16 results about "Mobility prediction" patented technology

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.

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.

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 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.

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.

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 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.

This invention discloses a method for three-dimensional fine modeling and stability probability evaluation of complex rock mass blocks, comprising three steps: first, three-dimensional modeling of complex blocks based on three-dimensional laser scanning, integrating real high-precision terrain and random boundaries; second, introducing full-space stereographic projection for kinematic mobility prediction, inputting the extracted random geometric parameters into a rigid body limit equilibrium vector model considering water-rock coupling effects such as pore water pressure reduction, and calculating the probability distribution of the safety factor; and third, based on a multi-source collaborative support inversion model of target reliability, dynamically calculating and outputting the probability distribution of anchor cable reinforcement force required to maintain the target's stable state. This invention overcomes the inherent defects of traditional deterministic analysis and simplified probabilistic models, and overcomes the shortcomings of existing technologies that often use fixed attitude parameters and are difficult to detect potential risks.

The present application relates to a kind of intelligent internet of things multi-modal data sensing method, belong to internet of things field.First, the weight of space-time attention mechanism is constructed when sensing platform to capture the influence of space-time relationship on worker mobility prediction, the mobility of sensing worker is predicted using GRU prediction model with space-time attention mechanism.Then based on the prediction result, using the multi-modal sensing task utility maximization task allocation method based on mobility prediction to the sensing worker in different time period appears in multi-modal sensing POI task area is allocated multi-modal sensing task.Effectively improve the prediction accuracy of sensing worker in different time period located in different multi-modal sensing task area, assign different sensing worker suitable multi-modal sensing task, improve the utility of sensing platform and the completion rate of multi-modal sensing task.

Apparatus and methods are provided for UE mobility prediction with AI. In one novel aspect, UE mobility prediction is performed based on UE measurement data through machine learning techniques. In one embodiment, the UE obtains a set of mobility-related data, feeds the set of mobility-related data to a mobility AI model for UE mobility prediction and obtains a UE mobility prediction based on the mobility AI model. In one embodiment, the UE mobility prediction is range-based. In another embodiment, two independent AI models are applied to predict the UE mobility under different situations, such as in-service and out-of-service. In one embodiment, the UE obtains mobility feedback from one or more UE applications and performs fine turning for the mobility AI model based on the mobility feedback.

Provided are a temporal domain mobility prediction method and apparatus, a communication device, a chip, and a storage medium. The method includes: a terminal obtaining K measurement instance(s) corresponding to a first downlink reference signal set, the K measurement instance(s) being obtained based on measurement performed by the terminal on downlink reference signals in the first downlink reference signal set at K measurement time instance(s), the first downlink reference signal set including first downlink reference signal subsets of N cell(s), the K measurement instance(s) being used to obtain F prediction instance(s) corresponding to a second downlink reference signal set based on a first model, different prediction instances corresponding to different prediction time instances, and the second downlink reference signal set including second downlink reference signal subsets of P cell(s), K, N, F, and P being integers greater than or equal to 1.

The invention provides a fund data flow control method and system and electronic equipment, and relates to the field of fund data control, and the method comprises the steps: constructing a closed-loop management system of global fund flow data acquisition-mobility prediction, fund flow threshold dynamic generation-fund allocation strategy updating; a management control mode of capital data flow from passive bookkeeping to active operation is realized, the use efficiency of capital is remarkably improved, and the anti-risk capability of enterprise capital is improved.