488 results about "Traffic prediction" patented technology

The invention provides a sequential network traffic prediction method and system based on swarm intelligence parameter optimization, and relates to the technical field of network traffic prediction. The method comprises the following steps: acquiring indexes such as throughput packet loss rate and round-trip delay of a target link by using a network probe, and performing deletion filling normalization and multi-scale decomposition to obtain a standardized traffic sequence; calculating information entropy, constructing a traffic complexity feature vector, and dividing a training set and a verification set; constructing a hybrid depth prediction model composed of a one-dimensional convolutional network and a gating cycle unit, and establishing a hyper-parameter search space; using particle swarm optimization and entropy-driven inertia weight adjustment and mutation probability mapping to reconstruct a speed and position updating strategy, and iteratively outputting a global optimal hyper-parameter; and generating a benchmark prediction result according to full-amount training, extracting a residual error, training a nonlinear residual error compensation model to carry out superposition correction and reverse normalization, obtaining a final flow prediction result, and improving prediction precision and generalization ability.

The invention relates to an intelligent traffic flow prediction analysis method based on artificial intelligence, and the method comprises the steps: collecting and fusing traffic flow, environmental factors and event information according to traffic levels, and achieving the standardization and automatic clustering preprocessing of multi-level space-time attributes through regional factor labels; and then, expressing a multi-dimensional structure and a dynamic attribute of each node by using regional factor vectorization, dynamically modeling a spatial node heterogeneous adjacency relationship in combination with a self-organizing graph neural network, introducing a cross-level dynamic attention mechanism to perform weighted fusion on multiple spatial and temporal features, and outputting multi-granularity traffic prediction through a hierarchical fusion decoding network. And the model is combined with actual feedback to realize self-adaptive optimization of the area factors and model parameters. The method has the advantages that high-precision prediction of the traffic flow under multiple scales of roads, blocks, cities and the like is achieved, the self-learning and self-adaptive capacity for heterogeneous information, emergencies and spatial dynamic changes is improved, and hierarchical decision making and flow management are supported.

The invention relates to the technical field of artificial intelligence, and provides a subway network traffic prediction method based on association modeling, comprising: acquiring a heterogeneous data source of a target subway network; the heterogeneous data sources are cleaned, aligned and fused, and a time-space association data set is constructed; based on the subway network topology and the real-time passenger flow state, constructing a dynamic relation graph representing the dynamic interaction between the line and the station; the space-time correlation data set and the dynamic relation graph are utilized to cooperatively train a space-time prediction module and a relation reasoning module in an alternate optimization mode, and the relation reasoning module iteratively updates an edge weight in the dynamic relation graph through a graph attention mechanism and a space-time convolution operation; and based on the dynamic relation graph and the optimized space-time prediction module, carrying out multi-step prediction on the passenger flow in the future period and outputting a prediction uncertainty quantitative index. According to the technical scheme of the application, the accuracy and reliability of subway passenger flow prediction are significantly improved by fusing multi-source data and dynamically modeling the site association relationship.

The invention relates to a traffic flow prediction method and system based on a dynamic perception expert network, and the method comprises the steps: obtaining traffic observation data, constructing a traffic network diagram, carrying out the feature embedding, and generating an initial spatial-temporal feature vector; inputting the initial spatial-temporal feature vector into a double-path time encoder, and respectively extracting a personalized time sequence feature vector and a time sequence dynamic feature vector through parallel channel independent paths and channel mixed paths; carrying out vector fusion through a gating mechanism to obtain a time context feature vector, inputting the time context feature vector into a multi-scale hybrid expert model, activating a plurality of most relevant time scale experts, and generating a corresponding routing weight; and generating a spatial dependency graph through a scale condition dynamic graph generator, performing graph convolution to extract a plurality of spatio-temporal feature vectors, performing weighted aggregation, sending the spatio-temporal feature vectors into a prediction model, and generating a traffic prediction value. Compared with the prior art, the model constructed by the method is relatively high in prediction precision and relatively high in robustness in a complex traffic scene.

