1064 results about "Traffic efficiency" patented technology

The invention relates to the technical field of unmanned vehicle control, and discloses a smart city environmental sanitation unmanned vehicle path planning and real-time monitoring method. The method comprises the following steps: firstly, acquiring a multi-source environment sensing data set such as laser radar point cloud data, a camera image sequence and real-time traffic flow information; local road network features are extracted based on laser radar point cloud data, a dynamic target motion prediction map is generated according to a camera image sequence, and real-time traffic flow information is processed to generate a regional traffic efficiency evolution curve. And inputting the data into a path optimization model to generate an initial path sequence, dividing cleaning task priorities, fusing related data and generating a final path planning scheme through a reinforcement learning algorithm. In addition, operation state data of the unmanned vehicle are collected in real time, and a path correction instruction set is generated through an anomaly detection model to update the strategy network. According to the method, the rationality and the operation efficiency of the path planning of the environmental sanitation unmanned vehicle can be improved, real-time monitoring is realized, and the operation safety and the management intelligence level are enhanced.

The invention discloses a perception intelligent driving complex traffic scene dynamic risk prediction method, and relates to the technical field of risk prediction, and the method comprises the steps: obtaining the multi-source traffic dynamic data of a target region in real time; performing space-time alignment processing on the multi-source traffic dynamic data; inputting the space-time coupling feature matrix into a pre-trained depth space-time prediction model to generate a risk thermodynamic map; calculating a dynamic risk index of each traffic sub-region, and generating a risk level distribution sequence; and triggering a self-adaptive early warning response mechanism according to the risk level distribution sequence, and dynamically adjusting operation parameters of the variable information sign and the traffic signal controller. The technical problems of frequent traffic congestion and high accident risk caused by inaccurate traffic risk prediction and difficulty in dynamic adjustment according to the real-time traffic condition in the prior art are solved, and the technical effects of accurately predicting the traffic risk and improving the safety and traffic efficiency in a complex traffic scene are achieved.

The invention provides an edge computing traffic light intelligent decision-making method integrated with AI video analysis, and the method comprises the steps: collecting a real-time traffic video stream of a target intersection through an edge computing device, and extracting a dynamic traffic feature set in the real-time traffic video stream, calling a pre-trained space-time analysis model to carry out multi-modal fusion processing on the dynamic traffic characteristic set, generating a traffic state vector of the target intersection, matching a candidate control strategy in a preset decision rule base based on the traffic state vector, and carrying out parameter adjustment on the candidate control strategy through a strategy optimization model to obtain a target traffic state vector; generating a target signal lamp control parameter; and issuing the target signal lamp control parameters to a traffic signal control terminal of the target intersection, and monitoring traffic state change data of the target intersection in real time to update weight parameters of the space-time analysis model. According to the invention, the traffic efficiency, pedestrian safety and traffic management intelligence degree of urban intersections can be improved.

The invention discloses an intelligent road alarm early warning patrol rescue linkage system and a method thereof, and relates to the technical field of road safety. An intelligent hectometer board, an unmanned aerial vehicle cluster and a mobile terminal are used for collecting accident alarm information and road multi-source data; performing accident target detection classification and abnormal condition classification based on the preprocessed data by utilizing edge calculation, and sending to the cloud; importing real-time road multi-source data into a digital twinborn model to obtain traffic situation spatial-temporal characteristics, and combining accident target detection classification and abnormal condition classification analysis to obtain an accident risk thermodynamic diagram for emergency decision making; and according to the accident risk thermodynamic diagram, generating alarm early warning information of different accident types and different risk levels, according to an emergency decision, generating a linkage mechanism of different departments, and sending the linkage mechanism to different department systems to realize automation of a disposal process. According to the invention, a full-chain linkage system of perception-analysis-early warning-linkage is constructed, rapid response and rescue are realized, the management cost is reduced, and the traffic efficiency is optimized.

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 traffic light intelligent adjustment method and system fused with Internet of Vehicles information, and relates to the technical field of intelligent traffic control, and the method comprises the steps: collecting multi-source traffic data through roadside equipment, and carrying out the time-space alignment and abnormal value filtering; constructing a deep learning prediction model based on the filtered data, and predicting a traffic demand in combination with historical and real-time Internet of Vehicles information; generating a dynamic timing strategy according to prediction, and synchronizing the dynamic timing strategy to an associated intersection through V2X; deploying an edge computing node to adjust traffic light parameters in real time, and sending a suggested speed to the vehicle; a closed-loop feedback mechanism is established, and strategies and models are corrected on line based on trajectory data and efficiency indexes. The technical problem that an existing traffic light timing system cannot fuse multi-source Internet of Vehicles data in real time to carry out accurate demand prediction and adaptive adjustment in a dynamic traffic environment is solved, and the technical effects of remarkably improving the traffic efficiency and shortening the emergency response delay in a complex traffic scene are achieved.

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 discloses an intelligent signal lamp phase optimization method and system based on vehicle infrastructure cooperation. According to the invention, through the dynamic game model, the system can sense multi-dimensional states such as traffic flow and pedestrian waiting time in real time, dynamically adjust demand weights of vehicles, pedestrians and roadside facilities, flexibly allocate green light duration, i.e., multi-direction multi-passing time of vehicles and multi-pedestrian time period to preferentially guarantee the street crossing demand; the average waiting time of vehicles and pedestrians is effectively shortened, the overall traffic efficiency of the intersection is obviously improved, and the suitability of daily traffic operation is obviously improved. The dynamic game model rapidly recognizes special requirements through a state value function, dynamically improves the weight priority of emergency vehicles or pedestrians, guarantees the efficient passing of key scenes (such as the rapid passing of emergency vehicles), avoids the long-time overstock of vehicles in other directions, and improves the safety of emergency vehicles or pedestrians. And the response capability and coordination level of the system to complex and emergent scenes are obviously enhanced.

