1064 results about "Smart transportation" patented technology

The invention provides an unmanned aerial vehicle low-altitude intelligent traffic dynamic airspace management and control method and system, and relates to the technical field of intelligent control, and the method comprises the steps: taking an electronic fence geographic coordinate set as a monitoring reference boundary, fusing ADS-B data, meteorological information and an unmanned aerial vehicle equipment state, generating a real-time risk thermodynamic diagram, and outputting a grading alarm instruction; receiving a real-time risk thermodynamic diagram and a grading alarm instruction, and combining wind speed prediction and dynamic airspace occupation data; when the grading alarm instruction is triggered, executing the following operations: constructing a route feasible solution space by taking a no-fly zone and a high-risk zone in the risk thermodynamic diagram as constraint conditions; and iterating an evolutionary path population through selection, intersection and mutation operations of a genetic algorithm by taking the lowest energy consumption as an optimization target, so as to output a global final obstacle avoidance bypassing path, and issuing a route updating instruction to an unmanned aerial vehicle flight control system. According to the invention, the utilization of airspace resources is maximized on the premise of ensuring safety.

The invention discloses a road intelligent induction and dynamic early warning method and system integrated with meteorological perception, and relates to the technical field of intelligent traffic and road safety. The method comprises the following steps: acquiring real-time weather, traffic and road data, performing multi-source data fusion by adopting an improved Kalman filtering and attention mechanism, and generating unified state estimation; dynamic risk assessment is carried out in combination with Bayesian reasoning and a Markov model, and speed-limiting adaptive adjustment is realized based on safety, traffic efficiency and energy consumption multi-objective optimization; and further calculating the length and position of the dynamic early warning area, and controlling devices such as intelligent spikes to issue induction information. The system comprises a data acquisition unit, a fusion estimation unit, a risk prediction unit, a speed adjustment unit, an early warning calculation unit and an induction unit. According to the invention, real-time monitoring, risk prediction and intelligent regulation and control of the road traffic environment in complex weather are realized, and the driving safety and the traffic efficiency are improved.

The invention discloses an intelligent traffic signal device and a remote control method, and relates to the technical field of intelligent traffic control. The method is used for solving the problems of low emergency traffic efficiency and global and local control imbalance under sudden traffic events. The method comprises the following steps: firstly, fusing an emergency vehicle navigation path, accident point vehicle motion abnormal parameters and pedestrian aggregation distribution data, constructing a multi-dimensional event evolution feature vector through a space-time encoder, and accurately describing a traffic situation; an event influence domain boundary is delimited based on vehicle trajectory and flow direction consistency analysis, a dynamic evolution model is constructed in combination with an acceleration abrupt change propagation path, and a space-time conflict probability matrix is generated; then executing a hierarchical control strategy, dynamically adjusting an intersection signal period starting time difference in a global level to form an emergency green wave coordination band, and inserting an adaptive full red phase in a local level according to a balance relation between a vehicle arrival rate and a dissipation rate; and finally, reversely correcting model parameters through an actual pass error, updating a conflict probability generation rule, and forming a closed-loop feedback mechanism.

The invention discloses a highway data fusion management method, and relates to the technical field of intelligent traffic management, and the method comprises the steps: collecting the global operation data of a highway in real time through a multi-source heterogeneous sensing network; performing space-time alignment and semantic annotation processing on the operation data to construct a fusion data set; inputting the feature subset into a lightweight space-time fusion model to form a multi-target decision set; executing the traffic control instruction sequence in a preset space-time window according to the multi-target decision set; and generating a self-optimization report including a model iteration path and road network health assessment based on the deviation degree of the execution feedback data and the expected optimization target. According to the expressway data fusion management method provided by the invention, collaborative optimization of passing efficiency and safety of the expressway is guaranteed.

