1610 results about "Intelligent transport" patented technology

The invention discloses a cargo transportation real-time path planning method based on artificial intelligence, and relates to the technical field of artificial intelligence and intelligent traffic, and the method comprises the following steps: collecting real-time data from a traffic monitoring system, a vehicle-mounted sensor, a road event platform and a weather information system in a transportation process, forming an original data set containing multi-source information; time synchronization and space alignment are carried out on source data in an original data set, and a standardized data frame is constructed to ensure the consistency of different source information in a space-time dimension. According to the method, the accuracy, the real-time performance and the intelligence of path planning are effectively improved through multi-source data consistency identification, weighted fusion and a dynamic re-planning mechanism. The system can timely identify data conflicts, dynamically adjust paths and enhance transportation safety and efficiency, has a self-learning capability, continuously optimizes path strategies and adapts to complex traffic environments.

The invention belongs to the technical field of vehicle trajectory prediction, and discloses a multi-scale interactive perception highway vehicle trajectory prediction method, which comprises the following steps: jointly modeling short-term burst features and long-term evolution trends through a convolutional neural network and a bidirectional gating cycle unit, and introducing a time sequence attention mechanism to improve the perception ability for key time slices; in combination with a dynamic graph attention mechanism including physical edge features such as relative position, relative speed and relative acceleration, a vehicle interaction relationship is updated in real time so as to improve spatial modeling precision and interpretability; in the decoding stage, the guide vector and the semantic information of the lane are fused, so that the predicted trajectory conforms to the geometric structure of the road in space and keeps smooth and continuous in time. According to the method, the robustness and adaptability of the model in the sparse adjacent vehicle environment of the expressway can be improved while the prediction precision is ensured, a more stable and reliable trajectory prediction result is provided for an intelligent traffic system, and powerful technical support is provided for traffic safety management and operation scheduling of the expressway.

The invention relates to the technical field of intelligent transportation control, in particular to a tomato transportation speed self-adaptive adjustment method based on path road condition feedback, which comprises the following steps: acquiring road images, vibration waveforms and altitude data through a multi-source sensing unit, and constructing real-time road condition information; identifying a driving mode and extracting a corresponding bumping parameter; detecting the maturity grade of the tomato by combining multispectrum and thermal imaging, and querying a maturity-compressive strength corresponding table to calculate a cargo damage threshold value; constructing a dynamic mapping model under multiple working conditions, predicting vibration response and converting the vibration response into equivalent pressure; comparing the equivalent pressure with a damage threshold value to obtain a safety margin, constructing a speed adjustment decision tree and generating a maximum allowable speed value of each road section; and dynamically generating a segmented variable-speed control instruction based on the speed decision matrix, and controlling a throttle valve and a braking system to cooperatively change speed. The method has the advantages of accurate working condition identification, dynamic fruit adaptation, closed-loop speed regulation and control and the like, and is suitable for fine speed control of a high-sensitivity fruit and vegetable transportation scene.

The invention discloses an urban road mobile source intelligent monitoring method and system based on multi-source data coupling, and relates to the technical field of environment monitoring and intelligent traffic. Utilizing a machine learning algorithm to construct a pollutant emission, carbon emission and energy consumption prediction model; acquiring vehicle inventory data in the city, and calculating the pollutant emission, energy consumption and carbon emission of the whole city; the method comprises the following steps of: constructing a dynamic distribution diagram of automobile emission in a city by using real-time position data of vehicles and urban road network information, introducing an atmospheric diffusion model, simulating pollutant migration by combining urban geographic information and environmental data, comparing and verifying a simulated migration result with actually measured data of a national control site, and optimizing parameters of the atmospheric diffusion model; and storing the result into a real-time database, and displaying the pollutant distribution, emission, energy consumption and carbon emission of the urban road network in real time through a visual interface. According to the invention, the temporal-spatial resolution and prediction precision of data are effectively improved.