The invention discloses a multi-mode urban traffic prediction system, belongs to the technical field of traffic, and solves the problems that a tunnel structure is not early warned in time due to hidden damage under the action of multiple coupling, and finally, a river-crossing tunnel bursts water suddenly and is forced to be closed under the triggering of peak traffic flow vibration, so that the tunnel structure cannot be early warned. Therefore, the problem of chain paralysis of the traffic system of the whole city is solved. Comprising a multi-modal data acquisition module, a damage evolution modeling module, a traffic influence analysis module, a collaborative optimization control module and a dynamic plan generation module. According to the method, the sensing capability is constructed by fusing multi-source monitoring data, hidden structure damage is identified and predicted by means of a multi-physics field coupling model, a traffic collaborative optimization strategy is generated based on adaptive dynamic planning, and then whole-process prevention and control from risk to emergency are realized through a dynamic plan and meta-learning. Therefore, the vicious circle of structural damage-traffic jam-rescue blocking is blocked, and regional paralysis is avoided.

The invention discloses a centrifugal pump flow dynamic detection system based on the Internet of Things, and relates to the technical field of intelligent sensing systems. Comprising a data acquisition module used for acquiring multi-source signals in real time and obtaining a sensing feature data set after preprocessing; the physical property parameter module is used for calculating the influence parameters of the physical property change of the medium on the pump performance based on the sensing characteristic data set to obtain a medium physical property parameter set; the flow prediction module is used for correcting a preset flow prediction model through the medium physical property parameter set to obtain a corrected flow prediction model; inputting a sensing characteristic data set acquired in real time into the corrected flow prediction model, and outputting to obtain a flow prediction value; the traffic detection module is used for judging whether the current traffic is abnormal or not based on the traffic predicted value, the historical traffic data and a preset operation threshold parameter set, and giving a traffic detection result; the influence of medium physical property changes on flow detection can be accurately captured, and the accuracy and stability of flow detection are improved.

The invention discloses a dynamic space-time diagram flow prediction method and system based on course learning, and relates to the technical field of supply chain logistics data analysis, and the method comprises the steps: building a space-time matrix based on historical multi-source data, generating a dynamic adjacent matrix through learning, and carrying out the smooth fusion through combining a static diagram, and forming a dynamic diagram structure. And then space and time features are respectively extracted by using a graph convolutional network and a gating loop unit, and deep interaction and fusion are realized through a bidirectional cross attention mechanism. A multi-dimensional difficulty estimator is innovatively introduced, the prediction difficulty of each training sample is quantified from three dimensions of space, time and time-space coupling, the selection sequence of the training samples is dynamically adjusted based on an adaptive course scheduler, and progressive learning is realized. And finally, feature representation is obtained through global pooling, and multi-step traffic prediction is realized by adopting a parallel independent decoder, so that error accumulation is avoided. According to the invention, prediction precision and model training efficiency in a complex supply chain logistics scene are effectively improved.

The invention belongs to the technical field of urban traffic flow prediction, and more specifically relates to a traffic flow prediction method based on self-supervision spatio-temporal representation and scene adaptation. The method comprises the following steps: acquiring traffic flow data, environment data and static region semantic embedding data, respectively performing preprocessing operation, and fusing the three data to obtain standardized tensor input; meanwhile, constructing structure prior data to constrain and correct subsequent model prediction output; constructing a traffic flow prediction model, inputting the preprocessed data into the prediction model, and outputting a future traffic flow prediction result; a road mask and a capacity upper bound are introduced in training and reasoning for constraint, and the stability of prediction result output is improved. The invention aims to enhance the modeling capability for nonlinear space-time dependence, sudden disturbance and exogenous environment coupling influence.

The invention discloses an inland waterway congestion dredging method and device, and relates to the field of traffic control, and the method comprises the steps: obtaining multi-source traffic data and waterway meteorological and hydrological data of each ship in a waterway; performing space-time registration and fusion processing on the multi-source traffic data, generating comprehensive traffic situation information of the whole section of the channel, and extracting traffic flow characteristic parameters and channel congestion indexes; inputting the traffic flow characteristic parameters, the channel congestion index and the channel meteorological and hydrological data into a trained traffic flow prediction model to obtain traffic prediction information of key nodes in the channel in a future preset time window; based on the comprehensive traffic situation information and the traffic prediction information, generating preventive control parameters for channel key node signal lamps; and according to the traffic prediction information and the preventive control parameters, traffic guidance information is generated and sent to the corresponding ship terminals, and the preventive control parameters are sent to the corresponding signal lamp control machines. The overall passing efficiency of the channel can be improved.

