1171 results about "Traffic conditions" patented technology

The invention provides a procurement supply chain collaborative intelligent management method and a procurement supply chain collaborative intelligent management system. Belongs to the technical field of supply chain management. Constructing a purchase demand prediction model; dynamically adjusting the purchasing strategy according to the prediction result; the record and the evaluation result are stored on the block chain; the system automatically sends order information to a supplier; the supplier confirms the order through the block chain platform and updates the production progress and the logistics information; the system adjusts an inventory strategy in real time; the logistics state is monitored in real time; the system analyzes data of each link of the supply chain in real time and identifies potential risks. Through dynamic path planning and real-time transportation optimization, the logistics path can be adjusted in real time according to traffic conditions, environmental factors and emergencies, the transportation time is shortened, the transportation cost is reduced, and therefore the overall efficiency of a supply chain is remarkably improved.

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 discloses a control strategy model construction method based on big data, and relates to the technical field of intelligent control, a traffic situation deduction and emergency strategy generation module uses a multi-agent traffic simulation technology, simulates a traffic situation in combination with real-time traffic data, evaluates risks and early warns potential crisis by integrating multiple factors, and provides a control strategy model for the traffic situation deduction and emergency strategy generation module. And after an early warning is received, an emergency strategy is generated by using an intelligent algorithm, the strategy is evaluated and optimized by using virtual rehearsal, a result is fed back to a management department, and a synergistic effect with other modules is achieved. Traditional traffic detection equipment data is integrated with multi-source heterogeneous data such as mobile phone signaling, shared bicycle and online hailed bicycle GPS, intelligent vehicle-mounted equipment and public traffic operation, more comprehensive and accurate information such as traffic flow, flow direction, travel behavior track and the like is provided, and rich and accurate basis is provided for traffic management decisions.

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

The invention provides a multi-modal remote sensing and knowledge graph-based urban planning decision-making method and system, and the method comprises the steps: integrating multi-source heterogeneous data, achieving the feature alignment and fusion of an optical image and SAR data in a satellite remote sensing image through a deep learning technology, and generating an urban ground feature feature vector; associating the urban ground feature feature vector with an urban planning policy database, outputting a structured early warning report of an illegal construction early warning event set and a policy compliance label, and forming a dynamic policy constraint condition for subsequent multi-objective optimization; processing historical traffic flow data based on the dynamic graph model, and outputting a time-space distribution prediction result of future traffic conditions; and generating a Pareto optimal city planning scheme by combining multi-objective optimization with a spatial-temporal distribution prediction result of a future traffic condition. According to the method, high-precision urban surface feature classification, real-time violation extension early warning and traffic flow accurate prediction are realized through multi-modal remote sensing data fusion and a space-time knowledge graph technology, and multi-target optimization and digital twinborn verification are combined, so that the planning efficiency is improved, and extension applications such as carbon neutralization are supported.

According to an embodiment, it is a system comprising, a sensor, a communication module, and a processor, wherein the processor storing instructions in a non-transitory memory that, when executed, cause the processor to scan, via the sensor of a host vehicle at a first frequency to observe surroundings for an erratic behavior, determine an erratic vehicle by analyzing for the erratic behavior, scan via the sensor at a second frequency to determine an identity of the erratic vehicle and analyze a traffic condition around the erratic vehicle, determine a possibility of a collision, alert via the communication module a nearby vehicle by sending a message, and determine an evasive action to avoid the collision.