The invention relates to the technical field of intelligent traffic, and discloses a road transportation key operating vehicle intelligent supervision system, which comprises a data acquisition and fusion module used for acquiring multi-source heterogeneous data of a vehicle terminal, an external environment and the like in real time and carrying out standardization processing; the cognitive digital twinning construction module is used for constructing a dynamic heterogeneous graph representation human-vehicle-road-environment system and outputting a cognitive state vector through a graph neural network; the risk prediction and evaluation module is used for predicting a risk evolution trend based on the state vector sequence and quantifying the uncertainty of prediction by using a Monte Carlo discarding method; and the self-adaptive intervention decision module is used for combining risk prediction and uncertainty, making a decision through a reinforcement learning model and executing an optimal active intervention instruction. By constructing cognitive digital twinning and introducing uncertainty quantification and closed-loop self-checking, prospective prediction and self-adaptive intervention of driving risks are realized, and the accuracy and robustness of supervision are improved.

The invention discloses a rail transit hub emergency regulation and control method and system based on real-time simulation, and relates to the technical field of intelligent traffic and rail transit emergency management, and the method comprises the steps: carrying out the time synchronization processing of real-time passenger flow data and train operation state data, and generating a hub state data set; constructing a multi-level simulation model, and simulating a passenger flow distribution evolution process by presetting passenger flow evacuation behaviors and train operation constraint conditions; based on an output result of the multi-level simulation model, positioning capacity limiting nodes in each evacuation path by adopting a bottleneck identification algorithm, calculating passenger flow carrying capacity, and generating an optimal path regulation and control parameter with shortest evacuation time as a target; and the optimal path regulation and control parameters are converted into a train automatic control system instruction and a passenger flow guide display instruction, the instructions are issued to each line control unit through a hub centralized dispatching system, and the train operation frequency and the passenger flow evacuation direction are adjusted. According to the invention, intelligence and collaboration of emergency regulation and control of the rail transit hub are realized.

The invention discloses an urban traffic event semantic recognition method based on a knowledge graph, and relates to the technical field of intelligent traffic and artificial intelligence, and the method comprises the steps: obtaining the multi-modal traffic data of urban traffic, and constructing a knowledge graph model; preprocessing and feature extraction are carried out on the multi-modal traffic data, the extracted multi-modal features are mapped to entity nodes of a knowledge graph model, a fusion feature vector is generated, and semantic embedding coding is carried out on the fusion feature vector through a graph neural network; constructing an event inference rule base based on a semantic embedding coding result, and performing multi-layer inference calculation on the fusion feature vector by using a graph convolutional neural network to obtain a matching strength score of the candidate traffic event and a standard event mode in the knowledge graph; and in combination with the event space-time constraint condition and the historical event mode, outputting a traffic event recognition result, confidence evaluation and disposal suggestions. According to the invention, the accuracy and practicability of urban traffic event identification are improved.

The invention relates to the technical field of intelligent traffic and energy-saving illumination, in particular to an intelligent management and control method and system for energy-saving illumination of a highway tunnel and a computer program, and is suitable for an intelligent illumination control system for improving tunnel driving safety and energy efficiency of an illumination system. Dynamic response and fine energy consumption adjustment of tunnel lighting are realized through out-of-tunnel brightness multi-source fusion prediction, entrance section feedforward, closed-loop dimming control, a segmented intelligent lighting strategy based on vehicle detection and an in-tunnel brightness closed-loop and stepless dimming mechanism. The system has a multi-sensor redundancy mechanism and can automatically return to a safety mode when equipment fails, so that the continuity and the safety of illumination are guaranteed; and meanwhile, data summarization and energy-saving statistics are combined, control parameters are optimized in cooperation with a digital twinning or self-learning algorithm, the energy-saving effect and the operation and maintenance efficiency are further improved, and the purposes of traffic safety and low-carbon operation are considered.

The invention relates to the technical field of intelligent traffic monitoring, in particular to a vehicle identification and traffic incident detection method and system based on the view angle of an unmanned aerial vehicle, and the method comprises the steps: obtaining the aerial traffic video data of the unmanned aerial vehicle and the flight control data of the unmanned aerial vehicle; performing target identification on the image in the video data by adopting a target detection algorithm to obtain vehicle information; tracking the vehicle in the video data by using a target tracking algorithm, and mapping the identified vehicle to a unified coordinate system in combination with the flight control data to generate vehicle trajectory data; constructing a comprehensive road description model based on the vehicle trajectory data and the vehicle information; and forming a traffic incident recognition module based on the comprehensive road description model, and recognizing the input aerial video according to a preset traffic incident recognition rule. According to the invention, by optimizing the vehicle identification algorithm, constructing the road description model and designing the efficient traffic event identification module, the application efficiency of the unmanned aerial vehicle aerial video in the traffic monitoring field is effectively improved.