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 relates to the technical field of intelligent network connection, and discloses a mixed traffic flow-oriented highway ramp confluence optimization control method, which comprises the steps of selecting different control methods according to state information of vehicles entering a formation control area, indirectly guiding manual driving vehicles to form a mixed formation mode through intelligent network connection vehicles, and controlling the formation control area according to the mixed formation mode. The driving states of all vehicles in the motorcade are monitored in real time; and dynamically calculating a confluence sequence and confluence time according to the driving state of the vehicle team entering the confluence control area, and dynamically adjusting the driving path and speed of the vehicle according to the confluence sequence and the confluence time. According to the method, the passing efficiency in the confluence process is greatly improved, and particularly in a complex traffic flow environment, the problem that in a traditional method, complex dynamic interaction during vehicle team confluence cannot be processed is solved.

The invention relates to the technical field of traffic control, and provides a regional green wave coordination control method and system for traffic safety early warning. The method comprises the following steps: traversing a target traffic area to collect traffic data, and constructing a regional dynamic traffic characteristic matrix; performing green wave coordination analysis based on the matrix, and generating a sub-region division scheme; traffic flow characteristics of the subareas are extracted, and green wave coordination control parameters are calculated; formulating and executing an initial control scheme according to parameters, and collecting feedback data; and compensating the optimization parameters according to a feedback result to obtain a final regional coordination control scheme. The technical problem that efficiency and safety are difficult to cooperate due to the fact that traditional green wave control only statically optimizes traffic efficiency and cannot dynamically respond to traffic safety risks is solved, deep coupling of safety early warning and green wave control is achieved through real-time traffic data feedback and dynamic parameter compensation, and the traffic safety is improved. And the traffic efficiency is ensured, and the accident risk is obviously reduced.

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 discloses a reversible lane traffic flow prediction method based on edge calculation, and relates to the technical field of intelligent traffic. The method comprises the following steps: acquiring reversible lane configuration data, service data, traffic flow data and road network data through an edge server and a cloud server; preprocessing the data; constructing a static road network diagram based on the reversible lane configuration data and the road network data; generating a dynamic road network diagram in combination with time granularity; constructing a generative adversarial network model fusing a multi-head attention mechanism and a graph neural network, and performing adversarial training; and deploying the optimized model to an edge server to realize prediction of the reversible lane traffic flow. According to the method, the precision and timeliness of traffic flow prediction are improved, the perception ability of the model to spatial-temporal characteristics is enhanced, and the improvement of traffic efficiency and the intelligent application of edge resources are promoted.

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 belongs to the field of intelligent transportation, and particularly relates to a non-signalized intersection multi-agent scheduling optimization method based on deep reinforcement learning. The method comprises the following steps: establishing an online network and a target network based on an Actor-Critic structure, wherein the online network and the target network are used for dispatching and controlling intelligent networked vehicles; a joint reward function is designed by combining the characteristics of the non-signalized intersection, and factors such as safe distance, traffic efficiency, speed deviation and collision punishment are comprehensively considered; historical interaction data of the vehicle is stored through an experience playback pool, time sequence correlation is broken, and the sample utilization rate is increased; through a deadlock detection and processing mechanism, it is ensured that the vehicle is prevented from being deadlocked in a complex traffic environment; and providing an optimal scheduling strategy for each intelligent network connection vehicle by using the trained model, wherein the optimal scheduling strategy comprises driving speed and passing time suggestions. According to the method disclosed by the invention, the traffic safety of vehicles is effectively ensured, and efficient scheduling and traffic control of the non-signalized intersection are realized while the waiting time is shortened and the traffic jam is relieved.

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 an intelligent intersection AI collaborative decision-making method and system. The method comprises the following steps: acquiring multi-source traffic perception data, and performing feature fusion processing on the multi-source traffic perception data to generate fused feature information; according to the fused feature information, traffic participant behavior prediction is carried out by using a space-time diagram convolutional network to obtain predicted behavior data; based on the predicted behavior data, signal timing optimization processing is carried out through multi-agent reinforcement learning and collaborative decision, and an optimized signal timing scheme is generated; the optimized signal timing scheme is used for adjusting passing parameters of the intelligent intersection. By adopting the method, the traffic efficiency of the intersection can be obviously improved, the traffic jam is reduced, the traffic safety is enhanced, and the intelligent, dynamic and collaborative control of the traffic signal is realized.

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.

The invention discloses a sensing intelligent driving multi-mode traffic signal optimization method and system, and relates to the technical field of traffic signal optimization, and the method comprises the steps: collecting traffic flow data; constructing a multi-mode traffic flow model; connecting a plurality of traffic lights in the target area to obtain a street lamp control signal; based on the traffic flow data and the street lamp control signal, using the multi-mode traffic flow model to predict a traffic flow change trend; and configuring a timing optimization scheme of a plurality of traffic lights in the target area according to the traffic flow change trend prediction result. According to the method and the device, the technical problem of low traffic intersection passing efficiency caused by the fact that a traffic signal lamp timing scheme cannot be dynamically adjusted accurately according to real-time traffic conditions and multi-mode traffic behavior interaction relations in the prior art is solved, and the purposes of optimizing the traffic signal lamp timing of the target area according to the prediction result and improving the traffic signal lamp timing efficiency are achieved. And the traffic efficiency of the traffic intersection is effectively improved.