34 results about "Event forecasting" patented technology

This invention discloses an event prediction method and apparatus based on subgraph segmentation and entity relationship model. It transforms an academic knowledge graph into a sequence of temporal historical subgraphs. Segmentation operations determine the segmentation points of the temporal historical subgraph sequence and the number of segments corresponding to different times. The number of continuous segments is also determined. A DQN network is used to perform secondary path search through target operations to obtain the final search path. The entity in the last node is the object to be predicted. This invention achieves this by segmenting the temporal historical subgraph sequence to give the input data a focus, enabling better selection of answer nodes. Secondly, the use of a DQN network better considers the intrinsic connections between entities and relationships. Finally, the global nature of convolutional neural networks captures the global features of the search path, significantly improving prediction accuracy.

This invention belongs to the field of overflow events, specifically relating to a hierarchical multi-joint learning-based method, system, and equipment for predicting well control overflow events. It aims to address the problems of equipment damage, production interruption, safety accidents, and environmental pollution caused by overflow event risks. The invention includes: training multiple well control overflow event prediction base models based on the training set; obtaining prediction results based on the test set; calculating the accuracy of the base models and selecting retained models to obtain a final feature set; integrating the retained models based on the final feature set to construct a voting-based ensemble model; and inputting the data to be predicted into the ensemble model to obtain the well control overflow event prediction result. This invention improves prediction accuracy and robustness by constructing an ensemble model, optimizes resource utilization, enhances operational safety and decision-making efficiency, effectively prevents well control overflow events, and ensures the safety and economic benefits of oilfield operations.

This invention discloses a method for predicting extreme positive IOD events based on ENSO and MJO. This invention uses the ENSO and MJO co-signals and extracts the key signals of ENSO and MJO that affect IOD. At the same time, it combines model forecast field information to perform statistical modeling for extreme positive IOD event prediction. This method avoids the systematic underestimation of strong IOD amplitude by dynamic models and can improve the prediction lead time and prediction accuracy of extreme positive IOD events.

The application discloses a kind of day-ahead wind power power ramp event prediction methods, comprising the following steps: S1.day-ahead wind power prediction: using extreme value driving model, based on historical wind power and meteorological characteristics, produce the wind power prediction of Q time points of next day;S2.predicting confidence interval construction: using wind power oriented conformal inference method, for each future time point, construct confidence interval C;S3.ramp event detection based on confidence interval: the prediction result of next day wind power ramp event is obtained by confidence perception detection algorithm.The application improves the prediction accuracy and reliability of wind power ramp event with significant operation risk by fusing multi-scale time series feature analysis, customized loss function for extreme event and adaptive confidence interval construction technology with statistical guarantee.

The present application relates to the technical field of wireless communications, and discloses an event prediction method and apparatus, a terminal, and a network side device. The event prediction method in the embodiments of the present application comprises: a terminal acquires a first parameter; the terminal acquires a predicted measurement result; and on the basis of the first parameter and the predicted measurement result, the terminal determines whether a trigger condition for a target event is satisfied.

The application provides a method and device for event prediction based on a timing diagram rule, the method comprising: obtaining timing diagram data, and adding event prediction results of a machine learning model to the timing diagram data to obtain timing diagram extension data; extracting a subgraph for verifying prediction rule reliability from the timing diagram extension data according to a verification ratio; training a rule creator according to the timing diagram extension data and the subgraph to obtain a timing event prediction rule set; and obtaining a prediction event set according to the timing event prediction rule set and the timing diagram data. By embedding the machine learning model for event prediction into a rule-based association relationship system as a predicate, the association rule can not only use the existing machine learning model event prediction result, but also use a logical condition to improve the prediction result of the machine learning model, has stronger expression ability, and does not have to constrain constant interval time or have a common focus constraint on a graph pattern.

This invention discloses a method, system, storage medium, and device for selecting emergency medical equipment, applied in the field of information processing technology. The emergency medical equipment selection system performs gridding processing on a target area to obtain multiple grid cells and acquires grid data for each cell, including current emergency medical equipment, traffic, weather, social factors, and historical out-of-hospital emergency events. Based on this, it acquires the grid characteristics, spatial connectivity characteristics, and temporal characteristics of each grid cell. After fusing these acquired features, it predicts the occurrence information of out-of-hospital emergency events corresponding to each grid cell based on the fused features. This allows for comprehensive consideration of multiple dimensions such as time, space, weather, traffic, and society for event prediction. Furthermore, by combining this with an emergency medical equipment coverage model, it determines the deployment information of emergency medical equipment in the target area, enabling more reasonable and accurate planning of emergency medical equipment.