The invention relates to the technical field of low-altitude traffic, in particular to a low-altitude traffic flow prediction and dispersion method based on a space-time diagram network, and the method comprises the steps: obtaining and fusing multi-source data; the takeoff and landing field monitors demand abnormity; monitoring the real-time state of the air route node; node traffic health degree combined diagnosis is carried out; quantizing spatial domain resource constraint conditions; performing multi-dimensional assessment on congestion risk levels; dynamically evaluating conflict risks; generating a dynamic grooming strategy; and potential risk evolution prediction and plan preparation. According to the invention, multi-source heterogeneous real-time and historical data are deeply fused through a time-space diagram network, multi-dimensional and progressive intelligent perception and prediction are carried out on the low-altitude corridor traffic situation, different levels of congestion risks from microscopic node overload to macroscopic region paralysis are diagnosed, a dredging strategy is automatically generated, and the traffic guidance efficiency is improved. The problems of decision lag, extensive study and judgment, low efficiency in dredging and incapability of coping with dynamic congestion due to adoption of an isolated static threshold and single-dimensional analysis are effectively solved.

The invention discloses a flight flow intelligent prediction and air traffic collaborative optimization system based on big data, and relates to the technical field of air traffic management. Comprising the steps that a data acquisition module acquires multi-source data from a civil aviation database and aligns the multi-source data to generate a fusion data matrix; the traffic prediction module extracts traffic feature vectors, generates a feature similarity matrix by calculating distribution differences, and generates a basic traffic prediction result based on migration prediction model parameters; the disturbance correction module detects a sudden disturbance event based on real-time weather and airspace state data and corrects a basic prediction result; the capacity adjusting module generates a capacity adjusting scheme when the capacity deviation value exceeds a threshold value according to the correction result and the airport operation capability data; and the collaborative optimization module finally generates a multi-airport collaborative scheduling scheme through a game equilibrium algorithm based on the correction result, the capacity scheme and the regional coordination data. According to the invention, the accuracy of flight flow prediction and the efficiency of multi-airport collaborative management are effectively improved.

The invention discloses a smart scenic area zoning and shunting method based on tourist flow prediction and dynamic bearing control, and relates to the technical field of smart scenic area passenger flow prediction, and the method comprises the steps: collecting historical tourist flow data and real-time tourist flow data, constructing a tourist flow prediction model, and generating a tourist flow prediction value; calculating a reference bearing threshold value, dynamically adjusting a current bearing threshold value in combination with the tourist flow predicted value, and when the tourist flow predicted value is greater than a preset proportion of the adjusted threshold value, marking the tourist flow predicted value as an early warning subarea; constructing a tourist flow transfer matrix between the early warning subarea and the adjacent subarea, calculating a tourist flow transfer probability value, and obtaining a shunting target subarea; and pushing the guide information of the shunting target zone to the early warning zone tourist terminal, synchronously adjusting the entrance gate of the early warning zone to reduce the passing rate, and improving the passing rate of the entrance gate of the shunting target zone. According to the invention, the execution success rate and tourist acceptability of the shunting scheme are improved.

The invention provides an intelligent prediction and dynamic optimization distribution method for computer network traffic, which relates to the technical field of computer networks and comprises the following steps of: acquiring and preprocessing network traffic data; constructing a multi-model fusion network traffic prediction model; generating a dynamic optimization distribution strategy based on a prediction result; and allocation strategy execution and dynamic feedback adjustment: executing the allocation strategy through the SDN controller, monitoring the network state in real time and feeding back the network state. A multi-model fusion strategy is adopted, the time sequence capturing capability of the improved LSTM, the feature fitting capability of the XGBoost and the long dependence processing capability of the time sequence attention Transform are combined, and the weight is optimized through PSO, so that compared with a single model, the prediction error is reduced, the burst flow and periodic flow features can be accurately captured, and the problem that in the flow distribution link, the flow distribution efficiency is greatly improved is solved. Most methods only aim at maximizing the bandwidth utilization rate, and ignore the problem of QoS demand difference of different services.

The invention relates to the technical field of intelligent traffic, and discloses a traffic flow prediction method and system based on traffic large model area signal optimization, and the method comprises the steps: obtaining road network structure data and traffic flow density distribution data; performing correlation analysis and feature fusion according to the road network structure data and the traffic flow density distribution data to obtain a regional traffic feature vector, and predicting vehicle flow based on the regional traffic feature vector to obtain a vehicle flow predicted value; according to the vehicle flow prediction value, evaluating the intersection congestion trend, and combining the regional traffic feature vector to carry out spatial dependence and network topology feature fusion processing to obtain an initial parameter set constrained by space; and carrying out optimization solution calculation on the initial parameter set subjected to spatial constraint to obtain a personalized signal control strategy. According to the method, the optimization range can be narrowed by introducing the physical topology constraint, and the response speed of signal control is remarkably improved while congestion overflow is prevented.

