algorithm, and generating three-dimensional sparse point cloud data; processing the three-dimensional sparse point cloud data by adopting a 3D Gaussian splashing technology, and converting the three-dimensional sparse point cloud data into three-dimensional Gaussian distribution data by using a pre-trained three-dimensional Gaussian kernel; gradually adding Gaussian noise to the three-dimensional Gaussian distribution data by adopting a three-dimensional diffusion model, and gradually removing the Gaussian noise to generate reconstructed three-dimensional Gaussian distribution data; and projecting the reconstructed three-dimensional Gaussian distribution data to a two-dimensional plane by adopting a rendering technology to generate a two-dimensional CT reconstructed image. According to the method, the advantages of the 3D Gaussian splashing technology and the three-dimensional diffusion model are combined, efficient, real-time and denoising medical image reconstruction can be achieved under the low-dose CT imaging condition, and meanwhile the problem of white floater artifacts possibly occurring in the reconstruction process is effectively solved.

The invention relates to an artificial precipitation enhancement operation area identification method and system based on multi-source data, and relates to the technical field of meteorological intervention operation, and the method comprises the steps: building a multi-resolution dynamic data grid system, and enabling a coarse-grained grid layer to cover the whole operation area, and to be used for bearing the macro data of a satellite cloud picture and a conventional radar; the fineness grid layer is used for receiving and mapping real-time data provided by the high-resolution radar; receiving real-time data of all cloud blocks, mapping the real-time data to corresponding grid layers, and projecting the data collected at different time points to a unified time reference frame to obtain a cloud system state view; predictive interpolation is carried out by using the latest data of the conventional radar and the macroscopic trend of the satellite cloud picture in combination with the motion vector and confidence weighting so as to obtain cloud system information; and forming a visual cloud system three-dimensional view, identifying and tracking a target cloud block with an artificial precipitation enhancement potential, and generating an operation instruction. The method has the effect that the target cloud block with the operation value can be identified and tracked to generate the accurate operation instruction.

The invention provides an intelligent adaptive projection lamp and a projection control method, and the method comprises the steps: collecting multi-source environment data which comprises at least one of environment illumination data, projection surface characteristic data and user operation data; performing preprocessing, feature extraction and feature fusion on the multi-source environment data to generate an environment feature vector; performing projection mapping on the environment feature vector through a parameter prediction model based on rate distortion optimization to generate projection parameters; performing image deblurring processing and adaptive k-space sampling reconstruction on the projection parameters to generate an optimized projection image; collecting projection effect feedback data for the optimized projection image; and performing online updating optimization on the parameter prediction model according to the projection effect feedback data. According to the invention, intelligent adaptive adjustment of the projection parameters is realized, and the projection quality is improved.

The invention discloses a method for measuring the navigation pose of a battery replacement robot based on three-dimensional visual features. The method comprises the following steps: 1, acquiring a color image and a depth image of a scene by using a 3D visual sensor; 2, positioning a locking and unlocking area in the color image through a convolutional neural network, and projecting depth data of the area into a point cloud by combining internal parameters of a camera; 3, performing statistical filtering and voxel filtering preprocessing on the initial locking and unlocking point cloud; 4, collecting five key points from the target point cloud by using an FPS sampling method, solving the coordinate of each key point in the target point cloud object coordinate system, and taking the coordinate as a directional vector; 5, points in the scene point cloud are converted into an object coordinate system, candidate points of the key points are screened according to the direction and length of the steering vector, and the key points of the scene point cloud are determined from the candidate points; and 6, fitting the pose of the scene and unlocking the point cloud by adopting a least square method according to the corresponding relation between the two groups of key points of the target point cloud and the scene point cloud, and optimizing the pose estimation result by using an ICP algorithm.

The invention provides a temperature modulation parameter optimization method for gas recognition based on a gas sensor, and belongs to the technical field of sensors. The method comprises the following steps: step S11, determining a plurality of classified gases; s12, building a data collection device; step S13, determining a search range and a search step length of modulation parameters; step S14, traversing the modulation parameter combination, and collecting sample data; step S21, projecting sample data of all gases of all modulation parameter combinations to a high-dimensional metric space; step S22, in a high-dimensional measurement space, measuring the similarity degree between the two samples by using Euclidean distance; s23, calculating a sample contour coefficient based on the cohesion degree and the separation degree of the sample; and S24, calculating a contour coefficient, and obtaining an optimization parameter according to the contour coefficient. By using the method, parameter tuning is performed, the required data is less, and the collection speed is high. Scientific basis and technical support are provided for parameter optimization.