The invention discloses an end-cloud cooperative detection method and system for traffic anomalies, and relates to the technical field of intelligent traffic control. The method comprises the following steps: collecting traffic video streams through edge equipment, and identifying abnormal behaviors and generating structured event data by using a lightweight YOLOv3-tiny model; when the confidence exceeds a dynamic threshold and the event type is a high-risk type, uploading a video clip and data to a cloud; the cloud integrates a historical road condition map, meteorological data and a real-time traffic flow state, reconstructs a three-dimensional event scene by adopting a space-time attention pyramid network, and verifies the authenticity of an event in combination with a traffic flow sudden change detection algorithm; and generating a signal lamp forced switching instruction for the risk level overrun event, and issuing the signal lamp forced switching instruction to roadside equipment within 3 seconds to execute emergency response. According to the invention, full-link closed-loop control of traffic accidents from identification to response is realized, and the false alarm probability is greatly reduced while the identification accuracy is guaranteed.

The invention discloses a dynamic vehicle scheduling intelligent decision-making system based on big data, and relates to the technical field of intelligent traffic and logistics scheduling, and the system comprises a data collection module, an event analysis module, a knowledge graph construction module, a hypergraph modeling module, a constraint processing engine, an optimization decision-making module, a strategy verification module and an output interaction module. The system has the advantages that multi-source data such as government announcement texts and social media information are acquired in real time through the data acquisition module, event key information is extracted through the event analysis module by utilizing a natural language processing technology, and an event knowledge graph of an incidence relation is constructed through the knowledge graph construction module; the strategy verification module simulates and verifies the strategy effect through the digital twinning technology and iteratively optimizes the strategy effect, the whole process does not need manual intervention to adjust rules, the limitation that a traditional system depends on manual processing is broken through, and the problems that dynamic strategy adjustment is time-consuming, labor-consuming and error-prone in an extreme scene are solved.

The invention relates to the technical field of traffic communication control, and discloses a vehicle-road cooperative communication optimization system for intelligent traffic. The system comprises a multi-source data sensing module for collecting multi-source heterogeneous data; the communication feature extraction module is used for analyzing the multi-dimensional features; the space-time fusion modeling module is used for constructing a combined space-time feature space; the double-layer resource scheduling library is used for storing an optimization strategy; and the dynamic collaborative decision module is used for generating a real-time scheme and instruction. The method also relates to the functions of abnormal track detection, path re-planning, communication link stability prediction and the like. According to the system, deep fusion and efficient processing of multi-source data are realized, communication resource scheduling and traffic flow regulation and control are optimized, abnormal behaviors of vehicles can be processed in time, the stability of a communication link is guaranteed, the operation efficiency, safety and communication reliability of an intelligent traffic system are effectively improved, and intelligent traffic development is promoted.

The invention discloses an unmanned vehicle intelligent obstacle avoidance method and system based on a multi-modal sensor, and relates to the field of automatic driving and intelligent traffic. According to the technical key points of the invention, data of a laser radar and a depth camera are aligned through a space-time registration module; the laser radar point cloud is preprocessed, obstacle point cloud is segmented, fusion clustering is carried out in combination with three-dimensional semantic seed points output by a depth camera, and an obstacle cluster with a semantic tag is generated; and calculating a three-dimensional directed bounding box (OBB) of an obstacle, dynamically adjusting a safety distance according to the speed of the vehicle, expanding the OBB of the vehicle, and carrying out collision detection by adopting a separation axis theorem. When a collision risk is detected, the system sequentially triggers a first-level early warning instruction and a second-level emergency braking instruction, and safe and efficient unmanned vehicle dynamic obstacle avoidance is achieved. According to the method, the advantages of multiple sensors are fully utilized, the obstacle detection precision and the response speed are effectively improved, and reliable technical support is provided for application of the unmanned driving technology in a complex road environment.