The invention discloses an intelligent traffic flow dynamic regulation and control method and system based on multi-source data fusion, and relates to the technical field of data processing. The method comprises the following steps: executing multi-source data acquisition through a data acquisition interface to obtain a multi-source traffic data set; performing spatial fusion on the multi-source traffic data set to obtain a flow marking map; performing current traffic state analysis based on the flow marking map, identifying local and global congestion, and constructing a traffic situation map; and performing traffic flow state prediction in a preset future time zone based on the traffic situation map, performing regulation and control decision based on a prediction result, and generating a traffic flow regulation and control strategy. The technical problem that traffic flow prediction and regulation are not accurate enough in the prior art is solved, and the technical effects that intelligent traffic flow dynamic regulation is achieved through multi-source data fusion and space-time analysis, and the traffic management precision and the response speed are improved are achieved.

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 discloses a bridge ship collision early warning system based on artificial intelligence, and relates to the field of intelligent traffic infrastructure safety protection. The method is used for solving the problems of difficulty in multi-source data fusion and low risk identification accuracy in a complex water area. The method comprises the following steps: firstly, uniformly mapping a radar point cloud, an AIS signal and a visual trajectory to a coordinate system taking a pier as a center through a Lie group transformation algorithm, and constructing a ship motion state tensor; then, ship behavior characteristics are extracted through a space-time diagram convolutional network, and intention probability distribution and behavior deviation degree are output; thirdly, an orthogonal constraint variational auto-encoder is adopted to decouple the autonomous control behavior and the environment disturbance factor, and a collision risk quantized value is generated according to the norm ratio of the autonomous control behavior and the environment disturbance factor; and finally, constructing a dynamic alarm threshold value according to the behavior deviation degree and the disturbance characteristics, realizing graded early warning, and driving a behavior pattern library to update through a feedback mechanism to form a self-adaptive closed-loop early warning system.

The invention relates to the technical field of intelligent traffic, in particular to an urban traffic flow prediction method fusing a dynamic graph convolutional network and a Transformer, which comprises the following steps: firstly, collecting traffic data in a road network to form a data set, then constructing a space-time dynamic GCN unit to capture dynamic evolution of road network topology, and extracting multi-scale space-time characteristics through expansion time convolution; then, constructing a Transform unit for modeling a global time sequence dependency relationship of the traffic flow; finally, in combination with a gating fusion prediction unit, spatial-temporal features are fused through a spatial-temporal cross attention mechanism, and the prediction precision and robustness are effectively improved. Through verification and evaluation of real data, the method is suitable for a traffic flow prediction scene in an urban road network dynamic environment, and especially has good performance in sudden road conditions and peak hours.

The invention relates to the technical field of intelligent traffic infrastructure engineering, in particular to a roadbed settlement high polymer grouting repair grouting parameter optimization method, which comprises the following steps of: firstly, constructing a multi-source fusion training data set, and learning a prediction model based on a training machine; establishing a driving mapping relation between the grouting parameters and the road lifting effect and the maximum stress data of the repair area; then, based on the mapping relation, a multi-objective optimization model including pavement settlement repair precision, road stress, economic cost control and environmental friendliness evaluation is constructed; solving the model by adopting a Bayesian optimization algorithm to obtain an optimized grouting parameter combination; a marginal contribution of each parameter to a prediction result is calculated in combination with an SHAP interpretability analysis method, and an engineering decision basis is provided for parameter selection; constructing a transfer learning adaptation framework to adapt to different disease, geology and road types; and finally, establishing a real-time monitoring system, and realizing optimal control by combining sensor data and model feedback.

The invention discloses an intelligent automobile interpretable abnormity diagnosis method and system, and relates to the technical field of intelligent traffic. The method comprises the steps of collecting multi-dimensional sensor data based on an intelligent automobile test platform, and constructing a directed causal graph and a causal adjacency matrix which are used for describing a causal relationship between the sensor data; designing a causal constrained graph attention mechanism based on the causal adjacency matrix, and constructing a causal constraint enhanced graph attention anomaly diagnosis model; and based on the directed causal graph and the graph attention anomaly diagnosis model, constructing a hierarchical anomaly diagnosis strategy integrating a feature reconstruction error, a variable causal relationship and a graph attention network weight, positioning an anomaly root cause and identifying a propagation path of the anomaly in the sensor network. According to the invention, the problems of false correlation and lack of exception explanation ability of graph attention network learning in the prior art can be overcome, and reliable exception detection and root cause diagnosis of intelligent automobile multi-sensor data are realized.

