240 results about "Intelligent transportation system" patented technology

The invention belongs to the field of intelligent traffic systems, and relates to a multi-mode space-time traffic flow modeling method supporting large-scale road network real-time prediction, and the method comprises the steps: firstly designing a space-time prediction framework facing a dynamic traffic network, and then carrying out the training to obtain a final prediction model; the space-time prediction framework comprises a data embedding layer, a space-time coding module and a deep modeling and output module based on an expert hybrid mechanism; the data embedding layer comprises two parallel channels of time embedding and spectral domain space embedding and a time-space data fusion module; the space-time coding module comprises a block-level sparse time attention module, a space attention-message passing module and a weighted fusion layer which are parallel; the deep modeling and output module based on the expert hybrid mechanism comprises an MoE dynamic expert modeling module, a full-connection mapping module, a jump connection layer and an output layer; according to the design, the response speed, the prediction precision and the cross-regional adaptive capacity of the model in a high-heterogeneity scene are improved.

The invention discloses a smart city traffic resource optimal configuration method, a platform and a storage medium, and relates to the technical field of traffic optimization. When a road accident is detected, accident information is collected, and a traffic accident position and traffic accident characteristics are obtained; time consumption prediction is carried out according to the traffic accident characteristics, and traffic control duration is obtained; calling real-time traffic data from an intelligent traffic system to obtain associated road data; executing multi-thread traffic flow back-stepping analysis to obtain associated traffic flow prediction information; carrying out shunting performance evaluation; and dynamically updating the signal lamp configuration of the plurality of shunting candidate roads by taking the associated traffic flow prediction information as a constraint. According to the invention, the technical problems of aggravation of traffic jam and low emergency management efficiency caused by lack of scientificity and dynamic adaptability of shunting planning and lagging of signal lamp regulation in a traffic accident scene in the prior art are solved, and the technical effects of optimizing traffic resource configuration, relieving traffic jam and improving traffic emergency management efficiency are achieved.

The invention discloses a lightweight space-time traffic flow prediction method based on GAT and Mama, and relates to the technical field of intelligent traffic systems. According to the method, the feature expression ability of the model is improved through multi-dimensional feature learning (time, date and space-time) of an embedded layer, rich features of input data can be deeply captured, and the learning ability of the model is enhanced; a graph attention module is used to adaptively pay attention to the relative importance between nodes, so that the graph structure relationship in the data can be more effectively captured and utilized, and the analysis accuracy of the model on the spatial data is improved; the GAT-Mama module optimizes the time sequence analysis and spatial data processing capability, and the model can more accurately capture the dynamic dependency relationship between time points and between spatial positions through a multi-layer graph attention mechanism and residual connection; the GAT-Mama layer structure supports flexible adjustment, a user can adjust the depth of the model according to specific task requirements so as to adapt to spatio-temporal data prediction tasks with different complexities, and good adaptability is achieved.

The invention provides a traffic flow prediction method and system based on an embedded physical information deep neural network, and belongs to the technical field of intelligent traffic system and deep learning crossing. The method comprises the following steps: firstly, carrying out variable grid division on an urban high-density road network, and establishing a physical model integrated with signal control to generate traffic state prediction; then constructing a physical information neural network, jointly inputting historical detection data and a physical model prediction result, and embedding a traffic flow conservation equation and a vehicle transmission rule as physical constraints through a loss function; weighted loss is utilized to optimize network parameters, and short-time density, flow and congestion propagation prediction conforming to the traffic flow theory is achieved. The problems that a traditional model is low in precision and a pure data driving method is insufficient in physical consistency are solved, and the accuracy and reliability of urban complex road network traffic situation prediction are remarkably improved.