The invention belongs to the technical field of intelligent traffic, and discloses a road vehicle speed and distance detection method, system and device based on unmanned aerial vehicle vision. The method comprises the steps of tracking a vehicle in a to-be-processed video aerially photographed by an unmanned aerial vehicle in real time through an improved YOLOv11 network and a BotSort algorithm to obtain a vehicle trajectory, and converting a vehicle pixel coordinate detected by each frame into a world coordinate in combination with a conversion matrix H of the pixel coordinate and the world coordinate to form a trajectory sequence of the vehicle in the world coordinate system; and for a certain vehicle i, according to the pixel coordinates at the t-th moment and the (t + t)-th moment in several continuous video frames, corresponding world coordinates are obtained through the conversion matrix H, the displacement between two points is calculated according to an Euclidean distance formula, and the average speed of the vehicle in the time period is calculated. According to the invention, efficient, stable and reliable vehicle speed measurement and vehicle distance calculation can be realized.

The invention relates to the technical field of intelligent traffic supervision, and discloses a vehicle tarpaulin coverage real-time detection method based on multi-modal feature fusion, and the method comprises the following steps: S1, constructing a bimodal input data stream; s2, the bimodal images are preprocessed respectively; s3, extracting features; s4, dynamically fusing the bimodal features through an adaptive feature fusion module; s5, inputting the mixed feature map into a lightweight convolution detection network, and outputting a segmentation mask; s6, motion trail compensation is carried out on the dynamic vehicle; by designing a multi-modal adaptive feature fusion mechanism, the robust detection performance in a complex environment is improved: the fusion weight is dynamically adjusted based on the environment illumination and the temperature gradient, so that the system automatically strengthens effective modal features under extreme conditions such as strong light, night, rain and fog and the like; by introducing a dynamic motion compensation framework, the problem of motion fuzzy interference of a running vehicle is effectively solved, and the boundary precision of dynamic vehicle tarpaulin coverage detection is improved in a breakthrough manner.

The present disclosure discloses a non-motor vehicle recognition method and system based on a multi-sensor collaboration and relates to the technical field of intelligent transportation. The method includes: constructing a sensor group based on a plurality of sensors, and performing a data collection based on a target range through the sensor group to generate a multi-class regional dataset; transmitting the multi-class regional dataset to a data fusion channel to generate an initial fusion dataset; synchronizing the initial fusion dataset to a data preprocessing unit to perform preprocessing to generate a target fusion dataset; utilizing a feature extraction unit to traverse the target fusion dataset to perform a feature extraction of a target non-motor vehicle, and generating a target feature information set; and constructing a target recognition unit, and intelligently recognizing the target fusion dataset through the target recognition unit.

The invention discloses an intelligent traffic control system and method based on multi-agent near-end strategy optimization, and belongs to the field of intelligent traffic, Internet of Vehicles and deep reinforcement learning. The method comprises the following steps: firstly, constructing a fog-cloud collaborative three-layer architecture, and realizing real-time monitoring and dynamic regulation and control of traffic flow through cloud global decision and local sensing collaboration of a road side unit (RSU); secondly, designing indexes of'road section overlap ratio 'and'road section time overlap ratio', and solving the problem of secondary congestion caused by rerouting; then, a multi-agent near-end strategy optimization (MAPPO) algorithm is adopted, so that the traffic signal lamp is used as an autonomous agent to dynamically adjust the phase, and the limitation of single-point control is broken; and finally, through integrated optimization of rerouting and adaptive signal control, an original multi-objective optimization problem is converted into a layered multi-agent reinforcement learning problem. According to the invention, vehicle driving time and system energy consumption can be effectively reduced, road traffic efficiency is improved, and active avoidance and dynamic alleviation of urban traffic congestion are realized.

The invention discloses a vehicle-road cooperation path planning decision-making system based on deep learning, and belongs to the technical field of intelligent traffic. According to the system, global traffic flow data, vehicle positions and vehicle driving destinations are collected, traffic flow prediction of each road section is carried out in combination with a GNN-LSTM fusion model, and a basis is provided for global path planning; the motion trails of the participants are predicted through a Transform model, and real-time interactive decision making between the vehicles and the participants is achieved through a DQN algorithm; vehicle state data, traffic light time sequence data and intersection geographic parameters are fused based on an MPC algorithm to generate a vehicle intersection passing track, whether a dangerous space-time window exists or not is detected through an IoU algorithm, whether the MPC algorithm with constraints is adopted to correct the vehicle intersection passing track or not is determined, and intersection collaborative decision making is achieved. According to the invention, the dynamic adaptability of vehicle path planning is improved, the safety of vehicle-road cooperation and interaction with surrounding participants is enhanced, and the traffic efficiency is effectively improved.

