416 results about "Driving simulation" patented technology

The invention discloses a single-view large-scale outdoor scene three-dimensional reconstruction method based on three-dimensional Gaussian splashing, and the method comprises the steps: collecting a pseudo aerial image, and constructing a panoramic multi-mode supervision end-to-end single-view three-dimensional reconstruction model; meanwhile, panoramic consistency supervision, semantic constraint depth regularization and a radial weighted luminosity loss and Gaussian cutting mechanism are introduced, so that the defect of insufficient geometric constraint of traditional single-view three-dimensional reconstruction is effectively overcome, and high-efficiency and high-fidelity three-dimensional modeling of a large-scale outdoor scene under single image input is realized; the method is suitable for various actual scenes such as smart city construction, automatic driving simulation, virtual reality / augmented reality, digital twinning and the like.

The present invention proposes an autonomous driving testing method based on multi-alliance cluster confrontation, aiming to improve the efficiency and accuracy of autonomous driving simulation test, and comprising S1—initialization of testing environment of autonomous driving; S2—decision-making for dividing background vehicle clusters; S3—decision-making for confrontation behaviors of the background vehicles; S4—trajectory planning for the background vehicles; and S5—looping through the steps S2, S3, and S4 until cluster confrontation testing tasks are completed. The method of the present invention uses reinforcement learning and alliance games to dynamically generate testing scenarios that are highly confrontational to vehicles being tested, which can find dangerous boundary scenarios for autonomous driving more quickly and improve the efficiency of simulation testing.

The invention provides an anthropomorphic automatic driving simulation test scene construction method. The method comprises the steps that all vehicles in a traffic scene are divided into two types of intelligent agents including a test vehicle and an environment vehicle, the traffic scene is modeled into a Markov decision process, the test vehicle is a vehicle controlled by an automatic driving algorithm, and the environment vehicle is a vehicle controlled by a GAIL-GRU driving strategy model; a Markov decision process is utilized to extract a driving track from the human driving data set, and an expert track data set is generated; and training a GAIL-GRU driving strategy model by using the expert track data set, realizing interaction between an environment vehicle and a test vehicle by using the trained GAIL-GRU driving strategy model, and constructing a simulation test scene. The automatic driving simulation test scene constructed by the invention can effectively expose decision defects of an automatic driving algorithm in a complex interaction situation, has good anthropomorphism and relatively high risk scene coverage, and provides support for a decision control simulation test of a high-level automatic driving vehicle.

The invention discloses an agricultural meteorological disaster monitoring and early warning method and system based on a remote sensing technology, and the method comprises the steps: generating a multi-dimensional data set through obtaining and processing multi-source remote sensing data, and generating a comprehensive data set through weighted average fusion. Then, extracting land surface temperature data, comparing the land surface temperature data with a historical value, calculating a temperature deviation value, and generating temperature anomaly distribution data; and calculating a disaster intensity index by combining the soil humidity and vegetation index data change trend, and generating disaster intensity distribution data. The data meteorology is imported into a driving simulation system, disaster evolution is simulated, and the disaster influence range and duration are predicted. And if the prediction data exceeds an early warning threshold, generating early warning data including disaster categories, influence areas and prediction time, and generating disaster risk distribution data by using a spatial interpolation method for dynamic monitoring. According to the invention, the accuracy and timeliness of disaster monitoring and early warning are improved.

The invention provides an intelligent driving simulation method and device based on big data joint training, relates to the field of intelligent driving, and solves the technical problem of performance degradation of a world model in a real scene due to distribution difference between virtual data and real data in the prior art. The method comprises the following steps: carrying out noise reduction processing on first environment data, carrying out feature analysis on the first environment data to determine an importance weight, and taking an area of which the clustering density of the importance weight meets a preset condition as a target scene; processing the first environment data based on a diffusion model to generate second environment data; using the second environment data and the target scene to construct a world model; processing the first environment data based on a visual language model to obtain dense description information of the target scene; generating a simulation scene according to the dense description information of the target scene and the world model, and performing an intelligent driving algorithm simulation test according to the generated simulation scene; the method and the device are used in an intelligent driving simulation training process.