The invention relates to the technical field of low-altitude unmanned aerial vehicle detection, in particular to an unmanned aerial vehicle detection method based on radar infrared visible light multi-mode fusion, which comprises the following steps: S1, respectively inputting an obtained unmanned aerial vehicle infrared image and an obtained visible light image into two visual branches of a double-flow feature extraction network, and extracting scale features by adopting a lightweight convolutional backbone network; s2, acquiring three-dimensional coordinate data of the unmanned aerial vehicle after radar detection and clutter removal processing in real time, and projecting the three-dimensional coordinate data into two-dimensional image coordinates through a camera external parameter matrix; s3, generating a two-dimensional Gaussian distribution probability graph based on the real-time two-dimensional image coordinates, and inputting the two-dimensional Gaussian distribution probability graph as a mask into the radar feature extraction network to extract a radar feature graph; and S4, under the same scale, performing dynamic weighted fusion on the scale features of the infrared branches and the visible light branches to generate a visual fusion feature map. The method greatly improves the capability of detecting the low-altitude unmanned aerial vehicle.

The invention belongs to the technical field of intelligent vehicles, and discloses an autonomous mobile robot multi-target tracking method and system based on a dynamic adaptive motion model. Detecting object information in the environment, and obtaining detection frame data of each frame of laser radar and camera; laser and camera data are projected to the ground to calculate the Euclidean distance so as to realize multi-sensor fusion and different frame data association operation. A corresponding adaptive state transition matrix is set for a previous frame trajectory, and a prediction bounding box and a covariance matrix of the previous frame trajectory in a current frame are calculated by using a Kalman filtering technology. The long-term and short-term memory module stores historical information of a management target and dynamically adjusts a tracking strategy and parameters. According to the invention, the tracking effect of the multi-target tracking algorithm on the autonomous mobile robot is successfully improved, powerful technical support and guarantee are provided for the intelligent robot to realize accurate multi-target tracking and efficient operation in a complex environment, and further development of the intelligent robot technology in practical application is powerfully promoted.

The invention discloses a virtual projection 3D oral and maxillofacial key point detection method based on deep learning, and aims to solve the problem of insufficient 3D image key point detection precision and efficiency in the prior art. The method comprises the following steps: projecting 3D image data into a plurality of 2D projection images by using a virtual projection technology; then key point detection and marking are carried out on the 2D image by using a deep learning network model, and 2D mark point coordinates are obtained; and finally, performing 3D reconstruction by adopting a virtual back projection method, and back-projecting the 2D mark point information to a 3D space to obtain 3D mark point coordinates. According to the method, 2D key point detection and 3D projection and reconstruction technologies are combined, rapid, high-precision and automatic detection of 3D image mark key points is realized, and the method can be widely applied to the fields of oral medical image analysis and the like.

The invention relates to an open source software supply chain security analysis-oriented LoRA enhanced large language model multi-source fusion fine tuning method and device, computer equipment, a computer readable storage medium and a computer program product, relates to the technical field of software supply chains, and can improve the multi-task security analysis efficiency for multi-modal security data. The method comprises the following steps: determining feature information corresponding to multiple pieces of security data of an open source software supply chain, and determining an association relationship among the security data according to the feature information; projecting the security data to the same semantic space for fusion according to the feature information and the association relationship to obtain fusion feature information of the security data in the semantic space; obtaining a safety analysis model; the safety analysis model is obtained by training a pre-trained large language model through a low-rank adaptation matrix group; and inputting the fusion feature information of the security data into the security analysis model, and outputting a security analysis result by the security analysis model.

The invention relates to the technical field of computer vision, in particular to a fabric color fastness analysis method and system based on image processing. Comprising the following steps: acquiring a multispectral image of a to-be-detected fabric, and synchronously acquiring multi-factor data; processing the multispectral image to generate an enhanced image; performing image recognition on the gray sample card in the enhanced image to generate a calibration image; performing spectral data projection on the calibration image through a dimension reduction algorithm, and performing color analysis and quantification on color change to generate color difference features; extracting texture features by using a conditional generative adversarial network, carrying out image recognition and calculation on the calibration image through a decoupling algorithm, and generating a region credibility graph; and inputting the chromatic aberration features, the texture features, the multi-factor data and the regional credibility map into an image feature fusion model for mapping, performing analysis through gradient visualization, and outputting an abnormal feature saliency map. According to the method, a color fastness objective analysis closed loop is created, so that the accuracy and the universality of an analysis result are improved.

