6357 results about "Transportation technology" patented technology

The invention discloses a traffic transportation operation monitoring early warning and decision analysis method based on vehicle infrastructure cooperation, and relates to the technical field of intelligent traffic. According to the method, real-time collection of dynamic traffic elements is realized through a multi-dimensional sensing network of a vehicle end, a road side and an environment and V2X communication, time-space reference unification of multi-source data is ensured, a road-vehicle-environment-event semantic network is constructed, multi-dimensional recognition of abnormal events such as accidents, congestion and severe weather is realized in combination with hierarchical feature extraction, and the method has the advantages of being high in practicability and high in practicability. The method is advantaged in that identification accuracy is improved, abnormal event propagation paths can be predicted, global road network situation prediction capability is realized, differential early warning is generated based on a comprehensive risk index, multi-level responses such as traffic signal adjustment and path planning are triggered, response time is greatly shortened, emergency response efficiency is optimized, a decision execution effect real-time feedback mechanism is established, and the method is suitable for popularization and application. The system performance is continuously optimized along with data accumulation, and the defect that a big data platform lacks an intelligent decision closed loop is avoided.

The invention belongs to the technical field of intelligent transportation, and particularly relates to a multi-view automatic driving scene reconstruction method based on three-dimensional Gaussian splashing, which comprises the following steps: S1, data acquisition; s2, fast system initialization: setting the received first frame data as a key frame, and processing the first frame data by adopting a Gaussian high-pass filter to reserve most of high-frequency information in the image; s3, key frame selection, wherein dynamic key frame selection under a sliding window is adopted; s4, estimating the pose of the vehicle-mounted camera; and S5, reconstructing an automatic driving scene. According to the method, the precision and the efficiency of three-dimensional reconstruction are improved, the robustness and the real-time performance of the system are enhanced, high-fidelity rendering and dynamic scene editing are realized, and a comprehensive frequency domain analysis and regularization strategy is provided; and the method has a wide application prospect, can also be applied to the fields of high-precision map updating, environment perception and path planning, unmanned aerial vehicle navigation, robot navigation and the like, and provides powerful support for the development of intelligent transportation and automatic driving technologies in the future.

The invention discloses a railway freight train data acquisition method and system based on multi-sensor fusion, and relates to the technical field of railway transportation, and the method comprises the steps: detecting the operation state and position of a train in real time through wireless communication, and carrying out the self-inspection of each sensor when the train is detected to be about to enter a monitoring region; constructing a three-dimensional health state model of the train through three-dimensional reconstruction and synchronous positioning according to the comprehensive state data of the train; in combination with a three-dimensional health state model and a side cloud collaborative architecture, layered processing and dynamic optimization of train comprehensive state data are accelerated by using a quantum algorithm, and abnormal features are identified in real time; the abnormal features are analyzed through deep learning and a time sequence analysis algorithm, and alarm information is generated and fed back to the edge computing terminal; according to the method, the running state of the train and the cargo state data are deeply fused through the convolution operation of the three-dimensional health state model and the space-time diagram, so that the global perception capability of the data is improved.

The invention discloses an enclosure system of a low-temperature storage tank and transportation equipment, and relates to the technical field of fluid transportation, the enclosure system is installed on the inner wall of the low-temperature storage tank, the enclosure system comprises a plurality of heat insulation modules installed on the inner wall of the storage tank, the heat insulation modules are spliced and cover at least part of the inner wall of the storage tank, and the heat insulation modules are arranged on the inner wall of the storage tank; assembling grooves are formed in the splicing positions of the heat insulation modules; the bridging blocks are installed in the assembling grooves, and every two adjacent heat insulation modules can be connected together through the corresponding bridging blocks; the shielding module is connected with the heat insulation module, the shielding module comprises a first shielding layer, a second shielding layer and a third assembly plate, the first shielding layer and the second shielding layer are arranged in a spaced mode, and the third assembly plate is located between the first shielding layer and the second shielding layer. According to the invention, a better shielding effect can be realized by arranging two or more shielding layers, and the first shielding layer and the second shielding layer are arranged at intervals, so that the capacitive coupling effect between the shielding layers is reduced, and the penetrating power of electromagnetic waves is reduced.