The invention provides an airspace simulation deduction method and system based on digital twinning, and belongs to the technical field of airspace simulation, and the method comprises the steps: integrating geographic bottom plate data and airspace element data, carrying out the meshing based on an airspace grid engine, and constructing an airspace digital twinning model; receiving at least one flight plan, and mapping the flight plan to the airspace digital twin model to generate a corresponding simulation aircraft entity; based on a preset simulation engine, driving the simulation aircraft entity to perform flight simulation in the airspace digital twin model, and calculating a simulation flight situation; in the simulation process, airspace conflict detection and early warning are carried out based on the simulation flight situation and the airspace digital twin model; and outputting a simulation deduction result, performing three-dimensional visual output on the simulation flight situation data and the early warning information, and generating an analysis report including conflict statistics and airspace evaluation. The airspace resource allocation is optimized, the flight safety is improved, and intelligent and efficient management is realized.

The invention provides an anthropomorphic virtual test scene multi-background vehicle collaborative decision-making method, which comprises the following steps of: constructing a dynamic traffic map of multiple vehicles based on a graph structure, and constructing a parameter-shared generative adversarial imitation learning framework, inputting the node features optimized by the graph attention network to a reinforcement learning network to generate a serialized control instruction, and comparing the generated control instruction with a state-action pair of expert data by using a discriminator sharing scoring parameters to iteratively optimize a control instruction generator; potential factor variables are introduced to represent driving styles, and strategy personification is enhanced in combination with explicit rule punishment and implicit confrontation awards; and a highly anthropomorphic multi-background vehicle sequence cooperative control instruction is generated through multiple rounds of iteration. According to the method and the system, the adversarial imitation learning is iteratively optimized and generated by utilizing potential factor variables and reward enhanced imitation learning, and a decision with diversity, compliance and personification is generated, so that the reliability of an automatic driving simulation test is improved in multiple aspects.

The invention belongs to the technical field of automatic driving test, and provides an automatic driving confrontation test scene generation method and system based on strategy switching, and the method comprises the steps: obtaining an automatic driving simulation scene; setting positions and initial speeds of a target vehicle and an intelligent body vehicle in the acquired simulation scene; in combination with the set position and initial speed of the target vehicle, the running state of the target vehicle is optimized by adopting a multi-agent near-end strategy, and the target vehicle is controlled; determining a current traffic state according to the control driving state of the target vehicle; in combination with the dynamic quantitative index of the artificial risk potential field, sensing the traffic flow risk of the intelligent body vehicle in the determined current traffic state in real time; and according to the obtained traffic flow risk level, automatically switching a defense strategy and an attack strategy of the agent vehicle, and driving the multi-agent vehicle to cooperatively generate an automatic driving confrontation test scene.

The invention belongs to the technical field of analog simulation, and particularly relates to an intelligent simulation system with an autonomous decision-making capability, which comprises simulation management used for task scene initialization, system parameter configuration and simulation process control and operation. The display system is used for 2D / 3D visual display and playback of simulation results; the model implementation system is connected with the simulation management and display system and is used for implementing a target model and a flight mode model according to configuration parameters; the intelligent target generation system is used for automatically generating a maneuvering combination strategy, a task load control strategy and a radar control strategy according to the simulation situation information generated by the model implementation system, and feeding back the maneuvering combination strategy, the task load control strategy and the radar control strategy to the model implementation system to drive simulation deduction; wherein the system can execute a plurality of simulation modes including large sample space random task simulation, task simulation without autonomous capability level and task simulation with certain autonomous capability level according to simulation types set by the simulation management and display system.

The invention discloses a vehicle driving simulation test method and system based on digital twinning, belongs to the technical field of vehicle driving, and realizes real interaction of a driving behavior model and a control algorithm in a virtual scene by constructing a digital twinning body highly corresponding to an actual vehicle and a driving environment. Time stamps and coordinate information of all the modules are collected, time differences and space residual errors are extracted, a space-time semantic consistency index model is constructed, and the loading process is intelligently judged and optimized; when inconsistency is detected, adjustment is performed through clock synchronization and coordinate recalibration, a closed-loop optimization mechanism is formed, and high collaboration of the model and the environment is ensured, so that the accuracy, the stability and the credibility of the simulation system are improved, and the method is suitable for complex and changeable automatic driving test scenes.

