148 results about "Flight delay" patented technology

The invention relates to the technical field of airport intelligent management, in particular to an airport intelligent operating system, which comprises a multi-modal data acquisition layer for acquiring multi-source data such as flights, passengers and luggage in real time through a heterogeneous sensor network; the intelligent analysis layer processes data by using deep learning and a unique algorithm, and generates information such as flight delay prediction and the like; the collaborative scheduling layer realizes cross-domain dynamic optimization of flight-boarding gate-luggage system-ground service based on digital twinning and multi-agent reinforcement learning; the man-machine interaction layer is equipped with AR navigation and voice interaction equipment and provides personalized services; in addition, a block chain evidence storage module is arranged to ensure that key operation records are safe and traceable, and a federal learning framework realizes multi-airport knowledge sharing and privacy protection; through multi-module cooperation, the airport operation efficiency is improved, the safety is enhanced, the passenger service experience is optimized, many problems faced by traditional airport operation are effectively solved, and the system has remarkable innovativeness and practicability.

The invention relates to the technical field of aerospace, in particular to a vertical take-off and landing airport scheduling method and system, electronic equipment and a storage medium, and the method comprises the steps: obtaining the state information of each aircraft associated with a vertical take-off and landing airport within a preset time period; and determining at least one scheduling strategy of the vertical take-off and landing airport based on the overall structure of the vertical take-off and landing airport and the state information of each associated aircraft, and performing scheduling according to the optimal scheduling strategy. According to the method, the scheduling process can be more comprehensively optimized, the urban air traffic congestion is effectively relieved, the flight delay cost is reduced, and the overall operation efficiency of the vertical take-off and landing airport is improved.

The invention discloses a large-area flight delay propagation prediction method and system based on a space-time attention deep learning network. The method comprises the following steps: acquiring flight data and meteorological message data of a plurality of airports, analyzing time sequence autocorrelation of the flight data and the meteorological message data, and dividing the flight data by time slices; analyzing and converting the meteorological message data to obtain meteorological message data with indexes; preprocessing the flight data divided into time slices and the meteorological message data with indexes by analogy of the input of the space-time attention network model to obtain a space-time data sequence; taking the spatio-temporal data sequence as modeling data, and obtaining a new weight model according to a spatio-temporal attention network model and a propagation dynamics method; according to the prediction result of the new weight model, improving the new weight model to obtain a multi-airport flight delay prediction model; according to the method, the space-time network model combined with propagation dynamics is constructed, and the evaluation quality of airline companies and airports on the next-day flight delay condition can be improved.

The invention discloses an airport group integrated operation management and control system, and the system comprises a data access module which is used for obtaining the operation data of an airport group; the operation resource dynamic management module obtains airport group operation data and optimizes time-space domain resource distribution based on the airport group operation data; the traffic flow conflict early warning module deduces potential conflict early warning of the whole airport group on the basis of airport group operation data and time-space domain resource distribution; the emergency recovery decision module generates a traffic flow recovery strategy based on airport group operation data, the current situation of time-space domain resources and conflict early warning, and performs simulation deduction and validity evaluation on the traffic flow recovery strategy; the visual intelligent management and control platform integrates and operates a resource dynamic management module, a traffic flow conflict early warning module and an emergency recovery decision module. According to the technical scheme of the invention, the method can effectively deal with the delay condition, reduces the disordered airport order, shortens the flight delay time, reduces the waiting time of passengers, and reduces the waste of airport group resources.

The invention provides a collaborative two-stage multi-runway departure flight ordering method, which is specially designed for relieving the congestion problem of a multi-runway airport in a peak period, ensures that the departure process of the flight is more efficient, and accords with scientific planning. The method comprises the following steps: constructing a multi-runway dynamic push-out rate control model to determine an optimal queuing threshold value of a taxiway; constructing a flight ordering and assigning model with the goal of minimizing flight delay and operation cost; and proposing a continuous Markov chain-based interactive whale algorithm, and carrying out flight push-out sequence and runway resource allocation. According to the method, the departure flight taxiing amount of the airport scene can be accurately identified, taxiway waiting time caused by runway occupation after the flight is launched is reduced, the actual significance is achieved for improving the flight punctuality rate and improving the overall airport operation efficiency, and the current green aviation development concept is met.