The invention discloses a network data intelligent management system and method based on artificial intelligence, and relates to the technical field of network data management, original sensor data is projected to a sparse domain through an auto-encoder, whether the sparsity of the original sensor data meets a compressed sensing condition is verified, if yes, a Gaussian random matrix is constructed, and if not, a Gaussian random matrix is constructed; the Gaussian random matrix maps sparse signals to a low-dimensional space, a server side uses a basis tracking reconstruction algorithm to recover original sensor data according to the Gaussian random matrix, and after performance data in actual deployment are collected, the performance score of the deployment is calculated and obtained according to the evaluation model. And a corresponding optimization strategy is generated according to a performance score calculation result. According to the invention, in an application scene with limited bandwidth and limited equipment energy consumption, the data quality is ensured, the cost of data acquisition and transmission is significantly reduced, and the overall efficiency and intelligent level of network management are improved.

A system or method according to the present disclosure includes a processor; and a memory coupled to the processor and storing data thereon that, when processed by the processor, enables the processor to: receive three-dimensional (3D) scan data depicting a portion of a patient generated with an imaging device; project the 3D scan data into a plurality of two-dimensional (2D) image planes including a first 2D image plane and a second 2D image plane; match, based on a geometry of the portion of the patient, an area of the first 2D image plane to an area of the second 2D image plane that corresponds to a common location in 3D space; and register, based on the matching, the 3D scan data and the imaging device.

The invention discloses a classification fault detection method based on a graph neural network, and the method comprises the steps: inputting time series data, projecting the input data to a set dimension through a linear layer, employing a two-layer one-dimensional convolutional neural network to extract time features, dynamically generating an adjacent matrix through a radius according to the Euclidean distance, and based on the adjacent matrix, in each layer of graph convolution, carrying out the classification fault detection of each layer of graph convolution. The method comprises the following steps of: performing spatial aggregation on adjacent node features, learning the weight of important nodes by using an attention mechanism, repeating three-layer GATv2 convolution, adding residual connection and normalization processing into output of each layer, finally obtaining node feature representation of spatial aggregation, and finally converting extracted spatial and temporal features into classification results through a decoder to detect faults; the method solves the problem that the prior art cannot be competent for multi-source data and diversified features in a modern industrial system, combines a memory one-dimensional convolutional neural network and a GNN deep learning method, can extract spatial and temporal features at the same time, and is significantly superior to a traditional statistical method in fault detection accuracy.

The invention relates to the technical field of wind power plant power control, in particular to a wind power plant dynamic power distribution optimization method based on projection dimensionality reduction. The method comprises the following steps: collecting a high-dimensional original operation data stream of a wind power plant, and preprocessing to output a high-dimensional state vector; extracting a dominant mode matrix through an intrinsic orthogonal decomposition algorithm, and projecting a high-dimensional state vector to a low-dimensional space; future modal coefficient evolution is predicted through an autoregression prediction model, and a low-dimensional rolling optimization problem is constructed and solved through a sequential quadratic programming algorithm; reconstructing the optimal low-dimensional modal coefficient vector into a target power instruction of each fan, and issuing and executing the target power instruction; and updating the dominant mode matrix through an incremental singular value decomposition algorithm and correcting parameters of the autoregressive prediction model. According to the method, projection dimensionality reduction from high-dimensional data to a low-dimensional space is realized through intrinsic orthogonal decomposition, and the problem of low optimization solution efficiency caused by high data dimensionality of a traditional method is solved.

The invention relates to an intelligent state monitoring method and system for a high-voltage isolation switch operating mechanism, and relates to the technical field of power equipment fault diagnosis, and the method comprises the steps: enabling a collected real-time sequence to update a standard operation response potential energy surface, obtaining a real-time operation response potential energy surface, generating a phase response difference tensor through data projection, and obtaining a real-time operation response potential energy surface; and depicting deviation characteristics of actual and standard operation states. And then, in the constructed graph attention network, on the basis of a real-time sequence, monitoring parameters and video motion characteristics, constructing a multi-modal sequence state graph, and determining a state typing result through fine-grained recognition and interpretable analysis of a fault. And finally, introducing a cyclic fingerprint residual model, performing clustering optimization based on a real-time sequence, determining an abnormal deviation index, and combining the abnormal deviation index with a state typing result to determine a fault diagnosis result. Therefore, the accuracy of intelligent state monitoring of the high-voltage isolation switch operating mechanism can be effectively improved, and the method can be suitable for non-ideal environments, low dominant anomalies and other conditions.