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 provides an inter-fleet dynamic electric quantity balance and collaborative replenishment system, which relates to the technical field of shipping transportation, and comprises an intelligent scheduling module used for obtaining a data set containing electric power conditions, navigation parameters, sea condition influences and task progress requirements of each ship based on ship motion states, ship equipment is monitored in real time to obtain data and analyze environmental influence to obtain adjustment parameters, then the total energy consumption contribution of a single ship is calculated, the total loss of a fleet is quantified to fuse to obtain the initial amount of total energy consumption and correct to obtain the navigation efficiency, and then a power output adjustment strategy is formulated for a main propulsion ship according to the navigation efficiency and an electric power balance target. And finally, a collaborative supply path is planned by taking the low-power ship as a core and combining multi-party information. According to the invention, efficient coordination of deployment and supply of the fleet on power management is realized, the transportation efficiency is improved, the cost is reduced, and the navigation safety is enhanced.

The invention discloses a low-altitude logistics unmanned aerial vehicle path planning method and system, and relates to the technical field of unmanned aerial vehicle logistics transportation, and the method comprises the steps: firstly, carrying out the optimization through calculating a preliminary path and combining the terrain and weather information; a sensor is used for detecting and dynamically avoiding obstacles; secondly, data transmission between the unmanned aerial vehicle and the ground control center is achieved, and the ground control center carries out monitoring, control and data processing. According to the low-altitude logistics unmanned aerial vehicle path planning system, efficient path planning is realized through multi-module cooperation. According to the invention, the flight safety, path planning accuracy and energy utilization efficiency of the unmanned aerial vehicle can be improved, the operation cost is reduced, and the system adapts to complex environmental conditions.

The invention relates to the technical field of intelligent traffic, in particular to a highway traffic incident recognition and early warning method and system, and the method comprises the steps: collecting traffic data in real time through sensing equipment arranged along a highway; performing preliminary identification and classification of conventional traffic events on the preprocessed traffic data through edge computing nodes; inputting a preliminary analysis result and unclassified traffic data into a cloud end, and carrying out secondary analysis and semantic understanding; and merging the logic relationship and the events of the same target to obtain a final event identification result. According to the traffic event attribute and the event identification result, generating early warning information; and inputting feedback data after each event processing into the recognition model for self-learning and optimization, and dynamically optimizing model parameters, event classification standards and early warning rules. According to the invention, the problems of false report and missing report in traffic event identification are effectively solved, the identification precision is improved, the repeated report is reduced, and the method can dynamically adapt to the problems of complex scenes and non-uniform standards.

The invention relates to the technical field of smart city traffic, in particular to a smart city traffic indication system and a working method thereof. According to the technical scheme, the system comprises a multi-source heterogeneous data acquisition module, a data preprocessing and fusion module, a feature extraction and analysis module, a traffic flow prediction and signal timing decision module and a signal display and communication module, the acquisition frequency is dynamically adjusted according to the traffic flow. Traffic and meteorological data are dynamically collected through multiple types of sensors, the quality of data preprocessing and fusion is guaranteed, features are accurately extracted, signal timing is optimized through reinforcement learning, the display and communication module effectively transmits information and cooperates with surrounding traffic, emergency situations can be dealt with, tail gas emission is reduced, pedestrian waiting time is shortened, and traffic safety is improved. And a basis can be provided for long-term traffic planning, and the traffic efficiency, the environmental protection property and the pedestrian friendliness are comprehensively improved.

The invention discloses an intelligent sensing system for multi-modal data fusion, and relates to the technical field of intelligent traffic. The multi-modal data fusion intelligent sensing system comprises a data acquisition and preprocessing module, a multi-modal data fusion module, an intelligent sensing module, a target detection and behavior prediction module and a system verification and optimization module, and high-precision and high-stability target sensing and tracking are realized by integrating millimeter wave radar and camera data. The multi-modal data fusion module effectively combines physical features and visual features, and improves the accuracy and robustness of perception. The intelligent sensing module adopts a volume Kalman filter and a Hungary algorithm, state estimation and target association are optimized, and continuous and accurate tracking of the target is ensured. The target detection and behavior prediction module utilizes an advanced YOLOv5 model and a DeepSORT algorithm to realize multi-target accurate detection and trajectory prediction, and the intelligent level of the system is further improved.

