191 results about "Traffic simulation" patented technology

According to the urban traffic automatic simulation plug-in based on the large language model and the implementation method, after a user inputs a request for a road network and a request for vehicle routing by using a natural language, the request is transmitted into a full-process automatic simulation module, and the full-process automatic simulation module carries out simulation; the input analysis agent extracts a key value from a natural language to generate a character string in a Json format, then the character string is spliced, a prompt input simulation input construction agent containing detailed simulation information is generated, the simulation input construction agent calls a packaged tool function, a vehicle route xml file and a road network xml file are generated respectively, and the vehicle route xml file and the road network xml file are connected with the simulation input construction agent. And the simulation execution intelligent body opens the SUMO-gu i to start simulation, and returns a result through a graphical interface. According to the method, the technical complexity of traditional traffic simulation is effectively simplified, a simulation tool is promoted to be transformed from professional modeling to intelligent decision support, and an innovative technical path is provided for optimization and management of a dynamic traffic system.

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 relates to the technical field of artificial intelligence and automatic driving, and discloses an automatic driving digital twin agent system based on reinforcement learning training, which comprises an agent library construction module, a simulation and configuration module, a distributed simulation execution module, a scheduling and learning module, a strategy training module and a simulation migration module. The method comprises the following steps: constructing a modular digital twin agent library, combining to generate an urban traffic simulation scene, executing large-scale parallel simulation on a distributed computing cluster, implementing dynamic task scheduling and course learning according to an agent learning progress, and training a reinforcement learning strategy and a value network based on simulation data. And verifying the strategy model and implementing migration deployment from simulation to reality. According to the method, the training efficiency can be improved, the diversity of simulation environments is enriched, and the sim-to-real migration difficulty is reduced, so that the robustness of the strategy model is improved.

The invention discloses an urban traffic three-dimensional model vegetation element automatic generation method and system based on streetscape recognition, a terminal and a storage medium. The method comprises the steps of constructing a vegetation element knowledge graph, defining a vegetation type and typical attributes, directionally collecting a three-dimensional model and a multi-view image, unifying metadata and storing the metadata. Pixel-level semantic segmentation is carried out on the streetscape image, vegetation categories are identified based on transfer learning and an attention mechanism, and ROI and feature vectors are output; screening the candidate set based on ontology mapping, selecting a best matched vegetation model for the local feature vector of the recognized ROI and a pre-stored feature vector in a material library, and recording mapping; based on reference object correction, knowledge graph fusion and automatic discrimination or optimization strategies, reliable scale correction, number or position deduction and batch three-dimensional model import placement meeting ecological constraints are carried out on the vegetation ROI. According to the invention, efficient, intelligent and standardized technical support is provided for traffic simulation and urban visualization.

The invention provides an ultra-large city comprehensive traffic data and intelligent research and strategy system, which establishes a multi-dimensional traffic research and strategy task through a traffic research and strategy task module, and processes the traffic research and strategy task through a traffic characteristic diagnosis module to obtain a traffic characteristic index vector rank and a candidate traffic root cause index set. The traffic traceability analysis module performs further processing to obtain main traffic root cause indexes and measure indexes, and the traffic strategy generation module outputs traffic characteristic indexes according to the main traffic root cause indexes and the measure indexes to optimize a pre-generation strategy; the traffic simulation deduction module optimizes a simulation task item distribution model generated by a pre-generation strategy according to traffic characteristic indexes to obtain a pre-selection result, and the traffic strategy evaluation module and the traffic strategy release module finally perform evaluation and release, thereby solving a problem that a single factor outputs a single execution scheme with relatively high precision. Therefore, when single execution schemes are overlapped and fused into a set of optimal executable solutions for the comprehensive traffic of the super-large city, scheme execution conflicts are easy to occur.

