1141 results about "Road traffic" patented technology

Provided are a method and an apparatus for constructing a road congestion prediction model, a device, a medium, and a product. A road traffic network is defined as a directed weighted graph. Historical dynamic traffic features of each road segment in the road traffic network are obtained as sample data, including recent dynamic traffic features and periodic dynamic traffic features. The sample data is input into a mixture of adaptive graph learners (MAGL) model for learning, and a probability prediction vector is output. The sample data is input into a trend expert model, and a trend distribution vector of a predicted probability of future traffic conditions is output. The periodic dynamic traffic features are fused to determine a periodicity prediction vector. An aggregated logit vector is obtained. An objective function is determined based on the aggregated logit vector. Congestion prediction training is performed to obtain a road congestion prediction model.

The invention discloses a complex scene traffic sign detection method and system based on dynamic frequency band focusing and double-domain attention screening. The method comprises the following steps: carrying out data preprocessing and data enhancement on a collected road traffic sign image; a CADPCM module and a CHAttention cross coordination attention mechanism are used to construct a CACHNet backbone network; designing a DWMSN neck network, and establishing a dynamic fusion mechanism of multi-scale features; a CACHNet and a DWMSN neck network are used to construct a CDWN model, and a traffic sign enhancement data set is used to train the CDWN model to determine the optimal model weight thereof. Compared with the prior art, the method has the advantages that the average detection precision is improved by 3.3% while the light weight of the model is maintained by constructing a three-level framework of the feature extraction unit, the attention feature expression enhancement and the dynamic feature fusion, the complex scenes such as illumination variation and shielding can be effectively dealt with, and the method is suitable for popularization and application. And high-precision traffic sign detection support is provided for a vehicle-mounted intelligent auxiliary driving system.

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 relates to the technical field of traffic scheduling, in particular to a road traffic analysis scheduling method and system based on event driving. The method comprises the following steps: collecting traffic state data in real time, and generating an original traffic data flow; feature extraction is carried out on the original traffic data flow, a space-time traffic flow graph is constructed, and a dynamic traffic network graph is generated; performing analysis and space-time correlation analysis on the dynamic traffic network diagram to generate a traffic event list; triggering an intelligent scheduling response based on the traffic event list, calling a corresponding scheduling strategy template according to the event type, and generating a candidate scheme set; a multi-objective optimization model is constructed for optimization, and an optimal scheduling scheme is generated; issuing the optimal scheduling scheme to road control equipment, and executing traffic scheduling; and collecting traffic feedback data, evaluating a scheduling effect, and if an expected target is not reached, adjusting scheduling parameters and regenerating an optimization scheme until the traffic state is improved. According to the invention, accurate analysis and efficient scheduling of road traffic can be realized.

The invention discloses a road congestion prediction system and method based on spatial-temporal feature extraction, and belongs to the field of intelligent traffic. The system adopts a layered distributed architecture, and comprises a multi-source data acquisition module, a data preprocessing unit, a double-flow spatio-temporal feature extraction network, a two-stage spatio-temporal attention mechanism module, a congestion prediction model and a result feedback interface. Multi-modal data such as a vehicle-mounted GPS track, checkpoint flow, video monitoring and meteorological data are integrated, a double-flow feature extraction network is constructed by adopting a graph convolutional network and a bidirectional gating circulation unit, and a key space-time region is dynamically focused in combination with a multi-head self-attention and time weighted dot product attention mechanism; and finally, optimizing the generalization ability of the model through a composite loss function. According to the method, a dynamic adaptive learning framework and multi-source data combined modeling mode is adopted for urban road traffic flow characteristics, the space-time precision and the real-time response capability of road network congestion prediction are remarkably improved, and reliable decision support is provided for intelligent traffic control.

The invention, which relates to the technical field of intelligent traffic and carbon emission monitoring, discloses a highway carbon emission energy consumption abnormity monitoring optimization system comprising a vehicle characteristic acquisition module, an environment dynamic sensing module, a carbon emission dynamic calculation module, an abnormity intelligent diagnosis module and an attribution optimization execution module. Calculating a real-time dynamic carbon emission value by combining environmental factor data such as vehicle characteristics, real-time driving speed and wind speed, road gradient and the like; setting a dynamic carbon emission reference value according to the traffic flow and environmental factor data of the current road section; and identifying an abnormal attribution type by associating data such as vehicle speed abrupt change, environmental factor abrupt change and road section traffic condition in the abnormal carbon emission time period, and generating a corresponding optimization adjustment instruction. The method improves the attribution accuracy of the carbon emission abnormity, supports the real-time optimization decision oriented to traffic management, and provides technical support for the operation of a green intelligent expressway.