The invention relates to an urban road carbon emission prediction and optimization control method and system, and belongs to the technical field of road traffic carbon emission monitoring, and the method comprises the steps: obtaining real-time monitoring data of a to-be-detected road, and carrying out the preprocessing; performing space-time alignment on the preprocessed real-time monitoring data, extracting dynamic traffic features and static road features, and generating a space-time feature matrix; inputting the spatial-temporal characteristic matrix into a pre-trained carbon emission prediction model to obtain a prediction result matrix of the to-be-detected road; on the basis of the prediction result matrix, identifying a road grid of which the carbon emission intensity prediction value exceeds a preset threshold value as a high emission area; acquiring real-time traffic state data of the high emission area; and based on the real-time traffic state data and the prediction result matrix of the high emission area, constructing a multi-objective optimization function and carrying out solving to obtain an optimization control strategy of the high emission area. According to the invention, traffic carbon emission optimization control and real-time traffic condition synchronization can be realized.

The invention discloses a vehicle and road cloud integrated traffic control method for automatic driving lane changing, and the method comprises the following steps: S1, carrying out the multi-mode fusion of preprocessed multi-source sensor data, and generating environment state parameters; s2, on the basis of the environment state parameters, predicting a future traffic scene by using a traffic flow prediction model, performing risk assessment on a traffic flow prediction result, and generating a risk map containing spatio-temporal information; s3, according to the current traffic condition, the vehicle characteristics and the task requirements, self-adaptive role allocation is carried out on the vehicles running on the expressway, and a local cooperation network is established based on the roles allocated to the vehicles; s4, under the framework of the optimized traffic control strategy, processing the current traffic condition data and the traffic flow prediction result by using a distributed negotiation algorithm based on an auction mechanism, generating a plurality of candidate lane changing schemes, evaluating the candidate lane changing schemes, and determining an optimal lane changing strategy; and S5, according to the optimal lane changing strategy, coordinating each driving vehicle on the expressway to execute lane changing operation.

The invention relates to the technical field of online car-hailing order dispatching, in particular to an online car-hailing intelligent order dispatching method and system based on a multi-dimensional rule, and the method comprises the steps: building a standardized multi-dimensional feature set through collecting passenger orders, driver states, traffic conditions, environmental weather and regional events, and carrying out the combined modeling of regional order demands and driver online conditions based on a recurrent neural network, thereby achieving the intelligent order dispatching of the online car-hailing. Outputting expected passenger and driver thermodynamic distribution, constructing expected difference thermodynamic distribution according to the expected passenger and driver thermodynamic distribution, training a scheduling strategy through reinforcement learning, enabling an unloaded vehicle to actively migrate to a supply and demand gap area before an order is generated, and further combining an order generation condition and driver expected off-duty information to obtain an order generation result; and calculating the accumulated driving time required by the driver to return to the expected off-duty place after the driver completes the service, and selecting the work order for distribution when the time difference between the accumulated driving time and the expected off-duty place is minimum, thereby realizing accurate matching between the order distribution strategy and the driver work cycle. The empty driving rate is effectively reduced, and the quick response capability and the resource configuration efficiency of the system in a dynamic supply and demand environment are improved.

The invention discloses a monitoring and early warning method, device and equipment based on a multi-modal large model and a medium, and relates to the technical field of monitoring and early warning, and the early warning method comprises the following specific steps: S100, multi-modal data collection and fusion: through sensors and cameras deployed in shopping malls, elevators and surrounding road areas, carrying out multi-modal data collection and fusion; the method comprises the following steps: comprehensively collecting multi-modal data of personnel flow, elevator operation and traffic conditions, preprocessing original data, and integrating different modal data into a unified feature vector by using a modal entropy weight fusion function; through a multi-modal data acquisition and fusion technology, the system can comprehensively capture complex environment information of shopping malls, elevators and surrounding road areas, data of different modals are efficiently integrated into a unified feature vector by using a modal entropy weight fusion function, the problem of multi-source heterogeneous data processing is solved, and the system can be widely applied to the field of multi-source heterogeneous data processing. And the accuracy and efficiency of data fusion are greatly improved.

