416 results about "Event forecasting" patented technology

The invention discloses an environment emergency aid decision-making method and system based on artificial intelligence, and the method comprises the steps: obtaining a plurality of historical environment disaster event instances, and constructing the multi-modal event features of each historical environment disaster event; analyzing an event triggering rule of each historical environment disaster event and a corresponding disposal scheme triggering rule based on the multi-modal event features, and constructing a disaster environment knowledge graph; environmental condition sensing information is obtained, and environmental emergency event prediction is carried out in combination with the disaster environment knowledge graph; constructing an event situation prediction model, and predicting the situation change of the current environment emergency event; a disaster environment knowledge graph is utilized to obtain candidate disposal schemes of real-time environment emergency events in a target area, and a multi-target grey wolf optimization algorithm is introduced to optimize the disposal schemes to generate an optimal disposal scheme for environment emergency decision assistance, so that the real-time performance, reliability and global optimality of the environment emergency decision are improved; and rapid and accurate disposal of environmental emergency events is realized.

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 relates to the technical field of health event prediction, in particular to an AI-based abnormal health event prediction system, which comprises a data acquisition module, a data preprocessing module, a causal analysis and health trend deduction module, a dynamic risk assessment and hierarchical management module, a feedback and optimization module and a user interaction and suggestion module, the data acquisition module collects health data of a user in real time; the data preprocessing module is used for preprocessing the collected health data; the causal analysis and health trend deduction module deduces the health trend of the user; the dynamic risk assessment and level-to-level management module dynamically assesses the health risk of the user; and the feedback and optimization module optimizes the health trend prediction model. According to the method and the system, the user is helped to take proper actions to prevent or deal with potential health problems, refined and personalized health management is realized, and the overall health level of the user is improved.

The invention discloses an event analysis method, system and device based on task generation and multiple modes, which are used for quickly and accurately mastering the development trend of an event. The method comprises the steps that structured event information is generated through multi-modal data, and an event relation is obtained; constructing an event graph; extracting a multi-modal feature, and obtaining a multi-modal emotion relationship vector by using a cross-modal attention mechanism; based on a preset graph neural network, combining the event graph to obtain an emotion feature vector; calculating a comparative learning loss value, and obtaining a target emotion feature based on the comparative learning loss value; generating a target event atlas by using a cross-modal attention mechanism; extracting relation characteristics, and combining the attention weight and a preset time sequence model to obtain an event prediction map; extracting a causal chain and reasoning, and obtaining a trigger condition and an emotion factor after reasoning; and combining the event prediction map and the target event map with the node feature sequence, the trigger condition and the emotion factor to obtain an event analysis result.

A plurality of resolved incident tickets of a technology landscape may be received from an incident handling system. A plurality of events may be received from a metric monitoring system monitoring the technology landscape. An incident cluster having related incidents may be generated from the plurality of resolved incident tickets, and a correlated event of the plurality of events may be identified for the incident cluster. The correlated event may be stored with an incident resolution obtained from the incident cluster, to obtain labeled training data. A machine learning (ML) model may be trained with the labeled training data to obtain an incident prediction model. A new event may be processed with the incident prediction model to provide a predicted incident and a predicted resolution.

The invention provides a monitoring method and device, equipment, a medium and a program product, and aims to solve the problem that the switching performance of a terminal cannot be guaranteed in related technologies. The monitoring method is applied to a network device, and the method comprises: performing function and / or model performance monitoring on first information, the first information comprising at least one of radio resource management (RRM) measurement prediction information, failure event prediction information and measurement event prediction information. According to the invention, the cell switching performance of the terminal can be improved.

The invention discloses an event prediction and early warning method based on Bayesian deep learning. The event prediction and early warning method mainly comprises the following two parts: constructing a Bayesian deep learning rule model based on event characteristics, and designing a prediction and early warning model on the basis of constructing the Bayesian deep learning rule model. According to the method, the Bayesian statistical method and the deep learning model are fused, the advantages of the Bayesian statistical method and the deep learning model can be fully utilized, the uncertainty of a prediction result can be expressed in a probability distribution form by combining priori knowledge and observation data, efficient modeling and prediction are performed on complex and nonlinear time sequence data, early warning of potential events is realized, and the prediction efficiency is improved. The accuracy and reliability of event prediction and early warning are improved.