The invention discloses an uncertainty perception simulation method based on a neural radiation field (NeRF), and aims to solve the problem of insufficient reality sense of a dynamic scene in an automatic driving scene. Aiming at the problems of rendering artifacts and uncertainty generated when a reflecting material and a shielding area are processed by a traditional simulation method, the invention provides a new framework fusing Bayesian uncertainty quantization and dynamic scene optimization. On the basis of an MARS simulator, the frame remarkably improves the visual quality and reliability of a simulation scene through a special rendering loss function and a physics-based rendering model. Experimental results show that the method is superior to a baseline MARS simulator in KITTI and VKITTI data sets, and particularly shows higher application value and generalization ability in the aspects of reducing artifacts and improving rendering fidelity, and provides a more reliable test environment for automatic driving simulation.

The invention relates to an automatic driving intelligent safety multi-task dynamic test system based on a Carla simulation platform, and belongs to the field of artificial intelligence safety and automatic driving simulation test. The system comprises an automatic driving perception model integration and training module which comprises a multi-task data collection module, a fine tuning perception model and a compilation perception model Carla API interface; the attack and defense algorithm integration and training module is used for finely adjusting an attack and defense algorithm, writing an attack and defense algorithm Carla API (Application Program Interface) and replacing Carla object textures with an adversarial patch; and the real-time attack and defense deduction and evaluation recording module comprises a sensor for acquiring Carla real-time data, replacing textures, starting a perception model and starting an evaluation record of the success rate and the accuracy rate of defense and attack. According to the method, the robustness of the automatic driving perception model in a complex attack scene can be comprehensively evaluated, security holes of a perception system can be found in time, and the reliability and the security of an automatic driving technology in a complex environment are improved.

The invention aims to provide a washout algorithm based on a data-driven parameter prediction model, and belongs to the technical field of driving simulation, a vehicle longitudinal acceleration signal and a motion platform state parameter are acquired and input into a pre-trained multi-layer perceptron neural network model, a washout filter parameter combination adaptive to a current working condition is calculated and output, and the washout filter parameter combination is determined. And then real-time and dynamic adjustment of parameters of the high-pass acceleration filter and parameters of the low-pass inclination coordination filter along with the state of the vehicle and the motion platform is realized, so that online real-time optimization of a control instruction of the motion platform is completed, and finally optimization of driving motion feeling simulation fidelity is realized. A data-driven parameter prediction model breaks through the limitation that a traditional washout algorithm is fixed in parameter and poor in adaptability; offline optimization and online prediction are combined, and real-time performance and fidelity are balanced; the active centering controller compensates the longitudinal acceleration by using the pitch angle, improves the space utilization rate, introduces a comprehensive somatosensory error evaluation index, and accurately evaluates the somatosensory simulation effect.

The invention relates to an automatic driving test scene simulation generalization generation method and system based on knowledge distillation. The method comprises the following steps: acquiring real trajectory data, constructing a scene-level multi-agent trajectory generation framework based on a denoising diffusion probability model, and generating diversified and vivid agent trajectories through an iterative denoising process so as to realize modeling of multi-agent interaction behaviors in a complex traffic environment; a mixed knowledge self-distillation framework is further proposed, multi-level knowledge of a teacher model is migrated in a student model, and task loss and distillation loss are dynamically balanced in combination with a simulated annealing strategy, so that the cross-scene adaptability and generation stability of the model are improved; simulation verification shows that the generated intelligent agent track can effectively reduce the collision event rate and the retrograde event rate, and interaction coordination and traffic rule constraints are ensured. According to the system, the authenticity, diversity and cross-scene adaptive capacity of an automatic driving simulation scene can be improved.

The invention relates to the technical field of automatic driving simulation, in particular to a performance evaluation method, system and device of an automatic driving simulation platform and a medium, and the method comprises the steps: constructing a simulation environment and a simulation vehicle equipped with a sensor, an actuator and a vehicle-mounted calculation unit simulation model; establishing a multi-dimensional platform evaluation system covering security, efficiency, response time and data processing capability, defining a specific sub-index, an evaluation method and a target data item needing to be collected for each dimension, and setting a comprehensive evaluation rule; running a closed-loop simulation test in the simulation platform, and synchronously collecting all target data; calculating evaluation results of the sub-indexes one by one based on the collected data; and judging whether the overall performance of the platform reaches the standard according to comprehensive evaluation rules. According to the invention, systematization, standardization and quantitative evaluation of the performance of the automatic driving simulation platform are realized, the reliability and comparability of a test result are improved, and an effective basis is provided for optimization of the simulation platform.

