19 results about "Event modeling" patented technology

The invention relates to a graph-based network security event modeling method and system, and the method comprises the steps: obtaining topological data and security policy information of a network where network security equipment is located, and carrying out the hierarchical construction of a knowledge graph, and obtaining a multi-layer security graph; acquiring real-time monitoring data of the network security equipment, and performing graph adversarial learning association with the multilayer security graph to obtain a dynamic evolution graph sequence; obtaining alarm data of the network security device, and performing anomaly detection on the multilayer security map to obtain an anomaly propagation situation; performing security assessment construction on the dynamic evolution diagram sequence and the abnormal propagation situation to obtain an initial security assessment scheme; performing alarm identification and attack link prediction on the alarm data to obtain a link prediction result; and performing evaluation and prediction on the initial security evaluation scheme and the link prediction result to obtain a security situation evaluation strategy. According to the invention, the security condition of the current network can be evaluated more accurately.

The invention relates to the technical field of engineering construction supervision, and discloses a building engineering construction supervision system based on big data analysis, and the system comprises a multi-modal data collection module which is used for collecting multi-source data of a construction site, and the multi-source data comprises structure sensor data, environment monitoring data, video image data, construction log data and building information model (BIM) state data; performing standardization processing and time synchronization on the data to generate a construction state data sequence; and the construction event modeling module identifies key events in the construction process based on the construction state data sequence and constructs a construction event graph, and the construction event graph is composed of event nodes representing construction events and event edges representing event collaboration or time correlation. By introducing an event atlas construction mechanism based on multi-source construction data driving, structured expression and semantic association mapping of key behavior units of a construction site are realized, and the problem of insufficient non-structured information processing capability in construction monitoring is overcome.

The invention discloses a sales resource distribution system based on a dynamic customer portrait, and relates to the technical field of artificial intelligence and big data application, and the system comprises an event modeling module, an abnormity monitoring module, a copy management module, a trial investment evaluation module, an unfreezing control module and an income backflow module. According to the method, a distribution offset map is generated by playing back customer behavior data in historical burst periods, and customer groups and key interaction contacts with easily distorted behaviors are identified as data monitoring references. According to the invention, through dynamic monitoring, label copy management, trial investment feedback evaluation, segmented unfreezing control and a negative pressure backflow mechanism, accurate identification and verification recovery of customer behavior abnormity are realized, the label stability is guaranteed, the sales resource distribution is dynamically optimized, and the response capability and configuration efficiency in an emergent environment are improved.

The invention discloses a deep learning-based valve opening and closing state recognition and diagnosis method, which comprises the following steps of: acquiring and synchronizing multi-source signals, and generating a standardized multi-mode time sequence sample; extracting modal features by multiple branches and fusing the modal features into a joint feature vector sequence; the combined features are input into a phase change layered decoder, and a layered recognition result is output; constructing a plurality of types of abnormal events in a point process layer modeling phase change stage, and outputting an event modeling result; constructing a condition reversible generation diagnostor, and outputting a consistency checking result; and fusing a result output state and diagnosis information, and executing alarming and filing. Through multi-mode deep learning feature fusion, phase change hierarchical decoding and conditional modeling, accurate recognition of the opening and closing state of the valve, fine division of the phase change stage and intelligent diagnosis of early faults are achieved.

The invention relates to the technical field of computers, and discloses an agricultural and sideline food whole-process information tracing method and system, and the method comprises the steps: deploying sensing and collection equipment in all links of production, logistics, storage and sales, and generating a standardized event record with a time-space stamp and a digital signature; executing data cleaning, event modeling and map construction through a centralized platform to form a multi-dimensional tracing network with commodities as nodes and operation events as edges; and in response to user query, dynamically generating a visual tracing path according to time, geography, responsibility subject or environmental parameter dimensions. The system comprises a production end acquisition subsystem, a logistics end acquisition subsystem, a storage end acquisition subsystem, a sales end acquisition subsystem and a central data fusion platform, and a permission control and credibility scoring mechanism is arranged in the platform. According to the method, through event-driven atlas and multi-source data fusion, full-chain information reversible restoration, multi-condition filtering query and compliance automatic judgment are realized, and the authenticity, integrity and decision support capability of tracing are remarkably improved.

The invention discloses a valley-type debris flow disaster intelligent early warning method based on multi-index fusion and dynamic optimization, which comprises the steps of data preprocessing, multivariable joint probability modeling, dynamic confidence optimization and threshold updating, and real-time monitoring and model updating, and aims to realize accurate early warning and quick response of valley-type debris flow disasters. According to the method, rainfall, muddy water level height, soil moisture content and other multi-source data are fused, and mathematical modeling and intelligent early warning of a debris flow disaster-causing mechanism are achieved through the steps of data preprocessing, multivariable joint probability modeling, dynamic confidence optimization, real-time monitoring, model updating and the like. According to the method, the Gumbel Copula function suitable for tail joint extreme event modeling is used for constructing the joint distribution model, the method is particularly suitable for valley debris flow disaster early warning scenes with complex terrains and diversified induction mechanisms, and disaster recognition and risk response capabilities in complex geological environments can be remarkably improved.