A disclosed method for incident dependency prediction includes identifying, for a newly-occurring network incident of interest, one or more similar historical incidents; obtaining causal dependency data for the one or more similar historical incidents, the causal dependency data identifying one or more historical responsible incidents responsible for causing the similar historical incidents; identifying candidate root cause incidents each occurring within a recent time interval and satisfying similarity criteria with a corresponding one of the one or more historical responsible incidents; and transmitting a prompt to an incident dependency prediction model, the prompt requesting prediction of a root cause incident responsible for causing the newly-occurring network incident of interest based on feature information for each of the candidate root cause incidents and for the incident of interest.

The invention relates to an interpretable event prediction method based on a graph attention network and a hokes process. The method comprises the following steps of: constructing a time prediction process as a sub-graph extension reasoning process by adopting a graph attention mechanism and a Horkes process in an event prediction time sequence point process, and taking a to-be-predicted node as a sub-graph base point; the attention value and a conditional intensity function value obtained in the Horkes process are combined to serve as the basis of sub-graph search expansion and node embedding vector updating, the event development context is deduced, and future events are deduced with the event development context serving as a forward condition. The method not only pays attention to the relative time interval information between the events and further improves the accuracy of the prediction result, but also can give a clear reasoning path, so that the prediction of the events has interpretability, the prediction accuracy of the urban safety events is improved, and the adverse effects of the safety events are reduced.

The invention discloses a mine slope risk prediction method based on multi-modal deep learning, and relates to the technical field of mine slope risk prediction, and the method comprises the steps: Stepl: inputting multi-modal data, Step2: preprocessing effectiveness data, Step3: constructing a sample set, and Step4: realizing mine slope stability prediction based on a gating cycle unit GRU of a mutual information mechanism. According to the invention, a multi-modal data integration GRU network is adopted to carry out sequence modeling on fused data, more complex sequence features are extracted, slope safety event prediction analysis is completed, high-precision monitoring of a mine slope is realized, and the accuracy and comprehensiveness of a prediction result are improved; according to the method, the mutual information mechanism is applied to the GRU network to maximize the mutual information between the sample and the feature, the dependency between the feature and the label is improved, the prediction capability is guaranteed, and the generalization capability on new data is also guaranteed.

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 relates to the technical field of meteorological prediction, in particular to an extreme meteorological prediction method based on a typical meteorological year, which comprises the following steps: firstly, constructing a data set of the typical meteorological year, and generating high-quality meteorological data through interpolation and fusion technologies in combination with ground, reanalysis and satellite data; the generative adversarial network is used for enhancing data, the problem that extreme event samples are insufficient is solved, and key meteorological variables are extracted through super-resolution and feature dimension reduction; based on a convolutional neural network and a long-short-term memory network, constructing a deep learning model to perform multi-task prediction; a physical constraint rule is introduced, such as energy and mass conservation, and it is ensured that prediction conforms to a meteorological rule; and through an uncertainty quantification technology, generating a confidence interval of a prediction result, and providing risk assessment and decision support information. According to the method, the precision and credibility of extreme meteorological event prediction are improved, and disaster prevention and reduction, resource scheduling, agricultural production and the like are supported.

The invention relates to the technical field of weather numerical mode / climate mode prediction and evaluation, in particular to a comprehensive evaluation method and device for climate mode prediction data and a storage medium. The method comprises the following steps: acquiring prediction data and corresponding observation data of different climate modes; calculating evaluation indexes of different climate modes based on the prediction data and the observation data, wherein the evaluation indexes comprise deviation, a spatial correlation coefficient, a root-mean-square error, a time correlation coefficient and a TS score; calculating the weight of each evaluation index according to the information entropy of each evaluation index; and according to the weighted sum of each evaluation index and the corresponding weight, determining a comprehensive evaluation result of the different climate modes. Therefore, according to the method, mode prediction precision, uncertainty estimation, prediction deviation and extreme event prediction capability are evaluated from time and space, one-sidedness of a single index is avoided, and an evaluation result is more objective and credible.