An airspace flow prediction method and device based on an ensemble learning algorithm are provided. The method includes the steps: collecting historical airspace flow data and related spatial structure data, and preprocessing; constructing a GNN model, and calculating an influence degree of each node and an influence degree between the nodes in an airspace network by using the GNN model, the node being any airport or any waypoint; performing, by the GNN model, feature conversion and attention fusion on the influence degree of the node, the influence degree between the nodes and time series data to acquire a fused feature vector; inputting the fused feature vector into an LSTM model to acquire a predicted airspace flow of the node; and applying the predicted airspace flow of the node to manage navigation of traffic in the airspace network.

The invention discloses a short-term traffic flow prediction method based on a dynamic multi-dimensional global perception graph convolutional network, and belongs to the field of traffic flow prediction. The method comprises the following steps: acquiring an input dynamic graph feature sequence, and generating space-time element parameters; carrying out feature enhancement on the dynamic graph feature sequence, fusing the dynamic graph feature sequence with the space-time element parameters, and then respectively carrying out space-time feature coding; fusing the spatio-temporal characteristics obtained by coding the spatio-temporal characteristics; and carrying out space-time trend alignment decoding on the fused space-time features. According to the method, the defects of limited modeling precision, insufficient dynamic adaptability, incomplete spatial-temporal feature capture and the like in short-term traffic flow prediction are overcome, and the accuracy and the applicability of short-term traffic flow prediction are improved.

The invention discloses an intelligent traffic emergency resource dynamic scheduling system based on flow prediction, which relates to the technical field of intelligent traffic scheduling and comprises a traffic data acquisition module, an integrated radar, a video monitor, a coil detector and a floating car GPS, 5G transmission, acquisition of traffic flow, vehicle speed and the like within 20ms and detection of abnormity within 3s; the traffic prediction module is used for predicting 1-4-hour traffic and updating parameters in 30 minutes based on 72-hour data and a fusion model; the emergency resource library module is used for storing rescue vehicle, personnel and material information and updating the state within one minute; the dynamic scheduling module is used for generating a scheme containing a route and arrival time within 30 seconds; the monitoring module is executed, resources are tracked, secondary scheduling is carried out when the deviation exceeds 10 minutes, and recording is carried out for more than 3 years. The traffic prediction accuracy and the resource matching degree are improved, the path is optimized, delay is reduced, cross-regional scheduling is supported, the emergency efficiency and the resource utilization rate are improved, and the accident influence is reduced.

The application provides a base station traffic prediction method and related equipment, and relates to the field of communication, wherein the base station traffic prediction method comprises the following steps: determining the adjacent base stations of a to-be-predicted base station according to the geographical distance information between base stations; determining the neighbor base stations in the adjacent base stations, and the historical traffic records of the neighbor base stations and the historical traffic record of the to-be-predicted base station have a causal relationship; performing feature extraction on the historical traffic records of the to-be-predicted base station and the neighbor base stations to obtain feature data; and predicting the predicted traffic of the to-be-predicted base station according to the feature data. In the screening process of the feature data object, the spatial factor is fully considered to avoid the singularity of feature data selection, and the causal relationship between the base station traffics is fully considered, so that the blindness of feature data selection is avoided, the redundant feature interference is reduced, the effectiveness of feature selection is improved, and the base station traffic prediction accuracy and efficiency are improved.

The invention discloses a traffic flow prediction method based on cross-modal interactive geographic image coding, and relates to the technical field of intelligent traffic systems and data processing. The method comprises the following steps: firstly, acquiring historical traffic data and a road network geographic image, and respectively constructing an embedded representation containing space-time periodicity and extracting node-level visual features; secondly, through a hybrid cross attention mechanism, utilizing a learnable global visual token as an abstract agent, compressing visual features and performing cross-modal alignment with the dynamic space-time representation to generate an enhanced space-time representation; meanwhile, a visual relation mode is constructed based on local visual patches between the nodes, and refining is carried out through a general relation matrix; and finally, dynamically fusing time, space and visual relation characteristics by using an adaptive gating mechanism, and predicting future traffic flow. Visual modes such as road geometry can be effectively captured, the problem of dynamic and static heterogeneous mode alignment is solved, and the accuracy of traffic prediction is remarkably improved while the calculation efficiency is guaranteed.

