40 results about "Mobility prediction" patented technology

According to the long-tail human movement prediction method based on adaptive hierarchical learning, coarse-grained semantic grouping is introduced through a hierarchical tree structure based on the Maslow human motivation theory, and the optimization process is rebalanced in a framework-independent mode. Firstly, thinking chain cues are designed based on the Maslow human motivation theory, a hierarchical structure for city customization is constructed by applying a large language model, and knowledge in human mobile data is fully developed through hierarchical learning. Secondly, exploring hierarchical position prediction through Gumbel interference and adaptive weight, so as to fully capture complex space-time semantics; wherein Gumbel interference is used for rebalancing learning of head and tail positions, adaptive weight performs node-level adjustment on head categories and tail categories in each layer, and these components effectively promote exploration of mobility knowledge. The invention further comprises a method adaptive to the advanced mobile prediction algorithm, and the effectiveness and applicability of other methods in long-tail mobile data are improved by embedding the adaptive hierarchical learning loss into other mobile prediction methods.

The invention discloses a human mobility prediction and uncertainty estimation method and system based on a sparse spatio-temporal trajectory, and the method comprises the steps: obtaining a spatio-temporal trajectory, and carrying out the double-graph convolution embedding and global timestamp embedding of the spatio-temporal trajectory, and obtaining a spatio-temporal vector; for the discrete time trajectory, predicting a place of the discrete time trajectory at the next moment based on a sequence encoder of an autocorrelation mechanism, evaluating a place prediction effect based on an evaluation index, and optimizing a discrete time trajectory prediction model; for the continuous time track, the location and time of the next time-space point of the continuous time track are predicted based on a relative time self-attention sequence encoder, the location prediction effect is evaluated based on an evaluation index, the accuracy of time is measured based on a mean square error, and a continuous time track prediction model is optimized; and for an uncertain trajectory, defining the data uncertainty as trajectory entropy based on the spatio-temporal trajectory, determining a trajectory entropy coordination loss function based on the trajectory entropy, and estimating the uncertainty of the model.

Certain aspects of the present disclosure provide techniques for mobility prediction with assistance information. An example method for wireless communications includes obtaining assistance information associated with user equipment (UE) mobility; predicting one or more candidate communication links for a communication link modification based at least in part on the assistance information; and communicating with a wireless communications device based at least in part on the one or more candidate communication links.

The invention relates to the technical field of semiconductors, in particular to a semiconductor carrier mobility prediction method and system, and the method comprises the steps: building a Markos jump rate which represents the transmission of charges in an organic semiconductor through a discontinuous jump mechanism; a main equation under the steady-state condition is established based on the Markos jump rate, and the main equation is used for representing that in the simulated charge transfer system, the charge occupation probability of each site depends on each other through the charge jump process and reaches balance under the steady state; establishing a hole mobility model and a mobility correction factor irrelevant to density, and establishing a mobility model based on the hole mobility model and the mobility correction factor; establishing a weak density dependency function based on the effective temperature, establishing an improved mobility model based on the weak density dependency function and the mobility model, and predicting the mobility of the semiconductor carrier based on the improved mobility model; the migration rate prediction error can be reduced, and the prediction precision can be improved.

The invention discloses an asset management information processing method, device and equipment based on a big data platform, and relates to the technical field of data processing. The method comprises the steps that an asset management data platform carries out data specification processing to obtain index data, the index data are sent to an information big data platform, an asset management graph neural network dynamic relation graph is constructed based on the index data, and asset early warning index prediction is carried out based on the asset management graph neural network dynamic relation graph and an asset management behavior prediction model. And based on the mobility prediction model, carrying out mobility index prediction to obtain response information. Data management is performed through the two data management platforms, repeated processing is avoided, risk early warning information is generated based on the model method and the information management graph neural network dynamic relation graph, risk early warning is realized, and the real-time performance of risk early warning is ensured.

Systems and methods are disclosed herein for User Equipment (UE) assisted data collection for mobility prediction. In one embodiment, a method performed by a UE for UE-assisted data collection for mobility prediction comprises receiving, from a network node of a cellular communications system, a UE trajectory prediction model for predicting a UE trajectory. The method further comprises executing the UE trajectory prediction model to generate a predicted trajectory for the UE, comparing the actual UE trajectory to the predicted UE trajectory, and sending, to a network node of the cellular communications system, a result of the comparison of the actual UE trajectory to the predicted UE trajectory. In this manner, mobility prediction performance is improved.

