211 results about "Event forecasting" patented technology

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.

The invention belongs to the technical field of extreme weather emergency management, and particularly relates to an extreme weather event prediction and emergency response system. The problems of multi-source data splitting, disaster chain prediction missing, static plan stiffness and lack of closed-loop evolution in the prior art are solved. A global disaster map is constructed through real-time fusion of multi-source data, a physical mechanism and machine learning are coupled to realize refined deduction of extreme weather and secondary disasters, a self-adaptive emergency scheme is generated based on a dynamic resource library and an optimization algorithm, and disaster feedback data is utilized to drive model and strategy iteration, so that the disaster situation feedback data can be used for driving the model and strategy iteration. And a'prediction-decision-feedback-evolution 'intelligent closed loop is formed. The method has the advantages that information islands are eliminated, the disaster chain modeling bottleneck is broken through, resource scheduling is dynamically optimized, a self-evolution mechanism is established, and the extreme weather response efficiency is improved.

The invention provides an intelligent prediction method and system applied to system log security audit, and the method comprises the steps: firstly obtaining a historical log data set of a power monitoring system, carrying out the event correlation modeling, generating a log event correlation model, generating a security event prediction rule library based on the log event correlation model through a rule mining algorithm, and carrying out the prediction of the security event. And then obtaining a real-time log data stream, inputting the log event association model to obtain a real-time event association result, matching the real-time event association result with the rule base to generate an abnormal event prediction result, finally generating a security audit report according to the abnormal event prediction result, sending the security audit report to the power monitoring terminal, and updating rule base parameters according to feedback information. Therefore, the abnormal event in the power monitoring system can be predicted in advance, and the intelligent level and the safety guarantee capability of system safety auditing are improved.

The invention relates to the technical field of artificial intelligence, and discloses an event prediction method and system based on multi-modal fusion, and the method comprises the steps: constructing a knowledge graph encoder, and converting domain expert knowledge into learnable vector representation; constructing a multi-granularity feature extraction network, and extracting features from different microcosmic, mesoscopic and macroscopic scales; realizing a knowledge-guided attention mechanism, and dynamically adjusting feature scale importance; constructing a prototype learning module, and establishing prototype representation of the abnormal category; a knowledge migration mechanism is constructed, and the generalization ability of the model to novel anomalies is enhanced; and multi-granularity abnormal event detection and early warning are realized, and a detection result and interpretable analysis are output. According to the method, through combination of knowledge guidance and multi-scale feature learning, efficient identification and early warning of social abnormal events under the condition of data scarcity are realized, and the method is suitable for the fields of public place safety monitoring, urban traffic safety management, large-scale activity safety guarantee and the like.

The invention provides a multi-dimensional user portrait and adaptive graph fused public opinion evolution prediction method and system, and the method comprises the steps: carrying out the unified modeling of user language styles, personality features, social structures and theme preferences, and depicting the multi-dimensional individual features of a user; and a multi-dimensional user vector and a multi-relation social contact propagation path are used as input, neighbor co-occurrence coding and partitioning technologies are combined, node representation of time perception is obtained through Transform and an attention mechanism, and dynamic self-adaptive learning modeling of public opinion elements is realized. And finally, dynamic link prediction and dynamic node classification are completed based on the time perception representation, and a visualization result is output. According to the method, through multi-dimensional feature fusion, dynamic graph structure learning and time sequence prediction, the limitation of traditional static modeling is effectively broken through, the precision and interpretability of public opinion evolution prediction are improved, high technical innovation and application value are achieved, and the method can be used for public opinion monitoring, event prediction and public opinion evolution and risk early warning.

The invention discloses an HE event early warning method, device and equipment, a medium and a product, and relates to the field of mine earthquake early warning. The method comprises the following steps: analyzing space-time correlation of mine earthquake data energy by adopting a semi-variation function according to acquired mine earthquake monitoring data, and determining a historical data window; constructing a dynamic sliding mechanism; based on a dynamic sliding mechanism, adopting a principal component analysis method and a kernel density estimation method to extract a kernel density peak value of the micro-seismic event, and adopting a fractal dimension analysis method to determine a fractal dimension index so as to analyze and quantify the geometric complexity change condition and obtain a quantification result; based on a quantification result, optimizing early warning parameters by adopting grid search, and determining a sliding window joint early warning model; and monitoring a kernel density peak value and a fractal dimension index in real time by adopting a sliding window combined early warning model based on the optimized early warning parameters so as to realize early warning of the HE event. The invention aims to improve the accuracy and timeliness of HE event prediction.