The application discloses a news event prediction method based on cross-time-step high-order correlation modeling, and belongs to the field of news event prediction. The method comprises the following steps: constructing an entity graph based on historical news events, performing relationship-time-aware message passing in the entity graph to obtain comprehensive entity representation, and capturing cross-time pair correlation between entities; passing the comprehensive entity representation through a learnable entity-superedge mapper to obtain an indication matrix describing the membership relationship between entities and superedges and construct an entity supergraph to capture the cross-time high-order correlation between entities; then, message passing is performed on the entity supergraph to update the superedge representation and further update the entity representation, which is used for predicting all possible relationships between entities in the future. The application effectively models the pair correlation and high-order correlation between entities in the news event prediction task across time steps, focuses on relationship semantic prediction, and adapts to the representation learning requirements of the relationship prediction-oriented representation learning in the news event prediction temporal knowledge graph.

The invention relates, on the one hand, to a method for predicting an individual's fitness level, correlated with a target activity event, based on previous activity events, and on the other hand, to a management system. Abstract figure: Fig. 2

The application relates to a vehicle safety event prediction method and device, computer equipment, a storage medium and a computer program product. The method comprises the following steps: acquiring vehicle safety event data, determining the statistical dimension type of a safety event in the vehicle safety event data; calculating the growth rate of the number of safety events in a preset statistical time period based on the vehicle safety event data; calculating the weight value of the safety events in the preset statistical time period; determining the training sample of a safety event prediction model based on the growth rate of the number of safety events and the weight value of the safety events, wherein the safety event prediction model comprises a machine learning algorithm for periodic prediction based on a time sequence; performing periodic prediction training on the safety event prediction model by taking the training sample as the input of the safety event prediction model, thereby obtaining a trained safety event prediction model; and outputting the safety event prediction result of a preset prediction period based on the trained safety event prediction model. The method can accurately and at low cost predict safety events.

This invention discloses a method and apparatus for constructing an event prediction model for a multi-infrastructure system. The method involves collecting basic data from each infrastructure component within the system; simulating the basic data of each infrastructure component to obtain operational data; coupling the spatiotemporal graph networks constructed from the infrastructure components to establish a spatiotemporal graph network model; and performing transfer training on the spatiotemporal graph network model based on the operational data and real-time monitoring data to obtain the event prediction model. The event prediction model constructed using this method can scientifically and accurately assess and predict the response and evolution of the multi-infrastructure system during events, and assist in the response and handling of urban disaster events.

This invention discloses a regional precipitation prediction method and system based on multi-source heterogeneous deep integration, belonging to the field of precipitation prediction technology. The method includes: classifying multi-source climate factors into atmospheric circulation, sea surface temperature, and comprehensive factor groups according to their physical properties; performing correlation screening, standardization, and PCA dimensionality reduction on each group of factors; constructing base models for each group of factors on a station-by-station basis using various machine learning algorithms, and employing a station-by-station hyperparameter search strategy; concatenating the out-of-place predictions of all base models and using the result as input to the ensemble layer of a deep neural network, learning dynamic ensemble weights station-by-station; and finally outputting the precipitation prediction results. This invention effectively eliminates the systematic bias of a single algorithm through heterogeneous integration and improves the model's adaptability to spatial heterogeneity through station-by-station hyperparameter search, exhibiting advantages such as high prediction accuracy, good interdecadal stability, and strong predictive ability for extreme precipitation events.

A system and method for temporal acceleration encoding in Lorentzian latent space enables real-time event forecasting within navigable spatiotemporal media. The system encodes media data into compact Lorentzian latent patches using variational autoencoders and organizes them within a multi-dimensional hyperspace spanning spatial, temporal, orientation, scale, and spectral coordinates. Temporal acceleration encoding computes velocity and acceleration vectors along geodesic trajectories, extracting event signatures through multi-scale aggregation over sliding windows. An acceleration-indexed memory stores dynamic descriptors with composite keys comprising hyperspace coordinates and motion characteristics. Event forecasting retrieves similar historical patterns and conditions a forecast head to produce event probabilities and time-to-event estimates with uncertainty calibration. The system streams forecast metadata to edge devices for real-time prediction and adaptive navigation, supporting applications in surveillance, autonomous systems, predictive media exploration, and anomaly detection where both temporal forecasting and multidimensional navigation capabilities are essential.