The invention discloses a highway intelligent emergency management method and system, and relates to the technical field of intelligent traffic, and the key points of the technical scheme are that multi-source heterogeneous data are fused to construct a three-dimensional situation awareness library, and accurate positioning and prediction of traffic congestion are realized; constructing a virtual emergency scene on a simulation platform based on a Bayesian network, evaluating traffic management efficiency indexes of different management and control plans, and analyzing accident influence and rescue path feasibility; an optimization objective function is established to solve an optimal resource scheduling scheme, and the optimal resource scheduling scheme is issued to the intelligent interaction device in real time; the Bayesian network and the deep reinforcement learning model are dynamically optimized through equipment feedback data, and scheme self-adaptive adjustment is achieved; according to the scheme, factors such as traffic flow dynamic change, road topology and environment are comprehensively considered, quick response is facilitated when an emergency occurs, rescue time and traffic jam loss are reduced, and the overall level of highway emergency management is improved.

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 discloses a traffic flow real-time optimization method and device for vehicle-road collaborative edge calculation, and relates to the technical field of intelligent traffic, and the method comprises the steps: carrying out the real-time collection of a vehicle-mounted unit and roadside equipment in a target road section, and generating a multi-source heterogeneous traffic data set; performing space-time fusion processing on the multi-source heterogeneous traffic data set, and extracting a dynamic traffic flow parameter set; traffic flow optimization is carried out based on the dynamic traffic flow parameter set, and a local optimization instruction set is constructed; and issuing the local optimization instruction set to a vehicle-mounted unit and roadside equipment in the target road section to realize real-time distributed optimization of traffic flow. The technical problem that the traffic flow optimization response is not timely in the prior art is solved, and the technical effect of improving the real-time performance and precision of traffic regulation and control is achieved by fusing the multi-source data of the vehicle-mounted unit and the roadside equipment and performing real-time processing and optimization instruction generation based on edge calculation.

The invention discloses a traffic flow prediction method and system based on multi-scale dynamic decomposition and a space-time Transform. According to the method, firstly, an original traffic flow sequence is decomposed into trend components and seasonal components; then, modeling the trend components by adopting a multi-layer perceptron to capture global changes; and meanwhile, a space-time Transform is used for modeling seasonal components, and the architecture effectively extracts dynamic space-time dependence characteristics by integrating space-time adaptive embedding and an adaptive Switch GLU gating mechanism. And finally, fusing trend and seasonal feature representation to generate a prediction result. According to the method, noise is effectively separated through decomposition, linear enhancement space-time self-adaptive embedding, a self-adaptive Switch GLU gating mechanism and a unified space-time self-attention Transform architecture are integrated, the modeling capacity for complex space-time dependence is enhanced, prediction precision and robustness are remarkably improved, and the method can be widely applied to the field of intelligent traffic management and control.

The invention discloses a traffic data interpolation method based on time-frequency feature fusion and a conditional diffusion model, and belongs to the field of intelligent traffic. The method comprises the following steps: firstly, acquiring data of each observation node and reconstructing to obtain traffic data; missing processing is carried out on the traffic data to obtain traffic data after missing processing, and missing data is used as interpolation target data; then, on the basis of the conditional diffusion model, a noise prediction network is constructed, forward noise adding is carried out on interpolation target data, reverse denoising is carried out through the noise prediction network, and the noise prediction network comprises a conditional information extraction module and a noise prediction module; and finally, training the noise prediction network, obtaining interpolation data by using the trained noise prediction network, and combining the interpolation data with the to-be-interpolated traffic data according to the observation mask of the to-be-interpolated traffic data to obtain complete traffic data. According to the method, error accumulation can be effectively avoided, meanwhile, the condition information fuses time domain and frequency domain characteristics of traffic data, and more stable interpolation is achieved.