The invention provides a high-precision railway map generation method and system based on one-way trajectory correction, and relates to the technical field of heavy haul railway transportation, and the method comprises the steps: collecting a continuous image sequence and spatial attitude data of a current train in a driving process at the same time; determining pose change data of the current train based on visual feature points in the continuous image sequence; performing synchronous positioning and map construction based on the pose change data and the spatial pose data to generate an initial electronic map; and based on mileage information and image positions corresponding to preset markers in the continuous image sequence, the mileage and the track of the current train in the initial electronic map are corrected, and a corresponding electronic map of the current train in the driving process is generated. According to the method and system provided by the invention, the generation efficiency and accuracy of the railway electronic map are improved.

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 relates to the technical field of lithium ore conveying, and discloses a lithium ore hopper energy-saving conveying system based on intelligent self-adaptive control. The system comprises a data acquisition module, a self-adaptive control module, an energy-saving optimization module and an execution adjustment module. The data acquisition module acquires multi-dimensional operation data of the lithium ore hopper through a distributed sensor network, and a hopper conveying state database is constructed after standardization processing; the adaptive control module performs dynamic feature extraction on the database, generates a real-time control parameter matrix, establishes an association rule graph of each parameter, and constructs an intelligent adjustment decision model based on the association rule graph; an energy-saving optimization module extracts a key energy consumption feature vector from the real-time control parameter matrix, calculates an energy consumption influence coefficient of each operation parameter, and screens out an optimal energy-saving control sequence in combination with an intelligent adjustment decision model; and the execution adjustment module drives an execution mechanism according to the optimal energy-saving control sequence, and self-adaptive adjustment of hopper conveying parameters is completed.

The invention relates to the technical field of cold-chain commodity transportation, in particular to a temperature monitoring and control method in a cold-chain commodity transportation process, which comprises the following steps: acquiring real-time temperature data, and generating a thermal response digital file of a cold-chain commodity according to the real-time temperature data and a cold-chain commodity category database; real-time positions and external air temperature data of the cold chain commodities are collected, and a temperature threshold value of the next time period is predicted according to the heat capacity characteristic parameters reflected by the thermal response digital archive; a three-dimensional temperature field distribution diagram is constructed based on the temperature threshold value of the next time period and the vibration data of the compartment where the cold-chain commodities are located; generating a control instruction according to the coordinates of the temperature anomaly region in the three-dimensional temperature field distribution diagram; after the control weight coefficient is calculated according to the control instruction and the preset energy consumption optimization objective function, the temperature control operation is executed according to the control weight coefficient and the control instruction, and balance temperature monitoring and control and energy consumption optimization are effectively achieved.

The invention provides a pneumatic conveying system optimization method and system based on a multi-target particle swarm, and relates to the technical field of pneumatic transportation, and the method comprises the steps: collecting key transportation data affecting the conveying efficiency, the energy consumption and the system stability, employing a particle swarm algorithm, regarding each particle as a group of optimizable parameter combinations, and obtaining a particle swarm optimization parameter combination; the method comprises the following steps: calculating a pneumatic transport performance index based on an engineering formula, generating a fitness value by adopting a weighted summation method, comparing the fitness value with a preset threshold value, determining whether optimization is needed or not, carrying out iterative optimization by updating particle positions and speeds by taking minimization of pipeline wear, energy consumption and pressure fluctuation as targets, and when the fitness value meets a threshold value condition, carrying out iterative optimization on the particle positions and speeds. And selecting an optimal solution based on a minimum deviation method. The pneumatic conveying system is optimized based on the multi-target particle swarm algorithm, and the conveying efficiency and the system stability are improved under the target of minimizing pipeline abrasion, energy consumption and pressure fluctuation.