The invention relates to the technical field of traffic management, in particular to a road traffic dispersion and emergency command system construction method based on multi-source data fusion, and the method comprises the steps: collecting traffic flow sensor data, video monitoring data, meteorological data, social media user feedback data and the like through a multi-source data collection module; a data fusion and processing module is used, and data-level, information-level and decision-level fusion algorithms are adopted to carry out standardization processing, key information extraction and comprehensive evaluation and prediction on multi-source data. And constructing a traffic flow prediction model based on machine learning, such as a long and short-term memory network model, and accurately predicting the traffic flow. And according to the prediction result and the real-time traffic condition, a traffic dispersion strategy making module is used for adjusting signal lamp timing, issuing traffic guidance information and allocating traffic police resources. In the aspect of emergency command, multi-source data are integrated to evaluate event severity, deploy rescue resources and determine an optimal rescue route, and multi-department information sharing and cooperative work are achieved.

The invention provides a pre-training enhanced space-time Transform traffic flow prediction method, which belongs to the field of intelligent traffic, aims to improve the prediction performance in a complex traffic environment by designing a space-time Transform model and a pre-training strategy, and comprises the following steps of: firstly, pre-training an encoder through a mask automatic encoding strategy by utilizing long-term historical traffic flow data, and then pre-training the encoder through a mask automatic encoding strategy; extracting long sequence features by adopting time perception attention embedded with time period information; constructing a pre-training space-time sparse graph generated by a data-driven graph learning layer to capture a dynamic spatial dependency relationship; in the prediction stage, a time feature extraction module and a spatial feature extraction module are designed, an improved multi-head self-attention mechanism is utilized to model correlation between time and space, and long sequence features and spatial-temporal features are adaptively fused through a gating mechanism to obtain a prediction result. According to the method, long-term historical traffic flow sequence information can be effectively extracted, and the traffic flow can be predicted more accurately under a complex traffic condition.

The invention relates to the technical field of information, and discloses an intelligent scheduling method and system for a vehicle-mounted mobile charging system, and the method comprises the steps: obtaining the emergency degree values, geographic positions and charging demand grade values of all charging demands in a current region, and building a demand priority list; acquiring real-time electric quantity states, position information and electric quantity consumption rates of all charging vehicles, and establishing a vehicle availability list; according to the two tables, combined with the target position distance, the road condition and the traffic condition, calculating the estimated time and the residual electric quantity of each charging vehicle arriving at the target position; when the estimated time is smaller than a preset time threshold value and the residual electric quantity meets the requirement, the vehicle is marked as an available vehicle; when the available vehicles are insufficient, judging whether cross-regional scheduling is carried out or not; and optimizing the global scheduling instruction by adopting a scheduling algorithm and dynamically scheduling the charging vehicles. The method can achieve the reasonable scheduling of the charging vehicles, and improves the overall efficiency.

The invention discloses a decision-making model optimization method and device based on a world model, a medium and a product, relates to the technical field of automatic driving, and aims to improve the ability of the world model to understand complex traffic scenes by training the world model in the first stage to understand structured traffic conditions. A world model is trained to predict a future driving scene according to structured traffic conditions and driving actions, and environment prediction and generation quality is improved; and based on the trained world model and the decision model, cooperatively constructing a closed-loop optimization framework based on the world model, inputting the driving action output by the decision model and the corresponding structured traffic condition into the world model, calculating the reward value of the driving action according to the obtained future driving scene sequence, and updating the decision model according to the reward value. According to the method, efficient closed-loop optimization of the decision-making model is realized, the perception capability and decision-making capability of the model can be remarkably improved, the safety and reliability of automatic driving decision-making are improved, and the automatic driving performance of the vehicle in a complex traffic environment is improved.