The present invention provides a method for modeling spatial lag relationships for geographic events, belonging to the field of data processing technology. The method specifically includes: Step 1: Modeling geographic events in a target area as spatial point elements and geographic factors as spatial surface elements; Step 2: Screening valid causal edges between different geographic events in the spatial point elements based on spatiotemporal rules; Step 3: Modeling an adaptive Gaussian field based on the valid causal edges and spatial surface elements, and outputting a quantitative result of the spatial lag effect. The present invention significantly improves the accuracy and robustness of spatial causal inference through refined filtering and dynamic modeling.

The invention discloses a state machine graphical modeling packaging method and device, equipment and a storage medium. The method comprises the steps of obtaining an event description file of a to-be-modeled encapsulation event implementation logic; performing logic analysis on the event description file, extracting a state machine structure, and obtaining hierarchical nesting information corresponding to a finite state machine in the event description file; according to the hierarchical nested information, plane view modeling processing is carried out, a state view model corresponding to a finite state machine in the event description file is obtained, and the state view model displays display information of all event states in a plane display mode; and performing graphic packaging processing on the sub-state machine in the state view model to obtain an event modeling packaging view corresponding to the to-be-modeled packaging event. According to the technical scheme, multi-layer nested state machine graph modeling can be realized, the complexity of the model is effectively reduced, and the readability and maintainability of the model are improved.

The embodiment of the invention belongs to the field of data analysis, and relates to a front-end plug-in implementation method based on artificial intelligence supply chain data analysis, which comprises the following steps: receiving supply chain data from a plurality of service systems and artificial intelligence analysis results corresponding to the supply chain data through a network interface; analyzing confidence coefficient information and a prediction interval of the artificial intelligence analysis result; generating a differential patch message for the standardized event stream and maintaining version vector and sequence control; a comprehensive scoring function is formed by the data type adaptation degree, the equipment side frame rate, the memory probe, the interaction complexity and the readability index; screening, drilling, linkage and backtracking interaction events are modeled into a state machine with priority and backspacing rules, and cross-view linkage and concurrent operation are arbitrated. The invention further provides a front-end plug-in implementation system based on artificial intelligence supply chain data analysis. According to the technical scheme, total redrawing and invalid transmission can be reduced, and the end-side real-time performance and the reading credibility are remarkably improved.

The invention discloses a converter station important event diagnosis method based on correlation causal, comprising the following steps: collecting and preprocessing a converter station historical message, and carrying out SER event modeling; recording sequence events in the database, namely SER events occurring according to a time sequence, and mining association rules by utilizing an Apriori method according to the events, fault information and manual operation information; redundant rule pruning is carried out by using a confidence enhancement method, hybrid variable control is carried out on the pruned rule, and potential hybrid variables are eliminated; and finally, reducing the causal rule through an OR statistical causal method to obtain the causal rule of SER event occurrence during the fault occurrence period and the manual operation period. According to the method, redundant rule pruning and hybrid variable control links are introduced, the rules mined from the association rules are subjected to previous rule pruning, the event scale is reduced, the calculation pressure of OR during causal statistics is relieved, and the method is low in data demand and high in diagnosis accuracy.

A computer-implemented method for material cyber event modeling includes: generating a cyber event catalog based on a past cyber event, the catalog including a plurality of cyber events, wherein generating catalog further includes: determining a distribution of all event parameters by extrapolating data from a past event; and assigning a set of restriction rules, wherein the parameter distribution and the set of restriction rules are used to create events in the catalog; simulating a cyber event, of the plurality of events in the catalog, to predict whether an organization is affected by a simulated cyber event, wherein the organization is an organization selected from a hazard table, and wherein the simulating cyber event simulates malicious activity; and estimating a damage of the cyber event on the organization by employing a damage function, including generating an exceedance probability (EP) curve for the cyber event, for at least one materiality category.

Machine learning systems and methods for extreme weather event modeling using generative diffusion models are provided. The system includes a weather modeling processor and a weather modeling engine executed by the processor. The weather modeling engine causes the processor to: receive a dataset including a plurality of vorticity samples; process the dataset using a deterministic mean model having a temporal attention unit to model spatial, cross-channel, and temporal dependencies using dynamical attention units; and process output of the deterministic mean model using a reverse diffusion model to capture stochastic fine scale features and to generate a denoised output. A downscaling pipeline can also be executed by the weather modeling engine to downscale outputs of the system.