The invention belongs to the technical field of intelligent traffic system and artificial intelligence crossing, and particularly relates to a traffic multi-agent simulation decision-making method and system based on a large language model. Road network state data are coded into a three-dimensional feature matrix containing channel dimensions, time dimensions and space dimensions, and joint representation of numerical road network data and text event reports is achieved through a hybrid embedding model. The decision-making layer comprises a dynamic Prompt generator which generates a candidate scheme set based on a four-layer progressive prompt structure; the Monte Carlo tree search multi-objective optimization is executed in cooperation with the decision core module; and the execution layer comprises a cross-language communication bridging device which adopts a gRPC bidirectional stream communication protocol and a Protobuf data serialization scheme to realize millisecond-level data interaction between services and support a hot plug mechanism of a strategy injection interface. According to the invention, dynamic optimization of urban traffic resource allocation and breakthrough improvement of simulation deduction efficiency are realized.

The invention relates to the technical field of intelligent traffic systems, in particular to a curve early warning method, system and terminal based on target detection, and the method comprises the steps: generating a visual twin image of a target curve; the visual twin images are displayed on display devices on the two sides of a target curve; if the movement track of the moving target is overlapped with the position of the obstacle or the movement track of the moving target is overlapped with the estimated movement track of the obstacle, early warning information is sent, and the early warning information comprises sound prompt information and image prompt information. The graphic prompt information comprises that a visual twin image corresponding to the moving target is rendered on a display device on one side of the road consistent with the moving direction of the moving target. A visual twin image of a target curve is generated through a multi-sensor fusion technology, the motion states of a moving target and an obstacle are reflected in real time, and early warning information is sent out when a potential collision risk is detected, so that the traffic accident rate of a curve area is effectively reduced.

The invention relates to the technical field of intelligent traffic systems, and discloses an expressway multi-source traffic data grading and classification processing system, which comprises a data acquisition standardization module for acquiring and preprocessing multi-source traffic data; the data source quality evaluation module is used for evaluating the credibility of the data source; the scene emergency degree calculation module is used for calculating the flow anomaly degree, the vehicle speed anomaly degree and the meteorological risk score and identifying an emergency event; the data grading module is used for calculating a comprehensive priority score of the data and obtaining a grading data set by adopting a threshold segmentation method; the data classification module is used for carrying out self-adaptive classification on the data; the data fusion module identifies the same traffic parameter, performs conflict detection and performs fusion processing on conflict data; the result output module is used for obtaining a processing result by adopting a grading and classification output and quality feedback mechanism; according to the invention, intelligent refined processing of the highway multi-source traffic data is realized, and the emergency response speed and the data processing accuracy are improved.

The invention discloses a traffic flow prediction method based on a clustering and space-time fusion graph convolutional network, and belongs to the technical field of intelligent traffic. The method comprises the following steps: firstly, acquiring traffic flow data and resampling, and constructing microscopic and macroscopic traffic flow data sets; then constructing a space-time fusion graph convolutional neural network, wherein the space-time fusion graph convolutional neural network comprises a space-time fusion module and a macro-micro interaction module; training the model by using the data set; and finally, traffic flow prediction is carried out by using the trained model. Compared with the prior art, through a clustering algorithm and a unique network structure, the traffic flow time-space information is effectively integrated, the prediction precision is improved, the adaptability of the model to a complex traffic scene is enhanced, more reliable prediction support is provided for an intelligent traffic system, and optimization of traffic management and alleviation of congestion are facilitated.

The invention discloses an intelligent traffic control method and system in a low-altitude economic environment, and relates to the technical field of intelligent traffic systems, and the method comprises the steps: collecting low-altitude aircraft and ground traffic spatio-temporal data through a multi-modal sensor network, and generating a multi-source heterogeneous data set; constructing a dynamic traffic situation map through spatio-temporal feature fusion; performing three-dimensional path planning to generate a three-dimensional guiding strategy; detecting conflicts and correcting strategies according to a preset rule base, and outputting an instruction set to distribute real-time traffic flow. The technical problem that in the low-altitude economic environment, a traditional traffic management and control method is difficult to meet the cross-domain cooperation requirement of the low-altitude aircraft and the ground traffic is solved, and the technical effects of three-dimensional cooperative management and control of the low-altitude aircraft and the ground traffic and further guaranteeing safe and efficient operation of the traffic in the low-altitude economic environment are achieved.

