53 results about "Intelligent transportation system its" patented technology

The application relates to a traffic capacity estimation method based on traffic basic graph parameter calibration, belongs to the technical field of intelligent traffic systems and traffic flow modeling, and particularly relates to a traffic capacity estimation method based on traffic basic graph parameter calibration. The application aims to solve the problem that the existing method cannot obtain accurate traffic basic graph parameters, resulting in low traffic capacity estimation precision. The process is as follows: step one, obtaining historical traffic flow single vehicle data samples of a road section; processing the obtained single vehicle data samples to obtain a final aggregated data set; step two, obtaining adaptive weights based on kernel density estimation based on the obtained final aggregated data set; step three, obtaining an optimal parameter set of a traffic basic graph model by adopting a particle swarm optimization algorithm based on the adaptive weights; and step four, obtaining traffic capacity based on the optimal parameter set of the traffic basic graph model.

This invention belongs to the field of intelligent transportation systems technology and discloses a method, system, and device for pedestrian trajectory prediction. The invention includes constructing a sparse directed prior interaction graph based on the observed trajectory, reflecting human perception patterns and the urgency of collisions; extracting spatiotemporal features from the historical trajectories of multiple pedestrians under the constraints and guidance of the sparse directed prior interaction graph; outputting a trajectory probability distribution parameter tensor for the prediction period based on the spatiotemporal features; and refining the trajectory probability distribution parameter tensor using prediction uncertainty and the risk of future proximity, obtaining the refined trajectory probability distribution parameters. This invention effectively suppresses noisy connections in the interaction graph, improves the model's sensitivity to potential collision risks, and enhances the safety, consistency, robustness, and interpretability of the prediction results.

The application discloses a kind of attention mechanism trajectory generation and simulation methods for sparse data completion, belong to intelligent transportation and trajectory prediction field.The application fills in trajectory missing value using high-precision sensor and map information, combines graph attention network (GAT) and multi-modal fusion technology;Adopt the attention module based on distance (D-GAT) and based on view (V-GAT), capture the interaction between vehicles, improve the understanding of complex traffic scene;Through prediction supervision generator and multi-modal trajectory generator, combine LSTM and Gaussian mixture model (GMM) to generate multiple possible trajectories, and use Kalman filter for online adjustment, ensure the accuracy and real-time of trajectory.The application realizes the intelligent completion of sparse traffic data and the accurate generation of trajectory, provides reliable data support and decision basis for intelligent transportation system, helps the efficient operation and sustainable development of urban traffic planning and management.

This application provides a two-stage highway lane detection method and related equipment, belonging to the fields of computer vision and intelligent transportation technology. The method includes: a first stage, using a lane detection network to perform preliminary detection on the input image to obtain an initial set of lane lines; and a second stage, constructing a lane line topology refinement module. Using the initial lane lines as graph nodes, edge connections are constructed based on geometric attributes and visual features to form a lane line topology graph. Iterative message passing and feature aggregation are performed through a graph neural network to refine the initial detection results, enhancing the confidence and positioning accuracy of detected lane lines, and recovering missed lane lines based on topological relationship inference. This application can effectively improve the accuracy, completeness, and real-time performance of lane line detection in highway monitoring scenarios, and is suitable for lane-level fine-grained perception tasks in intelligent transportation systems.

The application provides a blockchain-based unmanned aerial vehicle airspace management method and system, and relates to the technical field of intelligent transportation systems. The blockchain-based unmanned aerial vehicle airspace management method and system comprises the following steps: S1: based on multi-source sensor data, S2: based on a trusted dynamic airspace map, S3: based on a privacy protection compliance proof and a trusted dynamic airspace map, S4: based on airspace use authorization and a trusted dynamic airspace map, and S5: based on a dynamic risk insurance policy, airspace use authorization and real-time flight data. Through the integration of multi-source sensor data and the application of Kalman filtering and long short-term memory network deep learning algorithm for real-time data fusion processing, in combination with blockchain network storage and data snapshot verification, a high-precision airspace state model is constructed, the accuracy and reliability of airspace situation awareness are significantly enhanced, and a flight plan compliance proof is generated by using a zero-knowledge proof algorithm.