The invention belongs to the field of automatic driving, and particularly relates to a multi-scene fusion automatic driving analog simulation test method and system, and the method comprises the steps: obtaining multi-source heterogeneous driving data, carrying out the preprocessing and fusion of the multi-source heterogeneous driving data, and obtaining standardized scene data; based on the standardized scene data, constructing a basic simulation scene by adopting a photo-level real-time rendering technology, and optimizing and constructing the basic simulation scene into a three-dimensional scene model by adopting a 3D Gaussian splashing technology; based on a preset scene generation rule and through a 4D space-time modeling algorithm, performing expansion and variation on the three-dimensional scene model, and automatically generating a multi-dimensional test scene set; executing a simulation test corresponding to the multi-dimensional test scene set, and collecting test result data of the automatic driving system; and establishing a scene optimization and algorithm iteration closed-loop mechanism based on the test result data, and dynamically optimizing the multi-dimensional test scene set and the automatic driving system algorithm.

The invention relates to an automatic driving simulation test system based on a local cluster. According to the system, a local structured file is used as a database, a plurality of common computers are built into a distributed computing cluster through an SSH protocol, and a Web front end is integrated to provide a user interface. The core executes a priority scheduling algorithm fused with a dynamic adjustment and preemption mechanism through an intelligent scheduling and control module, and drives a simulation software API (Application Program Interface) to realize full-process automation from task submission, scheduling, distributed parallel execution, state monitoring to result collection and report generation. The invention further relates to a corresponding method and device, a processor and a computer readable storage medium. According to the automatic driving simulation test system, method and device based on the local cluster, the processor and the storage medium, the problems that an existing simulation test cloud platform is high in cost and complex in deployment are effectively solved, and an efficient, low-cost and high-flexibility test solution is provided for automatic driving research and development.

In order to overcome the defects that in the prior art, driving simulation training duration statistics is extensive, training quality cannot be effectively evaluated, and system fault interference is likely to happen, the invention provides a driving simulation training effective class hour evaluation method and device, and the method comprises the following steps: S1, collecting operation parameters of a driving simulator and current training scene information; s2, dividing the collected data into independent time slices according to a preset time interval, and calculating an operation characteristic index in each time slice; s3, performing effective class hour and comprehensive quality evaluation by utilizing an intelligent evaluation module, wherein whether the operation characteristic indexes in each time slice are effective or not is preliminarily judged on the basis of the basic threshold parameters; in combination with current training scene information, querying a scene-operation rule base, dynamically adjusting a basic threshold parameter, and re-evaluating whether the operation characteristic index in each time slice is valid or not; and performing quality evaluation and effective class hour evaluation based on a multi-dimensional data fusion algorithm, and outputting an accumulated effective class hour and comprehensive quality evaluation report.

Techniques are discussed herein for generating and evaluating driving simulations based on synthetic scenarios. Simulated objects may be controlled based on parameters determining the attributes and behaviors of the objects, and scenarios may be synthetically modified by changing the parameters for a simulated object. For a driving scenario with a synthetic simulated object, a simulation system may analyze driving log data to determine a probability or prevalence associated with the driving scenario. In various examples, the simulation system may determine marginal density estimates for individual attributes of the simulated object, as well as a cumulative distribution function modeling the dependence between the attributes. The joint probability distribution determined for the synthetic simulated object can be used for evaluating the efficacy of the simulation and the performance of the simulated vehicle controllers.

The invention relates to the technical field of industrial detection, and discloses a lithium niobate metasurface defect intelligent identification method based on reactive ion beam etching, which systematically solves the problem of scarcity of experimental data through physical driving simulation and a physics-based rendering algorithm, and provides large-scale field specific training data for a deep learning model. The bidirectional coupling simulation model generates defect morphology prediction data conforming to an etching dynamics law based on real process parameters and material parameters, the prediction data is converted into a synthetic image with real microscopic image statistical characteristics by a physical rendering algorithm, and the quality and consistency of the synthetic data are ensured by an automatic labeling and statistical verification process. According to the method, the number of samples of the ternary association database is multiplied, and extreme process conditions and rare defect modes which are difficult to obtain through experimental collection are covered.