The invention provides a class-independent motion prediction network system and method combining space-time and frequency domain features, equipment and a medium, and belongs to the field of automatic driving. The problem that the space-time dependency relationship and the dynamic characteristics cannot be fully captured in a complex dynamic scene in the moving target prediction is solved; comprising construction of a feature coding module, a hierarchical space-time integration module, a frequency-space fusion module and a feature decoding module, and the HTSIM fuses space-time information step by step through a multi-level space-time feature interactive learning mechanism, and can capture a long-term space-time dependency relationship and local dynamic change, so that a complex motion mode is modeled more accurately; fSFM combines the characteristics of a frequency domain and a space domain, multiband decomposition is carried out on input characteristics through two-dimensional discrete wavelet transform, and the capturing capability of dynamic characteristics is enhanced through deep fusion of the characteristics of the frequency domain and the space domain; the method is suitable for real-time track prediction tasks in intelligent traffic systems such as automatic driving and the like.

The invention provides a real-time multi-scene automatic driving system based on AI multi-sensor fusion. The system can realize multi-scene seamless high-precision real-time automatic driving of the automatic driving vehicle. Adopted key technologies comprise a multi-source heterogeneous fusion automatic driving technology based on AI assistance and an AI enabling enhanced decision execution mechanism. Aiming at the defects of the current automatic driving technology in the aspect of vehicle full situation awareness, the technology improves the multi-source fusion efficiency and accuracy through deep learning, directly maps from multi-sensor original data to a driving decision through an end-to-end learning model to improve the reaction speed, and deeply fuses multiple sensors from an information layer. Meanwhile, aiming at the contradiction between big data processing and real-time computing requirements under the current automatic driving vehicle-machine man-cloud interconnection, the edge computing technology, cloud processing and local computing are used for assistance, so that the power consumption is reduced and the real-time requirements are met. Furthermore, through access integration of an intelligent traffic system, deep combination of traffic big data and AI automatic driving is realized, and relative positioning between vehicles is realized through traffic big data interconnection characteristics. The purposes of responding to complex road conditions in real time and reducing the power consumption of a processing algorithm are achieved through information characteristics such as the vehicle machine, the man-machine and the accurate position between the vehicle machine and the specific marker. In a real-time multi-scene automatic driving system based on AI multi-sensor fusion, the generalization ability of the system is continuously enhanced, and the applicability and transportability of the system are concrete so as to adapt to wider driving scenes.

The invention discloses a highway event detection algorithm based on data situation analysis, and belongs to the technical field of intelligent traffic systems. According to the method, a dynamic graph structure is constructed by fusing multi-source data of an ETC portal, a toll station, a traffic detector, weather and the like, spatial-temporal characteristics are extracted by utilizing graph convolution and LSTM, and fuzzy distribution prediction of a traffic state is realized; calculating a node score mutation rate based on a prediction result, screening abnormal nodes in combination with a serious congestion probability, and extracting a connected abnormal sub-graph with a compact structure as a potential event region; a multi-factor event scoring function is designed, sudden change intensity, structural compactness and a state offset direction are fused, a risk level is quantified, and an adaptive boundary learning device is introduced to dynamically discriminate an alarm, so that scene limitation of a fixed threshold value is avoided; the detection accuracy and the alarm flexibility are improved, and the method is suitable for intelligent sensing and early warning of highway traffic events.

The invention discloses an air-railway combined transportation path optimization method based on XGBoost and a space-time attention network, and particularly relates to the field of intelligent transportation systems.According to the method, a traffic network basis is constructed by integrating flight and train historical data, topological information and weather data, delay time and consumed time of a critical path are predicted by means of an XGBoost model, and the time consumption of the critical path is predicted by means of the XGBoost model; a space-time dependency relationship is modeled through a space-time attention network, and complex space-time association is captured in combination with a space and time attention module and a multi-head mechanism; an optimal path is generated based on a weighted multi-objective function (covering time, economy, reliability and comfort), and users are supported to dynamically adjust weights to adapt to personalized requirements; and meanwhile, real-time adjustment of model parameters is realized by adopting an exponential weighted moving average and self-adaptive updating strategy, so that the prediction precision is remarkably improved, the reliability of the path and the user satisfaction are optimized, the real-time performance is ensured through a lightweight closed-loop updating mechanism, and a high-precision, personalized and real-time response intelligent solution is provided for air-railway combined transportation.

