848 results about "Traffic efficiency" patented technology

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

The invention discloses an urban traffic intelligent regulation and control system and method based on digital twinning. The method comprises the following steps: S1, collecting and fusing multi-source data in real time; s2, constructing a digital twinborn model; s3, traffic flow prediction and optimization; s4, multi-system cooperative control is carried out; the system comprises the following modules: a multi-source data real-time acquisition and fusion module which comprises a video data acquisition and processing unit, a geomagnetic sensor network construction unit and a floating car data integration unit; the digital twinborn model building module comprises a three-dimensional road network model building unit and a data fusion and calibration unit; the traffic flow prediction and optimization module comprises an intelligent prediction model unit and a real-time optimization control unit; and the multi-system cooperative control module comprises an emergency priority passing system unit, a reversible lane dynamic adjustment unit and a parking lot induction unit. The traffic efficiency can be remarkably improved, the cooperative response speed is greatly increased, and carbon emission is effectively reduced.

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

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

The invention provides a traffic participant behavior analysis-based edge calculation signal lamp control method and system, and the method comprises the steps: obtaining video stream data of a target intersection, carrying out the frame-by-frame behavior state analysis of the video stream data through a spatial-temporal feature coding network, extracting the behavior state features of each traffic participant, and carrying out the analysis of the behavior state features of each traffic participant; and inputting the behavior state characteristics into a preset behavior matching model, generating a behavior triggering identifier of the traffic participant in the current time window, generating a signal lamp control instruction of an edge computing node according to the time sequence relevance between the behavior triggering identifier and the phase state of the current signal lamp, and sending the signal lamp control instruction to the edge computing node. And finally, a signal lamp phase switching time sequence of the target intersection is adjusted based on the signal lamp control instruction, so that the traffic participants meeting the traffic rule conflict condition obtain the traffic priority under the signal lamp phase switching time sequence. According to the method, the traffic accident risk is reduced, and meanwhile, the overall traffic efficiency of the intersection is optimized by dynamically adjusting the minimum response period and the phase duration parameters.

The invention relates to a city-level path guidance method and system based on dynamic clustering in a vehicle-road cloud cooperation scene, and the method comprises the steps: constructing a vehicle-road cloud cooperation architecture, and collecting traffic data; the traffic management center updates the road weight and initializes a penalty matrix, and performs congestion detection at the same time; when a congested road section is detected, vehicles possibly affected by congestion are screened out, and a to-be-planned vehicle set is formed; performing spatial clustering on the to-be-planned vehicle set to obtain a plurality of vehicle clusters, and constructing an independent path planning sub-graph for each vehicle cluster; distributing priorities for the vehicles in each vehicle cluster; planning an alternative path for the vehicle; performing batch dynamic adjustment on the road weight and the penalty value on the path planning subgraph; the traffic management center issues the planned alternative path to the corresponding vehicle through the road side unit, and the vehicle runs according to the new running path to realize path induction; finally, reasonable distribution of traffic flow, improvement of road network traffic efficiency and balanced allocation of time-space resources are realized.

The invention belongs to the field of artificial intelligence and intelligent traffic systems, particularly relates to a vehicle path planning system based on multi-agent collaboration, and aims to solve the problems of path response lag, insufficient global optimization ability and conflict between individuals and group targets in a dynamic traffic environment. The system adopts a three-level collaborative architecture of a central coordination server, a regional scheduling agent and a vehicle-mounted agent, realizes macroscopic regulation and local resource allocation through a global guidance potential field and an improved auction algorithm, and improves the dynamic adaptive capacity by combining event-driven replanning and a robustness verification mechanism. Different scheduling and multi-time scale coupling operation of heterogeneous vehicles are supported, and the road network passing efficiency and the path reliability are effectively 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 aims to provide a large-range urban road network intelligent management and control method based on mobile phone signaling data, and belongs to the technical field of traffic management and control. Real-time traffic flow parameters are obtained by preprocessing the mobile phone signaling data, a high-fidelity microscopic traffic simulation environment is constructed, and on the basis, the real-time traffic flow parameters are obtained; the method comprises the following steps: establishing a multi-dimensional evaluation index system containing operation safety and efficiency, constructing a macro-micro collaborative double-layer planning model, adopting a deep reinforcement learning algorithm, taking continuous-discrete mixed decision variables such as intersection signal timing and a variable lane strategy as optimization objects, carrying out strategy learning and iterative optimization through an Actor-Critic architecture, and carrying out optimization on the optimization objects. And outputting the optimal control strategy combination. According to the invention, dynamic and accurate cooperative management and control of the large-range urban road network are realized, and the traffic efficiency is remarkably improved while the operation safety is guaranteed.