The invention discloses a high-density stereoscopic warehouse ex-warehouse scheduling and transportation system and method based on RGVs, and relates to the technical field of warehouse ex-warehouse scheduling and transportation, and the system comprises a scheduling management center which is in communication connection with a virtual model construction module, a task analysis and planning module, a task distribution module and a real-time monitoring intervention module. Wherein the modules are connected through electric signals; and the virtual model construction module is used for constructing a virtual model of the warehouse and the RGV. By constructing the virtual models of the warehouse and the RGVs and combining the digital twinning technology, the state of a physical entity can be synchronized in real time, the space-time paths of the multiple RGVs can be accurately simulated, path conflicts and traffic congestion are effectively avoided, the efficiency of warehouse-out operation is remarkably improved, task delay caused by the path conflicts is greatly reduced, and the working efficiency of warehouse-out operation is improved. And the accuracy and timeliness of warehouse-out operation are ensured, so that the overall logistics efficiency is improved.

The invention relates to the technical field of intelligent traffic, and discloses an expressway parking risk dynamic early warning method and system based on spatial-temporal characteristics and an adaptive threshold, and the method comprises the steps: collecting vehicle driving data through a multi-type sensor, obtaining vehicle internal data through 5G communication, and employing a D-S evidence theory for fusion; performing spatio-temporal feature processing on the fused data, constructing a spatio-temporal matrix, and extracting features by using a spatio-temporal convolutional neural network; constructing a parking risk assessment model by combining a gating circulation unit of an attention mechanism based on the extracted features, and outputting a parking risk probability in a period of time in the future; according to road sections, time periods and weather conditions, an early warning threshold value is dynamically adjusted by using a reinforcement learning algorithm, multi-channel early warning is triggered when the threshold value is exceeded, and the system comprises a data acquisition and transmission subsystem, a data processing and analysis subsystem and an early warning decision and release subsystem. The objective of the invention is to realize prediction and timely and effective early warning of the parking risk of the high-speed vehicle.

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 a dynamic maximum pressure traffic signal control method based on reinforcement learning, and belongs to the technical field of traffic. The method comprises the steps of traffic flow data collection and real-time traffic state observation. Establishing a dynamic maximum pressure traffic signal control method dynamically associated with dynamic traffic pressure parameters and upstream and downstream pressure; establishing a dynamic maximum pressure traffic signal control model based on reinforcement learning; setting a simulation environment and parameters, and training and testing a dynamic maximum pressure traffic signal control model; the invention relates to a dynamic maximum pressure traffic signal timing optimization strategy. According to the method, the advantages of a data driving method and a theoretical control model are combined, and a maximum pressure control model is optimized based on a deep reinforcement learning technology with a high self-adaptive characteristic. According to the method, the defect that a maximum pressure signal control model only takes a single pressure parameter as a pressure weight and is insufficient in consideration of upstream and downstream pressure association of the intersection is overcome, and the model has higher adaptability to complex and changeable traffic conditions.

The invention provides a stock matching method and device and electronic equipment, and relates to the technical field of cargo transportation. The shipping matching method comprises the steps of determining shipping information corresponding to a to-be-matched cargo source, wherein the shipping information comprises the to-be-matched cargo source, a to-be-selected driver corresponding to the to-be-matched cargo source, a first shipping score, a driver cargo source pool and a second shipping score corresponding to the driver cargo source pool; determining the goods source selection probability of each goods source selected by the to-be-selected driver based on the to-be-matched goods source, the first shipping score, the driver goods source pool, the second shipping score and the historical transaction order of the to-be-selected driver; and determining a target driver matched with the to-be-matched goods source from the to-be-selected drivers by taking the maximum sum of the transaction probabilities of the to-be-matched goods source and all goods sources in the driver goods source pool as a target based on the shipping information and the goods source selection probability. According to the scheme, invalid exposure of the goods source can be reduced, and exposure resources of the goods source are saved.