The invention discloses an ADAS test scene automatic generation method and system based on a parameterized preset risk field, and particularly relates to the technical field of automatic driving simulation tests.The method comprises the steps that a mixed risk field model is constructed, and the model at least comprises a static risk field and a dynamic risk field; extracting risk field physical parameters from the real accident data through a physical feature extraction channel to construct a static risk field conforming to real risk distribution; a natural language instruction is mapped into a risk field traffic parameter through a semantic large model analysis channel, and a hybrid GAN generative adversarial network for generating edge cases is generated by adopting a dual-channel hybrid mode of a physical feature extraction channel and the semantic large model analysis channel so as to construct a dynamic risk field; and a scene element reverse generation module is designed, a three-dimensional engine is driven to generate an ADAS test scene including a road network, traffic participants and environment variables according to the risk field physical parameters and the risk field traffic parameters, and directional enhanced generation of a high-risk scene is realized.

The invention relates to the technical field of automatic driving, and discloses a vehicle driving scene generation method based on an automatic driving simulation environment, and a track generation method, system and equipment, and the vehicle driving scene generation method comprises the steps: determining a dense depth map according to a real vehicle driving scene image and a sparse radar point cloud corresponding to the real vehicle driving scene image; generating a dense three-dimensional point cloud under the global coordinate system according to the dense depth map; and generating a future observation image of the vehicle driving scene based on the dense three-dimensional point cloud. According to the scheme, the vehicle driving simulation scene which is stable in geometric structure and good in picture consistency can be generated in the vehicle automatic driving closed-loop simulation process, and the consistency and controllability of driving scene generation are improved.

The invention relates to an automatic driving simulation test scene evaluation method and system and a medium, and relates to the field of automatic driving, and the method comprises the steps: recording a video through which a vehicle passes, converting the video into a scene image, generating a real scene image, and then extracting scene features in the real scene image; performing analogue simulation on a scene according to the scene features to generate a simulation scene image; comparing the simulation scene image with the real scene image, analyzing different data in the two groups of scene images, and calculating a difference value between the two groups of different data to generate a simulation difference value; and analyzing a simulation index of the simulation scene image according to the simulation difference value, and generating simulation evaluation information. According to the invention, a visual evaluation means and a systematic difference analysis mechanism can be provided, so that the accuracy and reliability of the automatic driving simulation system are improved.

The invention discloses an automatic driving test and evaluation method and device based on a large language model, and solves the problems that the pertinence of a test scene is insufficient, the result understanding depends on manpower, the evaluation interpretation is weak and the like in a traditional method. By means of semantic analysis, logical reasoning and domain knowledge support of a large language model and in combination with an automatic driving test domain knowledge graph, generation of a structured test portrait of a tested system, construction of a test demand list, generation of a semantic test scene description file, interpretable test result attribution, multi-dimensional comprehensive evaluation and report output are completed in sequence. And a test portrait is updated through report feedback to form a closed loop. The method is suitable for function verification and performance evaluation of an automatic driving simulation test, can also be used for post-processing analysis of real vehicle test data, realizes automation of a whole test process and interpretability of an evaluation result, and improves test precision and efficiency.

The invention discloses an automatic driving intersection passing method and device based on model offline reinforcement learning, and relates to the technical field of intelligent driving. The method comprises the following steps: establishing an intersection traffic scene simulation environment, and acquiring offline expert data by adopting a predefined true value track; constructing an initial world model; adopting offline expert data to train the initial world model, and outputting the trained world model; according to the offline expert data and the imaginary trajectory data, training the initial conservative behavior model until a second preset training condition is met, stopping training, and outputting the trained conservative behavior model; and deploying the two trained models to an automatic driving simulation environment, carrying out testing to evaluate the two trained models, and if the requirements of preset indexes are met, outputting a final trained world model and a trained conservative behavior model. According to the invention, trajectory planning of the autonomous vehicle in a complex intersection traffic scene can be realized.

The invention provides a system behavior simulation and logic verification method and system based on a state machine model. The method comprises the following steps: constructing a state machine model of system behaviors; generating an intermediate representation code embedded with a time constraint check function, wherein the time constraint calculates a state transition time upper limit based on a Lyapunov stability model; compiling the executable code framework; receiving real-time operation data through a human-in-the-loop interface, mapping the real-time operation data into an input event, driving simulation operation, and dynamically rendering a state conversion process by adopting a virtual reality technology; verifying the behavior correctness based on the state coverage and a logic closed-loop index, wherein the logic closed-loop detects a deadlock risk through an improved banker algorithm; and if the verification is not passed, the recent valid state is traced back, the model is adjusted, and the verification is executed again. According to the method, the problems of inaccurate time sequence verification, insufficient real-time interaction, single verification index and the like in a traditional method are solved, and the accuracy and reliability of system simulation are remarkably improved.

