892 results about "Traffic prediction" patented technology

The invention belongs to the technical field of intelligent traffic systems, and particularly relates to an intersection signal timing dynamic coordination system and method based on real-time traffic prediction.The method comprises the steps of collecting multi-source traffic data, generating a space-time prediction digital twin model, dynamically defining an intersection coordination cluster and executing cluster coordination optimization control. And signal timing and closed loop feedback are carried out. By adopting the technical scheme, the cooperative operation efficiency and the intelligent management level of the urban intersection group can be effectively improved, and the fundamental conversion from local and reactive active cooperative control to global and predictive active cooperative control is realized.

The invention provides a high-speed traffic flow high-precision prediction method based on multi-source disturbance characteristics, and relates to the field of data prediction, and the specific steps are as follows: firstly, a multivariable entropy driving interaction field module maps the multi-source disturbance characteristics into a unified energy field, calculates joint information entropy density and constructs a joint interaction field; processing the original feature sequence; secondly, the collaborative disturbance reconstruction module adopts a learnable mapping matrix and a multi-scale mechanism to extract dynamic differences of features under different time scales, and generates enhanced disturbance response features through a decoupling network after global disturbance collaborative response is fused; then, a spatial manifold mapping and partitioning module realizes spatial expression and partitioning modeling of a traffic flow tension evolution trend; and then, the prediction module constructs an asymmetric prediction structure in combination with the disturbance amplitude factor and the weighted disturbance characteristics, adopts a mean square error, introduces a disturbance constraint term to train the model, and outputs a final traffic flow prediction result through the trained high-speed traffic flow prediction model.

The invention relates to a gated multi-graph convolution perception modeling method for traffic flow prediction. The method integrates multi-graph structure construction, gating graph convolution and time feature extraction, and aims to solve the problems of strong time fluctuation and heterogeneous spatial relationship in traffic data. The method comprises the following steps of: firstly, respectively constructing a geographic map and a semantic map according to the maximum mutual information measurement between the spatial distribution information of a sensor and historical traffic data; and then, designing a dual-adaptive gating graph convolution module, and dynamically adjusting an information propagation path of a multi-graph structure by introducing an attention mechanism and a gating factor, thereby improving the modeling performance of the model on spatial isomerism dependence. On the time dimension, a time sequence interactive sensing module is constructed in combination with multi-scale causal convolution and an attention mechanism, time dependence characteristics of a short period and a long period are captured, and fusion and expression of time characteristics are completed. According to the method, the modeling precision and stability of the traffic prediction model in a complex traffic scene can be effectively enhanced, and the method has relatively high practical application value.

The invention provides a traffic control strategy self-adaption method and system based on simulation feedback, and belongs to the technical field of traffic prediction and control, and the method comprises the steps: firstly obtaining traffic control scene data containing traffic flow data and road condition information, then constructing a simulation evaluation environment, configuring scene parameters based on the traffic control scene data, and carrying out the simulation evaluation environment; the method comprises the following steps: simulating traffic operation states under different traffic control strategies, calling a pre-trained reinforcement learning model to perform simulation evaluation on each strategy in a traffic control strategy set, generating a strategy effect feedback set comprising a traffic operation efficiency index and a traffic order stability index, and according to the strategy effect feedback set, calculating the traffic order stability of the traffic control strategy. And performing parameter adjustment on the strategy in consideration of the index association relationship to obtain an adjusted strategy, and finally outputting the adjusted strategy to the traffic control system to realize strategy updating, thereby effectively improving traffic operation efficiency, ensuring traffic order stability, and realizing adaptive optimization of the traffic control strategy.

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 belongs to the technical field of air traffic management, and relates to an air flight flow prediction method based on a lightweight adaptive network, which comprises the following steps of: firstly, acquiring data and fusing the data to generate a flow time sequence matrix indexed by airport identification and timestamp; secondly, constructing a self-adaptive dynamic graph; performing mixed graph convolution on the output dynamic graph and node features to form a spatial feature tensor, and performing frequency domain enhancement on the spatial feature tensor to obtain an event enhancement sequence; then processing the event strengthening sequence through an hour-level large convolution kernel and a minute-level expansion small convolution kernel, amplifying a congestion peak based on trend-fluctuation gating after time alignment, and outputting a fusion feature sequence; and finally, carrying out recursive decoding and prediction to obtain the predicted air flight flow. According to the method provided by the invention, under the conditions of dynamically changing airspace topology and strong noise and multi-scale coupled time sequence data, a set of lightweight spatial-temporal model is constructed and updated online in a self-adaptive manner, so that high-precision multi-step prediction of the multi-element flight flow can be realized.