The invention discloses an automatic business travel itinerary optimization method, and relates to the technical field of intelligent itinerary planning, and the method comprises the steps: integrating the multi-source heterogeneous data of enterprise policies, personal preferences and real-time traffic through a federated learning framework, and achieving the cross-domain knowledge sharing; the method comprises the following steps: constructing a staged optimization engine by adopting an attention mechanism to dynamically balance cost, time, comfort and sustainability targets: in the first stage, modularly disassembling a travel through sparse constraint linear programming, and quickly generating a Pareto frontier candidate set; in the secondary stage, on the basis of a multi-agent reinforcement learning framework, complex interaction is simulated through a Markov decision process, and strategy iteration is driven through a special reward function for quantifying a comfort index; in order to cope with real-time disturbance, event-driven edge computing nodes are deployed, flight delay and traffic jam emergencies are responded in real time, an incremental topology updating algorithm is triggered, and only affected sub-modules are reconstructed to reduce computing complexity. According to the invention, the bottleneck of dynamic adjustment efficiency and multi-target balance capability is solved.

The invention discloses a departure peak period-oriented flight driving and taking-off collaborative optimization method, which comprises the following steps of: firstly, accurately identifying an optimal release control rate based on historical flight data, secondly, improving the sliding time prediction accuracy by utilizing integrated learning, and then, establishing a departure flight collaborative scheduling model according to an actual operation rule so as to realize the departure flight collaborative scheduling. And establishing a conflict mechanism to set flight priorities. A solving algorithm adopts a hybrid algorithm combining a genetic algorithm and tabu search, effective combination of global search and local search is ensured, and falling into a local optimal trap is avoided. The scheduling scheme covers key indexes such as runway throughput and average taxiing time, the airport scene congestion condition is relieved, the operation efficiency and safety of an airport in the departure peak period are improved, flight delay is reduced, and the utilization rate of runway time slot resources is increased.

The invention discloses a machine learning-based flight wheel block removal time prediction method, and relates to the technical field of flight prediction, and the method comprises the steps: collecting flight preorder state data, airport resource distribution data and meteorological data in real time, and generating an original data set through multi-source heterogeneous data fusion; constructing spatio-temporal features including a preorder flight delay propagation chain, a stand-vehicle conflict energy matrix and a meteorological attenuation factor, and screening and optimizing a feature set through distribution drift detection; training and verifying the Bayesian depth quantile regression model, and outputting a prediction result with a confidence interval; and combining the airport Internet of Things positioning feedback optimization feature set and parameters to generate a prediction deviation diagnosis report. According to the method, a preorder flight delay propagation chain and a stand-vehicle conflict energy matrix are constructed, flight dynamics, resource allocation and weather attenuation factors are embedded into a unified spatial-temporal feature space, and the problem of feature information loss caused by data isolation is solved.

The invention provides an aviation catering quality monitoring method based on artificial intelligence, and the method comprises the steps: obtaining flight delay information, passenger cabin configuration data and a real-time production line task sequence, evaluating the influence of delay on the task sequence and meal heat preservation time in combination with a preset meal standard, and generating a task recombination demand and a heat preservation time extension prediction value; carrying out real-time image acquisition on the meal semi-finished product required by task recombination, carrying out edge detection and texture analysis to obtain a raw material appearance quality index, and identifying a raw material attribute change trend based on the obtained raw material appearance quality index; and carrying out clustering analysis on the attribute change trend of the raw materials, identifying key characteristic parameters influencing the meal quality, and determining the quality risk level of the current production batch by combining the standardization requirement of the passenger cabin meal and the heat preservation time prolonging prediction value, so as to realize accurate control on the quality of the aviation meal.