A computing system includes a processor configured to: generate a first artificial intelligence (AI) model for device diagnostic information; generate a second artificial intelligence (AI) model for device temperature information; generate a third artificial intelligence (AI) model for device self-test information; generate a fourth artificial intelligence (AI) model for device-detected issues; generate a fifth artificial intelligence (AI) model for host-detected issues; generate an event prediction artificial intelligence (AI) model from an aggregation of a feature selection from the first AI model, the second AI model, the third AI model, the fourth AI model, and the fifth AI model; and operate the event prediction AI model to generate an event prediction for communicating an upcoming negative operational status.

The application discloses a kind of virtual-real fusion three-dimensional engine time axis control method, equipment and medium, the method includes: based on virtual-real fusion scene constructs multidimensional data mapping time axis model, establishes time axis basic framework, forms initial time axis model;Based on initial time axis model, the spatiotemporal alignment processing of real-time heterogeneous data stream and historical data is executed bidirectionally synchronized, and multi-source data is filled to time axis, and complete time axis dataset is constructed;Based on complete time axis dataset, event prediction and analog deduction based on timing deep learning are executed, and future time domain is extended to time axis, and form visual simulation timeline;Based on visual simulation timeline, realize visual interaction and dynamic scene real-time rendering based on event semantics, complete time axis data human-computer interaction output, and feedback interaction instruction to time axis basic framework.The application realizes the full cycle, high accuracy, event level time dimension control to complex virtual-real fusion scene.

A method of operating an interactive local-remote computer system, comprises receiving, by a local module (20) running on an edge device (2) of the computer system, first sensor data from at least one sensor (23, 202) which captures data of at least one of a local real environment (22) and the edge device (2) in relation to the local real environment (22), and transforming the first sensor data into at least one first scene state (44T1) which is indicative of at least a part of the local real environment (22) or the edge device in relation to the local real environment at a first point of time (T1), transferring, by the local module (20), the at least one first scene state (44T1) to a remote module (60) running on a server computer (6) or computer network of the computer system, and maintaining the at least one first scene state in a scene state memory (61) associated with the remote module, generating, by the remote module (60), at least one conditional reaction for a least one local actuator device (31, 32, 201, 203) associated with the local real environment (22) based on at least one event prediction processed from the at least one first scene state (44T1), and transferring, by the remote module (60), the at least one conditional reaction to the local module (20) and maintaining the at least one conditional reaction in a local trigger database (28) associated with the local module. The local module (20) receives at least one second scene state (44T2) which is indicative of at least a part of the local real environment (22) or the edge device in relation to the local real environment at a second point of time (T2), matches and associates the second scene state (44T2) with at least one of a plurality of conditional reactions maintained in the local trigger database (28), and if a trigger condition is met, triggers generation of at least one driving signal for the at least one local actuator device (31, 32, 201, 203) based on the associated at least one conditional reaction for driving a physical execution action (301) on the at least one local actuator device. The computer system is capable of reducing latency within local-remote interactive systems.

This application provides a deep learning-based predictive maintenance and fault detection system for power equipment, relating to the field of power systems and their automated monitoring. It addresses the problems of existing technologies, such as poor accuracy in predicting low-probability, high-risk events, weak generalization ability with small samples, insufficient adaptive adjustment across operating conditions, and a lack of deep integration between data-driven and physical mechanisms. The system includes: a data acquisition module, which collects time-series data of operating status, physical parameter sensor data, and environmental parameter data; a dual-modal heterogeneous inference module, comprising a data-driven subnetwork that outputs fault probabilities, a physical simulation subnetwork that generates simulation state variables and calculates physical residuals, and a cross-attention bridging module that calculates the difference between the two to obtain a consistency score; an environmental adaptive meta-controller that dynamically adjusts the fusion weights and corrects the physical simulation boundary conditions; and an output and decision module that generates prediction results, anomaly alarms, predictive maintenance strategies, and fault detection reports based on fault probabilities, residual vectors, and consistency scores.