The invention discloses a scenic spot traffic flow prediction-oriented multi-source heterogeneous data driven space-time modeling method. The method comprises the following steps: acquiring traffic historical data, a road network topological structure and scenic spot related auxiliary factors, and constructing multi-dimensional tensor and graph structure representation; spatio-temporal feature learning is realized through time slice embedding and spectrogram position coding, and a tourism enhancement tensor is constructed in combination with scenic spot auxiliary semantic factors and serves as node regulation and control guide input; and introducing a cross attention mechanism which is jointly regulated and controlled by a tourism regulation factor and a propagation delay factor into the encoder, and fusing multi-source embedded information to generate a traffic flow prediction result. The method is suitable for scenes with severe flow change such as holidays, holidays and hot scenic spots, the dynamic regulation and control relation of tourism activities to traffic states can be accurately reflected, and the response capability, interpretability and robustness of a traffic prediction model to high-association nodes are improved.

The invention discloses a multi-traffic-mode prediction method based on a hybrid expert model and space-time attention, and relates to the technical field of traffic prediction, and the method comprises the steps: dividing historical passenger flow sequences of multiple traffic modes according to tasks through employing a multi-gating hybrid expert model, and outputting a weighted expert feature corresponding to each task; wherein different tasks correspond to different traffic modes; performing feature structuring on the weighted expert features of each task in a traffic mode by using a space-time double-patch architecture to obtain space-time structural features; performing multi-scale feature extraction in a traffic mode and inter-modal feature fusion on the space-time structure features by using a space-time multi-attention convolutional neural network to obtain fused features; and traffic flow data of the multiple traffic modes are obtained through prediction according to the fusion features. According to the invention, through combined prediction of multiple traffic modes, flexible and steady multi-task learning is realized, different prediction tasks are adaptively balanced, and traffic flow data of different traffic modes can be accurately predicted.

The invention discloses a traffic flow prediction method based on a large language model, and the method comprises the steps: data preparation, construction of a road network diagram structure, collection of traffic sensor data, construction of a traffic prediction model, formatting of the data into a mixed format in which a natural language and a structure are combined, spatial feature extraction through LLM, formatting of time series data, and reprogramming. And then fusing the spatial features with the spatial-temporal features of the reprogrammed time series data, performing lightweight fine tuning on the language model by using LoRA, then performing forward propagation, abandoning a suffix part and obtaining an output representation, performing a flattening operation on the output representation, and obtaining a prediction result through a linear projection layer. According to the method, the LLM is conveniently used for traffic flow prediction under the condition that the backbone language model is kept complete, and the complexity and parameter quantity of model training are remarkably reduced while the prediction accuracy is improved.

A novel dynamic bandwidth allocation system integrates an intelligent traffic prediction module and an on-demand token replenishment module in a quality-of-service (QoS)-based media access control (MAC) layer controller, which is able to dynamically allocate data transmission capacity (i.e., bandwidth) to multiple incoming service flows of data packets for multiple subscribers more efficiently with less network congestions and service inconsistencies than conventional MAC layer management methods. The novel dynamic bandwidth allocation system utilizes network traffic predictions to identify potentially sudden or rapid changes in near-term network traffic, and if necessary, adjusts bandwidth allocations preemptively to service flows to minimize service quality degradations while also improving network resource usage efficiencies, compared to conventional static bandwidth allocation methods. The novel dynamic bandwidth allocation system also minimizes unnecessary packet droppages, network congestions, and / or speed degradations during bursty and oscillating network traffic, and improves QoS satisfaction rates for network subscribers.

The invention discloses a hierarchical prediction method and system for civil aviation passenger transport demands based on multi-source heterogeneous data, and the method comprises the steps: constructing a civil aviation passenger transport demand driving feature library, and constructing the civil aviation passenger transport demand driving feature library according to hierarchical items and upper and lower hierarchies of hierarchical feature items; constructing a civil aviation passenger transport demand level combination prediction model comprising a plurality of level item prediction sub-models; extracting causal association time series data taking daily passenger flow as a dependent variable and hierarchical feature item data as an independent variable in the historical feature data subset, and inputting the causal association time series data into a hierarchical item prediction sub-model for feature time series data prediction training; and the civil aviation passenger transport demand level combination prediction model sequentially outputs daily passenger flow volume corresponding to each level from bottom to top according to the level items. According to the method, daily passenger flow prediction according to multiple attribution levels of airlines, airports, cities and countries is realized, passenger flow demand data from macroscopic to microscopic linkage constraints can be obtained, and hierarchical rich data is provided for civil aviation management parties so as to improve the guarantee service capability.