The invention discloses a multi-lane highway toll station traffic state detection method and system, and relates to the technical field of intelligent traffic detection, and the method comprises the steps: building a multi-dimensional data collection network based on the lane distribution of a multi-lane toll station; collecting vehicle driving data and image information of each lane in real time by using the composite detection hardware to obtain an original detection data set; performing feature extraction on the original detection data set, and identifying traffic state feature parameters; and generating a traffic state grade judgment result of each lane according to the traffic state characteristic parameters. According to the invention, the technical problems of poor detection accuracy and low intelligent management level of the passing state of the multi-lane highway toll station in the prior art are solved, and the grade judgment of the passing state of each lane of the multi-lane highway toll station is realized. And the accuracy of toll station passing state detection and the intelligent management level are improved.

The invention discloses a real-time reservation and scheduling system for shared parking, and relates to the technical field of intelligent traffic and smart city management, and the system comprises a demand analysis module which is used for extracting parking lot scene types, time requirements and priority information from user parking request data, generating standardized demand description through dynamic weight distribution, and sending the standardized demand description to a user; the process decomposition module is used for decomposing a parking scheduling process into demand receiving, resource matching and path planning business units according to parking lot scene types and time requirements in the user demand analysis result, and determining a business unit set; according to the real-time reservation and scheduling system for shared parking, through dynamic weight distribution and real-time response optimization, efficient resource distribution and path planning are ensured, and the parking scheduling efficiency and the user experience are remarkably improved.

The invention discloses an intelligent intervention system and method based on multi-mode driving behavior analysis, and belongs to the field of intelligent traffic. The system comprises a multi-dimensional perception module, a behavior analysis engine, a risk assessment matrix and a self-adaptive intervention module. The multi-dimensional sensing module is integrated with a multi-source heterogeneous sensor and is used for acquiring physiological characteristics of a driver, driving operation data and environment information; the behavior analysis engine analyzes multi-modal data based on spatial-temporal feature fusion, and digs a driving behavior mode; the risk assessment matrix judges danger levels according to the multi-dimensional driving risk quantification result level by level; and the self-adaptive intervention module implements a grading progressive correction strategy. The system realizes active prevention and control of driving risks through cross-modal data association, behavior chain prediction and environment-behavior coupling analysis. The system solves the problems that a traditional system is single in sensing dimension, coarse in risk assessment, rigid in intervention strategy and the like, and effectively improves the driving safety.

The invention relates to the technical field of intelligent traffic monitoring, in particular to a toll station pedestrian and non-motor vehicle intrusion intelligent early warning system and method, a terminal layer comprises a sensing terminal composed of a camera, a hard disk video recorder and the like, and an early warning terminal composed of a directional sound post and the like; an edge computing unit is deployed on the edge layer, targets are detected in real time, tracks are tracked and classified, and cloud rechecking is triggered by low-confidence targets; the cloud layer utilizes a visual language large model to recheck a target, pre-mark data, generate an electronic fence and store data; the system adopts a dynamic grading early warning module, three-level early warning is triggered according to a target track, a position and staying time, and automatic degradation is carried out along with the presence of a worker; in addition, through a closed-loop optimization mechanism, a positive / false alarm feedback iteration visual small model is collected. According to the invention, cloud side-end cooperation is realized, multiple models and algorithms are fused, high-precision detection, real-time response, dynamic self-adaption and self-optimization capabilities are realized, the intrusion risk can be effectively prevented, and the safety management level of the toll station is improved.