The invention discloses a scheduling method for dynamic allocation of airport apron resources, and particularly relates to the technical field of airport apron resource scheduling, and the core of the method comprises the following three steps: 1, real-time data monitoring is carried out based on the Internet of Things, a sensor is deployed to collect multi-dimensional data, a resource snapshot is generated after processing, and abnormality and source tracing reasons are detected through an algorithm; dynamically pre-allocating standby resources and carrying out simulation evaluation; 2, machine learning-driven dynamic prediction is carried out, features are extracted to train a multi-task model, uncertainty weighting and gradient normalization optimization are combined, a prediction result is output, and a distribution suggestion is generated; and 3, reinforcement learning adaptive scheduling decision, modeling as a Markov process, adopting a layered architecture, training an agent to output scheduling actions, updating the model according to feedback, continuously optimizing the decision, reducing flight delay, and improving resource utilization rate and passenger satisfaction.

The invention belongs to the technical field of air transportation management, and discloses a multi-source data fused sorting method and system for airport flight coordinated launch. The method comprises the following steps: collecting a multi-source data source of a flight; performing multi-source data fusion, generating a five-dimensional dynamic situation matrix, and reflecting the comprehensive state of the flight in real time; judging whether manual priority ranking processing is carried out or not; by constructing an intelligent decision-making system for aircraft departure sorting, optimization target decision-making and intelligent sorting are carried out; the visual sorting interface displays the alternative push-out sorting schemes; obtaining a sorting result of flight coordination deduction; establishing a dynamic reentry mechanism for flights with updated resources or changed states, and reinserting the flights into the global sorting queue; and balancing the adaptation degree of the original priority and the new state of the flight through a dynamic weight distribution algorithm. The airport flight scheduling efficiency is improved, so that flight delay is reduced, the operation efficiency is improved, and the passenger satisfaction degree is enhanced.

The invention relates to the technical field of flight information processing, and provides a flight release plan determination method and device under flight delay, equipment and a medium, and the method comprises the steps: inputting the basic information of a current unreleased flight into a flight delay release sequence weighting model, carrying out the multi-dimensional weight value calculation of the flight, and obtaining a flight release plan; determining a total weight value of each flight; based on the response levels corresponding to the flights, determining flight release proportion ranges corresponding to the flights; and based on the flight release proportion range and the total weight value of the plurality of flights, permutation and combination are performed on the plurality of flights, and a flight release plan is determined. Through the flight release sequence weighting model, the release priority of each flight is quantified, and the optimal flight release plan is deduced according to the real-time condition, so that the flight release plan is quickly and accurately generated under the condition of flight delay, and the normal take-off rate and the normal release rate of the flight are improved.

The invention discloses a collaborative optimization allocation method and system for air route resources, particularly relates to the field of collaborative optimization of the air route resources, is used for solving the problems of dynamic allocation and real-time adaptation of complex resources, and is realized by constructing a basic resource node mapping graph in which dominant resources and recessive resources are combined. A resource dependency relationship path diagram is generated through time sequence disturbance analysis, collaborative response behaviors of resource nodes in a path are subjected to recursive analysis, path dynamic stability is classified in combination with offset parameters, and therefore the optimal allocation proportion of resources is accurately determined through a multi-target optimization model. And the allocation strategy is dynamically adjusted through real-time monitoring and a rolling optimization mechanism in actual execution, so that the allocation strategy of the air route resources is optimized, the risk of flight delay and airspace conflict is reduced, the response speed and decision accuracy of air route management are improved, and the safety and economic benefits of overall aviation operation are enhanced.

The invention belongs to the technical field of aviation management, and discloses a flight delay risk early warning and coping system based on big data analysis. Comprising a data acquisition module used for acquiring aviation operation data; the data analysis module is used for acquiring route flow; the delay prediction module is used for predicting the flight delay probability; the delay early warning module is used for judging whether an early warning instruction is generated or not according to the flight delay probability, and predicting flight delay duration if the early warning instruction is generated; the claim payment module is used for calculating the claim payment amount for claim payment based on the flight delay duration, and judging whether to perform meal payment or not; the travel and accommodation arrangement module is used for judging whether arrangement information is generated or not based on the flight delay duration; according to the method, big data and artificial intelligence technologies are fully utilized, intelligent prediction and refined service of flight delay risks are realized, the operation efficiency and service efficiency of airlines are improved, and the travel experience and satisfaction of passengers are greatly improved.