The invention provides a vehicle-road cloud collaborative mixed traffic flow optimization method, system and device, and relates to the technical field of intelligent traffic, and the method comprises the steps: extracting a mixed traffic flow feature set containing a road environment, a vehicle state and a driver behavior through obtaining and synchronously fusing the multi-source traffic data of a cloud end, a road end and a vehicle end; according to the method, the intention of a driver is predicted by using a double-layer LSTM model, a vehicle trajectory is predicted in combination with an RNN-LSTM model, confidence fusion analysis of the intention and the trajectory is performed on this basis, accurate prediction of traffic conflict events is realized, and global optimization of traffic flow is supported. According to the method, the intention recognition and trajectory prediction precision in a complex mixed traffic environment is effectively improved, the intelligent decision-making capability of a traffic management system is enhanced, and an efficient technical means is provided for relieving traffic congestion, improving the traffic efficiency and guaranteeing traffic safety.

The invention relates to the technical field of resource scheduling, in particular to a smart park resource scheduling method and system, and the method comprises the steps: constructing a heterogeneous information network representing the current state of a park; performing feature extraction on the heterogeneous information network by adopting a graph attention convolutional network, and generating a state embedding vector fused with high-order neighborhood information; inputting the state embedding vector into a scheduling strategy model, and predicting the expected variation of the candidate scheduling action to future park energy consumption, security index and traffic efficiency; according to the state embedding vector and an external management instruction, determining an optimization target of a current scheduling period from a preset operation normal form, and allocating a dynamic weight; and calculating a comprehensive utility score of each candidate scheduling action, and issuing and executing the candidate scheduling action with the highest score as a final scheduling instruction. According to the invention, the refinement level and the overall operation benefit of park management are improved.

The invention relates to a traffic condition-based signal lamp timing method, system and device and a medium. The method comprises the steps that target traffic parameters of all intersections in the current time period are acquired through a multi-source sensor network, the association strength between the intersections is calculated based on a preset traffic flow transmission model, an intersection set with the association strength exceeding a threshold value is screened, and a regional linkage adjustment framework is constructed. And when it is detected that the traffic flow density at the intersection exceeds a preset threshold value, priority ranking is performed on timing adjustment requirements according to the association strength and the over-limit degree, signal lamp timing parameters of the affected adjacent intersection are adjusted, and a regional timing scheme is generated. According to the method, by dynamically sensing the traffic flow state and quantifying the intersection cooperation relation, regional cooperation optimization of signal lamp timing is achieved, and the road network traffic efficiency and the traffic pressure balance are effectively improved.

The invention provides a vehicle safety early warning method and system for a highway network, relates to the field of traffic safety management and control, and solves the technical problem of inaccurate early warning in the prior art. The method comprises the following steps: acquiring environment data of a highway; dividing the expressway into a plurality of areas, and calculating an initial risk value of each area according to the environment data; calculating a risk propagation value of each region based on a dynamic risk propagation equation according to the initial risk value, and determining a risk level of each region according to the risk propagation value; and triggering early warning measures of each area according to the risk level. The method is used in the real-time safety management and control process of the highway network in severe weather, accurate prediction and graded early warning of risks can be realized through multi-dimensional data fusion and dynamic risk propagation modeling, and the safety and traffic efficiency of the highway network under complex meteorological conditions are improved.