The invention provides a tunnel event and warning lamp acousto-optic linkage strategy generation system based on AI decision, and relates to the technical field of intelligent traffic, and the method comprises the steps: collecting tunnel traffic event data, vehicle structured data, vehicle coordinates, vehicle flow, vehicle speed, and brightness data inside and outside a tunnel in real time, so as to obtain multi-source heterogeneous data; according to the multi-source heterogeneous data, taking the data matching degree, the space-time consistency and the event confidence as multi-objective optimization dimensions, constructing a weighted objective function, generating a Pareto optimal solution set through a multi-objective optimization algorithm, screening the solution set based on space-time constraint conditions, and removing conflict hypotheses; and determining the final event type and the structured event description of the three-dimensional geographic coordinates. The problems of large illumination energy consumption and poor system linkage in existing tunnel safety management are solved.

The invention relates to an Internet of Vehicles edge computing multi-target unloading method and system fusing dynamic environment modeling and improved SARSA, and belongs to the field of intelligent traffic and edge computing fusion. The method and the system comprise MEC environment perception and multi-dimensional state construction, dynamic reward feedback oriented to multi-dimensional performance indexes, intelligent decision model construction and learning based on improved SARSA, and antagonism training oriented to real disturbance. A high-fidelity environment model is constructed through a space-time attention mechanism, an SARSA algorithm is improved to realize hierarchical qualification trace attenuation and collaborative Q table updating, a multi-target hierarchical reward engine is combined to implement differential optimization on an emergency task and a conventional task, and an adversarial training mechanism is introduced to improve robustness. The core problems of high mobility, task diversity, resource limitation and the like in the Internet of Vehicles are effectively solved, the comprehensive performance is optimal in multiple dimensions of delay, energy consumption, resource utilization rate and the like, and the actual landing of the edge computing technology of the Internet of Vehicles is promoted.

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 invention provides a traffic abnormity and congestion cause analysis method and system based on a knowledge graph, and relates to the technical field of intelligent traffic perception, and the method comprises the steps: carrying out the target detection through employing preprocessed laser radar point cloud data, recognizing traffic participants, carrying out the continuous frame tracking of the traffic participants, and extracting the motion track and behavior characteristics in an event dimension; identifying a behavior event of the traffic target based on the motion trail and the behavior characteristics, binding the identified behavior event of the traffic target to a corresponding target entity, and performing target behavior modeling to form standardized structured information; based on structured information of a traffic target, an intersection-oriented traffic state knowledge graph is constructed, a rule-based abnormal event judgment module is utilized to perform semantic analysis on behavior and event nodes in the traffic state knowledge graph, abnormal traffic events are identified, and potential causes causing traffic congestion are traced. According to the invention, refined understanding and active perception of the intersection traffic state can be realized.

The invention relates to the technical field of computer vision and satellite video processing, in particular to a satellite video dense vehicle tracking method and system. The method comprises the following steps: constructing a satellite video dense vehicle data set VDD-VEH containing a motion vector label, and providing supervision information for space-time modeling; designing a motion position map (MPG), mapping a target space position and a motion flow into a three-dimensional space-time diagram structure, fusing space-time consistency, appearance features and detection confidence by using a multi-feature edge weight (MFEW) strategy, and quantifying node association strength; global optimal trajectory association is realized by adopting integral linear programming (ILP), abnormal trajectories are eliminated by combining a trajectory optimization module (TRM), trajectory fractures are repaired, and long-time-sequence tracking stability is enhanced. The method is remarkably superior to the prior art in indexes such as MOTA and IDF1, the identity switching frequency is reduced by 55%, and the method is suitable for intelligent traffic monitoring and remote sensing video analysis and has high precision and cross-scene generalization ability.