A rail-based intelligent transportation system includes: a vehicle frame; a control structure fixedly mounted on the upper wall of the vehicle frame and located at its center; and a guide rail structure that movably passes through the vehicle frame and is located at its center on the lower wall. This invention relates to the field of rail vehicle technology. The control console can monitor the weight of passengers on the floor in real time and identify overload or uneven loading. The console can also monitor the speed of the equipment in real time; if the speed is too high or too low, the console can control the motor reducer assembly to decelerate or accelerate to ensure a comfortable experience for passengers. The console can also monitor the distance to other equipment on the track in real time; if the distance is too close, the console will control the equipment to decelerate or even stop to avoid collisions. Through the AI ​​intelligent voice module inside the console, passengers can directly issue voice commands to have the equipment provide information about scenic spots or adjust the operating speed. The console can connect to the network to synchronize local weather in real time; if the weather is too severe, the console will remind the operator that operation is not possible. Through the above structure, intelligent control of the equipment is achieved, greatly improving the safety and comfort of the equipment.

A transportation system can present a set of vehicle operating states to a user of the vehicle and generally includes a system for presenting a set of vehicle operating states to a user of the vehicle, the system including a portion of a vehicle having vehicle operating states, a digital twin system that receives vehicle parameter data from one or more inputs to determine the vehicle operating states, and an interface for the digital twin system to present the vehicle operating states to the user of the vehicle.

This invention belongs to the technical fields of intelligent transportation systems, urban computing, and geographic information systems. It provides a method, device, equipment, and medium for predicting the utilization rate of electric vehicle charging piles. The method includes: preprocessing the raw data of the target urban area; determining a geographic adjacency graph and a travel tidal flow graph based on the obtained static and dynamic features; then performing feature weighting fusion using a time feature embedding method; performing two-layer graph attention aggregation on the geographic adjacency graph and the travel tidal flow graph based on the obtained node fusion features to obtain spatial feature embedding; using a gated recurrent unit to learn the evolution of the charging load of the grid over time; using the prediction error of the effective grid as a loss function and training with a dataset to obtain a grid charging load prediction model for subsequent predictions. This invention can effectively improve the model's environmental adaptability and prediction accuracy in different time periods.

This invention discloses a method for detecting and recognizing oil film on road surfaces based on the YOLOv8 model. By utilizing deep learning technology, this method not only improves the detection speed and accuracy but also adapts to different environments and lighting conditions, effectively identifying oil film areas on the road surface. Furthermore, the implementation of this method does not rely on high-cost hardware and is easy to deploy in existing road monitoring systems, providing an effective technical means for intelligent traffic safety management. Compared to the original YOLOv8 algorithm, this invention improves the detection of oil film on road surfaces, increasing the average recognition accuracy of the YOLOv8 algorithm for oil film on road surfaces. This invention provides a new approach in the fields of intelligent transportation systems and autonomous driving technology, and is significant for improving the visual perception capabilities of autonomous vehicles and for environmental protection.

This invention discloses an ultra-low latency sensing data processing method and system for vehicle-road cooperation, relating to the field of intelligent transportation systems. The method includes: collecting real-time traffic sensing data streams by deploying multimodal sensing devices on roadside units; cleaning, extracting, and encapsulating the data using a front-end lightweight data preprocessing module to generate structured sensing data packets; and then broadcasting these packets to connected vehicles within the communication range via a direct communication link. The vehicles then fuse the onboard sensing data with these data packets, performing spatiotemporal alignment and multi-source fusion to generate a real-time neighborhood traffic situation map, which is then visualized on the onboard display interface. This invention solves the technical problems of redundant data processing, low transmission efficiency, lack of continuity in cross-regional sensing, and insufficient sensing reliability in traditional vehicle-road cooperation systems. It achieves optimized processing of the entire vehicle-road cooperative sensing data link and efficient collaborative interaction between vehicles and roads, shortening data processing and transmission time.

The present application relates to the field of intelligent transportation systems and road safety technology, in particular to a risk real-time evaluation method and system for merging area and diverging area in human-machine mixed driving environment, comprising the following steps: S1, real-time collection of multi-traffic data of merging area and diverging area, S2, real-time calculation and output of risk feature set based on multi-traffic data; S3, based on the risk feature set, through a dynamic weighted risk fusion model, real-time calculation and output of a comprehensive risk value representing the current regional safety situation; the dynamic weighted risk fusion model dynamically adjusts the fusion weight of each risk feature according to the real-time traffic state parameter; S4, according to the risk level of the comprehensive risk value, generating and issuing a customized intervention instruction matched with the driving demand of the merging area or diverging area; meeting the real-time requirement of highway risk response, realizing accurate real-time risk evaluation in human-machine mixed driving scene.