The invention discloses an intelligent interpretable traffic signal adaptive control method. The method comprises the following steps: training an intelligent agent through reinforcement learning according to environment state information of an intersection; a timing decision of each phase of the intersection is generated by using the intelligent agent; guiding the first large language model to generate pre-training data by the timing decision and the cue word to perform LoRA fine tuning on the second large language model, and inputting the cue word into the fine-tuned second large language model to enable the second large language model to generate a plurality of reasoning tracks to generate positive samples and negative samples; performing all-parameter fine tuning on the second large language model to obtain a traffic control signal decision model; and inputting the constructed cue word into a traffic control signal decision model to obtain each phase timing scheme of the intersection. According to the method, the defects that an intelligent traffic signal control algorithm based on deep reinforcement learning lacks interpretability and the cross-scene generalization ability is poor are overcome, and the method has important significance in improving the decision credibility and the deployment efficiency.

The invention provides a 5G communication-based vehicle state sensing and intelligent traffic cooperative scheduling method, and relates to the technical field of intelligent traffic, and the method comprises the steps: receiving high-frequency vehicle state sensing data transmitted by a vehicle-mounted unit in a target road section in real time through a 5G base station cluster; collecting traffic environment sensing data of a target road section through a roadside sensing unit, fusing the high-frequency vehicle state sensing data with the traffic environment sensing data, and constructing a multi-dimensional real-time traffic situation map; performing collaborative analysis based on the multi-dimensional real-time traffic situation map to generate a traffic control instruction set; and issuing to a road side execution unit and a vehicle-mounted unit for cooperative scheduling control of the traffic flow. The technical problems that a traffic system in the prior art often depends on periodic data acquisition and a fixed signal control scheme, traffic control adjustment cannot be carried out in real time according to changing traffic conditions, traffic flow fluctuation or emergencies cannot be rapidly coped with, and then the traffic system is low in efficiency and unstable are solved.

The invention relates to a multi-distribution-center open type vehicle path intelligent optimization method and system, and belongs to the technical field of logistics distribution optimization and intelligent transportation, and the method comprises the steps: firstly obtaining the input data of a multi-distribution-center vehicle path optimization problem, selecting a multi-distribution-center processing strategy according to the problem scale and constraint conditions, and carrying out the optimization of the multi-distribution-center vehicle path; a vehicle path optimization model is constructed, the vehicle path optimization model comprises a single-target model and a multi-target model, a multi-algorithm collaborative optimization framework is adopted for solving, and the multi-algorithm collaborative optimization framework comprises an ant colony algorithm, a variable neighborhood search optimization ant colony algorithm and a non-dominated sorting genetic algorithm; and outputting an optimal vehicle path scheme, wherein the optimal vehicle path scheme comprises a distribution route, a distribution sequence and a corresponding objective function value of each vehicle. According to the method, strategy adaptive selection and algorithm collaborative optimization are carried out, global exploration, local optimization and multi-target equalization are carried out by combining the advantages of the ant colony algorithm, the variable neighborhood search algorithm and the non-dominated sorting genetic algorithm, and the method is good in reproducibility, high in scene adaptability and high in decision support capability.

The invention belongs to the technical field of intelligent traffic and new energy power supply, particularly relates to a traffic equipment solar power supply fault diagnosis system, and aims to solve the problems that a solar power supply system is not timely in fault diagnosis, low in precision and difficult to distinguish instantaneous interference and continuous faults. The system collects multi-source data through an environment sensing and electrical parameter monitoring module, generates a power deviation sequence and extracts time sequence characteristics by combining dynamic expected power modeling with actual output comparison; the fault identification module adopts a multi-level logic discrimination and 12-hour continuous verification mechanism, accurately identifies photovoltaic panel pollution, storage battery aging, poor line contact and controller faults, and distinguishes instantaneous interference; and the decision alarm module generates graded alarms according to fault types and grades, and realizes accurate positioning and operation and maintenance scheduling in linkage with geographic information. The system also has the functions of internal resistance pulse detection, dual-channel redundancy sampling, adaptive threshold adjustment and model self-learning, and the diagnosis accuracy and the operation and maintenance efficiency are significantly improved.