The invention relates to the technical field of intelligent traffic and road safety monitoring, and discloses a 4D imaging millimeter wave radar-based slope deformation monitoring and identification method, which comprises the following steps of: acquiring a continuous point cloud; carrying out voxel modeling on the point cloud in a three-dimensional space; selecting three continuous frames of point clouds, and identifying mutant voxels by calculating the point number variation of each voxel in adjacent frames; performing spatial clustering on the mutant voxels to obtain an obstacle region; performing feature extraction on the obstacle area, wherein the extracted features comprise the height difference, the average reflection intensity and the centroid drift of the obstacle area; carrying out linear weighted fusion on the extracted features, and constructing a landslide mutation index; and judging whether an obstacle target exists in an obstacle area or not according to a preset condition, and performing risk grading identification according to the landslide sudden change index. According to the invention, all-weather, structured and high-reliability perception of major traffic potential safety hazards such as landslide / rockfall is realized, and a new basic capability is provided for a traffic safety system.

The invention discloses a road shoulder vehicle exclusion method based on an inspection system, and relates to the technical field of intelligent transportation, and the method comprises the steps: obtaining a parking area image shot by an inspection vehicle, recognizing a vehicle target in the image, and extracting the license plate position information; based on vehicle three-dimensional feature reconstruction and space projection analysis, generating a space distribution thermodynamic diagram of the vehicle in the image coordinate system; constructing a dynamic segmentation line model, and adaptively generating a road shoulder region judgment boundary according to the parking space type and the road geometric parameters; and through a multi-target trajectory association algorithm, the berth vehicle and the road shoulder vehicle are distinguished, and the berth occupation state is output. According to the scheme provided by the invention, the spatial positioning precision and behavior judgment reliability in a complex scene can be remarkably improved, the limitations on environmental adaptability, vehicle type compatibility and time sequence continuity are broken through, and a road shoulder violation management and control solution with high robustness and full-time coverage is provided for urban traffic management.

The invention relates to the technical field of intelligent traffic, in particular to a dynamic road network collaborative capacity expansion decision-making system based on federated learning-digital twinning, which comprises the following steps of: calibrating pulse type and periodic type data flow weights through a dynamic weight distributor, and generating a normalized feature vector; fusing the normalized feature vectors of all the domains through a privacy protection aggregation engine to obtain a passenger flow pressure distribution prediction matrix; the reconstruction module is used for constructing a digital twinborn body based on the prediction matrix and initializing the digital twinborn body; and based on the initialized twinborn environment, recognizing and sensing a missing region through a blind area data reconstructor, and fusing historical features and real-time data streams of adjacent nodes by adopting a space-time correlation algorithm to reconstruct a complete road network state. According to the method, the dynamic road network collaborative expansion decision system is constructed by fusing federated learning and digital twinning technologies, and the expansion decision efficiency of the traffic road network is improved.

The embodiment of the invention discloses an intelligent traffic cooperative control optimization method, device and equipment based on quantum key distribution and digital twinning, and relates to the technical field of intelligent traffic. The method comprises the following steps: transmitting traffic data and spatial information through the quantum secure communication network, and constructing a traffic digital twin mapping model based on the traffic data and the spatial information; the traffic digital twin mapping model is configured to map to obtain a physical traffic system operation state corresponding to the traffic data and the spatial information; accessing the digital twin mapping model into a quantum computing environment, and generating a traffic control strategy based on the operation state of a physical traffic system by using a preset quantum algorithm; and transmitting the traffic control strategy to traffic equipment in a physical traffic system for execution, synchronously transmitting the traffic control strategy to a digital twin mapping model for simulation execution, and optimizing and adjusting the traffic control strategy according to a difference comparison result of a feedback actual execution result and a simulation execution result.

The invention relates to the technical field of intelligent traffic, in particular to an intelligent network connection vehicle driving risk assessment method and a visualization system based on multi-source data fusion. According to the method, multi-source data such as vehicle perception, communication and maps are fused, an artificial potential field theory is combined, a gravitational force risk potential field function, a repulsive force risk potential field function and a road boundary repulsive force potential field function are constructed respectively, and a unified total risk potential field TPF is formed by a gravitational force risk potential field, a repulsive force risk potential field and a road boundary repulsive force potential field; according to the method, the driving risk of the intelligent networked vehicle is obtained, risk grade division and real-time visual display are realized through clustering analysis, the accuracy and dynamic response capability of risk assessment are improved, the method is suitable for safety decision support of advanced driving assistance and automatic driving systems, real-time assessment of the driving risk can be realized more accurately and efficiently, and the risk assessment efficiency is improved. And the risk assessment precision and the dynamic response capability are improved.

