120 results about "Event model" patented technology

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 an intelligent data stream processing system based on Flink and an implementation method thereof, and belongs to the technical field of distributed streaming data processing, and the implementation method comprises the following steps: a data source access layer uniformly accesses multi-source data; the entity monitoring layer captures service entity change in a non-intrusive manner based on JPA, generates a standardized message body and asynchronously delivers the standardized message body to double channels; the unified event model layer maps the data into a standardized event and expands the standardized event; the Flink stream processing engine layer is used for complex event processing, state management and window calculation, and supports declarative pipeline definition and dynamic construction; the anomaly detection layer performs multi-dimensional anomaly detection; the distributed task scheduling layer schedules tasks according to a DAG model, and realizes load balancing through hotspot splitting and state transition; and the result storage and visualization layer adopts a cold and hot separation strategy to store data, and provides a dynamic visualization interface. The real-time performance, the consistency and the intelligent level of data processing are remarkably improved, and the development and maintenance cost is reduced.

A method of monitoring an assembly process is disclosed herein. The method includes obtaining an event model for each of a plurality of objects in the assembly process with the event model for each of the plurality of objects including a predetermined time frame for a change in presence to occur. The method includes collecting an image sequence of the assembly process for monitoring and identifying if a change in presence for each of the plurality of objects occurred with a detector model. The method further includes reviewing the event model for each of the plurality of objects to determine if the predetermined time frame lapsed without the change in presence of a corresponding one of the plurality of objects being identified and issuing an alert if the predetermined time frame lapsed without the presence of a corresponding one of the plurality of objects being identified.

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 combat event atlas construction method and a situation context analysis method and system, and the event atlas construction method comprises the steps: 1) designing a combat event mode, and determining a combat event type, a trigger word and a parameter role; 2) carrying out combat event extraction design based on the designed combat event mode; the combat event extraction design comprises trigger classifier design and parameter classifier design; the trigger classifier is used for detecting and identifying a combat event trigger word and allocating a predefined type to the combat event trigger word, and the parameter classifier is used for identifying a combat event parameter role; 3) identifying a combat event relationship based on a mode matching method; the combat event relationship comprises an affiliation relationship, a co-reference relationship, a time sequence relationship and a causal relationship; and 4) constructing the combat event atlas based on the combat event mode, the combat event extraction design and the combat event relationship The method has the advantages that the atlas is simple and rapid to construct, and the efficiency of acquiring event information is improved.

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 discloses a concrete mixing plant production management system comprising the following modules: a discrete event modeling module used for constructing a discrete event simulation model; the data acquisition module is used for acquiring a real-time operation data set of the concrete mixing plant and inputting the real-time operation data set as a discrete event simulation model; the simulation execution module is used for executing an event-driven simulation process; the ant colony optimization module is used for constructing a scheduling optimization objective function and a path heuristic function according to the simulation data set, and executing search to generate an optimal material sending scheduling sequence; the scheduling execution module is used for executing a scheduling process according to the optimal material sending scheduling sequence; and the feedback control module is used for monitoring the execution deviation, transmitting the latest operation state back to the discrete event modeling module, triggering scheduling reconstruction, and realizing the production dynamic closed-loop optimization management of the concrete mixing plant. According to the invention, continuity and dynamic optimality of production scheduling are realized.

The invention relates to a progressive memory delay monitoring and diagnosis method oriented to a virtual memory subsystem. The method comprises the following steps: mounting a BPF program to a plurality of key event points on a kernel; when the key event point is triggered, acquiring state information of a corresponding event; packaging the state information into an event, and writing the event into a perf ring buffer associated with the outputmap; and the user mode program asynchronously reads the packaged event from the perf ring buffer, performs classification and aggregation according to the process and the event type, generates a delay terminal log and a histogram, and outputs a diagnosis result. Through systematic architecture design, the state keeping capability and the kernel context access capability of the eBPF are combined with an event model of a virtual memory subsystem, and a set of closed-loop monitoring system oriented to a diagnosis target is constructed. High-precision and high-reliability memory operation delay measurement is realized.

Techniques are described that facilitate online monitoring of clinical data streams in association with detecting missing data and other suspicious data deviations. According to an embodiment, a computer implemented comprises receiving, by a system comprising a processor, a data stream from a plurality of different clinical data information systems configured to report defined clinical events within the data stream and recording arrival times of received events of the defined clinical events. The method further comprises detecting, by the system, data failure events associated with the data stream based on time differences between the arrival times for defined clinical events of the same type and estimated probabilities that the time differences are expected, wherein the detecting comprises estimating the probabilities that the time differences are expected using time-to-event models developed for each of the defined clinical events of the same type.