The application relates to the cross technical field of artificial intelligence and intelligent transportation systems, and discloses a low-altitude-highway infrastructure collaborative layout optimization method based on AI perception. The method deploys hierarchical composite infrastructure nodes, fuses multi-modal perception data, constructs a dynamic interaction relationship graph by using a lightweight space-time graph neural network, and realizes online collaborative optimization of calculation, communication and perception resources by combining a distributed task offloading algorithm; the hierarchical deployment of the composite infrastructure node system sinks the calculation, communication and perception capabilities to the network edge, thereby avoiding the long-distance transmission delay of centralized cloud computing; through the cooperation of the lightweight space-time graph neural network and the distributed task offloading decision algorithm, the utilization efficiency of infrastructure resources is dynamically optimized, the overall energy consumption and operation cost are reduced while the high-priority task response is ensured.

The application provides a kind of pan-port area collection and distribution automatic driving vehicle formation task scheduling method and system, it is related to intelligent transportation system and port logistics technical field.The method includes system initialization and data acquisition to construct pan-port area digital twin model and global state space;Based on K-means clustering, task grouping and preliminary allocation are completed;Fusion multi-agent deep reinforcement learning MADRL model and auction mechanism realize vehicle collaborative bidding and decision optimization;Based on the action sequence output by auction, dynamic formation and path planning are carried out;Based on the gate reservation decision output by MADRL model, gate intelligent allocation and execution are carried out;Through performance evaluation and safety guarantee by executing monitoring and adaptive optimization.The application effectively promotes vehicle collaborative formation, realizes peak shaving in peak period, significantly improves the efficiency and safety of pan-port area collection and distribution, reduces energy consumption and carbon emissions, and is suitable for large container port pan-port area scene.

The application discloses a kind of multi-modal sporadic abnormal environment generalization perception methods and systems for data closed loop, it is related to automatic driving and intelligent transportation system technical field.The method comprises: using a variety of sensors real-time collection and pre-processing environmental data, obtains the high-dimensional feature representation of each mode coding through the feature extraction module of perception model;Utilize the sporadic abnormality in high-dimensional feature representation to be identified by exception detection model, when detecting abnormality, determine abnormal state by reconstruction difference and trigger adaptive adjustment mechanism;Through multi-modal data fusion technology, the fusion feature is obtained by fusing each mode feature;Through environmental generalization learning algorithm, introduce environment-related constraint loss, prompt the perception model to learn environment-independent general features to optimize perception algorithm.The application aims to realize the accurate detection and efficient processing of sporadic abnormality in complex environment through data closed loop feedback and environmental generalization learning, improve the accuracy and robustness of perception task.

This invention discloses a vehicle-mounted Ethernet gateway device and its monitoring method based on a dual 5G network, aiming to solve the problems of insufficient bandwidth, single interface, and unstable connection of existing vehicle-mounted gateways. The device adopts dual 5G network modules (optional dual 5G or 5G+4G / Redcap configuration), integrating 6 vehicle-mounted Ethernet interfaces (3 1000 / 100BASE-T1 and 3 100BASE-T1), 2 M12 standard Ethernet interfaces (1000 / 100BASE-TX), 2 CAN interfaces (expandable to 3), 2 digital input (DI) interfaces, and 2 digital output (DO) interfaces. It supports V2X (vehicle-to-everything) and PTP (Precision Time Protocol) protocols, enabling remote vehicle status monitoring and multi-link backup. The device is based on a quad-core A55 CPU (maximum frequency 2GHz), 2GB RAM (expandable to 4GB), and 8GB eMMC flash memory (expandable to 16GB), providing secure data transmission through cellular networks, Wi-Fi (optional Wi-Fi6), and VLAN technology. This invention improves vehicle operating efficiency and is suitable for intelligent transportation systems.