A kind of micro-grid edge terminal multi-source fusion monitoring method and system, method includes: collecting micro-grid electrical data, equipment state data and environmental data and processing, calculate fusion encoding features;Determine periodic prediction trigger condition and event prediction trigger condition;When triggering periodic prediction or event prediction, short-term load forecasting is carried out;When the load difference calculated is greater than the predetermined load difference threshold, the abnormal score of the corresponding terminal is calculated;When abnormal score is not less than the predetermined local abnormal threshold, it indicates that there is local anomaly;The neighborhood consistency coefficient of terminal is calculated, to determine whether there is global anomaly;Get device fault alarm mark, when terminal exists device fault alarm or global anomaly, calculate rule score, and the abnormal classification of terminal is carried out.The present application realizes the high-precision monitoring, fast response and intelligent operation of micro-grid edge terminal by multi-source data quality evaluation fusion, adaptive prediction trigger mechanism and hierarchical anomaly identification.

The application relates to an event prediction method based on pair event relation learning and multi-view evidence fusion, and belongs to the technical field of natural language processing. The method comprises the following steps: pair event relation learning: a pre-training language model is trained by constructing positive and negative samples in a supervised learning framework; an event relation graph is constructed: the pre-training language model after pair event relation learning is used to deduce the dependency relation of the pair event, the probability output by the model is used as the weight of the edge, and thus the event relation graph is constructed; multi-view evidence learning: quantitative evidence of a candidate event is learned from a text semantic view and a graph structure view; credible evidence fusion: the uncertainty of each view is modeled by using a Dirichlet distribution, and multi-view evidence is dynamically fused by using Dempster-Shafer theory to generate a final prediction result. Under the condition of not depending on an external knowledge base, the application realizes a prediction accuracy of 64.62% on an NYT data set, and exceeds an existing baseline model.

The application discloses a news event prediction method based on heterogeneous evolution event clustering, comprising the following steps: generating event representation based on the preliminary updated entity representation and relationship representation in the constructed entity graph, regarding the event as a node, regarding the heterogeneous relationship between events as an edge, and constructing an event graph; obtaining event clusters by fuzzy clustering and constructing an event cluster graph; optimizing the event cluster representation according to the distance and similarity between event clusters on the event cluster graph by using a self-supervised optimization algorithm; capturing the implicit correlation between event clusters by using an implicit relationship encoder, and then updating the representation of the event cluster, the representation of the event, the entity and the relationship representation in sequence after sparsification and information aggregation; and predicting by a convolution-based news event model. The application effectively models the pair correlation, high-order correlation and multi-step time sequence evolution between events, and has important application value in international situation analysis, social governance and intelligent decision support.

This invention discloses a news event prediction method based on cross-time-step high-order correlation modeling, belonging to the field of news event prediction. It includes: constructing an entity graph based on historical news events; performing relation-time-aware message passing within the entity graph to obtain a comprehensive entity representation, capturing cross-time pairwise correlations between entities; passing the comprehensive entity representation through a learnable entity-hyperedge mapper to obtain an indicator matrix describing the membership relationship between entities and hyperedges, and constructing an entity hypergraph to capture cross-time high-order correlations between entities; then performing message passing on the entity hypergraph to update the hyperedge representation and subsequently update the entity representation, used to predict all possible future relationships between entities. This invention effectively models cross-time-step pairwise and high-order correlations between entities in news event prediction tasks, focuses on relational semantic prediction, and adapts to the representation learning requirements for relation-oriented prediction in temporal knowledge graphs for news event prediction.

A real-time data analysis and context-aware anchor broadcasting method, device and medium, through the integration of multiple levels of technologies such as real-time game data acquisition, character behavior feature analysis, action event prediction, tactical intention inference, deep context model emotion calculation and natural language generation, not only can identify single character behavior, but also can understand the cooperative tactical relationship of multiple characters, and further combine the deep context model to generate time-continuous emotion indicators, so that the voice broadcast and expression rendering of the virtual e-sports anchor can be dynamically adjusted according to the game situation, improving the realism and immediacy of the broadcast, thereby achieving the technical effects of improving the ability to capture key tactical events, optimizing the continuity of virtual anchor emotion performance, and improving the immediacy of the broadcast.