The invention discloses a traffic flow prediction method and system based on spatio-temporal hierarchical mixing, and relates to the technical field of traffic prediction, and the specific steps are as follows: obtaining an original traffic flow spatio-temporal sequence, mapping the original traffic flow spatio-temporal sequence, fusing spatial embedding and time period embedding, and generating an input feature; extracting time sequence features and multi-scale region features based on the input features, performing parallel extraction of node-level features and region-level features on the region features of each scale, and outputting the node-level features and a plurality of region-level features; fusing the region-level features step by step based on a hierarchical feature propagation mechanism, downloading the region-level features to the node-level features, generating space fusion features, and fusing the space fusion features layer by layer to generate time sequence fusion features; and fusing the highest-layer feature of the spatial fusion feature and the time sequence fusion feature to generate a fused spatial-temporal feature, and performing traffic prediction by using the fused spatial-temporal feature. According to the method, the efficiency and the prediction precision are improved.

Various embodiments include a wireless communication network that comprises resource allocation circuitry. The resource allocation circuitry hosts a traffic forecasting machine learning model, a resource forecasting machine learning model, and a resource allocation machine learning model. The resource allocation circuitry obtains traffic data for a provisioning engine cluster and provides the traffic data to the traffic forecasting model. The resource allocation circuitry obtains an output that comprises a traffic prediction for the provisioning engine cluster and provides the prediction to the resource forecasting model. The resource allocation circuitry obtains an output that comprises a hardware requirement prediction for the provisioning engine cluster and provides the hardware requirement prediction to the resource allocation model. The resource allocation circuitry obtains an output that comprises a hardware allocation recommendation for the network provisioning engine cluster. The resource allocation circuitry allocates hardware resources to the cluster based on the hardware allocation recommendation.

The invention provides a dynamic wavelength allocation method based on DCI wavelength division II type equipment. The method comprises the following steps: acquiring an occupation state and a transmission quality index of each wavelength channel of a current network; establishing a wavelength channel quality evaluation model, and scoring each wavelength channel according to the transmission quality index of each wavelength channel of the network; according to the bandwidth requirement, the priority and the QoS requirement of the service request, generating a traffic characteristic matrix containing the delay sensitivity and the bandwidth requirement through a traffic prediction algorithm; based on a network state and the traffic characteristic matrix, through resource availability calculation and path quality evaluation, forming an allocable wavelength resource pool and a quality score thereof; for the allocatable wavelength resource pool, generating an optimal wavelength allocation scheme by adopting an improved heuristic algorithm and comprehensively considering the resource utilization rate, the signal quality and the service priority; and according to the performance feedback of the optimal wavelength allocation result and the network state change, continuous optimization of wavelength allocation is realized through a self-adaptive adjustment mechanism. The method can effectively deal with the dynamic flow change in the DCI network, improve the resource utilization rate, ensure the signal quality and meet the QoS requirement of the service, thereby providing an efficient and reliable wavelength allocation solution for the data center interconnection network.

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 provides a time-varying graph neural network traffic flow prediction method based on a dynamic memory bank, and belongs to the technical field of traffic prediction. The method adopts a layered deep neural network architecture, and comprises a data embedding layer, a space-time coding layer, a memory enhancement layer and a prediction output layer. The data embedding layer preprocesses a traffic flow sequence, associates a collaborative coding time sequence mode with a road network, and synchronously constructs a dynamic graph structure; the space-time coding layer is subjected to space-time stream decoupling extraction, a space branch models multi-scale space dependence through a time delay graph convolution module and a space Mama module, and a time branch extracts multi-granularity time features through a hierarchical time sequence sensing module and a time Mama module; the memory enhancement layer performs pattern matching and reconstruction on the space-time fusion features by means of a dynamic memory bank; and the prediction output layer generates a prediction result by taking the GCRN as a decoder. According to the method, the space-time dependence of the traffic situation is accurately captured, the prediction curve is highly fit with the true value, and the high-precision prediction of the traffic flow is realized.

The present application relates to the technical field of communications, and discloses a path planning method and apparatus, a device, a system, and a storage medium. The method comprises: acquiring traffic prediction information respectively corresponding to at least one time period; and on the basis of the traffic prediction information respectively corresponding to the at least one time period and at least one selectable path respectively corresponding to each service, determining a target forwarding path of each service meeting path planning objectives. The path planning objectives comprise: the service traffic borne by any link in a hierarchical network in any time period is less than or equal to the bandwidth capacity upper limit of any link, load balancing is realized between multiple links connected to a same network node in the hierarchical network, and the transmission quality of the target forwarding path of any service meets a quality requirement of any service. Path planning of a global service is realized, network link congestion can be reduced, and better effects that load balancing between all network links is realized and a service forwarding path meets a service quality requirement are achieved.