The invention discloses an event entity prediction method based on perceptual contrast learning, which comprises the following steps: obtaining a time sequence knowledge graph of all events, each event being represented by a subject, a relationship, an object and a time tetrad; performing reverse operation on the tetrad to obtain a reverse tetrad, and respectively constructing a global historical graph and a local historical graph for all events in any day; then constructing an event entity prediction model, and inputting the global historical graph and the local historical graph into the event entity prediction model to train the model; and finally, predicting the event entity by using the trained event entity prediction model. According to the method, the accuracy of event entity prediction is improved through multi-tense dynamic embedding and relational graph comparative learning, the influence of time on event evolution can be more accurately captured by adopting the multi-tense dynamic embedding, and a dynamic context is provided for local historical node feature updating; the problem that the flexibility is insufficient due to the fact that event prediction excessively depends on recent events is solved.

Multi-stage hybrid network integrates relationship regularization links and explainable elements to improve alignment with human values, explainability, robustness, and efficiency. The network comprises neural components, event prediction elements, and probability models across multiple stages, with relationship constraints enforcing structured knowledge representation. Explainable elements provide interpretable rationales for decisions, enhancing transparency. Training incorporates supervised learning, human-guided refinement, semi-automated knowledge engineering, and adversarial robustness techniques. A Socratic reasoning module detects contradictions and refines outputs for logical consistency. Indexed model elements enable dynamic memory optimization for improved efficiency. Candidate outputs may be scored, verified, or selected using neural and symbolic criteria. The invention supports retry loops and configurable subsystem pipelines to improve output quality. Applications include text generation, speech recognition, translation, and decision support. By combining structured constraints, human oversight, and modular architectures, the system improves the trustworthiness, safety, and adaptability of AI systems across diverse modalities and tasks.

The invention belongs to the technical field of wind power climbing event prediction, and particularly relates to a wind power climbing event prediction method considering extreme weather and time-space correlation information. The method comprises the following steps: acquiring historical actually measured meteorological data of each wind power station of a cluster; carrying out cold-wave weather event identification on historical actually measured meteorological data, generating an antagonistic network based on a time sequence, and carrying out cold-wave event sample expansion; an extreme learning machine is constructed, and cold-wave weather prediction is carried out; performing historical climbing event detection on historical power output results of each station of the cluster; dividing the climbing events into various climbing conditions with different severity degrees by using a K-Means clustering algorithm; and carrying out climbing event prediction. According to the method, sample support is provided for training of the climbing prediction model, the climbing events are clustered and divided by fusing the fan operation state and the climbing characteristics, and the harm degrees of different climbing events, especially the climbing events in extreme weather, are finely measured.

The invention discloses an event prediction model construction method and device for a multi-infrastructure system. The method comprises the following steps: acquiring basic data of each infrastructure in the multi-infrastructure system; performing analog simulation on the basic data of each infrastructure to obtain operation data of each infrastructure; coupling the space-time diagram network constructed by each infrastructure, and establishing a space-time diagram network model; and carrying out migration training on the space-time diagram network model according to the operation data and real-time data obtained by monitoring to obtain an event prediction model. The event prediction model constructed by the scheme of the invention can scientifically and accurately evaluate and predict the response and evolution of the multi-infrastructure system in the event, and helps to deal with urban disaster events.

Techniques for predicting an impact of one or more events on service demand are disclosed. Some embodiments include first and second sets of data characterising properties of historic events using metadata tags, and demand for services that are then filtered to distinguish ordinary demand from extra-ordinary demand. Machine learning is used to determine correlations between metadata tags and extra-ordinary demand to produce a third data set operable for predictive determinations of future event impact on service demand.