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 discloses a visual monitoring system and method, and the system comprises a temperature and humidity monitoring module, a transportation path optimization module, a cargo state monitoring module, a cold chain breakage detection module, an artificial intelligence optimization scheduling module, a temperature control equipment intelligent optimization module, a cargo life cycle tracing module, and an intelligent abnormal event prediction and response module. According to the cold-chain logistics system, multiple advanced technologies (such as AI, Internet of Things, RFID and big data) are comprehensively applied, many problems in an existing cold-chain logistics system can be solved, the monitoring precision and real-time performance are improved, the transportation path and resource scheduling are optimized, and the transportation efficiency is improved. And the integrity and safety of cold chain transportation are ensured. Compared with the prior art, the system has stronger data integration capability, a more intelligent abnormity early warning mechanism and more efficient resource utilization efficiency, and brings remarkable technical progress for the cold-chain logistics industry.

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.

An event analysis and prediction system is disclosed that is configured to analyze at least one event data stream from a monitored system for the purpose of predicting future events in the system, e.g., for predictive maintenance of the system. The event analysis and prediction system advantageously predicts, in real-time, such failures in the monitored system that would otherwise lead to interruptions. The event analysis and prediction system advantageously leverages a novel data generation procedure that, in essence, converts the problem of failure prediction to a classification problem, which is solved using machine learning algorithms. The event analysis and prediction system is designed to work well even when there is limited labeled data available for model training.

The invention discloses a digital calendar event prediction reminding method and system based on artificial intelligence, and relates to the technical field of artificial intelligence, and the method comprises the steps: collecting calendar event record data, and executing semantic coding through a BERT model to generate a feature vector; constructing an event pattern recognition model based on the long and short term memory neural network to perform time sequence analysis; generating an event prediction probability distribution diagram by using a Transform model; screening high-probability candidate events in combination with the current calendar data; evaluating an event dependency relationship through a graph neural network to generate a reminding priority sequence; and pushing a prediction prompt according to the predicted occurrence time point. By fusing BERT semantic coding, LSTM time sequence analysis and the graph neural network, intelligent prediction and graded reminding of calendar events are realized, the prediction accuracy and reminding timeliness are improved, the false alarm rate is reduced, schedule conflicts are effectively avoided, and the working efficiency of users is improved.

A computer-implemented method for generating optimized user journeys leveraging artificial intelligence is disclosed. The method includes receiving a user text prompt describing a desired journey objective and extracting context data for multiple users from a customer data platform. This context data encompasses attributes, events, predicted traits and audience memberships. A journey generation prompt is constructed by combining the received user prompt and extracted context data. This prompt is input into a machine learning model which processes the prompt to produce a user journey definition comprising interconnected nodes representing journey phases like audiences, waits and messages. The journey system validates the definition, modifying nodes to conform to predefined schema rules. The validated journey is output to campaign orchestration systems for execution across customer touchpoints. As journeys run, engagement data is collected for retraining models to improve journey performance over time.

The invention discloses a news event prediction method based on time smooth constraint depth evolution clustering. The method comprises the following steps: constructing an entity map of a news event; performing information aggregation on the entity graph by using a relationship-aware graph convolutional neural network; in the deep evolution clustering module, fuzzy clustering is utilized to obtain a soft membership matrix of an entity to clusters, and the clusters of adjacent timestamps are aligned and fused to construct a cluster graph; in the cluster graph information transmission module, information aggregation is carried out by using implicit correlation between cluster pairs so as to update cluster representation, entity representation and relation representation; combining the updated entity representation and relationship representation with the initial entity representation and relationship representation by using a time residual gate to serve as a next timestamp input; inputting the entity representation and the relation representation updated by different timestamps into a time-dependent encoder based on an attention mechanism to obtain a comprehensive entity representation and a relation representation; and predicting through the news event model based on convolution. According to the method, time sequence evolution of high-order correlation between entities can be effectively modeled.