The invention discloses an expressway reconstruction and extension traffic diversion method, system and device and a medium, and the method comprises the steps: obtaining multi-source dynamic traffic data, and constructing a road network origin-destination matrix through data fusion and demand deduction; a traffic flow distribution result is obtained by constructing a system optimal traffic distribution model; identifying the first congestion time period, and calculating the traffic flow exceeding the residual traffic capacity in the congestion time period to obtain the traffic flow to be shunted; solving an optimal shunting path under the target of minimizing the total travel time of the system through a hybrid intelligent algorithm to obtain an optimal shunting path scheme; and verifying the optimal shunting path scheme by using microscopic traffic simulation software, updating traffic data based on a simulation result, and carrying out iterative optimization to obtain a differentiated traffic organization scheme. According to the invention, the traffic flow passing efficiency during the highway reconstruction and extension period is effectively improved.

The invention relates to a traffic simulation agent system construction method based on a large model, and the method comprises the steps: constructing a simulation tool library, and achieving the precise evaluation and continuous optimization of a simulation result through the fusion of multi-source heterogeneous traffic data, the construction of standardized input, and the establishment of a quantitative evaluation system. A large language model is utilized to understand a natural language instruction of a user, tasks are intelligently disassembled, an execution process is planned, dependence management and parallel scheduling are carried out in combination with a directed acyclic graph, and professional tools are driven to automatically execute. And performing evaluation, problem diagnosis and adaptive re-planning on an execution result through a large language model reflection mechanism to form an understanding-planning-execution-reflection closed loop. According to the method, the problems of how to assist a user to interact with a traffic system by utilizing an agent technology driven by a large language model, reducing the technical threshold of traffic simulation software use and saving time cost and labor cost are solved, the traffic simulation use threshold is reduced, the automation and intelligence level is improved, and efficient and accurate traffic system interaction and optimization are realized.

The invention discloses an intelligent decision-making system construction method for traffic signal control. The method comprises a model training and deployment stage and an application and evolution stage, and specifically comprises the following steps of: S1, generating a pairing training sample set of traffic state structured data and conflict-free signal control instructions based on a pre-stored road traffic conflict rule in the model training and deployment stage; utilizing the paired training sample set to supervise and finely adjust a large language model to obtain a basic model; s2, accessing the basic model into a traffic simulation environment for reinforcement learning training; and in each training step, the basic model outputs a signal control instruction according to the current traffic state, performs safety verification on the instruction according to the road traffic conflict rule, generates a safety reward signal and the like. The traffic signal intelligent decision-making system which is safe, credible, sustainable in evolution and suitable for edge independent deployment is constructed.

The invention discloses an encrypted domain name resolution protocol simulation and characterization system, and relates to the technical field of network security and network traffic analysis. The invention aims to simulate an encrypted domain name resolution process in a real network environment, collect the flow of the process and extract features to construct a data set, and ensure the quality of the generated data set through data enhancement and a data set evaluation scheme. The system comprises a traffic simulation module, a traffic representation module, a data enhancement module and a data set evaluation module. The flow simulation and characterization module simulates and encrypts domain name resolution flow and extracts a structured feature vector containing 34 side channel features; and the data enhancement and data set evaluation module is used for enhancing a feature set based on a conditional table generative adversarial network CTGAN so as to construct a feature data set which is closer to traffic in a real network environment, and ensuring that the constructed data set has engineering availability and theoretical rationality through evaluation. The system can be used for constructing a current scarce encrypted domain name resolution protocol side channel feature data set, and provides data support for related security detection and research.

This invention discloses a lane-level conflict prediction method for expressway weaving areas based on real-time traffic flow data, comprising the following steps: S1, selecting a suitable expressway weaving area as the survey location and conducting on-site investigation; S2, collecting traffic flow data and traffic conflict data for each lane, and defining thresholds for different conflict types; S3, building a traffic simulation model using the traffic parameters obtained from the survey, defining different conflict types according to the conflict thresholds obtained in step S2, and obtaining traffic conflict data for different types; S4, extracting feature variables from the data obtained in step S3, and constructing datasets for prediction models of different conflict types; S5, constructing a conflict analysis model; S6, constructing a conflict prediction model. This invention can achieve real-time lane-level traffic conflict prediction in expressway weaving areas, thereby providing more accurate lane-level conflict risk information for vehicles in each lane, helping drivers choose safer lanes and routes.