The embodiment of the invention provides a training method of a signal lamp control model, a signal lamp control method, a device, equipment, a medium and a product, and relates to the technical field of urban road traffic. The method comprises the following steps: acquiring a plurality of first traffic data, constructing a state characterization matrix and a reward function according to the plurality of first traffic data, and performing model training according to the plurality of first traffic data, the state characterization matrix and the reward function to obtain a signal lamp control model. According to the method, in a model training process, a state representation matrix is utilized to dynamically quantify passing priorities of buses in all directions, and meanwhile, a reward function is utilized to guide the model training process, so that a signal lamp control model obtained through training can recognize and preferentially respond to bus passing demands, and signal lamp actions are dynamically adjusted; therefore, the collaborative optimization of multi-direction bus priority requests in a complex scene is realized, and the coordination capability and response efficiency of a bus priority system in a complex urban traffic environment are improved.

The invention discloses an expressway tunnel abnormal event recognition method based on multi-scale polarization fusion, relates to the technical field of intelligent recognition of road traffic videos, and is used for solving the problem of robust recognition of trailing collision, fire smoke and abnormal static behaviors in an expressway tunnel scene. The method comprises the following steps: acquiring polarization angle and polarization degree information of each pixel point in a vehicle driving process; then, the image is divided into a plurality of partitions according to longitudinal illumination changes, a time sequence locking window is established in each partition, and the difference between traffic flow reflection characteristics and background interference is extracted; and identifying a smoke area by constructing a disturbance graph and a direction texture graph, calibrating a trailing collision risk, and judging an abnormal static behavior. And finally, an event atlas is generated, full-process identification closed loop and response cooperation are ensured through structure fingerprints and version numbers, and a high-robustness identification means is provided for fire, trailing and abnormal static targets in a tunnel scene.

The invention relates to the technical field of intelligent traffic control, and discloses a road traffic AI adaptive edge computing server. The server comprises a traffic situation sensing module, a traffic flow intention analysis module, a control strategy construction module, a control scheme generation module and an efficiency rolling optimization module. The server synchronously receives the original data flow of the heterogeneous traffic sensor, and extracts and generates a microscopic traffic behavior sequence after timestamp alignment and cleaning. A behavior sequence is matched with a historical scheme library, a control strategy knowledge graph based on a traffic entity relationship is constructed, and a candidate control scheme set is reasoned according to the control strategy knowledge graph. And performing conflict detection and rolling optimization on the candidate schemes based on a short-time traffic flow prediction result, and outputting a final adaptive control instruction set. According to the invention, deep understanding and foresight control of a complex traffic scene are realized, and the intersection passing efficiency and the control adaptive capability are improved.

Aspects of the disclosed technology relate to a system and methods for optimizing traffic flow along a roadway. The systems receive, from a connected-automated vehicle (CAV), a request for a travel time along a roadway having a managed lane and a general purpose lane. The system determines real-time traffic data, and using a trained machine learning model, predicts future traffic data along the roadway, determines a time saved by using the managed lane, communicates with the CAV, receives an acknowledgement that the vehicle will use the managed lane, and updates the predicted traffic data. Segment agents may be responsible for discrete segments of the roadway and may communicate with a coordination agent to result in a multi-agent reinforced traffic machine learning system. The system provides a mobility service to CAVs by implementing a reinforcement learning process to understand CAV behavior to optimize and maintain the service level along a roadway.

The invention relates to the technical field of intelligent traffic, and discloses a dynamic traffic guidance method based on traffic flow prediction under the influence of navigation information, and the method specifically comprises the steps: constructing a random dynamic traffic network model, and carrying out the quantitative description of OD demands and the time-varying characteristics of road traffic flow; establishing a path travel time perception model under the influence of navigation information, and calculating a path selection probability by adopting a Logit model; constructing a hybrid traffic distribution model based on dynamic system optimization and dynamic user balance, and performing iterative solution by adopting a continuous averaging method with a residual flow updating mechanism; forming a reinforcement learning environment by constructing a state space function, an action space function and a reward function; and training the model by using a DDQN algorithm, and optimizing a path selection strategy. According to the method, the problems of low induction precision and poor adaptability caused by neglecting node delay and lacking information fusion and utilization in the existing method are effectively solved, and the dynamic traffic induction effect is remarkably improved.