The invention relates to an internet of things quality of service (QoS) management method and system, and aims to solve the problem that the internet of things quality of service is unstable through a self-adaptive flow optimization technology. The system comprises a device identification module, a flow analysis module, a flow prediction module, a resource allocation module, a congestion control module, a safety guarantee module, a user feedback module and the like, predicts a flow trend by using a machine learning algorithm, dynamically adjusts resource allocation, and improves network efficiency and user experience. The method is suitable for various scales of Internet of Things environments, and is of great significance for promoting the development of the Internet of Things technology.

The invention discloses a passenger flow monitoring and analysis system, and relates to the technical field of scenic spot management, and the system comprises the following components: a data collection module which is used for deploying a plurality of Internet of Things devices at key points of a scenic spot, and is combined with a communication operator, social media and a scenic spot internal system; collecting and covering tourist behaviors, moving tracks, social feedback and consumption ticket buying data; according to the method, the multi-source heterogeneous data acquisition system is constructed, the scenic spot passenger flow related information is accurately and comprehensively acquired, the time-space sequence analysis and graph neural network technology is combined, the time and space correlation characteristics of the scenic spot passenger flow are fully mined, the constructed multi-scenic spot passenger flow dynamic correlation prediction model can realize accurate cross-scenic spot passenger flow prediction, and the scenic spot passenger flow dynamic correlation prediction method is high in practicability. Therefore, a scenic spot manager can grasp the visitor flow rate condition of each scenic spot in different time periods in the future in advance and prepare for coping in advance, the capacity of coping with the passenger flow peak of the scenic spot is effectively improved, safety accidents caused by passenger flow congestion are avoided, and the touring safety of tourists is guaranteed.

The invention discloses a traffic prediction method and device based on multi-modal feature fusion, and a medium, and relates to the technical field of traffic prediction. The method comprises the following steps: performing unmanned aerial vehicle video acquisition and preprocessing on an interleaving area to obtain a target area video; traffic flow parameters are extracted from the target area video based on a multi-target detection and tracking algorithm, wherein the traffic flow parameters comprise a space average speed, a space occupancy rate and a vehicle interleaving conflict index; a deep learning model is adopted to process the image sequence of the target area video, potential semantic features are extracted, and the deep learning model comprises a variational auto-encoder; carrying out weighting processing on the potential semantic features by adopting a double attention mechanism; and splicing the weighted potential semantic features and the traffic flow parameters into a multi-modal feature vector, and carrying out traffic state prediction. According to the method, the accuracy and robustness of traffic state prediction of the interlaced area are improved, and more accurate data support and decision basis are provided for dynamic traffic management of complex road sections.

The invention relates to the technical field of artificial intelligence and big data analysis, in particular to a short video traffic prediction method based on multi-modal data, which comprises the following steps: constructing and training a multi-modal short video traffic prediction model, and performing traffic prediction by adopting the trained multi-modal short video traffic prediction model; the multi-modal short video traffic prediction model comprises a multi-modal feature extraction module, a time sequence modeling module, a modal attention module, a global modulation module and a prediction head module; according to the method, video, audio and text three-mode information is mined in a combined manner, and fine prediction of the short video traffic is realized.

The invention relates to the field of intelligent traffic, and provides a cross-domain-oriented urban traffic road network toughness pre-judgment and evaluation method, which comprises the following three steps: step 1, based on input source domain and target domain urban traffic data, constructing a space-time cross-domain traffic prediction model, realizing traffic state transfer learning and generalization prediction among different cities, and establishing a space-time cross-domain traffic prediction model; generating traffic prediction in a cross-domain scene and a corresponding traffic map structure; 2, designing a hysteretic toughness index in combination with a prediction result and a traffic map structure, and fusing structure-function toughness and a hysteretic toughness factor to form a toughness index capable of reflecting a road network performance evolution mechanism; and step 3, based on the hysteretic toughness index, establishing a space-time fine-grained toughness quantization algorithm for quantizing node-level toughness of the road network. According to the method, the three major problems of lack of pre-judgment and cross-domain generalization ability, single toughness index representation and rough toughness quantification of an existing evaluation method are effectively solved, and the perspectiveness, the adaptability and the accuracy of road network toughness evaluation are remarkably improved.

The invention discloses an adaptive and interpretable traffic situation prediction method and system based on a large language model, and the method comprises the following steps: obtaining historical traffic data related to a prediction task, inputting a trained time sequence prediction basic model, and generating candidate traffic state tracks of a plurality of prediction periods based on the prediction task; acquiring context information related to a prediction time period, and evaluating the candidate traffic state trajectory based on a configured large language reasoning model to obtain an optimal trajectory conforming to the context information of the prediction time period; and based on the optimal track and the context information, generating a text report by utilizing the configured large language interpretation model. Probability prediction is carried out through the time sequence prediction basic model, context reasoning and text generation are carried out through the large language model, the accuracy, adaptability and interpretability of traffic prediction under abnormal events are remarkably improved, and powerful support is provided for intelligent traffic.