The invention discloses an intelligent parking space guiding system for a hospital parking lot, relates to the technical field of intelligent traffic, and is used for solving the problems that the hospital parking lot is seriously jammed in peak hours and special vehicles lack exclusive passing authority. The system comprises a parking space monitoring module, a vehicle identification module, an intelligent guiding module and a user interaction module. The parking space state is monitored in real time through the multi-mode sensor array; recognizing entering and exiting vehicles through a deep learning recognition algorithm based on cameras deployed at entrances and exits, and obtaining corresponding vehicle information; based on parking space state information and vehicle identification information, a route optimal value is dynamically calculated, and a whole-course navigation service from an entrance to a target parking space is provided for a driver, so that accurate detection of the parking space state, special vehicle priority distribution, high-low peak dynamic route planning and whole-process intelligent guidance are realized, the hospital parking efficiency is remarkably improved, and the hospital parking experience is improved. And the congestion condition in the peak period is relieved.

The invention provides an unknown key scene identification method and system based on an intersection scene space, and relates to the technical field of intelligent traffic, the method comprises the following steps: constructing a road network model based on topological structures of different types of intersection scenes, the road network model comprising the intersection scene space; scene sampling is carried out in the intersection scene space to obtain a plurality of known key scenes, and the known key scenes comprise at least one known risk factor; a plurality of key scene subspaces are constructed, a first center is determined, and each key scene subspace comprises at least one known key scene; and calculating an orthogonal basis of the key scene subspace, taking the first center as a center, extracting an unknown key scene space in the key scene subspace along the direction of the orthogonal basis, and sampling in the unknown key scene space to obtain an unknown key test scene set. The unknown key scene identified by the scheme is used in the test of the automatic driving technology, and the test comprehensiveness can be improved.

The invention provides a path planning method and system based on four-AGV cooperative carrying, and relates to the technical field of automatic logistics transportation. The method comprises the steps that a high-precision grid map is constructed, a global path is generated in combination with a simulation improved A * algorithm, dynamic weight factors are adaptively adjusted according to the obstacle density, and the path search efficiency is optimized; a time window constraint method is adopted to divide path segments and dynamically allocate time windows; real-time sensing of dynamic obstacles is fused, and a local path is adjusted based on Kalman filtering and a dynamic priority scheduling algorithm; a one-master and three-slave cooperative control architecture is adopted, a master AGV tracks a simulation path, and slave AGVs dynamically adjust control parameters through a fuzzy adaptive PID algorithm; the synchronous jacking operation of the four AGVs feeds back the state to the central server in real time through the Ethernet, so that the synchronism and safety of collaborative carrying are ensured. According to the invention, the technical problems of high path conflict rate, low trajectory tracking precision and slow obstacle avoidance response of a multi-AGV system in a complex environment are solved.

The invention discloses a roadside fusion tracking method and system based on Leiye frame fusion, and relates to the technical field of intelligent traffic. The method comprises the steps that when a certain MEC server triggers a fusion instruction and a timer of the MEC server reaches trigger time, image sensing data and radar sensing data in current cache data are acquired; performing frame fusion on the image sensing data and the radar sensing data to generate a thunder fusion frame; and carrying out target continuous tracking based on the Reighting fusion frame and the historical fusion data to obtain a tracking result, updating the historical fusion data, calculating to obtain a complete credible interval and a basic credible interval of the MEC server, and outputting the Reighting fusion frame and the tracking result at the same time. According to the method, the perception results of radar vision are fused, then multi-target tracking is carried out based on the fusion result, the continuous tracking effect of the target is good, the perception precision is high, the universality is high, the detection effect is good under the conditions of a static state and a long distance, and the problems of ghosting, splitting, position jumping and the like are effectively solved.