The invention relates to a method and a device for predicting the fluidity of alkali-activated mortar. The method comprises the following steps: determining a training set of the alkali-activated mortar; determining hyper-parameters of the deep belief network; inputting the training sample into the deep belief network, outputting a mobility degree predicted value corresponding to the training sample, optimizing hyper-parameters by adopting a grey wolf algorithm based on the mobility degree predicted value, and updating the deep belief network based on the optimized hyper-parameters, using the trained deep belief network as a mobility degree prediction model to predict the mobility degree of the target data; according to the method, the hyper-parameters of the deep belief network are optimized through the grey wolf algorithm, the high-precision prediction model is constructed, the complex nonlinear relation between the multiple parameters of the alkali-activated mortar and the fluidity of the alkali-activated mortar can be accurately extracted, the prediction precision of the fluidity degree of the alkali-activated mortar is improved, and the prediction efficiency is improved. And a theoretical basis and practical guidance are provided for mix proportion optimization and engineering application of the alkali-activated mortar.

A motor vehicle that features a dynamic vehicle mobility prediction system (VMPS) that uses a real-time 3D multibody physics-based simulation to analyze vehicle-terrain interactions. It dynamically adjusts vehicle components, including tire pressure, suspension height, power distribution, wheel speed, and traction control settings, to optimize grip, reduce sinkage, and maintain forward motion over challenging terrains. The vehicle is equipped with a vehicle control computer that manages acceleration and braking and is integrated with the vehicle's navigational systems, GPS, and sensors for precise movement. The vehicle control computer receives data and instructions from the dynamic VMPS that includes an onboard vehicle simulation module for real-time vehicle dynamics and terrain interactions, a deformable terrain soil program to assess vehicle to soil reactions, a tire / track soil interaction library, a digital terrain representation program offering a 3D model of the terrain, and a global positioning sensor used to determine the vehicle's position and speed relative to the digital terrain model. The dynamic VMPS is configured to identify possible travel routes and assigns a predictive value to each route. Routes with high and low predictive values are identified and presented to the vehicle operator as ‘Go / No-Go’ travel routes.

This invention relates to the field of routing technology, and more particularly to an on-demand predictive routing method for unmanned aerial vehicle (UAV) networks. The invention determines the state information of neighboring UAVs based on an improved Kalman filter model, predicts the latest state information of neighboring UAVs based on this state information, determines the communication status between the UAV and its neighbors and the message coverage rate of the neighboring UAVs for flooding commands based on the latest state information, sets replay delays and replay probabilities for the neighboring UAVs, determines the distance correlation factor and traffic load factor of the UAVs in response to the communication status between the UAV and its neighbors, calculates the routing cost value of candidate links, determines the optimal path to forward data, and for the failure of the optimal path, finds alternative links to replace the failed optimal path or performs path repair. This invention combines node mobility prediction and a neighbor coverage flooding mechanism to achieve fast addressing and dynamic optimization of routes.

Certain aspects of the present disclosure provide techniques for mobility prediction with assistance information. An example method for wireless communications includes obtaining assistance information associated with user equipment (UE) mobility; predicting one or more candidate communication links for a communication link modification based at least in part on the assistance information; and communicating with a wireless communications device based at least in part on the one or more candidate communication links.

The present invention discloses an active migration method for edge services based on mobility prediction, belonging to the field of mobile edge computing. Specifically, it is based on a hierarchical mobile edge computing network composed of mobile users, edge servers, and central servers. For a mobile user U, the target server for service migration is obtained through mobility prediction. Then, it is judged whether performance indicators such as the end-to-end delay and received signal strength indication received by the mobile device meet the service quality requirements. If so, the prediction of the next target server is carried out again; otherwise, the mobile user U notifies the target edge server s′ to reserve a container, and copies the disk data from the source edge cloud server s to the target edge server s′. Finally, the checkpoint / restore technology is used to achieve the data migration of the memory state, so that the edge server s′ provides services for the mobile user U. The present invention reduces the service migration time and end-to-end delay based on containers.

The present disclosure describes method for Non Terrestrial Networks (NTN) conditional and RACH-less handover comprising the steps: performing UE mobility prediction and grouping UEs, whereby a determination of different suitable conditional handover configurations and / or of a sequence of multiple conditional handover candidates is done, followed by pre-allocating uplink grants.

The invention relates to the technical field of unmanned aerial vehicle ad hoc networks of ultraviolet light communication, and provides a dynamic routing method and device for an ultraviolet light flight ad hoc network, a medium and a product, and the method comprises the steps: setting a communication mode of the ultraviolet light flight ad hoc network; determining a motion model; detecting network neighbor nodes, and generating a neighbor node information table; based on the motion model, updating position information of each node in the neighbor node information table; and dynamically forwarding a data packet by adopting a data packet forwarding mode based on the updated neighbor node information table. By setting a communication mode, determining a motion model, predicting mobility, and selecting and recovering a dynamic route, the problems of frequent disconnection of the route and decision error caused by high-speed motion of the node are effectively alleviated, the end-to-end time delay and energy consumption are reduced, the delivery rate of data packets is improved, and the robustness of the route is enhanced.