The invention provides a ground penetrating radar clutter suppression method and system and a medium, the method highlights a texture change area through first-order difference, can accurately capture change information of echo intensity and suppress background fluctuation and noise, meanwhile, introduces graph regularization to ensure similarity of adjacent pixels, suppresses isolated noise points, and improves noise suppression efficiency. And the stability of the texture structure is enhanced. By means of the combination, clutters can be effectively removed when the method is used for processing complex backgrounds, and meanwhile key structure information is reserved. Complex echo texture data are projected to a singular value feature space through singular value decomposition, singular values with a large change rate are screened out for reconstruction, redundant interference can be effectively removed, and stable echo textures are reserved. According to the mechanism, when the method is used for processing multi-target, dispersed and irregular echo signals, target signals and clutters can be effectively separated, and the detection precision is influenced.

The invention relates to the technical field of electrical detection, in particular to a multi-stress coupling environment reliability testing method for an electricity consumption information acquisition terminal. Comprising the steps of applying a combined stress field to an electricity utilization information acquisition terminal through an environment test box; acquiring power utilization information acquisition terminal parameters; the method comprises the following steps of: capturing metering pulse response and carrier communication interaction signals of an electricity utilization information acquisition terminal under the excitation of a combined stress field by utilizing an electrical sensor, extracting carrier signal-to-noise ratio data and metering reference drift data, and generating a reference voltage compensation coefficient and an impedance matching threshold value; projecting the carrier signal-to-noise ratio data to an impedance matching threshold value, and constructing a stress failure envelope surface according to a hardware overload protection threshold value; and mapping the communication quality distortion residual vector to a stress failure envelope surface, and calculating a reliability life prediction value. According to the method, a uniform excitation environment is constructed, and performance degradation of different dimensions is uniformly quantified into accumulative stress damage integral, so that the comprehensive anti-interference capability of the terminal in a multi-stress coupling environment can be truly reflected.

The invention discloses a customized part machining method, a storage medium and machining device.The customized part machining method comprises the steps that a model is generated, a drawing or model input by the outside is responded, a to-be-machined part model is constructed, and model data is output; a bottom surface confirmation step: responding to the model data, confirming a surface with the largest area in the to-be-processed part model, marking the surface as a bottom surface, and outputting bottom surface data; a projection confirmation step: responding to the model data and the bottom surface data, confirming a projection contour of the model on at least two mutually vertical surfaces based on the bottom surface, and outputting contour data; a hole confirmation step: responding to the model data, and judging whether holes or holes exist in the model; and an equipment selection step: responding to the model data, the bottom surface data, the contour data, the hole data, the cavity data and the solid instruction. According to the technical scheme, the beneficial effects that parameter analysis can be conducted on the workpiece to preset the machining steps, and the machining machine tool is selected can be achieved.

The invention belongs to the technical field of spatial perception positioning, and discloses a spatial perception positioning method and system for a multi-interval complex environment, and the method comprises the steps: obtaining a multi-interval environment video stream from a transformer substation monitoring camera, and synchronously collecting an environment scanning point cloud model; establishing a space coordinate transformation relation between the 2D image and the 3D environment model; acquiring a local area updating point cloud, splicing the local point cloud and the initial point cloud under the condition of no initial pose, identifying an overlapping area, and realizing dynamic map updating of equipment state change; projecting two-dimensional image data of a visible light / infrared / ultraviolet camera video stream to a digital twin three-dimensional space through perspective transformation based on a space coordinate transformation relation to obtain a three-dimensional perception result of multispectral information; and based on a three-dimensional perception result, after multi-camera target tracks are fused, space coordinates are optimized and output. Rapid updating of the local three-dimensional point cloud data and high-precision registration updating of the monitoring video stream and the digital twin space are realized, and the virtual-real consistency is ensured.

A system and method for implementing experiential manifold cognition that extends persistent cognitive machines beyond discrete thought caching to continuous geometric representation of experience. The system maintains an experiential manifold comprising a differentiable manifold with Riemannian metric tensor encoding semantic relationships, compression pressure field governing memory consolidation, and potential field encoding goals and attention. Input data is projected onto the manifold through adaptive geometric diffusion preserving semantic structure. The system executes geometric transformations including metric evolution, geodesic computation, and curvature estimation. During non-interactive periods, autonomous evolution occurs through trajectory recombination and selective pruning. A user interface enables visualization and direct manipulation of manifold geometry, translating navigation into geodesic traversal and edits into metric modifications. The system maintains persistence across sessions and enables controlled federation between multiple manifolds through consent-bounded synchronization. Applications include persistent narrative worlds, collaborative cognitive spaces, and experiential intelligence systems that learn through geometric evolution.