The invention relates to the technical field of intelligent traffic, in particular to an optimization method and device for a traffic control scheme generation model, equipment and a medium. The method comprises the steps of obtaining a traffic event training data set; inputting each traffic event into a traffic control scheme generation model, and generating at least one candidate control scheme of the traffic event; sequentially inputting the at least one candidate control scheme into a traffic simulation model for deduction to obtain parameters of at least one simulation index of each candidate control scheme; calculating a reward value of each candidate control scheme based on each candidate control scheme, the parameter of the at least one simulation index and a reward function; wherein the reward function comprises a format reward function, a rule reward function and a quality reward function; and based on the reward value of each candidate control scheme and the real annotation data of the traffic event, optimizing the traffic control scheme generation model by adopting a reinforcement learning algorithm based on strategy gradient to obtain an optimized traffic control scheme generation model.

The invention discloses a traffic signal optimization method based on multi-agent deep reinforcement learning, and belongs to the technical field of traffic signal control, and the method comprises the following steps: carrying out the tracking and track simulation of a vehicle according to the vehicle operation data, and constructing an urban traffic simulation model and a reinforcement intelligent learning body corresponding to the traffic signal lamp of each intersection; constructing a context enhanced state space, performing normalization processing on feature parameters in the context state space, and performing combination to obtain a real-time traffic environment state vector; a congestion index self-adaptive reward is obtained through calculation; according to a heuristic reward shaping method, defining a flow matching degree index and an indication signal period position reward, and combining a congestion index adaptive reward to obtain a traffic signal optimization reward; and according to the traffic signal optimization reward, a multi-agent double-depth Q network is adopted to train and strengthen an intelligent learning body to control traffic signal phase switching. According to the invention, the problem of insufficient traffic signal control flexibility and efficiency in a complex scene is solved.

The invention relates to an automatic parallel traffic simulation analysis method and device. The method comprises the following steps: in response to a received region delimiting instruction, generating an initial digital road network from a map data source, and executing topology calibration and connectivity repair operation on the initial digital road network to generate a digital road network file; generating structured traffic demand data based on the input of the user; generating a basic traffic scene file based on the digital road network file and the traffic demand data, and forming a traffic simulation operation scene based on the configuration of the basic traffic scene file by a user; generating a plurality of signal timing schemes based on the traffic simulation operation scene; and executing traffic simulation based on the plurality of signal timing schemes, analyzing a simulation result to obtain a multi-level key performance index, performing visual comparison on the multi-level key performance index, and generating a natural language evaluation report by using a large language model. Therefore, simulation efficiency, usability and scene construction flexibility can be remarkably improved.

The invention discloses a test method and device based on vehicle-in-the-loop, electronic equipment and a storage medium. The method is realized by a test system based on vehicle-in-the-loop, and the system comprises a scene simulation platform and a vehicle platform. The method comprises the following steps: constructing a traffic simulation scene by adopting a scene simulation platform, and obtaining virtual perception information; sending the virtual perception information to a vehicle platform for processing to obtain virtual scene information, and displaying the virtual scene information on a front windshield of the tested vehicle; and the tested vehicle is controlled according to the virtual scene information on the front windshield of the tested vehicle, and the pose information of the tested vehicle is collected and fed back to the scene simulation platform. By adopting the technical scheme of the embodiment of the invention, the tested vehicle in-the-loop test system is constructed to test the head-up display, and the vehicle head-up display function is comprehensively and accurately tested and verified in combination with the real running state of the tested vehicle and the simulated traffic simulation scene.

The invention provides a traffic simulation analysis method for an expressway construction area based on VISSIM. The method comprises the following steps: S1, collecting point cloud data, vehicle data and traffic video data of the landform of the construction area in real time; s2, preprocessing the collected multi-source data; s3, constructing a dynamic simulation model of the construction area; s4, simulating the traffic condition of the construction area; s5, dynamically planning an optimal driving path of the vehicle according to the simulated traffic condition and construction information; and analyzing the vehicle overlapping probability of the intersection in real time, and performing early warning according to an analysis result. Through the technical means of multi-source data fusion, high-precision dynamic modeling, real-time analogue simulation, intelligent path planning and the like, accurate simulation and optimal management of the traffic influence during the blasting demolition construction of the expressway overline overbridge group are realized, and remarkable technical advantages and application values are achieved; and a scientific and efficient solution is provided for urban traffic management.