The invention belongs to the technical field of flight delay prediction, and discloses a flight delay prediction method based on multi-modal data fusion and large model fine tuning. According to the method, firstly, a flight delay prediction state space model is established, and dynamic characteristics and random characteristics of flight delay are effectively captured by dividing flight operation stages and analyzing delay generation and propagation modes of each stage, so that system states and observation data which are crucial to prediction are focused on. Secondly, performing fine adjustment on the Qwen large model by adopting a LoRA method, performing fine adjustment on the large model by utilizing historical flight data, and performing real-time delay prediction in combination with real-time flight data so as to improve the prediction precision and response speed of the large model; a final flight delay prediction result is obtained by performing weighted fusion on a prediction result obtained by reasoning based on the fine-tuned Qwen large model and a state space model estimated value obtained through processing, and the accuracy and reliability of the prediction result are effectively ensured.

The invention relates to a flight delay time prediction method and system based on dynamic gating MoE, and the method comprises the following steps: collecting flight historical operation data, carrying out the data preprocessing, and constructing a prediction data set; performing feature vectorization coding on the prediction data set in the form of a time sequence matrix, and extracting time sequence features of the prediction data set by using a multi-head attention mechanism; inputting the time sequence characteristics of the prediction data set into a dynamic gating MoE, adaptively selecting and combining the outputs of the routing expert sub-model and the shared expert sub-model through a dynamic gating mechanism, and obtaining fusion characteristics; and generating flight delay time prediction results according to the fusion features, wherein the flight delay time prediction results comprise departure delay time prediction results and arrival delay time prediction results. Compared with the prior art, the method has the advantages that the accuracy and stability of departure and arrival delay time prediction are remarkably improved, and the adaptability and expandability of the model to the complex flight operation situation are enhanced.

The invention is suitable for the technical field of evaluation of external impact response capability of an airport, and provides an airport flight time toughness index calculation method, which comprises the following steps of: obtaining delay time sequences of a flight plan time in a certain season and an actual execution time in different days in a certain period of time; identifying the mode characteristics of the delay time sequence of a certain flight plan moment in the period of time; dividing the period of time of a certain flight plan moment into a plurality of periods, and identifying a flight delay normal stage, a delay increase stage, a stable delay stage and a delay decrease recovery stage of each period; calculating a normality index, a resistance index, a robustness index and a restorability index of a certain flight time in the period of time; and dynamically evaluating the toughness index of a certain flight time in the period of time. The method provides theoretical basis and decision reference for airport flight time delay prevention and control, recovery and toughness level improvement under different random interferences, thereby providing data support for reasonable planning and adjustment of flight time resources.

The invention discloses a passenger airport right and interest data processing method and device and electronic equipment. The method comprises the steps that initial airport rights and interests of passengers and flight states of to-be-taken flights of the passengers are acquired, the initial airport rights and interests at least comprise one of catering service rights and interests and rest room use rights and interests, and the flight states at least comprise one of a flight punctuality state, a flight delay state and a flight cancel state; the initial airport rights and interests are adjusted based on the flight state, target airport rights and interests of the passengers are obtained, and the target airport rights and interests are used for representing the adjusted passenger airport rights and interests. According to the invention, the technical problem of low processing efficiency of the right and interest data of the passenger airport in the prior art is solved.

The invention provides a flight time adjusting method and device, electronic equipment and a storage medium, and the method comprises the steps: carrying out the all-day data fusion of the basic capacity of an airport, the temporary air traffic control capacity and the ground guarantee capability, and taking the capacity with the minimum value as the capacity fusion data in each preset time period; based on the guarantee plan data, determining the total flight plan amount in each preset time period; performing evaluation processing based on the capacity fusion data and the total flight plan amount, and determining the flight calling-out amount of each airline in the excessive time period and the flight calling-in amount of each airline in the spare time period; and scoring each flight of each airline in the excess time period, and determining a plurality of timing flights based on the timing score of each flight of each airline and the number of scheduled flights of the airlines. Intelligent recommendation of the flight time adjustment scheme is realized, the flight time adjustment efficiency is improved, the influence of flight delay is minimized, and the problem of low manual operation efficiency is solved.