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.

Described are a system, method, and computer program product for multivariate event prediction using multi-stream recurrent neural networks. The method includes receiving event data from a sample time period and generating feature vectors for each subperiod of each day. The method also includes providing the feature vectors as inputs to a set of first recurrent neural network (RNN) models and generating first outputs for each RNN node. The method further includes merging the first outputs for each same subperiod to form aggregated time-series layers. The method further includes providing the aggregated time-series layers as an input to a second RNN model and generating final outputs for each RNN node of the second RNN model.

The embodiment of the invention discloses a traffic data processing method and device, electronic equipment and a readable storage medium, and relates to the technical field of computers. In the embodiment of the invention, through time sequence decomposition and edge attention extraction processing, the time-space dependency relationship between the nodes in the original traffic data can be extracted, and the adjacent matrix can be adaptively adjusted through the edge attention mechanism, so that the time-space flow between different areas can be better represented. Furthermore, according to the embodiment of the invention, a space-time dependency relationship can be established in the whole cascading process through multi-level sensor processing in the prediction process, so that accurate traffic flow prediction data can be determined. By performing traffic event analysis on the accurate traffic flow prediction data, the embodiments of the present application can determine more accurate traffic event prediction data, thereby improving the prediction effect of traffic information.

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 a meteorological monitoring method and system for low-altitude navigation and the Internet of Things. The meteorological monitoring method comprises the steps that a historical time sequence of meteorological monitoring data and historical observation records of meteorological events are obtained; preprocessing the historical time sequence of the meteorological monitoring data to generate standard sequence data; carrying out correlation analysis between the standard sequence data and meteorological event historical observation records to generate a meteorological event correlation; inputting the standard sequence data and the meteorological event association into an improved meteorological monitoring model, and executing model training; obtaining meteorological monitoring data in real time, inputting the meteorological monitoring data into the improved meteorological monitoring model, and generating real-time meteorological event prediction; and the real-time meteorological event prediction is sent to a low-altitude meteorological monitoring guarantee platform for real-time publishing, and if the real-time meteorological event prediction shows an extreme meteorological event, an early warning signal is broadcasted. The problem that a meteorological monitoring method and system specially oriented to low-altitude navigation and low-altitude internet-of-things scenes are lacked at the present stage is solved.

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.

The invention relates to the technical field of artificial intelligence, in particular to a tense knowledge graph reasoning method and device based on tense rule guidance and a storage medium. The method comprises the following steps: firstly, extracting a multi-hop historical sub-graph based on a query quadruple, and integrating entity association information under time constraint; secondly, designing a time coding model, and explicitly capturing the influence of a time interval on an entity relationship through periodic features and learnable parameters; further, entity representation is enhanced by utilizing a relation type, and entity embedding is dynamically updated in combination with graph structure coding and a tense attention mechanism so as to fuse spatio-temporal semantics; then, a time relation sequence is generated through cross attention iteration, and target entity prediction is achieved in combination with probability distribution; and finally, optimizing the model by adopting a composite loss function, and analyzing a high-confidence symbol rule to provide interpretability. According to the method, the reasoning precision of the tense knowledge graph is remarkably improved, and the method has wide application value in time-sensitive scenes such as event prediction and trend analysis.

The invention discloses a big data-based event prediction method, device and equipment and a storage medium, and the method comprises the steps: collecting an initial data set, carrying out the preprocessing, predicting a situation value set and a situation variable quantity through a situation evaluation model and a situation prediction model, further calculating an event state transition probability, and determining an event state. And finally, performing safety early warning according to the event state and the event importance. The method solves the problems that an existing event prediction method only pays attention to the event situation and ignores the situation change trend, and the prediction precision is low and the feature selection is unreasonable when large-scale and multi-dimensional data are processed.

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.