The invention discloses a multi-agent asynchronous collaboration method and system under a centralized architecture, and is suitable for task scheduling and resource coordination of heterogeneous agents in an intelligent Internet of Things environment. According to the method, a main scheduling node centralized control mechanism is adopted, a scheduling priority function is calculated in combination with a multi-agent game model according to real-time state information of a plurality of heterogeneous agents, and asynchronous allocation and feedback control of tasks are achieved. Meanwhile, an asynchronous and synchronous window mechanism is introduced, the scheduling continuity and efficiency can still be kept under the condition of incomplete information, and fairness and robustness of task distribution are achieved through agent feedback delay modeling and revenue function design. The system throughput and the response speed are improved, the isomerism and the expandability are considered, and the method is suitable for multi-agent cooperation scenes such as disaster emergency, industrial manufacturing, smart home and intelligent traffic.

The invention relates to the technical field of intelligent traffic control, in particular to a single-intersection signal lamp dynamic timing method and system based on holographic perception, and the method comprises the steps: collecting the information of a single intersection and surrounding traffic participants in real time through a multi-source holographic perception device, and carrying out the preprocessing, thereby obtaining a standardized traffic data set; based on the data set, combining vehicle, pedestrian and non-motor vehicle demand weights, constructing a traffic demand evaluation model, and calculating a demand priority coefficient of each entrance lane; constructing a signal lamp dynamic timing optimization objective function by taking minimization of intersection total vehicle waiting time, minimization of pedestrian crossing waiting time and maximization of intersection traffic volume per unit time as objectives according to the priority coefficient in combination with intersection traffic capacity constraint conditions, solving to obtain an optimal signal lamp timing parameter, and issuing the optimal signal lamp timing parameter to a control terminal; and traffic state feedback data of the intersection are collected in real time, and timing optimization is carried out again if the change exceeds a preset threshold value. The system can wait for a long time, improves the intersection passing efficiency, and relieves traffic congestion.

The invention relates to the technical field of urban intelligent traffic, in particular to a multi-source traffic data quality evaluation alarm method based on a time sequence large model, which comprises the following steps of: acquiring historical multi-source traffic flow of a target road, associating and screening the historical multi-source traffic flow, acquiring a confidence level grade of each pair of associated equipment by adopting a dynamic time warping algorithm, and calculating the confidence level grade of each pair of associated equipment; performing differential retention on the data according to the confidence level grade to obtain a historical trusted traffic data set; training a time sequence large model according to the historical credible flow data set, inputting the multi-source traffic flow in a previous time period at the current moment into the trained time sequence large model, and obtaining multi-source traffic flow prediction data and a double-source difference degree prediction value at the current moment; acquiring a multi-source traffic actual flow and a double-source difference degree value at the current moment, and comparing the multi-source traffic actual flow and the double-source difference degree value with a predicted value at the current moment to generate a multi-dimensional abnormal score; and performing data abnormity alarm according to the multi-dimensional abnormity score. According to the invention, the accuracy and real-time response capability of anomaly detection are remarkably improved.

The invention relates to a multi-type emergency vehicle signal dynamic priority control method based on vehicle-road cloud cooperation, and belongs to the technical field of intelligent traffic control. The method comprises the following steps: acquiring an emergency vehicle state and traffic environment data in real time through cooperation of a vehicle-mounted terminal, roadside equipment and a cloud control platform; the cloud control platform dynamically calculates priority scores based on vehicle types, task emergency degrees, predicted arrival time, real-time traffic influences and path complexity multi-dimensional factors; when multiple vehicles have conflicts, collaborative decision making is carried out based on scores; and finally, an optimized signal control strategy is generated and executed. The system effectively solves the problems that a traditional priority control mode is extensive, and traffic jam and multi-vehicle conflicts are easily caused, achieves the purpose that interference to social traffic is minimized while efficient passing of emergency vehicles is guaranteed, and improves the overall efficiency and safety of an urban traffic system.