The invention provides a double-chain-based air-space-ground fusion vehicle-mounted network security authentication system and method, and belongs to the technical field of intelligent transportation, in an air-space-ground fusion vehicle-mounted network, a vehicle-mounted unit, a road side unit, an unmanned aerial vehicle node and a satellite node interact through a trusted mechanism and a double-chain architecture, after interaction in a single domain is completed, cross-domain interaction is performed, and the security authentication of the vehicle-mounted unit, the road side unit, the unmanned aerial vehicle node and the satellite node is completed. The authentication token is stored on the block chain after being obtained in the first interaction, and the quick interaction can be completed by directly calling the authentication token subsequently. According to the invention, through the double-chain block chain architecture, the authentication time delay is optimized, the cross-domain authentication efficiency is improved, and the privacy protection problem of the node in a high dynamic environment is effectively solved.

The invention relates to the technical field of intelligent traffic, in particular to an intelligent scheduling method and device for unmanned taxis, and the method comprises the steps: constructing a spatial topological structure for connecting a target unmanned taxi, passengers and a charging station based on the received vehicle data, passenger data and environment data; capturing a spatial dependency relationship among the target unmanned taxi node, the passenger node and the charging station node to generate a spatial coding feature and a time coding feature, and combining the spatial coding feature and the time coding feature with corresponding weights to generate a spatial-temporal feature of the target unmanned taxi; and generating a passenger matching result and / or a path plan and / or a charging decision of the target unmanned taxi based on the spatial-temporal characteristics. Therefore, the problems that a traditional unmanned taxi scheduling method does not fully consider complex factors in an actual driving scene, scheduling decisions are still mostly single-target guiding, dynamic adaptability is lacked, and the increasing travel requirements of passengers cannot be met are solved.

The invention relates to the technical field of automatic driving, in particular to an automatic driving system based on intelligent traffic, which comprises a multi-source heterogeneous sensing fusion unit, a group collaborative decision-making unit and a dynamic control optimization unit, the group collaborative decision-making unit carries out space-time alignment on multi-source heterogeneous data, constructs and updates a global environment perception knowledge graph, carries out modeling on vehicle trajectories based on a Monte Carlo tree search framework in combination with deep reinforcement learning and risk perception, and trains a deep Q network model by adopting federated learning and adversarial training. A traffic flow distribution scheme is generated by applying multi-agent deep reinforcement learning and a game theory, storage data is proved by using homomorphic encryption and zero knowledge, a dynamic control optimization unit predicts, controls and plans an acceleration curve through a nonlinear model, and a transverse path tracking error is controlled by fusing sliding mode control and a fuzzy logic algorithm. And the safety of automatic driving is improved.

The invention discloses an intelligent control system and method based on an intelligent indication board, and relates to the technical field of intelligent traffic and Internet of Things control, and the method comprises the following steps: building a unified time mark baseline, collecting an exposure sequence, a saturation threshold sequence and a track residual error, and generating an exposure-track coupling spectrum for calibrating a rain and snow triggering time window; and calculating phase residual mapping under the constraint of an exposure-trajectory coupling spectrum, identifying an exposure oscillation root cause, extracting a saturated source region and a trajectory breakpoint set, and generating a fault anchor point. According to the method, rain and snow interference is identified through a time mark base line and a coupling spectrum, a fault anchor point is constructed, an optical path is played back to assess risks, multi-source signals are fused to generate a steady track, conjugate correction and feedforward constraint are adopted to suppress oscillation, a time reversal closed-loop instruction and multi-strategy joint debugging are combined, and self-adaptive optimization of exposure and induction information is achieved. And the stability and the control precision of the intelligent indication board in extreme weather are improved.

The invention discloses an intelligent transport capacity dispatching system and method based on a GPS. The system comprises a transport capacity state monitoring subsystem and an intelligent dispatching management subsystem. And by deploying a GPS positioning and multi-parameter sensor, real-time acquisition and analysis of information such as the position, the speed and the energy consumption of the transport capacity unit are realized. According to the system, CNN and Transform structure extraction features are fused, LSTM is combined to predict a transport capacity demand, and a PPO reinforcement learning algorithm is adopted to dynamically plan a path. Abnormality detection (SVM), PID feedback adjustment and cloud edge cooperative calculation are integrated in the scheduling process, and the scheduling precision and the response speed are improved. The system has the capabilities of task filing, online optimization and data security guarantee, and is suitable for complex traffic and logistics scheduling scenes.