The invention belongs to the technical field of image processing and deep learning, and particularly relates to an image defogging method based on dynamic wavelet prior and double-domain learning. Aiming at the requirements of all-weather clear imaging in the fields of intelligent traffic systems, safety monitoring and the like, and in order to overcome the defect that a static convolution kernel adopted by a traditional defogging method is difficult to adapt to different haze degradation, the invention provides a method for dynamically generating a convolution kernel by using haze priori contained in a multi-scale wavelet LL sub-band; and an efficient, robust and accurate image defogging model is constructed. According to the invention, based on a multi-scale U-shaped coding-decoding architecture, a dynamic wavelet depth separable convolution module DyWConv is embedded in front of each level of a coder to realize content adaptive feature extraction, and a double-domain feature learning module SPAFormer Block cooperatively utilizing Fourier domain global modulation and wavelet domain multi-scale decomposition is designed. And double-domain features are fully fused through an adaptive gating fusion mechanism, and finally a clear image is reconstructed and output step by step. According to the method, a method for explicitly encoding frequency domain degradation prior into dynamic convolution kernel parameters is innovatively provided, the complementary advantages of Fourier transform and wavelet transform are cooperatively utilized, spatial non-uniform haze can be effectively removed, image details can be recovered, leading performance is achieved in a synthetic data set and a real scene, and the method has a wide application prospect.

The invention relates to a vehicle optimal lane changing opportunity prompting method and system based on pre-training LLMs trajectory prediction, and belongs to the field of intelligent driving and intelligent traffic systems. The method comprises the following steps: establishing vehicle motion trajectory data through a trajectory prediction module, and generating an information joint encoding feature vector through an information joint encoder; processing the joint coding feature vector through a pre-trained LLMs and a multi-layer vector machine to obtain an advanced interactive representation feature vector; a long-short-term memory neural network of a multi-modal trajectory prediction module is established, and trajectory information of the target vehicle and surrounding vehicles in a future time domain is obtained through prediction; future trajectory information is input into a lane changing intention prediction module, and a lane changing scene coding module extracts vehicle lane changing intention features; the surrounding vehicle interaction modeling module is used for modeling and extracting context feature information of a lane changing scene; the lane changing intention output module outputs the maximum lane changing probability; and if the target vehicle needs to change the lane, the optimal lane changing opportunity is predicted through the optimal lane changing opportunity prediction module.

The invention belongs to the field of intelligent transportation system planning and industrial artificial intelligence, and particularly relates to a one-order system coal multimodal transport interpretable customer portrait construction method, which comprises the following steps: A, acquiring a customer coal multimodal transport mode selection questionnaire and historical one-order system coal multimodal transport waybill data, and carrying out data processing and data fusion; b, using a causal discovery FCI algorithm to construct a causal graph, screening out feature variables having a causal relationship with customer intermodal mode selection, and removing pseudo-correlation features at the same time; c, performing clustering by using a k-prototype hybrid clustering method to obtain customer categories; and D, training a LightGBM classification model to predict the category of the customer, checking the portrait precision, describing the customer portrait in combination with an SHAP interpretable analysis method, and providing decision support with scientificity and business landing for coal multimodal transport customer accurate classification and single system service strategy optimization.