A video to event prediction pipeline system includes a backbone conversion network having a model that is configured to receive a raw active pixel sensor video sequence and convert it into 3D predicted voxels. An event sampling module is configured to receive the 3D predicted voxels and create event timestamps in a continuous scale by leveraging nonlinear dynamics of event firing trends in each voxel of the 3D predicted voxels. The backbone conversion network comprises a series of training loss function modules, the training loss function modules teaching the backbone conversion network to account for variations in the active pixel sensor video sequence caused by adjustable camera parameters of the active pixel sensor video sequence.

The invention relates to the technical field of network security asset risk pricing and management, in particular to a business influence-oriented network security asset risk pricing and management method and system. Historical business data, infrastructure resource data and external environment data are integrated to construct a business-resource portrait library; a potential interruption event prediction model is trained based on a random forest or a long short-term memory (LSTM) network, a multi-target cost optimization function including resource preset cost, service interruption loss cost and resource idle penalty cost is constructed, and an optimal resource preset strategy is solved by adopting a non-dominated sorting genetic algorithm NSGA-II or a particle swarm optimization algorithm. And after execution, feedback data update models and functions form closed-loop optimization. According to the method, accurate risk prediction, dynamic cost balance and continuous strategy adaptation are realized, the service continuity is effectively guaranteed, the overall operation cost of an enterprise is reduced, and the pertinence and effectiveness of network security asset risk pricing and management are improved.

The embodiment of the invention discloses a site selection method and system for first-aid equipment, a storage medium and equipment, and is applied to the technical field of information processing. The site selection system of the first-aid equipment performs gridding processing on a target area to obtain a plurality of grid units, and obtains grid data of each grid unit, including current first-aid equipment, traffic, weather, society and historical out-of-hospital emergency attack events. On the basis of the acquired grid characteristics of each grid unit, spatial connection characteristics among the grid units and time sequence characteristics, after the acquired characteristics are fused, occurrence information of extrahospital emergency attack events corresponding to each grid unit is predicted according to the fused characteristics; therefore, factors of multiple dimensions such as time, space, weather, traffic, society and the like can be comprehensively considered to perform event prediction, and on the basis, the deployment information of the first-aid equipment in the target area is determined in combination with the coverage rate model of the first-aid equipment, so that the first-aid equipment can be planned more reasonably and accurately.

The invention discloses a regional extreme drought and flood event prediction system based on a climate model, and relates to the technical field of climate model prediction and disaster early warning, and the system comprises a data input module which is used for receiving regional multivariable prediction data processed by downscaling of the climate model, and the multivariable prediction data comprises rainfall, soil humidity and evapotranspiration. According to the regional extreme drought and flood event prediction system based on the climate model, equation strong constraint and variational optimization are carried out on key variables such as rainfall, soil humidity and evapotranspiration through the physical constraint dynamic coupler, the physical law of water circulation mass conservation and surface energy balance is forcibly met, the distortion phenomenon is eliminated, and the prediction accuracy is improved. According to the method, physical correction residual errors are converted into weight factors, Copula function parameters are dynamically adjusted, it is ensured that joint probability distribution of extreme drought and flood events strictly follows a physical mechanism, the path of misinformation physical impossible events is blocked from the source, and the problem of prediction distortion caused by multivariate physical inconsistency in downscaling output of a climate model is solved.

The invention discloses an architecture performance intelligent alarm and root analysis method based on big data analysis, and belongs to the technical field of artificial intelligence, and the method comprises the steps: obtaining monitoring data in a distributed architecture system, and carrying out the preprocessing; generating alarm event data through an alarm rule engine based on the preprocessed monitoring data, mining a frequent mode in the alarm event data by using an Apriori algorithm, and performing association rule analysis based on the frequent mode to obtain an alarm mode feature vector; splicing the alarm mode feature vector with the time sequence feature of the alarm event, inputting a splicing result into an LSTM model for prediction, obtaining an alarm event prediction trend feature, and splicing the alarm event prediction trend feature with the alarm mode fusion feature to obtain an alarm fusion feature; and classifying the alarm fusion features by using an alarm classification and root cause analysis model based on a random forest algorithm, and outputting an alarm category and a root cause. According to the invention, the accuracy and effectiveness of the alarm event are improved.