The invention discloses an urban garbage collection and transportation scheduling method and system based on path optimization, and relates to the technical field of urban garbage collection and transportation and intelligent scheduling optimization, and the method comprises the steps: collecting multi-source original data, carrying out the standardization and time-space registration, and generating the structured observation data of vehicles, roads and collection and transportation points; constructing a dynamic garbage quantity prediction model according to the structured observation data, and predicting garbage increment and liquid level trend of each collection and transportation point; generating a task package and establishing a mapping relation based on a dynamic garbage quantity prediction model result in combination with road passage, vehicle load and time window constraints; and constructing a multi-target path optimization model on the basis of the mapping relation and the constraint, outputting an optimal driving path of the vehicle, and converting a path optimization result into a scheduling instruction. According to the method, the task package is generated on the basis of the dynamic garbage quantity prediction result in combination with road traffic, vehicle load and time window constraints, hierarchical matching of task targets and vehicle resources is achieved, and therefore the reasonability of task allocation and operation feasibility are improved.

The invention relates to the technical field of environmental noise monitoring, and discloses a road traffic noise intelligent monitoring and three-dimensional sound field reconstruction system, which comprises an acoustic sensor module, a data collection and storage module, a data analysis and evaluation module, a three-dimensional sound field construction and display module and a traffic flow feature library. According to the system, a differential geometry principle is adopted, a sound field is regarded as a Riemannian manifold with a local microstructure, and accurate description of an irregular sound field is realized through a curvature self-adaptive sound field manifold construction technology; introducing a covariant derivative in Riemannian geometry, and constructing a sound propagation model adapted to a complex road environment; and realizing hierarchical decomposition and reconstruction of the sound field by using a multi-scale analysis theory. According to the method, the sound source positioning precision and the calculation efficiency are improved, seamless analysis from microcosmic to macroscopic is realized, an innovative solution is provided for traffic and noise collaborative management, and intelligent traffic and environmental noise management are effectively supported.

The invention discloses a low-altitude ground collaborative distribution method and system considering a real-time traffic state of a road, and relates to the technical field, and the method comprises the steps: constructing a traffic speed function of a driving time function of a distribution vehicle along with the change of time and a road section in a segmented manner; constructing an energy consumption function for the unit time power consumption rate of the unmanned aerial vehicle; constructing a mixed integer programming model according to the passing speed function and the energy consumption function; generating an initial solution; performing variable neighborhood search of multi-thread parallel disturbance in the search space based on the initial solution, and performing iterative solution to obtain an optimal solution of the mixed integer programming model; resetting the optimal solution to obtain a primary optimal solution; and optimizing the primary optimal solution to obtain a final solution. According to the invention, through constructing the mixed integer programming model, the distribution efficiency can be improved in a scene of cooperative distribution of the distribution vehicle and the unmanned aerial vehicle.

The invention belongs to the field of intelligent traffic systems, and particularly relates to a congestion scene emergency decision-making method based on dynamic traffic environment modeling, which comprises the following steps: by introducing a congestion potential field concept, fusing multiple factors such as traffic density, speed, cart proportion and the like into a quantifiable risk index, and distinguishing a current congestion index from a future congestion index; the layout problem of the monitoring points is converted into a mathematical optimization problem with the aim of improving the decision effect from experience design; in the decision-making process, not only is passing efficiency considered, but also dimensions such as safety, user experience and resource utilization rate are included; according to the closed-loop intelligent management system integrating traffic flow long-time-sequence evolution prediction, congestion risk assessment based on the physical potential field theory, multi-dimensional comprehensive decision and monitoring point layout collaborative optimization, the opening and closing opportunity of an emergency lane is scientifically and prospectively determined, and the layout of sensing equipment is synchronously optimized; the traffic jam is relieved to the maximum extent, the road passing efficiency and safety are improved, and meanwhile energy consumption is reduced.