The present application relates to the field of intelligent transportation system and big data analysis technology, and more particularly to a shared bicycle rail connection analysis method considering travel stage built environment, comprising: obtaining shared bicycle order trajectory data in a target area, identifying travel behavior, performing OD mapping of the identified connection travel starting point-destination, and generating shared bicycle travel OD samples; performing shared bicycle trajectory data corresponding to the OD samples for riding path reconstruction and path restoration; for different travel stages of shared bicycle connection travel, corresponding built environment features are extracted respectively; based on the built environment features of different travel stages, prediction models for predicting shared bicycle use probability of rail transit inbound connection and outbound connection are constructed respectively; the prediction models are explained and analyzed, and key influencing factors and their action relationships are output. The present application can more comprehensively and truly reflect the formation mechanism of shared bicycle and rail transit integrated travel.

An OD estimation method based on flow data of an ETC gantry, which method solves the problem of it being difficult to reflect traffic mobility by means of an existing OD estimation method, and belongs to the field of traffic engineering and intelligent traffic systems. The method in the present invention comprises: determining that the research objective is region-level OD estimation, city-level OD estimation or expressway-level OD estimation; dividing constitution regions involved in the research objective into direct traffic impact regions and indirect traffic impact regions according to the degree of influence; dividing the direct traffic impact regions and the indirect traffic impact regions into traffic regions according to administrative region units and expressway sections; acquiring flow data of selected ETC gantries in real time, so as to obtain the hourly traffic volume of each ETC gantry, and then in view of the positions of the ETC gantries, matching the obtained hourly traffic volumes into an existing road network, so as to obtain road network section flows; using the obtained traffic regions as origins and destinations, selecting road network section flows of ETC gantries for OD estimation, and performing OD matrix estimation; and verifying an estimated OD matrix.

This invention relates to a large-scale collaborative ANIL target recognition method and system for cameras operating in icy and foggy conditions, belonging to the fields of computer vision and road safety technology. The invention employs an edge-cloud collaborative architecture. On the edge, the dark channel defogging coefficient and gamma correction coefficient are dynamically adjusted based on visibility and icing conditions to adaptively preprocess the image. A lightweight ANIL network is used for real-time target detection, and a confidence threshold is dynamically determined based on visibility. When the confidence level is insufficient, a large-scale Grounding DINO 2.0 model is triggered in the cloud for accurate inference. Simultaneously, the output of the large-scale model is used as prior knowledge to perform online transfer learning and parameter optimization on the edge-side ANIL network, forming a continuous evolutionary closed loop. Finally, millisecond-level priority warnings are achieved for sudden situations. This invention is used in intelligent transportation systems for target recognition and safety warnings of cameras in severe weather conditions such as icing and fog, improving the recognition accuracy, real-time performance, and environmental adaptability of traffic monitoring.

The application belongs to the cross technical field of artificial intelligence and intelligent transportation system, and specifically discloses a freight electrification station path intelligent planning method based on multi-source OD data. The method comprises the following steps: constructing a multi-source OD data credible evidence and completion system based on a blockchain and a geographic weighted regression fusion cellular automaton; establishing a strategic layer stochastic robust optimization model with logistics cost, power grid load, construction investment and battery attenuation as multiple targets; designing a tactical layer flexible path scheduling algorithm combining an improved genetic algorithm and an ant colony mechanism; deploying an execution layer real-time response module based on digital twinning and SAC reinforcement learning; and integrating a three-dimensional collaborative perception and health management closed loop driven by the Internet of Things and digital twinning. Through the above technical scheme, the four-dimensional collaborative optimization of logistics efficiency, battery life, power grid stability and comprehensive cost is realized, and the robustness, response speed and operation benefit of the freight electrification network are improved.

This invention provides a multimodal trajectory prediction method based on a conditional variational autoencoder, belonging to the fields of computer vision and machine learning. The technical solution includes the following steps: S1, data preprocessing and trajectory sample extraction; S2, multimodal feature extraction module; S3, cross-modal attention fusion; S4, conditional variational autoencoder generation; S5, loss function optimization; S6, model evaluation, calculating standard trajectory prediction metrics on a test set to evaluate model performance. This invention is applicable to trajectory prediction tasks in autonomous driving, robot navigation, and intelligent transportation systems. It can effectively integrate multimodal information such as historical trajectories and lane features to generate diverse trajectory prediction results with uncertainties, improving the accuracy and robustness of trajectory prediction.