The invention relates to a power grid abnormal event prediction and early warning method and device based on big data analysis. The method comprises the following steps: acquiring power grid operation data, external environment data and equipment state data of a target power grid; performing abnormal event analysis on the power grid operation data, the external environment data and the equipment state data, and identifying each abnormal event key factor of the target power grid; performing time sequence analysis on the power grid operation data, the external environment data and the equipment state data according to each abnormal event key factor, and predicting abnormal event data and an abnormal event triggering probability of the target power grid; and generating power grid early warning information according to the abnormal event data and the abnormal event triggering probability. By adopting the method, a prediction result obtained under the condition of predicting complex or atypical faults is more real-time and accurate, and operation and maintenance personnel are helped to pre-judge potential risks, so that the self-healing capability of a power grid is improved, and the reliability and efficiency of power grid operation are greatly improved.

The application discloses a network security relation extraction method, wherein the method steps are as follows: S1: obtaining a security event data set, cleaning, preprocessing and segmenting and marking data in the security event data set, and finally dividing the data set after completion of marking into a training data set and a test data set S2: constructing a relation extraction prediction model S3: inputting the training data set into the relation extraction prediction model, obtaining a security event prediction result, and adjusting parameters of the relation extraction prediction model according to the prediction result S4: inputting test data set data into the relation extraction prediction model to obtain performance parameters of the model, then repeating step S3 until the performance parameters are higher than preset values, and finally saving the model as a security relation extraction model.

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.

The present disclosure provides a threat intelligence information processing method and device and a storage medium, wherein the method comprises: respectively performing fusion processing on vulnerability intelligence information and event intelligence information to generate vulnerability intelligence fusion information and event intelligence fusion information; extracting attribute information from the vulnerability intelligence fusion information, and determining whether the vulnerability intelligence information corresponding to the vulnerability intelligence fusion information is threat vulnerability information based on the attribute information and asset information; processing the event intelligence fusion information by using a trained event prediction model to determine whether the event intelligence information corresponding to the event intelligence fusion information is industry threat event information; and performing security processing on the threat vulnerability information and the industry threat event information. The present disclosure can improve the accuracy of threat intelligence information identification, reduce the situation of missed reporting of threat intelligence and false reporting of invalid threat intelligence, reduce the response time of security operation personnel, improve the network security protection capability, and reduce the operation cost.

The invention discloses an event prediction method and system based on a time sequence hypergraph, and belongs to the technical field of event prediction. The method comprises the following steps: acquiring multi-source heterogeneous data of a target region, defining a unified time index, and generating a time sequence feature sequence of each variable of the region through preprocessing; then, identifying a variable causal relationship in the region based on a frequency domain anti-fact condition mutual information algorithm, and fusing the variable causal relationship with a time sequence evolution relationship to construct a time sequence hypergraph structure representing the interior of the region; a dynamic filter is used for filtering the graph, and deep features of the graph are learned by means of a multi-band spectrum gating mechanism, so that internal complex causal and time sequence modes are effectively captured; and finally, performing dichotomy prediction based on the learned graph representation, and outputting the occurrence probability of future events in the region. According to the method, accurate and explainable event prediction is realized, training and prediction do not need to cross regions, data privacy and calculation efficiency are guaranteed, and stronger robustness is shown for specific data distribution change of the regions.

The application provides a city base model training method and device for heterogeneous spatiotemporal data, and relates to the technical field of artificial intelligence. The method comprises the following steps: using a city base model to perform city event prediction according to a pre-training data set, obtaining first spatiotemporal features and a first prediction data set; updating preset initial mask weights based on an error-guided spatiotemporal mask strategy to obtain updated feature masks; using the city base model to perform city event prediction based on the updated feature masks to obtain a second prediction data set; calculating a loss function according to the pre-training data set and the second prediction data set, and optimizing parameters of the city base model to obtain a first optimized city base model; and using a memory network-based double-channel prompt generator to fine-tune parameters of the first optimized city base model to obtain a second optimized city base model. The application is an efficient and accurate city base model training method for heterogeneous spatiotemporal data.