The invention belongs to the field of artificial intelligence and intelligent traffic systems, particularly relates to a path control system combining multi-modal perception and dynamic trajectory prediction, and aims to solve the problems of insufficient perception fusion, low trajectory prediction precision and control response lag in a complex dynamic environment. The system comprises a multi-modal perception fusion unit, a dynamic interaction modeling unit, a space-time coupling prediction unit, a risk field construction unit and an adaptive path generation unit. Multi-source sensor data are fused through confidence coefficient weighting, an interaction weight matrix under an attention mechanism is constructed, a future trajectory is predicted in combination with individual dynamics and a social force model, a four-dimensional space-time risk field is generated, and a minimum risk path is solved based on an improved fast marching algorithm. The system realizes sensing-prediction-control closed-loop cooperation, the end-to-end delay is less than 250 milliseconds, and the driving safety and comfort in a complex traffic scene are improved.

The invention belongs to the technical field of conveyor coal flow monitoring, and aims at solving the problem that an existing coal flow monitoring method is insufficient in precision. According to the belt conveyor coal flow monitoring method based on the inspection robot and the monocular vision, coal flow images are dynamically collected through the inspection robot, accurate segmentation of a coal flow area is achieved through a deep learning algorithm, and a coal flow three-dimensional point cloud is reconstructed in combination with a monocular depth estimation technology; and finally, qualitative and quantitative analysis of the coal flow is realized. Compared with a traditional method, the method has the advantages of non-contact measurement, full conveying belt coverage, high calculation efficiency, adaptability to complex environments and the like, and reliable data support can be provided for a coal mine intelligent transportation system.

The invention relates to the field of intelligent transportation systems, discloses an intelligent highway maintenance system and method, and aims to solve the fundamental defects that in the prior art, the data acquisition dimension is single, the maintenance decision depends on artificial experience, and prospective prediction is lacked. The method comprises the following steps: acquiring multi-modal dynamic sensing data covering a whole road domain; constructing and updating a four-dimensional space-time digital twinborn model in real time; driving the causal graph neural network model to perform structure health state prediction and diagnosis; generating an active maintenance instruction through a multi-objective optimization engine; and the autonomous maintenance execution unit is dispatched to complete unmanned closed-loop operation. According to the method and the system, fundamental transformation of road maintenance from passive response to active prevention, from artificial experience to intelligent decision making and from discrete operation to closed-loop automation is realized, and the maintenance efficiency, the safety and the health level of the whole life cycle of the road are remarkably improved.

The invention discloses an intelligent vehicle fusion perception and decision-making method and system based on low-orbit satellite communication and high-precision positioning, and the method comprises the steps: firstly, collecting the perception data of an intelligent vehicle through the low-orbit satellite communication and high-precision positioning technology, and achieving the time-space consistency processing through a cloud end, the method includes delay compensation, semantic information matching and multi-observation joint state estimation, improves complex scene perception precision, and reduces dangerous decision-making behaviors and high takeover rate caused by perception defects of an intelligent automobile. Secondly, a behavior guiding strategy is constructed based on track data of a large number of vehicle groups, and a macroscopic decision basis is provided for the intelligent vehicle groups; and through a multi-agent reinforcement learning algorithm, the intelligent vehicle group can carry out self-vehicle optimal decision making, so that efficient traffic flow management and group collaborative optimization are realized. A brand new technical scheme is provided for dynamic traffic flow management in a complex traffic scene, traffic safety and efficiency are improved, and important support is provided for application of an intelligent traffic system and automatic driving.

The invention discloses a multi-index dynamic fusion traffic hidden danger identification and completion method and system, and relates to the technical field of intelligent traffic systems, and the method comprises the steps: obtaining a multi-dimensional traffic data sequence containing missing values in a target monitoring region, the multi-dimensional traffic data sequence comprises a traffic flow index sequence, a driving behavior characteristic sequence and a road environment parameter sequence; and interpolating missing values in the multi-dimensional traffic data sequence based on a spatio-temporal dynamic graph completion network to generate a completed spatio-temporal data tensor. According to the method, the missing data are accurately completed through a space-time dynamic graph completion network, a space graph convolution layer of road network topology, a gating circulation unit of a modeling time sequence rule and a cross-index attention module, multi-index dynamic association is fully fused, and propagation delay and a space-time evolution rule are considered; and deep fusion features are extracted by using a shared feature encoder, hidden danger identification branches are linked, traffic flow, driving behaviors and environmental parameters are dynamically fused, and hidden danger dynamic formation paths are captured.