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

The invention provides a traffic signal lamp intelligent control method based on a fiber bragg grating sensing network, and relates to the technical field of traffic signal lamps, and the method comprises the steps: obtaining the detection data of each fiber bragg grating sensing line, and enabling the fiber bragg grating sensing lines to be transversely laid at a selected traffic crossroad along a lane, three fiber bragg grating sensing lines parallel to the stop line are laid on each road; determining the length range of the motor vehicle queuing length of each lane according to the detection data of each fiber Bragg grating sensing line; and controlling the working state of the corresponding traffic signal lamp according to the length range of the motor vehicle queuing length of each lane. The actual traffic flow can be determined through the detection data of the fiber Bragg grating sensing lines, the green light time can be dynamically allocated based on the actual traffic flow, the vehicle queuing time is shortened, and the passing efficiency can be effectively improved.

The invention provides an intelligent path planning method and system based on dynamic road condition prediction. The method comprises the steps of firstly obtaining multi-source dynamic data of a target area; then, constructing a weather influence prediction model to predict weather influence parameters in a future time period; secondly, constructing a weather-traffic coupling model, and respectively establishing correlation models of corresponding precipitation, traffic flow density and average vehicle speed according to road types through historical data analysis, so as to estimate the traffic efficiency of each road section under a dynamic weather condition; and finally, generating a plurality of candidate paths according to the passing efficiency, screening out an alternative path set meeting a multi-target optimization condition from the candidate paths, performing simulation evaluation on the alternative path set, and determining an optimal path according to a simulation result. Compared with a traditional static path planning method, the method has the advantages that the responsiveness of a traffic system to meteorological disasters is remarkably improved, and predictable navigation service is provided for intelligent network connection vehicles.

The invention discloses a global traffic signal control method based on a regional context enhanced large language model, and the method comprises the steps: dividing a target region into K traffic signal lamp control regions according to a traffic network structure of the target region; global real-time traffic information of the target area is collected and converted into global traffic text information; and inputting the global traffic text information into the large language model after LoRA fine tuning, and outputting a global traffic signal lamp control strategy of the target area, namely a traffic signal lamp phase of each intersection. In the fine tuning process of the large language model, an optimal phase decision is generated by using a reinforcement learning agent, a decision result is converted into text data, a region-level optimal phase decision data set is constructed according to an adjacent region division strategy, fine tuning is performed on the model by using the data set, region loss is calculated, and the large language model is optimized through the loss. According to the invention, optimization of global traffic signal lamp control can be realized, the overall traffic delay is reduced, and the traffic efficiency is improved.

A lane-changing trajectory planning method, an autonomous driving method, and a related apparatus. The autonomous driving method comprises: on the basis of first motion state information of an intelligent driving device and second motion state information of an opponent, controlling the intelligent driving device to change to a target lane within a first lane-changing time period and to be located in front of the opponent, wherein the opponent is located on the target lane for the intelligent driving device, and is located between a first position and a second position, wherein the first position is located behind the intelligent driving device and longitudinally distant from the intelligent driving device by a first distance, and the second position is located in front of the intelligent driving device and longitudinally distant from the intelligent driving device by a second distance. The technical solution of the present application can be applied to intelligent vehicles such as a new energy vehicle and an electric vehicle, and facilitates improvement of lane-changing success rates and traffic efficiency when the vehicles are in an autonomous driving mode in complex lane-changing scenarios such as merging into congested roads and merging into ramps.

The invention discloses an expressway toll collection robot system, relates to the technical field of expressway toll collection, and constructs a full-process automatic expressway toll collection robot system through collaborative design of a toll collection control module and a special situation scheduling module. The charging control module integrates various types of hardware, realizes automatic identification of vehicle information, fee calculation and passage control, replaces manual work to complete a standardized charging process, and remarkably reduces manual operation errors and time consumption. The special situation scheduling module is used for quickly positioning the type of a special situation through object association relationship analysis for an abnormal state in a charging process, and scheduling staff to intervene; different from a traditional post response mode, the module predicts the severity level of a special situation based on flow characteristics, equipment states and environment characteristics by training a special situation prediction model and configures the number of workers in advance, so that lane congestion can be effectively avoided, and the overall traffic efficiency is improved.