Aspects described herein may use machine learning models to predict one or more remediation actions to mitigate reoccurrence of an incident that has become restored based upon previous incidents of an entity. Historical incident data is compiled into two incident datasets: one representative of incidents that were assigned a remediation action to mitigate reoccurrence of the incident, and a second representative of incidents that were not assigned a remediation action. A machine learning model matches relationships between data in the two datasets and outputs scores representative of similarities. Based on the scores, one or more remediation actions are mapped to an incident in the second dataset and the remediation action is performed for the incident.

The invention discloses a graph reasoning-based judgment document event joint extraction method. According to the method, a lexical graph inference mechanism and a trigger word centralization decoding architecture are fused, the lexical graph inference mechanism strengthens the relevance between trigger words and argument roles and solves the problem of semantic segmentation of entities and events, and the trigger word centralization decoding architecture avoids multi-stage cascade errors and improves the complex logic chain processing capacity. The method comprises the following steps: constructing a joint event extraction model: performing corpus labeling and BIO labeling on a data set, and constructing an event type-role label set; processing the text subjected to BIO labeling into numeric vector representation capable of being input into a model, and constructing a lexical element pair label matrix; the hidden lexical elements output by the text embedding layer are classified through a classification layer, a softmax function is adopted to predict relation marks between lexical element pairs, label distribution corresponding to the lexical elements is generated, and a basis is provided for subsequent event extraction; and establishing an event center graph according to lexical tag distribution, performing graph structure decoding to obtain an event prediction result, and training a model.

The invention relates to a vehicle event prediction device (12) for a vehicle (10). The vehicle event prediction device (12) comprises a data collection module (14) configured to receive and store input data (16) and vehicle event data (18) for training a prediction model (20), wherein the vehicle event data (18) relate to the occurrence of predefined vehicle events; the prediction model (20) provides a prediction of the occurrence of each of the predefined vehicle events; and a prediction model module (22) configured to host the prediction model (20) and to generate a prediction calendar (26) based on the prediction model (20), wherein the prediction calendar describes a probability of the occurrence of the vehicle events in predefined future time periods.

The invention provides a measurement event prediction method, a program product, a device and a storage medium, relates to the technical field of communication, and is used for solving the technical problem of frequent cell switching in the prior art. The measurement event prediction method comprises the following steps: receiving first indication information sent by a second node, wherein the first indication information is used for indicating a first node to predict a target measurement event configured by the second node; and second indication information is sent to the second node, the second indication information comprises a first time point, and the first time point is the predicted time point for reporting the measurement report corresponding to the target measurement event.

To predict possible future events based on events described in documents.SOLUTION: A future event prediction system predicts possible future events based on documents describing past events and the dates on which the past events occurred in natural language. The system includes: a past event tree generation unit configured to read past events from documents and generate a past event tree for documents whose vector distances are below a predetermined threshold; a language model generation unit configured to generate a future event prediction model from the past event tree; a prediction unit configured to accept event specifications and predict future events from the future event prediction model based on the accepted events; and an output unit configured to output the future events determined by the prediction unit.SELECTED DRAWING: Figure 1

Methods and systems for predicting events during hydraulic fracturing of a target subterranean formation, inccluding: obtaining a time-series of measurements associated with the hydraulic fracturing; decomposing the measurements into a plurality of respective components that collectively represent nonstationary and nonlinear properties of the pressure measurements; extracting respective attributes that are representative of events occurring in the target subterranean formation from the respective components; and predicting, based on the extracted attributes, a likelihood of one or more future events being induced by the hydraulic fracturing.

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.

The invention discloses a rail transit abnormal event prediction method based on spatial-temporal feature fusion, and relates to the technical field of public traffic data analysis and prediction, and the method specifically comprises the following steps: S1, data collection and preprocessing; s2, spatio-temporal feature extraction and fusion; s3, selecting and training a model; s4, abnormal events are predicted in real time; according to the method, a closed-loop process from data collection to prediction feedback is realized, spatial-temporal feature fusion plays a key role in identifying rail transit abnormal events, the influence of the abnormal events on a rail transit system can be effectively reduced, the operation efficiency and safety are enhanced, and the method is suitable for popularization and application. The method achieves the precise prediction of the abnormal events of train delay, equipment faults and passenger flow congestion through the multi-source data fusion and deep learning model through the features of spatial distribution and time dynamic changes in a rail transit system, and provides powerful technical support for the operation safety management of rail transit.