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 discloses a hydroelectric power station state monitoring communication method, system and device and a medium, and the method comprises the steps: carrying out the channel attenuation prediction through employing an integrated learning algorithm, and obtaining a channel attenuation prediction result; based on a channel attenuation prediction result, communication parameter optimization is carried out through a control optimization algorithm, and the optimal transmitting power and coding modulation scheme are obtained; analyzing through a deep learning framework to obtain a communication traffic prediction result; dynamically adjusting resource allocation of each service flow through a bandwidth allocation strategy based on a communication flow prediction result to obtain optimized bandwidth resource allocation; through the configuration instruction, the communication parameter setting is dynamically adjusted, and the fine management of the communication system is realized. The satellite communication system of the hydropower station is more reliable and efficient, and better support is provided for operation management of the hydropower station.

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 provides a user terminal flow prediction method, medium and system, and belongs to the technical field of flow prediction model fine tuning, and the method comprises the steps: firstly constructing a general flow prediction basic model comprising a mathematical prediction module and a convolutional neural network; acquiring historical traffic data of a plurality of user terminals, preprocessing the historical traffic data and generating a traffic time sequence vector; clustering analysis is carried out on the vectors, and the users are divided into a plurality of clusters; and on the basis of the cluster with the maximum clustering scale, calculating the clustering similarity between other clusters and the cluster with the maximum clustering scale. Next, an LORA fine tuning model is set for each cluster, and the smaller the similarity is, the larger the parameter scale of the fine tuning model is; and on the basis of the trained general basic model, performing fine tuning on each cluster to obtain a corresponding LORA model. And for the to-be-predicted user terminal, the cluster to which the to-be-predicted user terminal belongs is determined according to the traffic time sequence vector of the to-be-predicted user terminal, and the corresponding LORA fine tuning model is adopted for prediction, so that personalized prediction of a user group can be realized.

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 an intelligent electric vehicle queue optimization method based on congestion prediction and DRL. The method comprises the following steps: (1) modeling a queue position optimization problem of an electric vehicle queue into a mathematical model which takes energy balance as a target and is constrained by a traffic environment; and (2) solving the mathematical model in the step (1) by adopting a DRL method based on a TRPO algorithm to obtain an optimal queue adjustment strategy of the electric vehicle queue. In the step (2), congestion state information in an external traffic environment is obtained through an LSTM traffic prediction and FCM method, and the congestion state information, the remaining electric quantity of each vehicle in the motorcade and the accumulated driving distance are jointly used as state input of a TRPO algorithm; according to the TRPO algorithm, a strategy network and a value network are adopted to respectively obtain a strategy for adjusting the queue position, and the return expectation in the current state is evaluated. The TRPO algorithm dynamically adjusts the updating step length of the strategy through the KL divergence updated by the constraint strategy, and guarantees the stability and convergence in the strategy updating process.

The invention discloses an active congestion avoiding method and system based on flow prediction, and relates to the technical field of network communication, and the method comprises the steps: collecting the network state information of a physical network in real time; dynamically synchronizing and mapping network state information of a physical network to a virtual mirror image by using a digital twinning technology; under the virtual mirror image, traffic prediction is carried out on the network state information based on a time domain convolutional network model, and a traffic prediction result is obtained; the traffic prediction result is transmitted into a scheduling strategy engine for dynamic analysis and evaluation, and an active traffic scheduling strategy for traffic prediction is determined; issuing the active flow scheduling strategy to a physical network for execution; and according to the latest network state information, an active flow scheduling strategy is dynamically adjusted to form a closed-loop adaptive system. According to the method, the virtual mirroring of the network state information is realized by using the digital twin technology, the traffic prediction result is obtained by using the time domain convolutional network model, and the traffic scheduling strategy is actively adjusted before traffic congestion occurs, so that congestion is effectively avoided.

The invention relates to an expressway traffic flow prediction and accident identification method and system, and relates to the technical field of intelligent detection, and the method comprises the steps: firstly, carrying out the real-time monitoring of an expressway through a mobile robot, obtaining the traffic flow feature data, and obtaining the traffic flow prediction data of a specified time period through feature extraction analysis and fusion; and then, according to the traffic flow prediction data and traffic flow data acquired in real time, accident identification and accident grading are carried out, when an accident on the highway is identified, dynamic path planning is carried out by using an algorithm and combining the data identified in real time, and based on the planned path, the mobile robot is controlled to execute a dredging task on the lane of the highway. Through robot automatic monitoring and an intelligent algorithm, whole-process unmanned operation of traffic flow prediction and accident identification and disposal is realized, manual intervention is reduced, the monitoring efficiency and the safety of workers are improved, and the operation cost is reduced.