A real-time multimodal pedestrian image detection method and system is disclosed. This invention relates to the fields of intelligent transportation systems, autonomous driving environmental perception, and computer vision, specifically a real-time multimodal pedestrian image detection method and system. The invention constructs a MAFD-DETR detection model: In the visible light branch, an adaptive frequency-domain gated wavelet convolution is introduced. By constructing a global frequency-domain gated unit, frequency band weights are dynamically learned to adaptively suppress high-frequency noise and enhance texture features. In the infrared branch, a thermal gradient-guided windmill-shaped convolution is introduced. Central difference convolution is used to extract edge temperature gradients, and gradient masks are used to modulate the windmill-shaped intensity features, effectively filtering background thermal noise. Finally, a progressive semantic fusion module integrates dual-stream features and outputs the detection results. This invention significantly improves the accuracy and robustness of pedestrian detection in all-weather scenarios and meets the real-time requirements of in-vehicle systems.

This invention belongs to the field of intelligent transportation systems, and particularly relates to a method for intersection signal safety control based on behavior perception and dynamic scheduling. The method includes: S1, identifying the travel intention of the last vehicle in the queue of the current phase within a preset time window before the current phase of the green light ends; S2, determining whether the phase switching conditions are met; if so, proceeding to S3; S3, calculating the residual value of each red light phase using a green light residual value model; selecting the phase with the highest residual value as the next permitted phase; S4, monitoring whether any vehicles have mistakenly entered the intersection within a monitoring time window after the red light of the current phase turns on; if so, postponing the green light activation time of the next permitted phase. This method can accurately and dynamically adjust the timing of phase switching while ensuring the traffic efficiency of the intersection, and actively monitor and prevent mistaken entry after the red light turns on.

The application belongs to the technical field of intelligent transportation system and artificial intelligence, and discloses a highway tunnel traffic event intelligent question and answer system and method. The method comprises the following steps: receiving a natural language query input by a user; guiding the context understanding output by prompting engineering to output a standardized event label in a preset five types of events; calling a preset Cypher template, dynamically filling an event name, generating a structured query statement, and executing a query to a pre-constructed tunnel management knowledge graph Neo4j to obtain a structured subgraph containing a trigger condition, an impact area and a response device; constructing a structured knowledge dictionary; guiding a large language model to generate a natural language answer through a strong constraint prompt word; and directly executing a refusal if the identification result is a non-five type of event. The application provides an interpretable, verifiable and landable technical path for intelligent operation and maintenance of a highway tunnel, and has important engineering application value and promotion prospects.

This invention discloses a method and system for air-ground cooperative route planning for flying cars under dynamic congestion, belonging to the field of air traffic route planning technology. Addressing the temporal variation of urban road network congestion and the travel process of flying cars switching between ground driving and flight, this invention establishes a unified air-ground cost metric. Under the constraint of ensuring that different candidate routes experience comparable congestion impacts, a constraint search method based on reference path alignment is constructed to efficiently enumerate and search for takeoff / landing switching points within congested road segments. Finally, by scanning the time preference weight parameter, an energy-time Pareto solution set is generated and a compromise solution is output. This invention achieves optimal trade-off decisions for air-ground cooperative travel of flying cars under dynamic congestion conditions, reducing total energy consumption or shortening total travel time and improving planning interpretability. It has significant application value for improving the operational efficiency and user experience of urban air traffic and intelligent transportation systems.

This invention discloses an accident emergency management method and system based on a smart road model, belonging to the field of intelligent transportation system technology. The method includes: running a three-dimensional perception network to acquire real-time traffic data of the road network; performing preliminary screening of edge anomalies after spatiotemporal consistency filtering to locate suspected abnormal events; transmitting relevant data to the smart road model to complete accident pattern recognition, obtaining recognition results including high-confidence accident types and lane-level positioning; executing situation-driven linkage decisions based on these results and outputting an emergency command set to implement traffic emergency management coordination. This invention solves the technical problems in road network accident emergency management, such as the lack of comprehensive and accurate collection of road condition data, insufficient accuracy in accident judgment, and imprecise location positioning. It achieves intelligent and collaborative processing of the entire process of road network accidents from data collection to emergency coordination, improving the accuracy of accident judgment and the precision of location identification.