The invention provides a fee deduction method, system and device of an intelligent traffic system and a medium, and belongs to the technical field of intelligent traffic. The method comprises the following steps: acquiring a vehicle image, vehicle driving information and traffic flow information at a preset position of a traffic system; the vehicle image comprises a license plate image of a vehicle; identifying and analyzing the vehicle image by adopting a deep learning algorithm to obtain license plate number information and vehicle type information; predicting a driving path and passing time of the vehicle based on the historical driving data of the vehicle; dynamically calculating a fee deduction amount according to the driving path, the passing time and the real-time traffic flow information of the vehicle; and sending the deduction amount to the mobile terminal of the user, and providing payment service. According to the invention, the intelligent identification system is constructed, the vehicle information is obtained in real time, dynamic fee deduction is carried out in combination with various factors, and accurate and reasonable traffic and parking management is realized.

Transportation systems have artificial intelligence including neural networks for recognition and classification of objects and behavior including natural language processing and computer vision systems. The transportation systems involve sets of complex chemical processes, mechanical systems, and interactions with behaviors of operators. System-level interactions and behaviors are classified, predicted and optimized using neural networks and other artificial intelligence systems through selective deployment, as well as hybrids and combinations of the artificial intelligence systems, neural networks, expert systems, cognitive systems, genetic algorithms and deep learning.

The invention relates to the technical field of computer vision and target detection, and particularly discloses a lightweight real-time two-wheeled vehicle helmet detection method. According to the method, firstly, a video stream is collected and preprocessed through a traffic monitoring camera, then feature extraction and fusion are carried out through a lightweight backbone network, an encoder and a neck network in sequence, finally, a detection result is output through a decoder, a StripCGLU module is introduced to reduce the calculation complexity, a Pola Former module is adopted to enhance the feature interaction capability, and finally, the detection result is output through a decoder. And a GLBiFPN network is designed to optimize multi-scale feature fusion. According to the method, the calculation complexity and parameter quantity of the model are remarkably reduced, the requirement of edge calculation equipment for efficiency is met while high detection precision is guaranteed, and an effective technical means is provided for safety monitoring in an intelligent traffic system.

The invention discloses a sequence-based vehicle type category correction method and device, and relates to the technical field of intelligent traffic systems, and the method comprises the steps: obtaining a vehicle detection model, and collecting a backflow video data set; traversing the set, detecting each frame of vehicle target by using the model, and associating cross-frame targets by using a target tracking technology to obtain a vehicle trajectory and form a plurality of vehicle target sequences; traversing the sequence to count vehicle types, and correcting the vehicle types according to a statistical result to obtain a plurality of corrected vehicle target sequences; identifying a missing detection frame based on an original sequence, predicting a missing detection target position, and obtaining a plurality of missing detection data sets; and iteratively updating the vehicle detection model in combination with the correction sequence, the missing detection data and the original sequence. According to the invention, the technical problems of vehicle type judgment errors and target detection deficiency due to the fact that multi-frame associated information is not combined in a traffic scene in traditional vehicle detection are solved, and the technical effects of improving the vehicle type detection accuracy and integrity and realizing continuous optimization of the detection capability are achieved.

The invention relates to the field of intelligent traffic systems, in particular to a multi-source data fusion-based vehicle-road collaborative bus priority system, which comprises the following modules: a data acquisition module, which comprises an electronic police, a bayonet device, a monitoring device, a thunder-vision all-in-one machine and a vehicle-road system device and is used for acquiring traffic flow data at an intersection and related road sections thereof; the data processing module comprises a data cleaning unit, a data classification unit and a data analysis unit and is used for cleaning, classifying and analyzing the collected data and extracting traffic flow features, and the data fusion module is used for fusing the traffic flow features through three-level fusion (vehicle data fusion, traffic flow feature fusion and vehicle state fusion). Accurate vehicle position and state information and real-time traffic conditions of the intersection and the vicinity of the intersection are obtained, and the traffic prediction module can accurately predict the traffic flow condition of the whole intersection within a short period of time by combining real-time data and historical data.