The invention provides an urban traffic management intelligent evaluation system and method based on a large language model, and belongs to the technical field of intelligent traffic management. The system comprises a data processing module, an MECA traffic flow analysis module, a feature engineering module, a driving behavior evaluation module, a clustering analysis module, an LLM intelligent decision module, a visualization generation module and a visualization module. The system analyzes the road traffic data collected by the unmanned aerial vehicle, adopts the MECA technology to automatically identify the road type and the traffic environment, intelligently judges the congestion level, evaluates the driving behavior, and combines a big language model to generate a targeted traffic management optimization suggestion. According to the method, the adaptive context sensing technology is innovatively combined with multi-criterion learning, adaptive congestion judgment of different road types is realized, different traffic characteristics of urban expressways, common urban roads and expressways can be accurately recognized, and differentiated management strategies are provided accordingly. The system supports real-time processing of large-scale traffic data, and provides scientific and accurate decision support for urban traffic management departments.

The invention relates to a two-stage road traffic abnormal event identification method and system based on a visual large model, and the method comprises the steps: collecting the monitoring image and video data of an expressway and an urban expressway, and building a static image semantic data set and a dynamic video traffic semantic data set; utilizing the static image semantic data set to train a visual large model to obtain a first visual large model; constructing a same-preference data pair, and performing direct preference optimization training of the first visual large model by using the same-preference data pair to obtain a second visual large model; intercepting an abnormal video key frame based on the dynamic video traffic semantic data set, and performing parameter fine tuning on the second visual large model based on the abnormal video key frame to obtain a road traffic abnormal event recognition model; and collecting a monitoring image or video sequence in real time, and performing abnormal event identification by using the road traffic abnormal event identification model. Compared with the prior art, the traffic abnormal event identification method provided by the invention can effectively combine dynamic and static characteristics of data and is efficient.

The invention belongs to the technical field of near-roadside pollution diffusion simulation, and particularly relates to a road traffic pollution diffusion rapid simulation method based on a physical structured proxy model. Comprising the following steps: S1, meteorological characteristic engineering and sampling; s2, road network standardization decomposition: carrying out topology discretization decomposition on the complex urban road network into standardized unit line sources which can be independently calculated; s3, reconstructing spatial geometric features; s4, physical mechanism dual partitioning: constructing an orthogonal physical mechanism partitioning system according to the atmospheric stability level and the relative position of the receptor point; s5, proxy model training: respectively training an XGBoost regression model for each physical partition; and S6, area field rapid reconstruction: receiving real-time boundary conditions for parallel inference, and rapidly reconstructing an area pollutant concentration field through linear superposition. On the premise of ensuring the simulation precision highly consistent with that of a full-physical model, the calculation efficiency is remarkably improved, and the method is suitable for real-time air quality simulation of a large-scale urban road network.

The invention discloses a multi-dimensional flood risk assessment method and system oriented to personnel, traffic and economy. The method comprises the following steps: acquiring multi-source data, and generating flood spatio-temporal evolution data through hydrodynamic simulation; constructing a time-dependent traffic network driven by flood power, wherein the road traffic capacity is dynamically attenuated along with hydrodynamic parameters; on the basis of a space-time racing mechanism, calculating the time difference between the personnel danger arrival time and the shortest evacuation time consumption, and constructing an evacuation time margin and evacuation success probability model so as to dynamically correct the static personnel exposure risk and obtain a personnel safety risk index; and in combination with physical state attenuation and asset exposure characteristics, traffic operation and economic loss risk indexes are calculated respectively, and multi-dimensional fusion evaluation is carried out. According to the method, the problem that dynamic interaction between flood routing and personnel evacuation is neglected in traditional static assessment is solved, accurate calculation of personnel trapped probability and critical road section rush-through marginal contribution is realized, and scientific decision support is provided for flood control command and emergency rescue.

The invention relates to an unmanned aerial vehicle aerial video vehicle robust tracking method in an anti-shielding scene, and the method comprises the steps: obtaining a road traffic video through the fixed visual angle high-altitude shooting of an unmanned aerial vehicle; marking a specific area in the video frame based on the polygon mask, and generating coordinate points of a polygon area; performing target detection on the video frame based on a pre-trained target detection model, and outputting a vehicle detection frame and category information; through adaptive target tracking, shielding area tracking is carried out on video vehicles in a shielding area, non-shielding area tracking is carried out on video vehicles in a non-shielding area, and a vehicle tracking trajectory with a stable ID and a flow statistical result are obtained; and matching a lane for each video vehicle, and outputting a vehicle tracking trajectory with a stable ID and a traffic statistical result, wherein the vehicle tracking trajectory has a serial number of the lane where the tracked vehicle is located. Compared with the prior art, the problems that the vehicle cannot be continuously tracked and the tracking precision is low due to the shielding object are solved.

