88 results about "Flight delay" patented technology

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 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 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 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 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 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 present invention discloses a method and system for intelligently allocating landing runways based on flow data balance analysis. The method comprises: performing flight 4D trajectory prediction; screening available runways that meet operating conditions according to mandatory runway allocation rules; setting runway landing capacity, flight delay threshold, runway balance fixed points, and runway flow balance ratios at fixed points; calculating flight flow at fixed points in the air and on the runway within a statistical unit time based on the flight's estimated landing time and estimated transit time obtained from the flight's 4D trajectory prediction; and performing optimal runway allocation calculations based on a runway flow balance strategy. The present invention utilizes flight plan information and 4D trajectory prediction information, while taking into account mandatory runway allocation rules and flight flow prediction information on each runway, to perform intelligent landing runway allocation based on flow balance analysis, thereby achieving an effective balance between runway flow and controller load, and improving runway time slot resource utilization.

The invention relates to the technical field of air transportation management, in particular to a flight delay response grade evaluation method and central control equipment in an airport, and the method comprises the steps: obtaining a delay feature evaluation index value based on flight actual operation data and flight plan data in a central control system in a specified airport; according to the response level interval information to which the delay feature evaluation indexes belong and the delay feature evaluation index values, an improved matter-element model is adopted for processing, and an association degree matrix and a weight sequence of the delay feature evaluation indexes and response levels are obtained; according to the correlation degree matrix and the weight sequence, obtaining an evaluation value of flight delay; and obtaining an evaluation result of the flight delay response level according to the evaluation value and the evaluation rule of the flight delay. According to the method, factors comprehensively considered by a large-area flight delay index system in different time periods are fully considered, optimization of a future development trend on a current early warning level judgment result is considered, and the current early warning level judgment result is dynamically corrected, so that an early warning decision has perspectiveness.

The invention discloses a space-time enhanced aviation network flight delay prediction method and system, and belongs to the technical field of data mining. The method comprises the following steps: constructing an aviation network dynamic congestion degree matrix; processing a congestion degree matrix by adopting a graph embedded network of an encoder-decoder structure, and extracting a dynamic congestion mode through short-term time enhancement; extracting a spatial dependency relationship through long-term spatial enhancement by using the hidden state of the encoder and the static characteristics of the airport; fusing short-term and long-term features to generate node embedding, and constructing an adaptive adjacency matrix for graph convolution; multi-step prediction is performed based on an encoder-decoder framework in combination with time attention and graph convolution. According to the method, the implicit relationship of delay propagation between airports can be effectively captured, the space-time modeling capability is enhanced, and the accuracy and practicability of flight delay prediction are improved.

The invention belongs to the technical field of civil aviation operation and intelligent scheduling, and discloses a history-based flight delay probability dynamic judgment and sorting method and system. The method comprises the following steps: acquiring and standardizing single flight data; standardized time sequence characteristics of the flight guarantee progress are formed, and delay probability dynamic judgment is carried out; through propagation risk modeling, the shared resources or the connection relations are propagated to other flights; calculating an operation loss expected value of each flight, and generating a flight delay risk sorting list; and explaining and relieving action output is carried out on the generated flight delay risk sorting list. The invention aims to provide a more scientific, more real-time and more executable delay management means for airport operation through progress perception, probability updating and risk sorting.

The invention relates to an early warning processing method for controlling an airport unmanned bus based on a cloud platform, and belongs to the technical field of intelligent transportation, and the method comprises the steps: S1, sensing vehicle driving data through a sensor, transmitting the data to an automatic driving system, and synchronizing the data to the cloud platform; s2, after the cloud platform carries out noise processing on the received data, anomaly detection is carried out; s3, when an abnormal result is detected, identifying an abnormal type of the vehicle; s4, flight information is obtained, and traffic influence grade division is carried out on the airport ground according to the flight information; s5, generating a pre-scheduling scheme based on the traffic influence level, the vehicle state and the road condition for the remote scheduling abnormity; and S6, generating an exception handling personnel scheduling scheme for exceptions which cannot be remotely dispatched. According to the invention, the operation state of the unmanned bus is monitored in real time, the abnormity of the unmanned bus is handled in time, the operation efficiency of the whole airport is improved, and the delay of airplanes is reduced.

The invention provides a large-area flight recovery energy efficiency optimization method fusing deep learning and a genetic algorithm, belongs to the technical field of intelligent scheduling and optimization, and solves the problem of multi-target efficient recovery after large-scale flight delay. According to the technical scheme, the method comprises the following steps: firstly, constructing standardized scheduling input characteristics and economic loss factors by using real flight data; secondly, designing a multi-objective function, and comprehensively considering total delay, economic loss, the number of serious delay times and scheduling fairness; then, generating a scheduling sample based on an evolutionary strategy and constructing a training set; thirdly, training a deep neural network agent model to replace a high-cost evaluation function; and finally, combining an agent model and an elite screening mechanism, and quickly searching an optimal scheduling scheme in evolutionary optimization. The method has the advantages that large-scale flight recovery scheduling tasks are efficiently completed, global efficiency and local fairness are both considered, a command department is helped to rapidly formulate a recovery strategy, and the method has high practical application value.