Systems and methods for multiple players to predict outcomes of live micro-events in a macro-event and for comparing predictions to actual outcomes of the live micro-events and calculating point scores for the predictions.

This invention relates to the field of database technology, and in particular to an AI-based intelligent database operation and maintenance method and apparatus. The method includes collecting raw data from different dimensions; preprocessing and extracting features from the raw data of each dimension; integrating features from the same time window across different dimensions to obtain a feature vector; acquiring the target feature vector corresponding to the current time window; performing collaborative analysis on the target feature vector to obtain detection results, predicted events, and abnormal events generated based on the detection results; reasoning about the abnormal events to determine their root causes; comprehensively analyzing the abnormal events, predicted events, and root causes; dynamically selecting a self-healing strategy from a preset strategy library; and using an automated script to run the self-healing strategy to restore the database. This invention can reduce the time-consuming and labor-intensive manual troubleshooting steps in traditional operation and maintenance, and shorten the average database recovery time.

The application relates to the technical field of video processing, and discloses a game event prediction method, device and equipment and a storage medium. The game event prediction method comprises the following steps: receiving a game video to be predicted, wherein the game video comprises a game live video or a game video recording; video frame sampling is performed on each time point in the game video respectively to obtain a time-sequenced video frame sequence corresponding to each time point; a structured text prompt in a predefined format is constructed, and the video frame sequence corresponding to each time point and the structured text prompt are taken as model input data respectively; the model input data is input into a pre-trained game event prediction model for processing, and the output comprises the burst probability of the type and position of various game events in the future. The application improves the understanding of the model for the key content and time-sequenced content of the video, and realizes the forward-looking prediction capability of the video content.

The system will allow users to identify their risk of incidents occurring based on electronically stored support record document data. [Solution] To predict incidents occurring among users of welfare support facilities, the present invention provides an incident prediction program for welfare support facilities characterized by the following functions: a support record acquisition means that acquires support record text data from a support record database where support records of users of welfare support facilities are stored; a risk value derivation means that extracts entries correlated with incident occurrence (incident correlation entries) from the support record text data acquired by the support record acquisition means, scores each extracted incident correlation entry to derive an incident risk value for each user; and a risk value transmission means that transmits the risk value derived by the risk value derivation means to a user terminal.

This disclosure relates to the field of robot energy scheduling technology, and discloses a robot energy scheduling method, system, device, and storage medium. The method includes: real-time acquisition of external scene perception signals and internal body operation status signals of the robot; construction of a high-power event prediction model; feature extraction and fusion of the acquired scene perception signals and body operation status signals to generate a time-series feature vector sequence and input it into the high-power event prediction model, outputting the trigger probability of a high-power event occurring within a specified future time window; generating a corresponding energy pre-scheduling instruction based on the trigger probability; and executing the energy pre-scheduling instruction before the high-power event actually occurs to complete the robot energy scheduling. This disclosure realizes a fundamental shift in energy management from "passive response" to "proactive prediction," significantly improving operational continuity and system reliability.

A method for use with a subterranean well drilling operation can include training a predictive model with historical well data to predict a probability of a historical wellbore event occurring, then inputting to the trained predictive model parameters of a target well to be drilled, and the predictive model predicting a probability of a wellbore event occurring in the target well. A system for use with a subterranean well drilling operation can include a predictive model trained to predict at least one historical wellbore event, based on historical well data. The predictive model can be configured to predict a probability of a wellbore event occurring in a target well. The predictive model can be configured to provide guidance for drilling the target well.

The application discloses a kind of based on multi-mode data's inspection monitoring method and device, comprising: in response to the confirmation of the inspection subject in starting node, activate inspection monitoring task;According to the inspection topological relationship, in combination with historical transfer probability matrix, determine the initial next inspection node of the inspection subject travel, and wake up the image acquisition module of initial next inspection node;Based on the monitoring state of the inspection subject in starting node, in combination with topological path prediction model, determine next inspection node, and give the time of the inspection subject travel to next inspection node;Adjust the image acquisition module of initial next inspection node;Obtain the monitoring state of the inspection subject in next inspection node;Repeat the above acquisition of the monitoring state of each node of the inspection subject, to terminal node, complete based on multi-mode data's inspection monitoring.Event prediction mechanism is carried out active inspection monitoring, response speed is improved, system calculation amount is reduced, and cost is reduced.