The invention discloses an attack resisting method and device for a prediction model, and belongs to the field of artificial intelligence security. And indirect targeted countermeasure attacks are realized aiming at the network range flight delay prediction model. According to the method, significance of non-target nodes is evaluated based on gradient, then a preliminary candidate range including a first damaged node subset is determined by using binary search external circulation and nesting internal circulation, and on this basis, gradual refining of the external circulation and nesting of the internal circulation are performed to approach a minimum damaged node subset. In the two circulation processes, the intersection of the first damaged subset and the second damaged subset of two adjacent external iterations is accumulated to obtain a second initial sample and a third initial sample, disturbance information is fully utilized, the antagonistic potential of damaged nodes is maximized, the number of the damaged nodes needed by attacks is reduced, and the attack efficiency is improved. The minimization capability of the victim node subset is effectively improved, and the attack budget is saved.

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.

The invention discloses a flight ordering planning method based on flight path operation. The method comprises the following steps: obtaining an operation time deviation value of an in / out flight; quantifying the uncertainty of the running time deviation value of the in / out flights; and establishing an in / out flight optimization scheduling random chance constraint model, introducing the uncertainty corresponding to the operation time deviation into the model through random chance constraint, and solving to obtain an optimal scheduling moment, sorting, runway and time slot distribution scheme of each flight. According to the method, the operation uncertainty is converted into the specific numerical value constraint through the random chance constraint, it is guaranteed that the flight take-off and landing time meets the safety requirement under the preset confidence level, and the delay occurrence probability is greatly reduced; the problem of flight delay in the actual operation process is solved, and the effectiveness of the flight scheduling means is enhanced.

The invention discloses a flight delay prediction method and system based on digital twinning, and relates to the technical field of aviation services, and the method comprises the steps: constructing a multi-source heterogeneous data fused digital twinning body comprising an airport operation state, an aircraft health state and airspace dynamic traffic; accessing data of multiple dimensions in real time through the digital twinborn body; wherein the plurality of dimensions comprise weather, airspace and guarantee resources; the method comprises the following steps: carrying out deep learning training on historical delay data by utilizing a long short-term memory network, extracting time sequence characteristics of flight delay, and capturing a delay propagation rule and multi-node interaction influence in an aviation network in combination with multi-dimensional data accessed in real time so as to predict flight delay probability and influence range in a future time period.

The invention relates to an intelligent travel payment preferential recommendation system, in particular to the field of intelligent travel, and the intelligent travel payment preferential recommendation system realizes a personalized and flexible recommendation function by introducing a dynamic user state space, a reinforcement learning strategy and a real-time updating mechanism. The method can predict the future payment demand of the user according to the historical payment behavior of the user, the real-time flight dynamic state and the relevance of the cross-platform itinerary, and can provide timely and accurate preferential recommendation when the user demand changes, such as flight delay and itinerary change; besides, through a real-time feedback mechanism and a dynamic optimization module, the system can continuously adjust and optimize a recommendation strategy to adapt to user requirements and market environment changes, so that the challenges of inaccurate personalized recommendation, insufficient real-time updating and user behavior differentiation faced by a traditional recommendation system can be effectively solved, and the user experience is improved. And more accurate and real-time preferential recommendation is provided.

The present disclosure provides for informed de-icing by identifying a travel time range for an aircraft from a de-icing station to a runway; identifying a holdover window based on predicted weather conditions during the travel time range; estimating a takeoff time for the aircraft based on a takeoff queue for the runway and the travel time range; and in response to the holdover window expiring before the estimated takeoff time, delaying the aircraft from de-icing. In some aspects, informed de-icing includes, in response to identifying an aircraft scheduled for de-icing within a caution threshold of a scheduled takeoff time and to determining that a push time cannot be delayed: evaluating an effect of repeating a de-icing operation for the aircraft on flight operations; and in response to the effect exceeding an impact threshold: rescheduling the de-icing operation for the aircraft based on a new takeoff time.