The invention discloses an airport delay level evaluation method based on space-time correlation. The method comprises the following steps: constructing an associated airport set according to associated airport index analysis; analyzing the time relationship of the delay influence of the associated airport on the target airport by using the grey correlation degree; establishing an airport delay evaluation index system; an entropy weight method is adopted to carry out weight assignment on each airport delay evaluation index weight; and carrying out quantitative grading on the airport delay index based on a one-dimensional kernel density clustering algorithm so as to realize airport delay level evaluation. According to the method, the airport flight delay level can be objectively measured according to the operation characteristics of the target airport and the associated airport.

The invention discloses a dynamic aircraft stand redistribution method based on a passenger gathering degree and an electronic terminal, and the method comprises the steps: obtaining aircraft stand information and initial flight plan information of an airport, and obtaining flight delay information; according to the flight initial plan information, according to a flight first-arrival first-service rule, carrying out machine position distribution, and generating a machine position pre-distribution scheme; calculating a single-flight passenger gathering probability at a boarding gate; and setting parameters of a taboo search algorithm, taking the minimum passenger adjustment loss time after the aircraft position changes as a target function, inputting the aircraft position pre-distribution scheme and the flight delay information into the taboo search algorithm for solving, and generating an aircraft position redistribution scheme. According to the invention, extra adjustment time of passengers caused by change of the boarding gate can be reduced to ensure the satisfaction of the passengers; the tabu length is adjusted to be a variable which changes along with the frequency of a tabu object, and a neighborhood moving method is finely divided, so that the solving speed is improved, and rapid optimization is realized.

The invention relates to the field of computer technology application, and provides a flight delay prediction method and device based on space-time multi-modal fusion and a medium, and the method comprises the steps: firstly obtaining flight delay associated data and airport basic information of multiple airports in a preset time window, and then generating a node embedding matrix; and three spatial dependence matrixes are constructed based on route physical connection, a historical cooperative delay rate and a time-space relationship. And carrying out time feature mining on the preprocessed flight data through a time feature extraction module, and fusing node embedding and a multi-source spatial dependency matrix by utilizing a layer-by-layer graph learning module to realize hierarchical learning of spatial features. And finally, integrating time and space features through a multi-modal feature fusion module, and inputting the time and space features into a delay prediction module to obtain a flight delay time prediction result. According to the method, through joint modeling of multi-modal spatial-temporal characteristics, the spatial-temporal propagation rule of flight delay in an airport network can be effectively captured, and the flight delay prediction accuracy is improved.

The invention relates to an aircraft hydraulic system failure monitoring method and device and a storage medium. The method comprises the steps that firstly, historical flight data are acquired; decoding and analyzing the historical flight data to obtain a hydraulic system pressure distribution diagram; and transversely comparing the hydraulic parameters of each flight according to the time sequence of the flight data, and judging the performance state of the hydraulic system based on the change trend of the parameters of the hydraulic system. Compared with the prior art, the system degradation sign can be observed in advance through trend monitoring, intervention is performed in advance before system failure, flight safety margin decline caused by system completion failure is avoided, flight delay AOG loss caused by system completion failure can be effectively avoided, the component overhaul occurrence rate is reduced, the maintenance cost is reduced, and economic benefits are obvious.

The application relates to the field of computer technology, and provides a flight delay prediction method, equipment and medium based on space-time multi-modal fusion, which comprises the following steps: firstly, flight delay correlation data and airport basic information of multiple airports within a preset time window are acquired; then, a node embedding matrix is generated; and three space dependence matrices are constructed based on route physical connection, historical collaborative delay rate and space-time relationship. Time feature extraction is performed on the pretreated flight data through a time feature extraction module, and a layer-by-layer graph learning module is used to fuse the node embedding and the multi-source space dependence matrix, so that hierarchical learning of space features is realized. Finally, the time and space features are integrated through a multi-modal feature fusion module, and the flight delay time prediction result is obtained by inputting the delay prediction module. Through joint modeling of the multi-modal space-time features, the method can effectively capture the space-time propagation law of flight delays in the airport network, and improve the flight delay prediction accuracy.

The application discloses a kind of off-site flight dynamic collaborative sequencing methods, specifically comprising the following steps: step 1: for different traffic states of airport, respectively establish off-site flight collaborative sequencing model;Step 2: by dynamic method, obtain the off-site flight list to be sequenced of to-be-sequenced period;And determine the traffic state of to-be-sequenced period airport;Step 3: the off-site flight list to be sequenced obtained in step 2 is input into the off-site flight collaborative sequencing model corresponding to the traffic state of to-be-sequenced period airport, solves off-site flight collaborative sequencing model, obtains flight sequencing.The application provides a kind of method for realizing off-site flight dynamic collaborative sequencing, the method fits collaborative decision-making concept, comprehensively considers the benefit demand of control unit, airline and airport three parties, can carry out optimization sequencing to off-site flight, guarantees the dynamicity of flight sequencing, significantly reduces flight delay, effectively improves fairness.