The invention relates to a semantic-driven traffic simulation scene generation method based on a large language model. The method comprises the following steps: receiving traffic simulation scene natural language description input by a user; constructing a tool knowledge base, performing retrieval in the tool knowledge base based on user input, and combining a retrieval result with the user input to obtain extended input; calling the large language model to carry out multi-step reasoning on the extended input, determining a tool needing to be called in a tool knowledge base, and generating a structured simulation configuration element by utilizing the determined tool; and converting the generated simulation configuration elements into a configuration file which can be directly loaded to a traffic simulation platform, and executing simulation verification in the traffic simulation platform. Compared with the prior art, natural language input can be automatically converted into a structured and executable traffic simulation configuration file under the condition that manual intervention is not needed, and high efficiency and intelligence of scene construction are achieved.

The invention relates to an intelligent traffic scheduling technology, and discloses an urban traffic network intelligent scheduling method and system based on digital twinning, and the method comprises the steps: obtaining the multi-source traffic data of a plurality of intersections in a preset target region, carrying out the precision alignment and fusion, and obtaining the fusion traffic data, constructing digital twin bodies of two adjacent intersections in the target area according to the fused traffic data to obtain a first dynamic virtual traffic body and a second dynamic virtual traffic body, and generating an initial scheduling strategy according to the multi-source traffic data by using the first dynamic virtual traffic body and the second dynamic virtual traffic body, and performing traffic deduction on the first dynamic virtual traffic body and the second dynamic virtual traffic body by using the initial scheduling strategy, performing a game-type collaborative scheduling decision according to the obtained deduction data to obtain an optimal scheduling strategy, generating a control instruction set according to the optimal scheduling strategy, and realizing intelligent scheduling of the traffic network according to the control instruction set. According to the invention, the traffic network scheduling effect can be improved.

The invention discloses an intelligent vehicle autonomous lane changing decision-making system and method based on near-end strategy optimization. Belongs to the technical field of intelligent traffic and automatic driving. The technical problem that in the prior art, defects still exist in the aspects of state space design, security constraint modeling, training strategy optimization and the like is solved. The system comprises a traffic simulation environment module, a state observation and feature construction module, a near-end strategy optimization lane change decision module, a reward calculation and safety supervision module, a training control and data recording module and a strategy export and application module. A training environment is constructed based on a microscopic traffic simulation platform, a training scene is closer to real road traffic by configuring different traffic flow densities and speed distributions, and the strategy has better adaptability under different working conditions.

The invention relates to the technical field of traffic simulation, in particular to an automatic driving traffic flow simulation method and device based on closed-loop reinforcement learning, and the method comprises the steps: obtaining a real driving data set based on a pre-constructed traffic flow simulation frame with an automatic driving vehicle as the center; performing imitation learning pre-training according to the real driving data set to obtain an initial trajectory generation model; and constructing a closed-loop reinforcement learning fine-tuning target function according to the initial trajectory generation model, and updating the initial trajectory generation model by using the closed-loop reinforcement learning fine-tuning target function to obtain a final trajectory generation model. Therefore, the problems that in the prior art, authenticity and controllability cannot be considered at the same time, or covariant offset, mode collapse, unstable training and the like are faced in closed-loop deployment are solved.

The invention relates to the technical field of intelligent traffic simulation and heavy vehicle cooperative control, and discloses an intelligent cooperative control simulation verification system for a tractor or a trailer, which comprises three modules and four modules: a three-dimensional environment modeling module based on multi-source data fusion and hierarchical modeling and combined with a timing and event double-trigger mechanism to update dynamic obstacles; the cooperative motion control module integrates Bezier trajectory planning, a geodesic theoretical dynamics coupling model and triangular collision detection, and realizes vehicle-towing cooperation and risk early warning. The visual simulation interaction module constructs a multi-window view based on OpenGL / Unity 3D, and simulates light and shadow and friction characteristics in combination with material attributes; the result verification module outputs a conclusion based on the quantitative index and the qualitative report. According to the system, the goodness of fit between a simulation environment and a real scene is remarkably improved, the collision early warning response is less than or equal to 100ms, more than 80% of roll-on-roll-off ship scenes can be covered, the entity test cost is reduced by 60%, and a high-fidelity verification tool is provided for vehicle-tow intelligent cooperation.