The invention relates to the technical field of airport flight delay prediction, and provides an airport flight delay prediction method based on multi-dimensional data fusion, which comprises the following steps: selecting multi-attribute data; converting the multi-attribute data into a spatial data variable, a spatio-temporal data variable and a time data variable; inputting the spatial data variables into a graph convolutional neural network, and extracting data spatial features by using the graph convolutional neural network on the basis of the congestion index of the window moment transport topology network; inputting the spatio-temporal data variables into a three-dimensional convolutional neural network, calculating features from time and space dimensions, and obtaining spatio-temporal features of dynamic data with space-time dependence; inputting the time data variable into a long short-term memory network to extract time characteristics of the data; and fusing the spatial features, the spatial-temporal features and the time features, and inputting the fused features into a long-short-term memory network for delay identification to obtain the delay time of the take-off flight. The airport delay can be effectively and accurately identified.

The invention relates to the technical field of air traffic management monitoring, in particular to an airport flight delay prediction system and method.The system comprises a data preprocessing module, a spatial feature module, a time feature module and a feature fusion and output module.The method comprises the steps that firstly, data preprocessing is conducted based on spatial-temporal feature decoupling, and standardized input is provided for subsequent modules; processing the problem of insufficient spatial heterogeneity by adopting a spatial feature module of a double attention mechanism, and considering short-time sudden fluctuation and a long-period rule through a time feature module parallel expansion convolution and dynamic pooling strategy; and finally, hierarchical attention-guided space-time fusion is used for splicing the multi-scale features of the bidirectional LSTM through expansion convolution, the recognition precision of the model is improved, lightweight output driven by affine transformation replaces a full-connection layer with LayerNorm, the feature expression ability is ensured, the reasoning time delay is optimized, and delay prediction is completed.

The invention provides a flight delay prediction method and system, computer equipment and a storage medium, and belongs to the technical field of traffic delay prediction.The method comprises the steps that firstly, a bacterial foraging algorithm (BFA) is optimized through a genetic algorithm (GA) so as to improve the global search ability and convergence speed of the algorithm; secondly, optimizing the structure and parameters of a deep neural network DNN by using the optimized BFA algorithm, and constructing a deep neural network model based on double hidden layers; according to the model, an attention mechanism and a residual block are introduced, so that the nonlinear mapping capability and the generalization capability are improved, and model overfitting is effectively prevented. In addition, the flight data and the weather condition data are combined, so that the prediction accuracy is further improved. The performance of the model is evaluated through multiple indexes, the result shows that the method can effectively solve the problem of DNN structure and parameter selection, and the training efficiency and generalization ability of the model are remarkably improved.

Examples are disclosed that related to providing flight-delay estimations of airborne flights in a real-time environment. In one example, an aircraft information message for a current aircraft flight is received. The aircraft information message has a designated format consumable by a machine learning model previously trained to assess delay predictions for aircraft flights. The aircraft information message includes one or more aircraft flight-plan parameters, one or more aircraft surveillance parameters, and one or more weather parameters for the current aircraft flight. The aircraft information message is provided as input to the machine learning model to assess a real-time delay prediction for the current aircraft flight based at least on the one or more flight-plan parameters, the one or more aircraft surveillance parameters, and the one or more weather parameters included in the aircraft information message.

The invention relates to the technical field of flying around of aircrafts, in particular to an airport entering and leaving thunderstorm flying around scheme analysis system based on historical flight paths. And obtaining a thunderstorm fly-around scheme of the current aircraft at the airport by combining the meteorological data of the specified area in the specified time period in the future and the geographical map information of the airport and the surrounding area. The method has the beneficial effects that through data analysis and processing, the comparison between the flight historical track based on the geographic map and the flight program design standard track is realized, so that the flight fly-around rule under the thunderstorm weather is obtained, and a pre-fly-around scheme reference under the thunderstorm weather is provided for pilots and controllers. A more reasonable and safer fly-around scheme is formulated, and the thunderstorm weather coping capacity is enhanced. The system aims at improving the flight safety, reducing flight delay and cancellation caused by thunderstorm weather and improving the flight punctuality rate.