The invention provides user equipment and a method for performing mobility prediction by using artificial intelligence. In one novel aspect, UE mobility prediction is performed through machine learning techniques based on UE measurement data. In an embodiment, the UE obtains a set of mobility-related data, inputs the set of mobility-related data into a mobility AI model for UE mobility prediction, and obtains UE mobility prediction based on the mobility AI model. In an embodiment, UE mobility prediction is range based. In another embodiment, two independent AI models are applied to predict UE mobility in different cases, such as within and not within the service area. In an embodiment, the UE obtains mobility feedback from one or more UE applications and fine-tunes the mobility AI model based on the mobility feedback.

Systems and methods for user equipment (UE)-assisted data collection for mobility prediction are disclosed herein. In one embodiment, a method for UE-assisted data collection for mobility prediction performed by a UE includes: receiving, from a network node of a cellular communication system, a UE trajectory prediction model for predicting a UE trajectory. The method further includes: executing the UE trajectory prediction model to generate a predicted trajectory of the UE; comparing the actual UE trajectory with the predicted UE trajectory; and sending, to the network node of the cellular communication system, a result of the comparison between the actual UE trajectory and the predicted UE trajectory. In this way, the mobility prediction performance is improved.

The embodiments of the present disclosure relate to measurement methods, devices, and a storage medium. The method comprises: a terminal executing mobility prediction on the basis of a measurement event, wherein the mobility prediction can be used for performing prediction to obtain mobility information of the terminal, and the mobility information can be used by the terminal to perform a mobility operation. In this way, the mobility prediction of the terminal can be flexibly controlled and managed, thereby improving the communication reliability.

The present invention relates to the field of mobile communications technology, and more specifically to a method, apparatus, device, and medium for service migration decision-making based on an MEC environment. The method specifically comprises: constructing a user mobility prediction model based on a Bidirectional LSTM model for many-to-many prediction using known human activity mobility data; establishing a DRL framework, wherein the DRL framework includes a state s, an action a, and a reward r, wherein the state s represents the distance between the user and each MEC server and the user's current location, the action a represents an MEC server that the user can select, and the reward r represents the reward for taking action a under state s; training the DRL framework using a DQN algorithm, and obtaining a service migration decision model based on the user mobility prediction model. The present invention addresses the problem of providing mobility services by combining the proposed DRL framework and the user mobility prediction model.

The embodiments of the present disclosure relate to measurement methods, devices, and a storage medium. The method comprises: a terminal executing mobility prediction on the basis of a measurement event, wherein the mobility prediction can be used for performing prediction to obtain mobility information of the terminal, and the mobility information can be used by the terminal to perform a mobility operation. In this way, the mobility prediction of the terminal can be flexibly controlled and managed, thereby improving the communication reliability.

The embodiment of the invention relates to a measurement method, equipment and a storage medium. The method comprises the steps that the terminal executes mobility prediction based on a measurement event, the mobility prediction can be used for predicting mobility information of the terminal, and the mobility information can be used for the terminal to carry out mobility operation. Therefore, the mobility prediction of the terminal can be flexibly controlled and managed, and the communication reliability is improved.

This invention discloses an adaptive application deployment method in an edge computing network based on mobility prediction. The deployment framework implemented by this method is based on a general adaptive mobility prediction framework, achieving good accuracy for different movement trajectories while saving on model design and training costs. An adaptive application deployment method is proposed, using mobile user mobility predictions over multiple future time periods as input. The goal is to optimize and maximize user service quality while minimizing deployment costs at edge nodes, dynamically deploying mobile user services to the most suitable edge nodes. This invention constructs a joint optimization mechanism to determine the deployment location and duration of user applications.

The embodiment of the invention provides a time domain mobility prediction method and device, communication equipment, a chip and a storage medium, and the method comprises the steps that a terminal obtains K measurement instances corresponding to a first downlink reference signal set, the K measurement instances are obtained by measuring downlink reference signals in the first downlink reference signal set at K measurement moments based on the terminal, and the first downlink reference signal set comprises first downlink reference signal subsets of N cells; wherein the K measurement instances are used for acquiring F prediction instances corresponding to a second downlink reference signal set based on a first model, different prediction instances correspond to different prediction moments, and the second downlink reference signal set comprises second downlink reference signal subsets of P cells; k, N, F and P are integers greater than or equal to 1.