The invention provides a dynamic system sequence representation learning method and device based on a pre-training network, and relates to the technical field of artificial intelligence. The method comprises the following steps: acquiring observation data of a dynamic system; the observation data comprises an observation value of an object of the dynamic system; dividing the observed value into a first sequence and a second sequence; gaussian noise is added to the first sequence, and the first sequence after normalization processing is obtained through normalization processing; constructing a pre-training model for dynamic system modeling; performing data projection on the normalized first sequence by adopting a linear data projection module to obtain a linearly projected sequence; a pre-trained language model is adopted to reconstruct the sequence after linear projection, and a reconstructed sequence and a prediction sequence are obtained; constructing a loss function of the model by adopting the minimum reconstruction loss; and training according to the loss function to obtain a pre-training model for dynamic system modeling. By adopting the method, the modeling accuracy of the dynamic system can be improved.

The invention provides a space target three-dimensional feature construction method and system based on block spherical projection, and relates to the technical field of three-dimensional reconstruction, and the method comprises the steps: projecting multi-view image data to a spherical surface, and generating spherical projection data containing depth and surface feature information; then, dividing the spherical surface into a plurality of overlapped spherical surface blocks by adopting a self-adaptive spherical surface blocking strategy, and extracting point cloud, edge and texture features of each spherical surface block by utilizing a multi-level feature extraction network; then, establishing a feature corresponding relation in an overlapping region of adjacent spherical blocks by using an attention mechanism, and constructing a feature connection graph; and finally, optimizing local features by using a graph convolutional network, generating a three-dimensional grid model of the space target through a three-dimensional reconstruction algorithm, and further constructing a multi-level three-dimensional feature model comprising geometric shapes, surface attributes and local details for feature matching and attitude estimation of the space target. According to the method, the precision and robustness of space target three-dimensional feature construction can be effectively improved.

The invention relates to the technical field of optimization of pneumatic clamps, in particular to a simulation optimization design method for structural size parameters of a pneumatic clamp. The method comprises the following steps: acquiring standard cylinder data and target fixture workpiece data, extracting geometric boundary data to reconstruct a three-dimensional cylinder structure, identifying fixture workpiece interface data and analyzing installation constraints, thereby projecting the workpiece data into a three-dimensional cylinder to form a projection combination structure, and simulating the working process of a pneumatic fixture. A guide limiting structure in the clamp is determined based on the simulation result, a clamp guide rail is adjusted to generate an optimized structure, a clamping force path is captured, workpiece deviation is predicted, clamping stability adjustment is conducted, and finally the optimized pneumatic clamp structure is obtained by combining the optimized guide rail and a clamping structure. High-precision and high-efficiency pneumatic clamp design optimization is achieved, and the clamping speed and the machining efficiency are improved.

This invention belongs to the field of airport public transportation allocation technology, and discloses an airport public transportation pre-allocation processing system and method based on a big data model. The method includes: acquiring historical data; using big data processing technology and machine learning and deep learning models to infer passenger demand and public transportation usage from historical data, generating a data projection model; inputting real-time weather, time, and passenger data into the data projection model, employing intelligent optimization algorithms, and dynamically adjusting public transportation resource allocation in conjunction with real-time traffic conditions; monitoring the public transportation resource allocation results in real time, continuously optimizing and dynamically adjusting the results using a feedback mechanism, and pushing the latest adjusted results to downstream third-party systems. This invention utilizes real-time data analysis and intelligent optimization algorithms to achieve a more efficient traffic scheduling scheme, and has significant practical implications and broad application prospects.

The invention discloses an image-based sorted object pollution dynamic tracking method, which belongs to the field of sorted object pollution monitoring, and is characterized in that a pollution identification area is arranged on a sorted object conveying line, and image acquisition, edge detection and 3D point cloud data projection technologies are combined, so that the actual contour of a sorted object is accurately identified; by extracting color, texture and shape characteristic parameters of an appearance image and internal spectral characteristic parameters and density distribution parameters, the appearance and internal pollution conditions of the sorted objects are quantitatively evaluated; the pollution diffusion risk is comprehensively evaluated by calculating the pollution intensity value, the self-expansion evaluation value and the external expansion evaluation value of the polluted sorted objects; and finally, the positions of the polluted sorted objects are tracked in real time, task processing queues of the sorting stations are sorted according to pollution evaluation values, the optimal sorting station is intelligently distributed, the sorting efficiency is improved, and the pollution diffusion risk is reduced. The problems that the sorting object contour recognition precision is insufficient, the pollution degree judgment is not specific enough, and sorting station distribution is rigid are effectively solved.