The invention provides a vehicle-road cooperation method and system, and the method comprises the steps: S1, obtaining original road side data and original vehicle end data which are reported by a road side and a vehicle respectively, and carrying out the standardization processing; and S2, carrying out sensing fusion on the standardized roadside data and the vehicle end data to obtain fused sensing data, carrying out sensing calculation by using the sensing data, and meanwhile, re-engraving the original vehicle end data, the original roadside data, the fused sensing data and a sensing calculation result. And S3, acquiring duplicated data in real time, performing quasi-real-time scene calculation in combination with the signal lamp, the roadside equipment and the vehicle end equipment, and acquiring and duplicating a quasi-real-time scene calculation result. And S4, issuing the quasi-real-time scene calculation result to the vehicle end or the roadside, and sending an early warning signal to the vehicle end or controlling roadside equipment. According to the invention, accurate replication and dynamic early warning of the vehicle-road data are realized, the traffic efficiency and the driving safety are improved, and the reliability and expansibility requirements of a large-scale vehicle-road collaborative network are met.

The invention is suitable for the technical field of automatic driving, and provides an automatic driving vehicle blind area track prediction and planning method based on Transform, and the method comprises the steps: firstly constructing a key entrance point, calculating a shielding region, and then generating and maintaining a belief state of a ghost target in combination with a risk score; then, real and ghost targets are coded into Token in a unified mode, the Token is input into Transform for interactive modeling, and multi-modal track distribution is output; and finally, a variable anisotropic repulsive force potential field is constructed based on the predicted risk, and a safety control instruction is obtained through optimization solution and hard safety filtering. According to the method, continuous probabilistic estimation and interactive reasoning of the potential risk of the blind area are realized, a flexible defensive track can be generated according to the risk confidence and the dynamic state of the vehicle, and the traffic efficiency is guaranteed while the safety is improved.

The invention relates to the field of intelligent traffic control, discloses an intelligent control method and system for a bidirectional driving wheel type sightseeing train, and aims to solve the problems of low efficiency, poor safety, weak adaptability and the like caused by dependence on a fixed track, path solidification and control lag of a traditional sightseeing train. According to the method, a dynamic environment map is constructed by deploying a front-back symmetric multi-mode sensor array and fusing laser radar, vision, millimeter wave radar and inertial data; an improved mixed A star algorithm is adopted to plan a global collision-free trajectory, high-precision tracking is realized in combination with model prediction control, bidirectional autonomous operation, multi-vehicle intelligent cooperation and full-scene adaptive control under a trackless condition are realized, and the traffic efficiency, safety and intelligent level of a scenic spot are remarkably improved.

The invention relates to the technical field of ETC special situation processing, in particular to an efficient ETC special situation processing system and a working method thereof, and the system comprises a special situation monitoring module which monitors the ETC lane transaction state in real time and obtains ETC lane special situation information; the special situation scene diagnosis module is used for carrying out special situation scene diagnosis analysis on the ETC lane special situation information to obtain special situation type information; the processing scheme matching module is used for substituting the special situation type information into a pre-constructed special situation processing scheme library for comparison to obtain a special situation processing scheme; the multi-modal guide execution module is used for guiding the user to complete special condition processing operation through visual and auditory dual channels based on the obtained special condition processing scheme, and obtaining a special condition processing result; the processing result verification module is used for verifying the special condition processing result in real time, and recording the special condition processing result in response to the fact that the special condition processing result is not abnormal; the parking time of vehicles at the toll station can be shortened, traffic jam is relieved, and the overall passing efficiency of the expressway is improved.

The invention provides a building interior design layout optimization method, and relates to the technical field of indoor modeling and layout optimization, and the method comprises the steps: obtaining building structure boundary data and current situation layout information, constructing an indoor three-dimensional space model, and completing the marking of node passing attributes and a spatial topological structure. On the basis, crowd behavior data of a daily scene and an emergency scene are collected, behavior characteristics such as walking speed, obstacle avoidance response and congestion sensitivity are extracted, and a walking characteristic model used for simulating the individual motion trend is established. And performing multi-scene simulation under a daily event and an emergency evacuation event by using the walking feature model, calculating performance indexes such as passing time, local congestion degree, key node congestion probability and total evacuation time, and identifying a congestion sensitive area. And further constructing a multi-target evaluation model including traffic efficiency, congestion degree and evacuation performance, and performing global optimization on layout adjustable parameters by adopting an algorithm to obtain a candidate layout scheme set.