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

The invention discloses a tunnel ventilation control system and method based on artificial intelligence, and relates to the technical field of ventilation control, and the method comprises the steps: in a tunnel design stage, building an unsteady-state computational fluid dynamics model based on tunnel three-dimensional linear parameters, calculating turbulence structures under different traffic conditions through a large eddy simulation method, and calculating an unsteady-state computational fluid dynamics model; determining an optimal space configuration scheme of the fan group according to the distribution of the velocity field and the pressure field; in the tunnel construction stage, multiple types of sensor arrays are arranged along the vault and the side wall of a tunnel in a layered mode; in the tunnel operation stage, real-time vehicle tracks and speed distribution information of a traffic monitoring system are obtained; establishing a ventilation demand dynamic prediction model based on space-time correlation analysis, constructing a fan cooperative control model considering airflow organization optimization, and solving an optimal operation strategy by adopting a multi-target adaptive weight distribution algorithm; when a fire characteristic signal is monitored, the multiple sets of fans are coordinated to form a relay type smoke exhaust airflow organization.

The invention, which relates to the technical field of intelligent traffic and illumination control, discloses a street lamp vehicle-road cooperative control system comprising a data acquisition module, a data processing module, a control execution module and a decision management module. The data acquisition module is used for acquiring traffic signal lamp phase, vehicle position and speed, street lamp working state and environment illumination intensity data; according to the invention, through a cross-system collaborative algorithm, the traffic flow direction is predicted in combination with the phase of the traffic signal lamp, the brightness of the street lamp illumination area is adjusted in advance, the energy distribution according to needs is realized, the energy consumption is effectively reduced, and through a vehicle-lamp-cloud three-level decision-making mechanism, the computing power priority is dynamically distributed according to the traffic condition. Traffic signal lamp control and vehicle driving path planning are optimized, the road passing efficiency is improved, traffic congestion is relieved, by pushing the lighting optimization path to the vehicle, the vehicle is driven under the proper lighting condition, the emergency brake risk is reduced, and the road traffic safety level is remarkably improved.

The invention discloses an intelligent parking method and device based on multi-source data fusion and a medium, and relates to the technical field of big data technologies. The method comprises the following steps: collecting multi-source parking data, and analyzing the multi-source parking data to generate a parking lot label library; generating a user tag based on historical search records and residence time preferences of the user, and constructing a user information database; when it is detected that a user triggers a parking request, performing multi-dimensional matching calculation on a user tag and a parking lot tag library to obtain a recommended parking space sequence; and optimizing the recommended parking space sequence based on the real-time traffic state and the user credit score to generate a final navigation path. According to the method, a dynamic parking recommendation mechanism is constructed by collecting and analyzing the multi-source parking data and the user preference data.

The invention relates to the technical field of intelligent sensing control, in particular to an intelligent sensing control method and system based on multi-modal sensor fusion. According to the method, vision, millimeter-wave radar, geomagnetic and other multi-mode sensors are deployed to collect traffic data, feature information is extracted after preprocessing, an adaptive weighted fusion algorithm is adopted for fusion, and an intelligent control decision is generated according to a fusion result. The method comprises the steps of signal lamp timing optimization based on fuzzy logic, traffic flow induction based on reinforcement learning and traffic event detection and response based on event rules, and finally an instruction is sent to traffic control equipment to adjust the flow. The advantages of various sensors are integrated, the limitation of a single sensor is overcome, traffic information is accurately and comprehensively obtained, intelligent decision is made on the basis, the traffic condition can be comprehensively and accurately sensed, traffic control is intelligently optimized, the self-adaptive adjusting capacity is high, the traffic operation efficiency can be improved, and the traffic safety can be enhanced.

The invention provides a highway situation awareness method and system, and the method comprises the following steps: collecting camera video stream data to extract traffic flow data and parking data, and inputting the traffic flow data into a graph neural network to construct a traffic flow propagation model; constructing an abnormal event influence evaluation model based on the recurrent neural network and the long-short-term memory network; and through an abnormal event influence evaluation model, outputting influence range data including an affected road segment set and predicted abnormal recovery time, integrating the data and outputting the data to a visual interface. According to the method, the traffic flow state of the expressway is accurately evaluated by collecting, processing and analyzing the video stream data of the roadside camera, and a reliable situation awareness model is constructed in combination with toll station entrance and exit data, portal snapshot data and the like, so that real-time and accurate monitoring and prediction of the traffic condition of the expressway are realized, powerful decision support is provided for traffic management, and the traffic flow state of the expressway is accurately evaluated. And the operation efficiency of the expressway is improved.