The invention discloses a traffic jam prediction management method and system based on artificial intelligence, and a medium, and relates to the technical field of intelligent traffic systems, and the method comprises the steps: collecting multi-source traffic data to construct a space-time traffic data set, carrying out the traffic state classification based on the space-time traffic data set, and generating a multistage jam probability distribution diagram; performing congestion prediction in combination with the multi-stage congestion probability distribution diagram and the real-time traffic event data, and determining a congestion evolution path; and carrying out traffic management according to the congestion evolution path, generating a dynamic dispersion instruction set, and sending the dynamic dispersion instruction set to traffic control equipment of the target road section to carry out traffic congestion management. The technical problems that a traditional traffic management means cannot adapt to complex and changeable traffic conditions, and accurate congestion prediction and efficient management are difficult to achieve are solved, accurate prediction of traffic congestion is achieved, a dynamic dispersion strategy is efficiently generated and executed according to the real-time traffic conditions, and the traffic congestion prediction efficiency is improved. Therefore, the traffic management efficiency and the road traffic capacity are improved.

The invention discloses an intelligent decision-making system construction method for traffic signal control. The method comprises a model training and deployment stage and an application and evolution stage, and specifically comprises the following steps of: S1, generating a pairing training sample set of traffic state structured data and conflict-free signal control instructions based on a pre-stored road traffic conflict rule in the model training and deployment stage; utilizing the paired training sample set to supervise and finely adjust a large language model to obtain a basic model; s2, accessing the basic model into a traffic simulation environment for reinforcement learning training; and in each training step, the basic model outputs a signal control instruction according to the current traffic state, performs safety verification on the instruction according to the road traffic conflict rule, generates a safety reward signal and the like. The traffic signal intelligent decision-making system which is safe, credible, sustainable in evolution and suitable for edge independent deployment is constructed.

The invention discloses a traffic drainage signal management method and system based on a Beidou system, and relates to the technical field of Beidou satellite navigation, and the method comprises the steps: obtaining the real-time traffic data of the position and speed of a vehicle at each intersection based on the Beidou system, and transmitting the real-time traffic data to a traffic management platform; carrying out feature matching on the accident identification model trained based on historical emergency data and a deep learning algorithm and real-time traffic data, and identifying an emergency; and constructing a real-time traffic situation map in combination with the identified emergency information and the traffic condition information of the intersections. The vehicle position and speed data are obtained in real time through the Beidou system, emergencies are rapidly recognized and signal lamp timing is dynamically adjusted in combination with a deep learning algorithm, and compared with a traditional fixed timing scheme, traffic flow changes can be responded in real time, frequent start and stop of vehicles are reduced, the road passing efficiency is improved, and the traffic safety is improved. And particularly, in a sudden traffic accident or construction scene, a signal strategy can be quickly optimized, and traffic congestion aggravation is avoided.

The invention provides a driving behavior dynamic reminding method and system, a vehicle and electronic equipment, and belongs to the technical field of vehicle control, the driving behavior dynamic reminding method comprises the steps that a driving position monitoring video, steering wheel data, the vehicle speed and road traffic information are acquired in real time, and the driving position monitoring video is a video used for detecting the sight direction of a driver; based on the driving position monitoring video and the steering wheel data, analyzing the distraction degree of the driver, and determining a distraction degree value; determining a reminding priority based on the distraction degree value, the vehicle speed and the road traffic information; and reminding the driver under the condition that the reminding priority reaches a preset triggering condition. According to the embodiment of the invention, the attention of the driver can be reliably evaluated, and the driver can be dynamically reminded according to the vehicle speed and the road traffic information.

The invention relates to a different intelligence traffic subject interaction information model construction method based on a three-dimensional digital model. The method comprises the following steps: S1, constructing a multi-level scene based on spatial levels of a road traffic system and designing functions of the multi-level scene; s2, defining autonomous levels of the traffic system, and describing the perception capability, decision logic and execution precision of each terminal device of the vehicle road cloud of each level; s3, according to different attributes of traffic entities, designing a perception-transmission-decision-control four-stage traffic information circulation process and realizing unambiguous collaboration among different intelligence subjects; and S4, converting the standardized and defined information model into a plurality of instantiation units, and deploying the instantiation units into computing entities of various end-edge-cloud traffic subjects to support actual operation of a traffic service scene. According to the method, the problems of difficult cross-domain collaboration, non-uniform information models and insufficient global optimization caused by non-uniform intelligent level of traffic subjects in the prior art are effectively solved, and methodological support is provided for design, verification and implementation of a vehicle-road cloud integrated system.