The invention is suitable for the technical field of intelligent driving regulation, and provides an intelligent traffic flow line optimized driving behavior regulation method and system, and the method comprises the steps: continuously collecting the behavior characteristic data of the following distance, lane changing frequency, accelerator stability and the like of a driver, and generating aggressive or prudent driving style DNA tags; in combination with vehicle navigation destinations and routes, a temporary motorcade containing one aggressive leader vehicle and two cautious following vehicles is established in the vehicles sharing the main road path; sending a team establishment application through the Internet of Vehicles, establishing a temporary collaborative team after confirmation of all the members, and planning an exclusive collaborative route; the leader vehicle broadcasts control instructions such as lane changing and speed changing, a vehicle team is regarded as a whole to calculate a cooperative safety space, instruction transmission intervals are dynamically set, and a unified driving strategy is generated. According to the invention, vehicle complementation and cooperation are realized, the traffic flow line is optimized, the driving behavior is adjusted, and the traffic efficiency and safety are improved.

The invention discloses an urban elevated expressway congestion relieving method and system based on detector data. The method comprises the steps that multi-dimensional traffic parameters of key sections of an elevated expressway are collected in real time through arranged traffic detectors; establishing a multi-level threshold matrix based on the multi-dimensional traffic parameters, and dynamically dividing traffic congestion levels; when the real-time traffic parameters meet the threshold condition of any congestion level, a disposal instruction of the corresponding level is automatically triggered; according to the processing instruction, executing a multi-device cooperative control strategy covering a congestion point space correlation area; and dynamically optimizing the threshold matrix or the cooperative control strategy according to traffic parameter change feedback after strategy execution. According to the invention, by monitoring the traffic data in real time, dynamically setting the congestion threshold and automatically matching the optimal mitigation strategy, rapid early warning and accurate intervention of traffic congestion can be realized, and the traffic efficiency and intelligent management level of the elevated expressway are improved.

The invention is suitable for the technical field of traffic control, and provides a road section pedestrian crossing signal control method and system based on human and vehicle postures. The method comprises the following steps: acquiring instantaneous speed and position data of a head vehicle on each lane in front of a pedestrian crossing in real time, and calculating the maximum crossing time by combining the length of the pedestrian crossing and the type of a pedestrian; determining the length of the upstream and downstream judgment area of the pedestrian crossing based on the road design speed and the street crossing time; monitoring the number of pedestrians and the longest waiting time in the waiting area, and judging whether a first triggering condition is met or not by combining a reasonable waiting time threshold value and a capacity threshold value; judging whether a second triggering condition is met or not by comparing the theoretical driving time of the vehicle with the pedestrian crossing time in the judgment area; and when the first and second trigger conditions are met simultaneously, starting a pedestrian green light phase, otherwise, maintaining the current signal state. According to the invention, the waiting time of pedestrians and vehicles can be effectively reduced, and the intersection passing efficiency and safety are improved.

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 relates to the technical field of intelligent traffic management and control, and particularly discloses a reinforcement learning-based highway hard shoulder dynamic management and control method and system, which dynamically responds to traffic state data of a management and control road section through an optimization model obtained through training, and outputs a hard shoulder management and control strategy taking both traffic efficiency and traffic safety into account. Therefore, the overall optimization of the operation state of the road section is realized in a complex and changeable traffic environment, and the rationality and adaptability of dynamic management and control of the hard road shoulder are improved.

The invention relates to a multi-source data fusion monitoring system linked with tunnel events and warning illumination, and belongs to the technical field of intelligent traffic systems, and the system comprises a multi-source data sensing and preprocessing module which is used for obtaining original data inside and outside a tunnel, and carrying out the normalization processing of the obtained original data, so as to generate a structured data stream; the dynamic event feature comprehensive evaluation module is used for receiving the structured data stream and performing quantitative evaluation to generate an event severity score and an event influence range radius; the heterogeneous linkage strategy generation module is used for receiving the event severity score and the event influence range radius, and constructing a space-time warning field model to generate normalized warning intensity; and the gradient control instruction execution module is used for receiving the normalized warning intensity, converting the normalized warning intensity into an equipment control instruction, and issuing the equipment control instruction to the terminal controller.