The invention relates to a text event extraction method in a text scene based on consistency solution and event linear clustering. Compared with the prior art, the defect that text event extraction is difficult to achieve in a complex text scene is overcome. The method comprises the following steps: acquiring an unstructured text in a text scene and preprocessing the unstructured text; forming an event draft; constructing a mutual correction consistency graph; executing joint judgment on the mutual correction consistency graph and adopting a dual-threshold hysteresis strategy; and generating a text event extraction result. According to the method, key technologies such as multi-constraint joint consistency solving, iterative diffusion consistency calculation, a double-threshold hysteresis strategy, event linear clustering and thread management, monotonic constraint dynamic planning stage labeling and multi-view robust fusion are adopted; the defects of an existing text event extraction technology in the aspects of cross-document consistency, long-period event modeling and result stability are effectively overcome, and extraction precision, cross-document consistency and time sequence interpretability can be considered at the same time in a complex text scene.

Machine learning systems and methods for extreme weather event modeling using generative diffusion models are provided. The system includes a weather modeling processor and a weather modeling engine executed by the processor. The weather modeling engine causes the processor to: receive a dataset including a plurality of vorticity samples; process the dataset using a deterministic mean model having a temporal attention unit to model spatial, cross-channel, and temporal dependencies using dynamical attention units; and process output of the deterministic mean model using a reverse diffusion model to capture stochastic fine scale features and to generate a denoised output. A downscaling pipeline can also be executed by the weather modeling engine to downscale outputs of the system.

The application provides an AIGC content self-adaptive method and system based on user feedback, comprising: acquiring and preprocessing a multi-modal user feedback dataset, event modeling and causal discovery, acquiring and performing feedback event quality evaluation according to a first feedback event dataset and a causal influence dataset, generating a second feedback event dataset, synchronously acquiring event exploration intensity, performing counterfactual reasoning based on a preset neural jump differential equation and the causal influence dataset, acquiring a corrected event exploration intensity and a third feedback event dataset, performing AIGC self-adaptive generation based on a preset multi-scale stochastic differential equation, synchronously combining the second and third feedback event datasets and the corrected event exploration intensity, acquiring an original AIGC content dataset, performing quality evaluation on the original AIGC content dataset, and combining a Bayesian optimization algorithm to perform optimization fine-tuning to generate an optimized AIGC content dataset, thereby improving the accuracy of the generated content.

The invention relates to the technical field of grassroots governance, and discloses an emergency handling method recommendation system based on grassroots governance, comprising a system module assembly which comprises an information collection module, a modeling module, a learning module, an adjustment module, an HTTP push module and an alarm module; the information collection module is used for collecting information of various emergencies, and the emergencies refer to public security dispute events during grassroots governance; the modeling module is connected with the information collection module, and the modeling module is used for establishing an emergency processing model; the learning module is used for inputting the information collected by the information collection module into the modeling module for learning, and obtaining logical emergency handling capacity through learning; the recorded information is subjected to detail analysis through the analysis module, and matching of types, grades and verification degrees is subsequently carried out, so that an effective emergency scheme is obtained, and a reasonable solution is conveniently and timely provided for basic management personnel.

The embodiment of the invention provides a lag prediction method and device, equipment, a storage medium and a product. The method comprises the following steps: acquiring a streaming media mutation event of a target object in a streaming media transmission process and corresponding event change information; determining an event feature vector of the streaming media mutation event according to the event change information; and according to the event feature vector and the time sequence feature vector of the target object at the current moment, carrying out lagging prediction to obtain a lagging probability of the target object in the target time period. According to the embodiment of the invention, the defect of lack of accurate event modeling in the prior art is made up, the profound influence of sudden events such as network switching and CDN switching on the playing state can be effectively captured, prospective prediction is realized, and the prediction precision is improved, so that an effective support is provided for service optimization decision making in an uncertain environment, and the service optimization decision making efficiency is improved. And the video playing fluency and the user experience are effectively improved.

The application discloses a valve opening and closing state recognition and diagnosis method based on deep learning, comprising the following steps: collecting and synchronizing multiple source signals to generate standardized multi-modal time sequence samples; extracting modal features by multiple branches and fusing them into a joint feature vector sequence; inputting the joint features into a phase change layered decoder to output layered recognition results; constructing a point process layer to model multiple abnormal events in a phase change stage and output event modeling results; constructing a conditional reversible generation diagnostic device to output consistency checking results; and fusing the results to output state and diagnosis information and execute alarm and archiving. Through multi-modal deep learning feature fusion, phase change layered decoding and conditional modeling, the application realizes accurate recognition of the valve opening and closing state, fine division of the phase change stage and intelligent diagnosis of early faults.

A computer-implemented method for material cyber event modeling includes: generating a cyber event catalog based on a past cyber event, the catalog including a plurality of cyber events, wherein generating catalog further includes: determining a distribution of all event parameters by extrapolating data from a past event; and assigning a set of restriction rules, wherein the parameter distribution and the set of restriction rules are used to create events in the catalog; simulating a cyber event, of the plurality of events in the catalog, to predict whether an organization is affected by a simulated cyber event, wherein the organization is an organization selected from a hazard table, and wherein the simulating cyber event simulates malicious activity; and estimating a damage of the cyber event on the organization by employing a damage function, including generating an exceedance probability (EP) curve for the cyber event, for at least one materiality category.