The invention provides a multi-modal collaborative delivery scheduling method, which comprises the following steps of: constructing a dynamic environment model according to topographic data and traffic condition data, extracting delivery demand characteristics from real-time order information, and splitting a complex order into a plurality of sub-task units by adopting a task decomposition technology to obtain a decomposed sub-task set; extracting sub-task features of adjacent areas and similar time windows from the optimized sub-task sequence, performing clustering analysis on the sub-tasks by adopting an intelligent combination technology, determining space-time relevance among the sub-tasks, and generating a preliminary sub-task aggregation group; and calculating the path cost and the time cost of each delivery batch according to the final resource allocation scheme and the sub-task aggregation grouping, and carrying out optimization iteration on the batch path by adopting a genetic algorithm to obtain a time table and a route plan of the efficient delivery batch.

The invention provides a 5G communication-based vehicle state sensing and intelligent traffic cooperative scheduling method, and relates to the technical field of intelligent traffic, and the method comprises the steps: receiving high-frequency vehicle state sensing data transmitted by a vehicle-mounted unit in a target road section in real time through a 5G base station cluster; collecting traffic environment sensing data of a target road section through a roadside sensing unit, fusing the high-frequency vehicle state sensing data with the traffic environment sensing data, and constructing a multi-dimensional real-time traffic situation map; performing collaborative analysis based on the multi-dimensional real-time traffic situation map to generate a traffic control instruction set; and issuing to a road side execution unit and a vehicle-mounted unit for cooperative scheduling control of the traffic flow. The technical problems that a traffic system in the prior art often depends on periodic data acquisition and a fixed signal control scheme, traffic control adjustment cannot be carried out in real time according to changing traffic conditions, traffic flow fluctuation or emergencies cannot be rapidly coped with, and then the traffic system is low in efficiency and unstable are solved.

The invention relates to the field of intelligent logistics transportation carbon emission real-time tracking, in particular to an intelligent logistics transportation carbon emission real-time tracking system and method. The method comprises the following steps: firstly, dividing a logistics transportation path into path sections, and generating a candidate path section set at the starting point of each path section to obtain candidate path sections; then, on the basis of the obtained traffic data, vehicle parameters and scheduling plans of the candidate path segments, the predicted carbon emission of the candidate path segments is calculated through a path segment carbon emission prediction algorithm; constructing an optimal path set based on the predicted carbon emissions of the candidate path segments; and finally, on the basis of the path segments in the optimal path set, obtaining actual operation data of the vehicle, and on the basis of the actual operation data of the vehicle, calculating the actual carbon emission. The technical problems that carbon emission prediction and optimization lack pertinence, obvious influences of speed change behaviors such as acceleration, deceleration and idling on energy consumption in actual driving are ignored, and comprehensive influences of vehicle load weight, driving speed and traffic conditions cannot be dynamically reflected are solved.

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 belongs to the technical field of logistics monitoring, and discloses a cable coil rapid transfer monitoring method and system based on the industrial Internet of Things, and the method comprises the steps: obtaining real-time data, building a space model, predicting a vehicle track, calculating space overlapping, and recognizing potential conflicts. A cooperative control instruction is generated according to the conflict type, the task priority, the cable coil specification and the regional traffic condition, an instruction is issued to adjust the vehicle motion state, and potential conflicts are avoided; potential conflicts can be recognized prospectively, a cooperative control instruction is generated based on multiple factors, conflicts are avoided by adjusting the vehicle motion state, vehicle pauses are reduced, the continuity of the transfer process is guaranteed, the transfer speed is increased, and the logistics efficiency is improved.