The invention discloses a road traffic blocking identification and accident site positioning method. The method comprises the following steps: acquiring expressway vehicle running data in real time through an ETC door frame; data preprocessing and data correction are carried out by comparing timestamps of adjacent records; extracting the identification information of the vehicles with the same journey in the front and rear gantries, and calculating the average running speed of the vehicles on each journey; drawing a vehicle space-time diagram of each portal section; and taking an intersection point of the spatial-temporal trajectories of the fast vehicle and the slow vehicle at the starting moment and the ending moment of the abnormal time difference as a blocking ending spatial-temporal position judgment point. No extra monitoring equipment needs to be deployed, the data reliability problem and the real-time problem of the jump door frame data are solved only based on the ETC door frame data, and second-level road blocking and accident site positioning are achieved.

The invention discloses a deep reinforcement learning highway variable speed limit control method based on multi-target dynamic weight distribution, and the method comprises the steps: 1, collecting the traffic flow information of a road section, and obtaining the traffic state data; 2, defining an action parameter and a reward function of the deep reinforcement learning method; 3, constructing a network model of deep reinforcement learning, and establishing a playback memory bank storage sample; and 4, reward function weight updating is carried out based on an entropy weight method, an optimized control strategy is obtained based on a sampling training deep reinforcement learning network model, and speed limit control is carried out on each control unit. According to the method, dynamic weight distribution of the traffic efficiency and the safety reward function is achieved, the method can adapt to a complex traffic environment, meanwhile, the operation efficiency of the expressway is effectively improved, and the safety risk is reduced.

The invention belongs to the technical field of traffic intelligent perception, and particularly relates to a road area traffic perception and intervention low-speed operation vehicle collision early warning system and method, and the system comprises a data collection module, a data transmission module, a data processing module, a collision risk judgment module and an early warning information issuing module. Data used by the method are collected data of millimeter wave radar and camera detection equipment installed on a movable device, the method is convenient and rapid to use, real-time radar data is adopted, the method has the characteristics of high detection precision and high detection speed, real-time video data is adopted, the method has the characteristics of light weight and low deployment cost, and the method is suitable for popularization and application. And real-time sensing of vehicle position and size information, weather information and personnel position information can be realized through the edge calculation unit. And the constructor out-of-bound early warning module can judge the position information of the constructors according to the outer contour of the construction operation area and perform early warning prompt on the constructors exceeding the construction operation area, so that the possible interference of the constructors on traffic operation is reduced.

The invention relates to a multi-store distribution vehicle management method and system, a terminal and a medium, and relates to the field of logistics distribution, and the method comprises the steps: obtaining real-time data, the real-time data comprises store data, vehicle data and traffic data, the store data comprises order demands, inventory states and distribution customer priority information of all stores, and the vehicle data comprises the order demands, the inventory states and the distribution customer priority information of all the stores; the vehicle data comprises vehicle load and volume parameters, and the traffic data comprises road traffic conditions; constructing a data model based on the real-time data; based on the data model, a dispatching algorithm is adopted to generate a distribution plan, and the distribution plan comprises vehicle distribution and distribution paths; and pushing the distribution path to the distribution vehicle. The method has the advantages that the fresh goods can be delivered on time in a fresh-keeping manner, the use efficiency of the vehicle is improved, and the delivery cost is reduced.

The invention relates to the technical field of auxiliary driving, and discloses a deep learning-based road signboard recognition method, which comprises the following steps of: acquiring a road traffic video sequence stream in a vehicle driving process; inputting the road traffic video sequence stream into a deep learning detection framework based on improved YOLOv11 for initial positioning to obtain an initial positioning result of the road signboard; performing cross-frame target tracking on the initial positioning result of the road signboard through an improved Kalman algorithm to obtain a road signboard identification result with a motion track; and displaying the identification result of the road signboard on a vehicle-mounted head-up display of the vehicle. According to the road signboard identification method and system based on deep learning provided by the invention, a scheme based on deep learning and optimized Kalman filtering is adopted, traffic signboards in a complex road traffic environment are identified, and a perfect auxiliary driving system is established, so that key signboard information can be quickly and accurately found in the complex road environment; and a driver is assisted to make correct judgment.