49 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 an urban population flow prediction method based on multi-source data, and the method comprises the steps: collecting and preprocessing mobile equipment position data, public transport card swiping data and POI point location data, and extracting time and space features; calculating a regional attraction coefficient, an inter-regional distance attenuation function, a regional connectivity matrix, a time dynamic parameter and an external influence parameter, constructing a comprehensive population flow prediction mathematical model, and optimizing model parameters through a machine learning method to realize accurate prediction of population flow among urban regions in a future time period; the urban population flow prediction method comprehensively utilizes multi-source heterogeneous data, fully considers the influence of a space factor, a time factor and an external environment factor on population flow, introduces an innovative index of a regional connectivity matrix, realizes adaptive updating of parameters, can effectively improve the accuracy of urban population flow prediction, and improves the urban population flow prediction efficiency. And a scientific basis is provided for smart city construction and urban traffic planning.

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.

The present disclosure relates to a method (200) for orchestrating paging of a user equipment, UE, (101) connected to a communication network (100) The method (200) comprises identifying (201) a location of a mobility prediction model of the UE (101). The method (200) comprises obtaining (202) a predicted cell location of the UE (101), by executing the mobility prediction model in the identified location, using a last know cell location of the UE (101) as input to the mobility prediction model. The method (200) comprises initiating (203) paging in the predicated cell location of the UE (101). Further the disclosure relates to a network node (600) for orchestrating paging of the UE (101).

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.

The invention provides a typhoon effect evaluation method and device, electronic equipment and a readable storage medium. The method comprises the following steps: capturing unobserved time-varying confounding factors by analyzing typhoon data and geographic area covariants; learning representation of time-varying hybrid factors by using machine learning technologies such as a recurrent neural network and a graph convolutional network in combination with expanded observable data and regional covariable data of a previous time step; based on the confounding factors and observable data of the current time step, the conditional density is calculated to estimate the expected value of the human mobility index, and an average dose-reaction curve is drawn to evaluate the causal effect of the continuous weather handling level on the human mobility. Through the method, accurate prediction of human mobility influence and accurate evaluation of the causal relationship are realized, and the problems of insufficient accuracy of human mobility prediction and causal effect evaluation under extreme weather conditions are solved.

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.

The invention discloses a human mobility prediction method and device based on cooperation of a large language model and a small model plug-in, and the method comprises the steps: preprocessing original user sign-in data, and constructing a time sequence knowledge graph which comprises the global interaction relation between a user and a place in each time period, and is used for representing the sign-in behavior of the user; meanwhile, constructing a long-term historical sign-in track and a recent sign-in track of the user; a user memory vector is constructed, and a plug-in small model composed of a plurality of encoders is trained. The plug-in small model is not limited by the form described in the application, and other plug-in small models can be freely replaced; extracting a historical sign-in track and a recent sign-in track of a user part, and generating cue words by combining data such as first K prediction tags, confidence scores and reference truth values of various preferences generated by the small model; and supervising and finely tuning the large language model by using historical data, and predicting the track of the user according to the prompt without the reference truth value to obtain a final prediction result. According to the invention, a more accurate prediction result can be provided.

The invention relates to the technical field of routing, in particular to an on-demand prediction unmanned aerial vehicle network routing method, which determines state information of a neighbor unmanned aerial vehicle based on an improved Kalman filtering model, and predicts latest state information of the neighbor unmanned aerial vehicle based on the state information. Determining a communication state between the unmanned aerial vehicle and a neighbor unmanned aerial vehicle and a message coverage rate of the neighbor unmanned aerial vehicle to a flooding instruction based on the latest state information, setting a replay delay and a replay probability for the neighbor unmanned aerial vehicle, and responding to the communication state between the unmanned aerial vehicle and the neighbor unmanned aerial vehicle. Determining a distance correlation factor and a traffic load factor of the unmanned aerial vehicle, calculating a routing cost value of a candidate link, determining an optimal path to forward data, and searching a standby link for a failure state of the optimal path to replace the failed optimal path, or performing path repair. According to the method, node mobility prediction and a flooding mechanism of neighbor coverage are combined, and rapid addressing and dynamic optimization of routing are realized.

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 provides a method, system, client device and server for secure prediction of personnel mobility, the method comprising: obtaining trajectory data of a user belonging to a client, and randomly dividing it into first secret shared data according to the trajectory data; applying a secure interactive protocol to interact with a server to perform inference on a pre-trained personnel mobility prediction model according to the trajectory data, model parameters of a personnel mobility prediction model transmitted by a server, and a first random number set generated by a trusted third party, and obtaining a first inference result; receiving a second inference result obtained by interactive inference of the personnel mobility prediction model by the server according to the first secret shared data, model parameters and a second random number set generated by a trusted third party, and combining the first inference result and the second inference result to obtain a prediction result. The present invention can achieve secure and effective prediction, while ensuring accuracy, reducing communication overhead.

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 invention relates to the technical field of intelligent production scheduling, and discloses a digital twin-driven production full-process optimization method, which comprises the following steps: in a first preset time period, acquiring personnel flow data and production efficiency data of K production lines in a target production system according to a fixed time interval T, and labels of the production lines with flow faults; extracting corresponding personnel flow data and production efficiency data to obtain a personnel flow data set and a corresponding production efficiency data set; based on the personnel flow data set and the corresponding production efficiency data set, constructing a characteristic relation model of personnel and production efficiency; taking the label of the mobility fault as a sample label of the corresponding sample data, and training to obtain a mobility prediction model; and adjusting the number of redundant personnel of each production line of the K production lines to obtain an updated target production system.

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.

A device may receive geolocation data associated with a user device in a first cell region, and may determine a location of the user device. The device may determine whether the user device is located in an overlapping region or a non-overlapping region between the first cell region and a second cell region, and may calculate a velocity of the user device. The device may estimate a trajectory of the user device, and may calculate a first distance associated with the user device entering the overlapping region. The device may calculate a second distance associated with the user device exiting the overlapping region, and may determine a first time period to travel the first distance. The device may determine a second time period to travel the second distance, and may determine a handover time period based on the first time period and the second time period.

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.