The invention discloses a cross-city traffic prediction method and system based on multi-modal fusion and spatial expert routing, relates to the technical field of traffic prediction, and provides an adaptive modal selection mechanism based on a signal-to-noise ratio for the problems of missing, noise and uneven quality of multi-modal data in different cities. And a low-quality mode is dynamically suppressed in combination with comparative learning, and robust multi-mode fusion is realized. In order to solve the problems of large space structure difference and weak generalization ability among cities, a multi-modal guided space expert routing architecture is designed: modal sharing experts and routing experts are activated by using a multi-modal context, and local space dependence is adaptively modeled for different functional regions. The method supports any modal combination input, city specific fine tuning is not needed, and the zero sample cross-city prediction performance is significantly improved.

The invention provides a traffic flow prediction method based on deep learning, and belongs to the field of intelligent traffic systems and traffic management. The method comprises the following steps: collecting traffic flow data of a road, and obtaining regional road network map data; constructing a memory tensor enhanced Transform fusion model, wherein the memory tensor enhanced Transform fusion model comprises a data embedding layer, a feature extraction layer, a jump connection layer and an output layer; inputting the traffic flow data and the regional road network map data into a data embedding layer for preprocessing to obtain preprocessed data; inputting the preprocessed data into a feature extraction layer for space-time memory triple attention and multi-layer perceptron processing to obtain a plurality of space-time memory feature extraction layer outputs; accumulating the output and input jump connection layers of each space-time memory feature extraction layer to obtain a jump connection result; and inputting the jump connection result into an output layer, and carrying out convolution operation twice to obtain a traffic flow prediction result. According to the method, a more accurate traffic prediction basis can be provided for decision making, and the method has a remarkable engineering application prospect.

The invention relates to the technical field of intelligent traffic control, particularly discloses a traffic flow dynamic prediction and scheduling method based on artificial intelligence, and aims to solve the problems that the traffic prediction precision is insufficient and the scheduling strategy lacks adaptability. According to the method, continuous optimization is realized through multi-source traffic data fusion, spatial-temporal feature depth extraction, multi-scale dynamic prediction and adaptive scheduling strategy generation in combination with closed-loop feedback. A deep convolutional network and a graph attention network are adopted to extract spatial-temporal features, and a signal control and lane management strategy is dynamically generated through multi-target reinforcement learning, so that the road network traffic efficiency and the system adaptive ability are effectively improved.

The embodiment of the invention relates to the technical field of traffic flow prediction, and provides an anti-poisoning traffic flow prediction method based on federated learning, and the method comprises the steps that a traffic region client trains an anti-poisoning dynamic graph neural network model; and the traffic area client calculates the credibility of the central server and the credibility of other traffic areas. And selecting a centralized aggregation mode or a decentralized aggregation mode for aggregation according to a credibility calculation result. And each traffic area client updates local model parameters according to the corresponding aggregation result to obtain a local anti-poisoning dynamic graph neural network model. According to the method and the system, the interference of malicious clients can be resisted, the traffic knowledge sharing effect between normal clients is ensured, an optimal balance point is found between prediction performance and safety protection, and the safety of model training is improved.

The invention provides an airport security check passenger flow volume prediction method and system, electronic equipment and a storage medium, and is applied to the technical field of airport security check. Target historical airport security check queuing data before target time is acquired; fusing the queue information of each target historical queue to obtain fused queue information of a target fused historical queue; performing empirical mode decomposition processing on fusion queue information of the target fusion historical queue to obtain a plurality of target data sequences and target residual components; determining a prediction model matched with the target data sequence, and performing airport security check passenger flow prediction according to the target data sequence or the target data sequence and the target residual component through the prediction model matched with the target data sequence to obtain a target prediction result; and determining the airport security check passenger flow volume corresponding to the target time according to each target prediction result, thereby being capable of adapting to unstable data, meeting actual requirements, and improving the accuracy of predicting the airport security check passenger flow volume at the same time.

The present specification relates to the technical field of satellite, and particularly relates to a satellite traffic flow prediction method and device. The method comprises: acquiring geographical feature data of a wave position; acquiring coverage time data of the satellite for the wave position in a historical period; constructing first feature data of the satellite according to the geographical feature data and the coverage time data; the first feature data is used for representing geographical features cumulatively covered by the satellite in the historical period; acquiring second feature data, the second feature data is used for representing traffic flow of the satellite in the historical period; and predicting future traffic flow of the satellite according to the first feature data and the second feature data. The embodiment of the present specification can improve prediction accuracy.