The invention discloses a large-load unmanned aerial vehicle group structure, a control method, system and device and a storage medium, and relates to the technical field of material transportation in the power industry, and the method comprises the steps: selecting a proper unmanned aerial vehicle connection mode according to the working environment of transportation and the load requirement, and constructing an unmanned aerial vehicle group and a material carrying platform; based on the constructed unmanned aerial vehicle group and the material carrying platform, synchronous take-off is achieved in combination with environment and load data; the horizontal attitude of the material carrying platform is controlled through a hierarchical cooperative control architecture based on the synchronous take-off unmanned aerial vehicle group data; on the basis of unmanned aerial vehicle group data after horizontal attitude flight is controlled, the height and the attitude are combined, and a multi-mode hierarchical control strategy is adopted, so that the unmanned aerial vehicles safely land and unload materials; the method can effectively guarantee the safety, stability and high efficiency of transportation in the whole process of cargo transportation, and has important practical application value.

The invention discloses an intelligent scheduling and transportation system for a port unmanned flat car, which belongs to the technical field of port unmanned transportation, and is technically characterized by comprising a cloud intelligent scheduling platform, an unmanned flat car transportation system and a car cloud collaboration module, the cloud intelligent scheduling platform comprises a vehicle cloud control system, an operation task management system, an automatic driving operation monitoring system, an intelligent network connection operation scheduling system and a port scheduling collaborative management system; the unmanned flat car transportation system comprises intelligent sensor hardware, global fusion situation awareness, multi-redundancy fusion positioning, high-precision motion control and anthropomorphic driving decision planning. And the vehicle cloud cooperation module comprises task instruction interaction and vehicle real-time state reporting, and has the advantages that through seamless butt joint with automatic container hoisting equipment, the whole-process unmanned operation of transportation and loading and unloading operation is realized, the port operation efficiency and the intelligent level are improved, and the intelligence and the high efficiency of the port unmanned transportation system are greatly improved.

The invention relates to the technical field of intelligent traffic, in particular to an integrated fog measurement type highway safe driving guidance device and method, and provides the following scheme that local visibility data is acquired based on a plurality of highway safe driving guidance devices and uploaded to a region controller to generate guidance information. And the regional controller performs research and judgment calibration and gradient calibration on the data, corrects measurement errors and generates road visibility space distribution information by screening calibration anchor points and adopting an exponential attenuation function and iterative gradient calculation. Based on the information, a connectivity algorithm is used to identify a fog area, and in combination with road environment and historical accident data, an optimal induction strategy is matched, and induction information is dynamically adjusted. The fog area detection accuracy is improved, the induction information matching is optimized, and the driving safety and the passing efficiency under the low-visibility condition are improved.

The invention relates to the technical field of brick sculpture transportation, in particular to a brick sculpture conveying device which comprises an assembling track structure and an assembling conveying belt, a driving mechanism is arranged at the bottom of the assembling track structure, the assembling conveying belt comprises a plurality of rubber connecting pieces, and brick sculptures are placed on the rubber connecting pieces; symmetrically-distributed baffle connecting blocks are arranged on the upper side face of the splicing conveying belt, brick carving baffles are rotationally arranged on the baffle connecting blocks, and each brick carving baffle comprises a blocking plate and a baffle rotating shaft. The splicing track structure and the splicing conveying belt are both of splicing structures, a certain length can be spliced according to needs, the use range is widened, the use flexibility is improved, meanwhile, the whole device can be spliced through flexible splicing, the whole device is convenient to carry and transport, the driving mechanism directly drives the splicing conveying belt, and the splicing track structure and the splicing conveying belt are convenient to use. And after a certain angle is adjusted, the brick bodies can be conveyed horizontally and upwards, and the flexibility and efficiency are further improved.

The invention belongs to the technical field of intelligent traffic, and particularly relates to a road intersection perception and scene understanding method combining a deep learning algorithm and a large language model, and the method comprises the steps: obtaining traffic visual data; performing deep learning target detection based on the traffic visual data to obtain an object corresponding to the traffic visual data, and recording related dynamic information to form a prompt label; based on a traffic monitoring image and a prompt label of a certain view angle of the intersection, scene description of a corresponding view angle of the intersection is obtained through processing of a large language model; and based on the intersection single-view scene description and the prompt label, enhancing the chained thinking fine-tuning large language model to obtain the traffic scene understanding and semantic description of the whole intersection.