This invention discloses a pedestrian trajectory prediction method based on anisotropy and target guidance across dimensions, belonging to the technical field of trajectory prediction and intelligent transportation systems. The method takes the pedestrian's historical trajectory and the positions of surrounding pedestrians as input. After trajectory feature extraction and Transformer temporal modeling, spatial interactions are decoupled by dimension, and the effectiveness of the interaction is determined by a discriminator on a target-by-target, time-step-by-time, and spatial-dimensional basis. Multimodal target points are predicted based on a conditional variational autoencoder network and explicitly encoded and marked with their positions, which are then introduced as structural constraints into the decoding stage. Masking is removed, and non-autoregressive global decoding is employed, ensuring that the predicted positions are all globally constrained by the target points. This method improves the precision of spatial interaction modeling and the stability and rationality of trajectory prediction, while reducing computational overhead. It can be applied to autonomous driving and intelligent transportation systems, providing accurate prediction basis for path planning and risk warning.

The application provides a time-dependent label constraint efficient query method based on partition tree decomposition, relates to the technical field of data processing, and is used for path planning application in an intelligent transportation system and comprises the following steps: acquiring road network information and generating a road data graph; performing reduction on the road data graph based on a label partition tree decomposition algorithm and generating a tree structure; constructing a parameter separation index mechanism and converting the tree structure into a parameter separation index; acquiring a departure point and a destination point and taking the departure point and the destination point as a source vertex and a target vertex respectively; and calculating a shortest path from the given source vertex to the target vertex based on the parameter separation index. Through the design of the label partition tree decomposition and the parameter separation index, the application greatly reduces the index storage redundancy of a time-dependent label constraint graph, can efficiently and compatibly support ordered label constraints and time-dependent weights to adapt to an intelligent transportation scenario, and can linearly expand with the scale of a data set and maintain stable performance in a network with more than ten million vertices.

This invention relates to a traffic flow prediction method based on a discrete-continuous hybrid spatiotemporal network. First, a tensor is constructed from the feature data of traffic flow data within the time period to be analyzed. Then, a time-varying graph convolutional network and a time-space-time module are applied to process the tensor, obtaining tensors constructed from traffic features extracted from discrete channels and tensors constructed from traffic features extracted from continuous channels, and finally, a tensor constructed from fused traffic features. Next, the tensor is processed sequentially by an encoder with a bidirectional gated recurrent unit structure and a decoder consisting of two-stage time bottleneck attention modules to generate and output corresponding spatiotemporal features. Finally, a fully connected network is applied to process the spatiotemporal features output by the decoder to achieve the prediction of traffic flow data for the predicted time period. This design significantly improves the accuracy of traffic flow prediction without incurring significant computational overhead, highlighting its practicality and efficiency as a core component of intelligent transportation systems.

This invention discloses a method for managing and optimizing the merging of multiple vehicles from adjacent lanes into an empty lane, based on Stackelberg game theory. Due to fluctuations in vehicle movement caused by acceleration, deceleration, or lane changes, temporary empty spaces may appear in some lanes. Given a limited empty space, how to allocate this limited space to multiple vehicles attempting to merge into an empty lane from adjacent lanes is a critical problem that urgently needs to be solved in intelligent transportation systems. To address this problem, this invention presents a method for managing and optimizing the merging of multiple vehicles from adjacent lanes into an empty lane based on Stackelberg game theory, and conducts in-depth theoretical analysis and application exploration, aiming to provide a unique and efficient solution.

This invention discloses a time-space scheduling method for electric vehicles based on dual prediction of SOC (State of Charge) and location, belonging to the technical field of commercial data processing and intelligent transportation systems. The method collects multi-source data and identifies vehicles in fixed or moving scenarios. Then, a dual prediction mechanism is executed: LSTM is used to predict the SOC change trend, SARIMA is used to predict the spatiotemporal location, and a clustering algorithm is used to predict regional charging volume to dynamically calculate the regional saturation threshold. Simultaneously, a two-level optimization mechanism based on a genetic algorithm (global and individual levels) is employed to deeply mine users' personalized preferences for distance and time. Finally, a two-layer collaborative framework of environment and decision-making unit layers is constructed, combining predicted data and preference parameters to calculate scheduling costs, and conflict resolution is performed under dynamic saturation threshold constraints, supplemented by a rolling optimization mechanism to correct decisions in real time. This invention achieves forward-looking, personalized, and globally balanced charging resource scheduling, effectively alleviating local congestion and significantly improving charging efficiency and user satisfaction.