The invention discloses a model optimization method and a related device, which can be applied to automatic driving, auxiliary driving, intelligent traffic, traffic simulation, vehicle-mounted scenes and the like. Perception data of the target object is obtained, an interpretation information prompt text is obtained, and the interpretation information prompt text is used for indicating the content understanding model to output the text content meeting the expectation. And outputting an object behavior control signal of the target object and decision explanation information matched with the explanation information prompt text through a content understanding model according to the perception data and the explanation information prompt text. If the action executed by the target object based on the object behavior control signal does not meet the preset target, that is, the object behavior control signal cannot control the target object to execute the correct action to cause avoidance failure, which link has a problem can be determined based on the decision interpretation information, so that the avoidance failure is avoided. Therefore, the content understanding model is adjusted more accurately and conveniently based on the decision interpretation information, and the performance of the content understanding model is improved.

The invention provides an automatic driving formation hardware-in-the-loop simulation platform based on a satellite position simulation platform and a medium, and relates to the technical field of automatic driving and car networking simulation. The platform comprises a satellite position simulation module, a traffic simulation platform, a hardware verification platform, a simulation bus and a clock synchronization module. The satellite position simulation module generates satellite data with a timestamp; the traffic simulation platform constructs a formation driving scene and operates a formation control algorithm; the hardware verification platform comprises a real phased-array antenna module and a PC5 communication device. The modules exchange data through a simulation bus, and the clock synchronization module unifies the time reference to form a closed-loop verification environment of simulation hardware simulation. The problems that space-based data and ground simulation are separated and real communication characteristics are difficult to quantitatively evaluate in the prior art are solved, the satellite-ground cooperation and formation control strategy can be efficiently and reliably verified in a laboratory environment, and the real vehicle test cost and risk are remarkably reduced.

The invention relates to the technical field of urban traffic management, in particular to a traffic simulation data generation method and system based on a data distillation and knowledge distillation technology, and the method comprises the steps: obtaining original data of a real traffic scene, carrying out the cleaning, standardization and spatial-temporal feature extraction of the obtained original data, and obtaining a structured feature set; performing data distillation analysis on the structured feature set to obtain an initial simulation data set conforming to real distribution; inputting the initial simulation data set into a knowledge distillation model, and outputting the knowledge distillation model to obtain knowledge distillation parameters; performing fusion processing on the knowledge distillation parameters and the initial simulation data set to obtain a fusion enhanced data set; and performing dynamic scene adaptability verification and multi-dimensional distribution correction on the fusion enhanced data set to obtain a final simulation data set.

A system is provided.SOLUTION: A system comprising: means for real-time analysis of traffic information collected from cameras and sensors; means for generating a traffic simulation using a generative AI model; means for calculating optimal traffic routes and signal light timing; means for sending instructions to signal lights and digital signage based on the calculation results; means for continuously monitoring and optimizing traffic conditions; and means for notifying users of traffic information.SELECTED DRAWING: Figure 1

The invention provides a charging pile layout optimization method, system and device, and relates to the technical field of charging facility management, and the method comprises the steps: obtaining historical traffic flow and power grid node capacity, generating a charging pile coordinate set, and determining a related road section; wherein the related road section is a road section inevitably passed by a navigation path of each charging pile in an influence range; identifying potential congested road sections in each time period by using a traffic simulation model; if congestion exists, the total number of detained vehicles is calculated, and a time-varying charging load curve is generated in combination with the electric vehicle proportion and the single vehicle fast charging power; mapping the curve to a power grid node, evaluating whether the node voltage is lower than a threshold value, and confirming a to-be-repaired pile road section; generating a candidate coordinate set around the road section to be supplemented with the piles, screening out the position coordinates of the supplementary charging piles, and merging the position coordinates into the original coordinate set to form a complete layout scheme. According to the invention, optimization of the charging pile layout is realized, the utilization rate of the charging piles is improved, and stable operation of a power grid is ensured.