The invention belongs to the field of computer software and traffic, and particularly relates to a mixed traffic flow complex interactive behavior-oriented data-driven simulation method and system, and the method comprises the following steps: obtaining real trajectory data of motor vehicles, non-motor vehicles and pedestrians, carrying out the group division of the non-motor vehicles and the pedestrians, and extracting an interactive scene; the method comprises the following steps: constructing a mixed flow strategy simulation model, and by taking each traffic participant as an intelligent agent, generating a simulation track based on a hierarchical strategy network for guiding a lower-layer action by an upper-layer strategy constructed for three types of participants respectively: further constructing observation-action pairing samples respectively based on real interaction data and simulation generation data, and integrated efficient simulation of complex interaction behaviors of the mixed traffic flow is realized through an adversarial training mechanism. According to the method, integrated simulation modeling of multiple types of traffic participants in the mixed traffic flow is realized, group travel characteristics of non-motor vehicles and pedestrians can be fully described, and a dynamic decision process of heterogeneous traffic participants is accurately described.

The invention provides an intelligent driving scene reconstruction method and system and a storage medium, and the method comprises the steps: obtaining an OpenStretMap map file based on the real vehicle GPS data, and carrying out the lane-level road network matching based on the OpenStretMap map file; the OpenStreetMap map file obtained after matching of the lane level road network is converted into an OpenDR IVE map file; based on real vehicle GPS data, generating a main vehicle track by using a FastDTW algorithm; based on the environment vehicle information, generating a dynamic interaction object track through a Hungary matching algorithm; and fusing the OpenDRIVE map file, the main vehicle track and the dynamic interaction object track, and reconstructing a scene file in an OpenSCENARIO format in preset simulation software. According to the method, automatic reconstruction is carried out by utilizing the real vehicle data, the real vehicle data can be automatically stored as a universal OpenSCENARIO scene file, simulation testing can be carried out in different types of automatic driving simulation software, a real driving scene can be accurately simulated, and a reliable simulation environment is provided for development, testing and verification of an intelligent driving algorithm.

The invention relates to a multi-vehicle automatic driving simulation system and method supporting DORA middleware, and belongs to the technical field of automatic driving. The system comprises a simulation platform and a domain controller group. The simulation platform comprises a multi-vehicle Dora-bridge, traffic scene simulation software and vehicle dynamics simulation software, the multi-vehicle Dora-bridge receives multi-vehicle state data, sensor data and vehicle cooperative control information generated by the traffic scene simulation software, and the multi-vehicle Dora-bridge publishes the multi-vehicle state data, the sensor data and the vehicle cooperative control information in the form of a DORA topic for each domain controller to subscribe; meanwhile, control instructions issued by the domain controllers in the form of DORA topics are received and issued to the vehicle dynamics simulation software. The domain controller group comprises a plurality of domain controllers, each domain controller corresponds to one automatic driving vehicle, and software of the domain controller group consists of a plurality of DORA function nodes to realize an automatic driving function. According to the invention, an effective solution is provided for simulation of the multi-vehicle automatic driving system based on the DORA middleware, the development efficiency is improved, and the development cost is reduced.

The invention relates to the technical field of business travel management, and discloses a business travel intelligent optimization management system comprising a data processing module used for collecting and preprocessing multi-source data related to business travel and integrating the multi-source data into a structured business travel data set; the optimization engine module is used for performing simulation-based solution based on input of the structured business travel data set and outputting a candidate business travel scheme set and corresponding baseline data; the evidence storage contract module takes the baseline data as well as transaction data, performance data, settlement data and abnormal data generated by the business travel process as input to generate a corresponding event abstract and perform uploading; a data learning module; a user interaction module; and an emergency simulation module. Monte Carlo simulation and event-driven simulation are combined and applied to business travel scheme generation, calculation can be carried out under the conditions of price fluctuation, flight delay and uncertain resource availability, multiple selectable schemes are formed, baseline data are determined according to the selectable schemes, and optimization processing of complex uncertain scenes is achieved.