316 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.

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 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 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 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.

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.

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 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 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.

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.

Systems and methods for reducing network traffic associated with generating event predictions based on cognitive image analysis are disclosed. The system receives a user input indicating a location identifier of a location of interest. The system queries a data store to determine whether an image associated with the location of interest satisfies a data recency criterion. In response to the image failing to satisfy the data recency criterion, the system queries a remote data store to determine whether another, additional image associated with the location of interest satisfies the data recency criterion. In response to the additional image satisfying the data recency criterion, the system extracts one or more image-based attributes of the additional image and determines a distance between objects of the image. The system generates an event prediction based on the image-based attributes, the distance, and location-based attributes using a machine learning model to be displayed.

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.

The invention discloses a news event prediction method based on heterogeneous evolutionary event clustering, which comprises the following steps: generating event representations based on preliminarily updated entity representations and relationship representations in a constructed entity graph, and constructing an event graph by taking events as nodes and taking heterogeneous relationships between the events as edges; obtaining event clusters through fuzzy clustering and constructing an event cluster graph; using a self-supervised optimization algorithm to optimize the event cluster representation according to the distance and similarity between the event clusters on the event cluster graph; capturing implicit correlation among the event clusters by using an implicit relation encoder, and sequentially updating representation of the event clusters, representation of events, and representation of entities and relations after sparsification and information aggregation; and predicting through the news event model based on convolution. According to the method, the pairwise correlation, the high-order correlation and the multi-step time sequence evolution of the events are effectively modeled, and the method has important application value in the aspects of international situation analysis, social governance, intelligent decision support and the like.

The present invention provides a method for constructing an event prediction model based on multi-modal data and an event prediction device. By using independent feature extraction layers to separately extract features from multiple personal medical images and multiple personal non-image data, after obtaining the feature vectors of multiple personal medical images and multiple personal non-image data, a global self-attention transformation is performed on the feature vectors of multiple personal medical images, multiple personal non-image data, and a preset token vector based on a multi-modal fusion layer to obtain the self-attention vector of the preset token vector. Then, event prediction is performed based on the self-attention vector of the preset token vector to obtain the event prediction result of the sample object, forming an end-to-end prediction structure. Thus, the model can be trained with the constructed sample data set and the parameter settings of the model can be adjusted to finally obtain an event prediction model. This model effectively utilizes multi-modal data information on the basis of simplifying the network structure and improves the prediction accuracy of the event prediction model.

A system (100) for real-time financial reconciliation using SAP-integrated forecasting engines, the system comprising: (a) a data ingestion and integration module configured to collect and normalise financial transaction data from SAP systems, external general ledgers and third-party financial sources; (b) a reconciliation module configured to perform multi-level transaction reconciliation across ledgers using configurable rules and adaptive algorithms; c) a predictive analytics and forecasting module integrated into SAP, trained on historical data to detect anomalies, predict reconciliation periods, and prioritize discrepancies; (d) an exception management and resolution module to categorise unreconciled transactions and trigger automatic or semi-automatic correction workflows; e) a dashboard and visualisation module embedded in SAP interfaces to display reconciliation metrics, alerts and drill-down views for user interaction; (f) a compliance and audit trail logging module configured to log reconciliation events, predictive actions and user decisions for regulatory reporting; g) and a configuration and management module that allows administrators to manage rules, learning parameters, access roles and workflows; h) the system enables continuous real-time reconciliation of financial data with predictive intelligence and automatic exception handling.

The invention discloses a GNSS occultation open-loop forecasting method based on a cubic spline interpolation algorithm, and relates to the field of occultation open-loop forecasting. The method comprises the following steps: judging whether a current navigational star is atmospheric occultation or not through an occultation event forecasting algorithm; acquiring position and speed information of atmospheric occultation and a low earth orbit; carrying out occultation forecast calculation on atmospheric occultation by adopting an atmospheric model to obtain model Doppler caused by satellite motion and model pseudo-range caused by neutral atmosphere; the method comprises the following steps: caching n-second model Doppler, model pseudo-range and open-loop forecast time of occultation in a 1HZ task, and obtaining interpolation parameters of the model Doppler and the model pseudo-range by using cubic spline interpolation; through the interpolation parameters, carrying out interpolation to obtain a 100HZ model Doppler and a 100HZ model pseudo-range; through the model pseudo-range, signal emission time is obtained through calculation; through signal emission time of a 100HZ task period, a signal emission time deviation value is obtained through calculation and converted into a hardware adjustment count, and the hardware adjustment count is transmitted into FGPA adjustment so as to carry out open-loop tracking of atmospheric occultation.

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.

The invention particularly relates to a video parsing label optimization method, device and equipment based on weak supervised learning, and the method comprises the steps: carrying out the data preprocessing of a to-be-parsed video in a target data set, obtaining the audio features and visual features of the to-be-parsed video, carrying out the analysis after the feature aggregation, and obtaining a video parsing label; performing preliminary event prediction on the to-be-analyzed video according to an analysis result, obtaining an audio track of a first target video clip and a visual track of a second target video clip in the target data set, exchanging the audio track and the visual track to obtain a new to-be-analyzed video, and performing feature aggregation on the new to-be-analyzed video to obtain the to-be-analyzed video. Performing event prediction on a new to-be-analyzed video according to an analysis result, and respectively generating a first modal perception event tag and a second modal perception event tag according to the preliminary event prediction and the new event prediction, and optimizing the first modal perception event tag and the second modal perception event tag according to a comparison result of the preliminary event prediction and the new event prediction.

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 invention relates to the technical field of ambient air quality forecasting, in particular to an O3 pollution forecasting method and system based on multiple linear regression. The O3 pollution forecasting method based on multiple linear regression comprises the following steps: S1, acquiring data; s2, preprocessing the data, and constructing an O3 pollution case library; s3, typing the O3 pollution cases according to the meteorological elements; s4, screening proper meteorological elements as forecasting factors for each type of O3 pollution case; s5, establishing a step-by-step multiple linear regression model of meteorological data and pollutant concentration for each type of O3 pollution case, and evaluating a regression effect; and S6, obtaining a future day-by-day weather factor forecast result of the high-resolution atmospheric chemical numerical model, and predicting a future O3 pollution condition by using a multiple linear regression model. According to the O3 pollution event forecasting method provided by the invention, the calculation method is simple, the accuracy of the output forecasting result is high, the O3 pollution forecasting effect is effectively improved, an open research framework is provided for O3 pollution forecasting, and powerful support is provided for related researches such as forecasting and evaluation of O3 combined pollution.

The invention discloses an AI object management method and device, a terminal and network side equipment, and belongs to the technical field of communication, and the AI object management method comprises the steps that under the condition that the terminal determines to execute first operation, second operation is executed on target configuration; wherein the first operation comprises one of the following items: AI object activation, AI object deactivation, operation of returning to a non-AI object, and AI object switching; the AI object comprises an AI model or an AI function, and the AI object is used for at least one of radio resource management (RRM) measurement prediction, target cell prediction, measurement event prediction, radio link failure (RLF) prediction and handover failure (HOF) prediction; the target configuration is a configuration associated with the AI object, and the second operation is an operation corresponding to the first operation.