The present invention relates to the technical field of intelligent production scheduling, and discloses a method for optimizing the entire production process driven by digital twins. During a first preset time period, personnel flow data and production efficiency data of K production lines in the target production system are obtained at fixed time intervals T, as well as labels of mobility failures occurring in the production lines; the corresponding personnel flow data and production efficiency data are extracted to obtain a personnel flow data set and a corresponding production efficiency data set; and based on the personnel flow data set and the corresponding production efficiency data set, a characteristic relationship model of personnel and production efficiency is constructed; the label of the mobility failure is used as the sample label of the corresponding sample data to train a mobility prediction model; and the number of redundant personnel on each of the K production lines is adjusted to obtain an updated target production system.

The invention discloses an unmanned aerial vehicle MEC network data caching and trajectory optimization method, and relates to the technical field of information and communication engineering, and the method comprises the following steps: deploying a semantic analysis platform and a mobility prediction platform to a plurality of unmanned aerial vehicles, setting flight parameters, caching capacity and an initial strategy of a deep reinforcement learning agent, and forming a system operation basis; receiving a ground user data stream, performing fragment-level analysis on the data content by using a semantic analysis platform, generating a semantic value sequence, and determining a semantic value integral based on the semantic value sequence; according to historical user position information, a position prediction track of each user is output through a mobility prediction platform, and a future link quality change trend is evaluated; fusing the state of the unmanned aerial vehicle, the user prediction position, the semantic value integral and the link quality evaluation result to construct an environment state vector; and inputting the environment state vector into a deep reinforcement learning agent to generate a joint action vector.

Various aspects of the present disclosure generally relate to wireless communication. In some aspects, a first network node in a radio access network (RAN) may transmit mobility history data for a user equipment (UE) to a second network node in a core network associated with the RAN. The first network node may receive a UE mobility prediction model that is based at least in part on the mobility history data from the second network node. Numerous other aspects are described.

This technology provides an improved prediction accuracy for coal expansion pressure information. [Solution] The coal expansion pressure information prediction method includes an acquisition step of obtaining the average maximum reflectance, inert ratio, and maximum fluidity of the coal to be predicted, and a prediction step of predicting expansion pressure clusters corresponding to the expansion pressure based on the acquired average maximum reflectance, inert ratio, and maximum fluidity of the coal to be predicted.

The embodiment of the invention discloses a fund liquidity analysis method and device based on an operation process and a medium, and relates to the technical field of data analysis, the method comprises the steps that fund liquidity analysis demand information triggered by a user is acquired, and the fund liquidity analysis demand information comprises a target analysis organization and a target analysis timestamp; based on the fund mobility analysis demand information, a corresponding subject balance table intermediate table and a financial voucher entry intermediate table are matched, and the subject balance table intermediate table comprises operation node information; according to the subject balance table intermediate table and the financial voucher entry intermediate table, determining multi-dimensional financial index data corresponding to each operation node according to a plurality of preset financial index dimensions; and performing sand table modeling according to the multi-dimensional financial index data through a preset node index value table and an operation node connection line table, generating a fund flow sand table model corresponding to a plurality of operation nodes, and performing fund flow prediction risk analysis based on the fund flow sand table model.

The present disclosure describes a method for non-terrestrial network (NTN) conditional handover and RACH-free handover, the method comprising the steps of performing UE mobility prediction and grouping UEs, thereby determining different appropriate conditional handover configurations and / or a sequence of a plurality of conditional handover candidates, followed by pre-allocation of uplink grants.

This invention discloses a method for UAV access capacity in the 1090 MHz band based on variable channels and mobility prediction. It introduces S-mode and A / C-mode messages in the real-world 1090 MHz band; and allows for the setting of corresponding channel propagation models according to different environments. All aircraft are modeled as mobile devices; and the ADS-B message update cycle can be adjusted according to the UAV's speed. Finally, the continuous packet loss situation and corresponding packet loss rate of civil aircraft and UAVs can be obtained. Under the condition of meeting the minimum message update rate of civil aircraft, i.e., maintaining the normal operation of the civil aviation surveillance system, the method analyzes how many UAVs equipped with ADS-B OUT devices can be added to this frequency band.

The present disclosure describes a method for non-terrestrial network (NTN) conditional handover and RACH-free handover, the method comprising the steps of performing UE mobility prediction and grouping UEs, thereby making a determination of different suitable CHO configurations and / or a sequence of a plurality of CHO candidates, thereby pre-allocating UL grants; qoS requirements and mobility characteristics of the UE are considered.