This invention discloses a system for identifying and simulating key nodes in traffic networks based on deep reinforcement learning. Addressing the high computational complexity of key node search and the lack of dynamic verification in traffic networks, this invention employs the following system: a road network topology mapping module parses GIS map data and removes redundant information to generate a weighted topology map; a deep reinforcement learning identification module, based on a deep Q-network, maps node features to graph embedding information through graph representation learning, with the optimization objective of minimizing cumulative normalized connectivity, outputting the optimal key node sequence; and a traffic effect simulation verification module uses a traffic simulation platform to establish a realistic road network model, quantitatively analyzing the impact of key node failures on average travel time and waiting time. This invention features low time complexity, the ability to generalize from small synthetic networks to extremely large-scale real networks, and simulations have verified its high application value in actual traffic management and disaster prevention.

The invention discloses a traffic scene simulation method based on an accident report, and aims to solve the problem of insufficient complex dynamic scene data in traffic simulation. According to the method, physical clues (such as vehicle speed, collision angle, quality and the like) and context information (such as road types and environmental conditions) are extracted from a real traffic accident report, and a high-fidelity traffic scene data set is generated. In foreground processing, the extracted physical clues are used for accurately constructing a vehicle motion trajectory, diversified dynamic traffic scenes are generated through a pre-collision trajectory planning algorithm, and trajectory prediction is optimized in combination with a large language model to ensure that the real physical law is met. In background processing, high-quality three-dimensional scene reconstruction is carried out according to environment information in an accident report, a vivid background image is generated, and the consistency of a visual angle and illumination is kept. And finally, through fusion of the foreground vehicle track and the background image, a visual and physical double real traffic simulation video is generated. The method significantly improves the diversity and authenticity of traffic scenes, reduces the dependence on real data collection, is widely suitable for high-fidelity traffic simulation scenes such as automatic driving test and traffic flow analysis, and effectively promotes the progress of the traffic simulation technology.

The application relates to a semantic driving traffic simulation scene generation method based on a large language model, which comprises the following steps: receiving a traffic simulation scene natural language description input by a user; constructing a tool knowledge base, searching in the tool knowledge base based on the user input, combining the search result with the user input to obtain an extended input; calling a large language model to perform multi-step reasoning on the extended input, determining tools required to be called in the tool knowledge base, and generating structured simulation configuration elements by using the determined tools; converting the generated simulation configuration elements into a configuration file which can be directly loaded into a traffic simulation platform, and performing simulation verification in the traffic simulation platform. Compared with the prior art, the application can automatically convert natural language input into structured and executable traffic simulation configuration files without manual intervention, and realizes the efficiency and intelligence of scene construction.

This invention discloses a collaborative control method for highway merging zones based on deep reinforcement learning. A LiikeSim-Python co-simulation environment is established, and loop detectors are set up in the simulation environment to acquire traffic flow data upstream and downstream of the highway merging zone. An EM algorithm based on Gaussian mixture distribution is used as a traffic state classifier, taking the traffic flow data of the highway merging zone as input to classify the traffic state of the merging zone. A state space, action space, and reward function are designed. Using the state space of the highway merging zone as input and the actions of the variable speed limit agent and the ramp metering agent as output, a multi-agent shared experience network model under time-series characteristics is constructed. An independent experience pool is set up for the variable speed limit agent and the ramp metering agent, and the interaction experience between the agent and the traffic simulation environment is collected at the control cycle frequency. The agent model is trained using sampled data. The trained agent model is used to realize collaborative control of the highway merging zone. This invention can reduce travel delays in highway merging zones.

The invention discloses a satellite network-oriented ground service flow generation simulation method, and belongs to the technical field of communication. According to the method, by simulating generation factors, including geographic areas, population, time and other multi-dimensional factors, influencing ground satellite communication services, flow generation models of different levels are input according to different requirements, and finally area flow information is generated, so that the requirement of simulating satellite network input is met. Geography-population distribution information is utilized, flexible region merging is adopted, and region flow simulation models of different levels can be flexibly set according to simulation requirements, so that the simulation accuracy is ensured, and meanwhile, the ground service flow generation simulation complexity is reduced.