The invention discloses a large transport vehicle bridge safety assessment method based on fuzzy comprehensive evaluation, which comprises the following steps: constructing a bridge bearing capacity reduction coefficient model, integrating four factors of material performance degradation, structural damage, environmental corrosion and load combination, and determining index weights by adopting an analytic hierarchy process; and a multi-stage evaluation system is established in combination with a fuzzy mathematics theory. A prestressed concrete continuous beam bridge in Jiangsu section of Jinghai highway is taken as an object, finite element simulation is used for analyzing the load effect of large vehicles under independent and mixed passing conditions, and verification shows that when the large vehicles pass at the mixing rate of 20%, the most unfavorable section safety coefficient of the bridge reaches 1.35, and the standard requirements are met. According to the method, fuzzy mathematics and structural mechanics analysis are creatively fused, a standardized process of parameter collection, model calculation and safety judgment is established, the efficiency is improved by 40% or above compared with a traditional static load test, the method is applied to transportation examination and approval of five bridges in Jiangsu province, a scientific basis is provided for selection of large transportation channels, and the risk of bridge damage is effectively prevented.

The invention discloses an intelligent traffic flow statistics and prediction platform based on multi-source data fusion, and relates to the technical field related to traffic prediction, and the platform comprises a data collection layer which is used for collecting road traffic flow data, meteorological data, public traffic operation data and data including traffic condition description on a social media platform; a data preprocessing layer; a data fusion layer; a traffic flow calculation module; and the traffic flow prediction module constructs a prediction model framework based on the space-time convolutional neural network, optimizes hyper-parameters of the ST-CNN, and applies the optimized model to traffic flow prediction. According to the invention, data collected by the annular induction coil and the camera focuses on actual traffic conditions of specific roads and intersections at a microscopic level, and meteorological data, public traffic operation data and social media data reflect traffic influence factors of the whole city or the region from a more macroscopic perspective, so that macroscopic and microscopic aspects are combined, and the traffic influence factors of the whole city or the region are reflected. And the operation condition of the urban traffic system can be known more comprehensively.

The invention discloses a dynamic traffic signal control method based on a large language model, belongs to the technical field of artificial intelligence and intelligent traffic control, and aims to solve the problems of poor phase duration flexibility and weak adaptability caused by the fact that most traditional traffic signal control methods are limited to single-stage traffic phase control. According to the invention, the real-time traffic condition is input to the large language model in the form of natural language, and more efficient and intelligent traffic signal control is realized by using the strong generalization ability and the human-like reasoning mechanism of the large language model. The invention provides an efficient fine tuning architecture comprising two stages, the first stage training model learns an answer normal form and a reasoning track of a large parameter quantity model, and the second stage training model promotes the system to effectively learn excellent strategies and keep away from poorer strategies. The method provided by the invention can provide a new thought and technical support for urban intelligent traffic management in the future.

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.

The invention relates to the technical field of traffic data processing, and discloses a short-time traffic flow prediction method, system and device based on spatial-temporal characteristic analysis and a storage medium, and the system comprises a data processing module, a spatial-temporal characteristic mining module, a characteristic fusion and model construction module and a prediction and result display module. Through cooperative work of the data processing module, the spatial-temporal feature mining module, the feature fusion and model construction module and the prediction and result display module, the spatial-temporal features of the traffic flow are comprehensively and deeply analyzed, the prediction precision is effectively improved, a more reliable basis is provided for traffic management decisions, and the traffic flow prediction efficiency is improved. Multi-source external data is introduced and an attention mechanism is adopted to perform feature fusion, so that the adaptability of the model to complex traffic influence factors is enhanced, a prediction result is more fit with an actual traffic condition, a visual result output mode is adopted, different users can conveniently and quickly know the future change trend of traffic flow, and reasonable travel and management strategies can be conveniently formulated.