The invention discloses a self-supervised dynamic space-time diagram traffic flow prediction method and device, a medium and a product, and relates to the technical field of intelligent traffic systems, and the method comprises the steps: obtaining a current traffic flow sequence; inputting the current traffic flow sequence into a traffic flow prediction model to obtain a current traffic flow prediction sequence; the current traffic flow prediction sequence comprises traffic flow prediction data within a second preset time period after the current moment; the traffic flow prediction model is obtained by training a self-supervised dynamic space-time diagram convolutional network by using historical traffic flow data; the self-supervised dynamic space-time diagram convolutional network comprises a double-branch diagram feature extraction structure and a self-supervised learning module which are connected in sequence. The double-branch diagram feature extraction structure comprises a space-time diagram branch and a region diagram branch which are connected in parallel; according to the method, the precision of traffic flow prediction can be improved by constructing the self-supervised dynamic space-time diagram convolutional network.

The invention discloses a point cloud intelligent cutting and dynamic optimization method based on edge computing, and belongs to the technical field of intelligent traffic systems and edge computing, and the method comprises the steps: collecting three-dimensional point cloud data through a multi-line laser radar disposed on an RSU; generating a three-dimensional bounding box through a three-dimensional target detection network in an RSU edge calculation unit, generating an ROI mask, expanding a buffer region, and performing adaptive region cutting on the point cloud to form an ROI point set and a background point set; a perception-driven cutting decision is realized, and ROI extraction is converted into a self-learning process based on semantic features from fixed threshold judgment; voxelizing the non-uniform resolution point cloud, generating a unique hash index for each voxel unit, comparing a hash set of a current frame with a hash set of a previous frame, extracting newly added and disappeared point sets, and constructing a differential data packet according to a change ratio; while high timeliness is kept, data redundancy is reduced, and the point cloud updating rate and the incremental transmission efficiency are improved; and dynamic optimization of communication-calculation cooperation is realized.

The invention discloses a traffic flow management method combining dynamic and static induction information in a complex traffic environment, and the method comprises the steps: constructing a temporal-spatial data cube containing a temporal label, fusing static road network constraints (forbidding rules and lane attributes) with dynamic real-time data (flow and events), purifying abnormal data through a double-layer verification mechanism, and guaranteeing the compliance. Based on a hierarchical multi-objective optimization model, an NSGA-AM algorithm with an attention mechanism is adopted, a Pareto optimal induction strategy considering travel time, carbon emission and road network fairness is generated, the calculation complexity is reduced to O (NlogN), and the strategy generation time is less than 10 ms. Through a three-level feedback mechanism (immediate response, short-term adjustment and long-term evolution) of edge computing and cloud collaboration, system self-evolution is realized, and traffic environment changes are dynamically adapted. Experiments show that according to the method, the violation induction rate is reduced from 15% to 2% or below, the average travel time is reduced by 18%-25%, carbon emission is reduced by 12%-18%, the passing efficiency and safety of a complex road network are remarkably improved, and the method is suitable for engineering application of an intelligent traffic system.

The invention relates to the technical field of intelligent traffic systems, in particular to a highway network global traffic flow prediction method, device and equipment, and the method comprises the steps: constructing an initial feature sequence based on the multi-source data of an ETC portal system; inputting the data into a multi-head attention model driven by a query mechanism, and outputting a spatial dependency relationship between ETC door frame nodes; inputting the spatial dependency relationship into an extraction fusion model fusing a causal convolutional network and a selective state space network, extracting short-term time sequence features through the causal convolutional network, modeling long-term time sequence features through the selective state space network, and fusing the long-term time sequence features into fused time sequence features; and a global traffic flow prediction result is generated based on the fusion time sequence features, so that the problems of high traffic flow prediction cost, data coverage missing and the like in related technologies are solved.