Systems and methods for creating a model include converting historical data into categorical time series data; de-noising the categorical time series data by organizing events into transition sets and removing noisy transitions sets according to a coefficient of variation. A relationship graph is generated that determines relationships between pairs of nodes, where the nodes relate to respective data sources and where the relationships indicate a degree of correlation between nodes based on the de-noised categorical time-series data, using a Hawkes process that determines a likelihood of a category transition based on historical events. An anomaly threshold is determined based on anomaly scores for a validation dataset using the relationship graph, wherein a likelihood output of the Hawkes process that exceeds the anomaly threshold indicates an anomaly.

Systems and methods include converting (300) historical data into categorical time series data and de-noising (302) the categorical time series data by removing noisy transitions sets according to a coefficient of variation. A likelihood of a category transition is determined (304) based on historical events using a Hawkes process to generate a relationship graph. Relationships between pairs of nodes are determined (306) using the relationship graph, where the relationships indicate a degree of correlation between the nodes based on de-noised categorical time-series data. An anomaly threshold is determined (308) based on anomaly scores for a validation dataset using the relationship graph, wherein a likelihood output of the Hawkes process that exceeds the anomaly threshold indicates an anomaly.

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.

A vehicle device may be configured to execute one or more event models, such as based on input from one or more of the audio capture devices, image capture devices, and / or other sensors associated with the vehicle device, to intelligently detect safety events, such as unsignaled lane departures, in real-time. Detection of a safety event may trigger an in-vehicle alert to make the driver aware of the safety risk. The vehicle device may include logic for determining which asset data to transmit to a backend server in response to detection of a safety event, as well as which asset data to transmit to the backend server in response to analysis of sensor data that did not trigger a safety event. The asset data transmitted to the backend server may be further analyzed to determine if any action should be taken by the user of the backend server.

The invention provides a target event detection method based on natural language processing and related equipment. The method comprises the following steps: acquiring event data of a to-be-detected event; detecting the event data based on a trained target event model, and determining that the to-be-detected event is a target event; wherein the trained target event model is obtained by training based on training text data about a target event, and the method comprises the steps that the training text data are acquired, and event types and target features of the target event are marked on the training text data; performing data preprocessing and data enhancement on the training text data to obtain enhanced text data; and training a natural language processing model based on historical text data and the enhanced text data to obtain the trained target event model.

The invention relates to a dynamic multi-objective optimization method and device, equipment and a medium. The method comprises the following steps: establishing an event model of a multi-objective optimization event; if it is detected that the environmental information changes, a first group and a second group are obtained based on a multi-objective evolutionary algorithm and a historical optimal set, an original optimal set of an event model is determined according to the first group and the second group, the first group is obtained based on a representative individual prediction algorithm, and the second group is obtained based on an inflection point prediction algorithm; otherwise, taking the Pareto optimal solution of the event model at the current moment as an original optimal set; and inputting the original optimal set into a social optimization learning model, and outputting a target optimal set of the event model through the social optimization learning model. According to the method, when the change of the environment information of the multi-target optimization event is detected, the target optimal set is output through the social optimization learning model by improving the population diversity and improving the precision of the Pareto optimal solution from the perspective of the population diversity.

The invention belongs to but not limited to the technical field of flight safety, and particularly relates to a fixed-wing unmanned aerial vehicle fusion flight safety analysis method and system, and the method comprises the steps: S1, building an improved Event model; s2, longitudinal collision risk calculation; s3, calculating a lateral collision risk; and S4, calculating the collision risk in the vertical direction. The invention provides a collision risk assessment method based on an MGFPE improved model by improving an Event model. The provided collision assessment method is subjected to precision calculation, and compared with a traditional cuboid and sphere collision box, the MGFPE improved model is optimal in precision. The optimized safety interval is subjected to a simulation experiment, a certain airport in the southwest is introduced to serve as a simulation experiment scene, a capacity evaluation method based on the dynamic interval is used, the calculated leg capacity is 20.9 sortie, compared with the leg capacity 9.6 sortie before optimization, the leg capacity is improved by 2.18 times, and the leg capacity is greatly improved.

Systems and methods for creating a model include converting (302) historical data into categorical time series data; de-noising (302) the categorical time series data by organizing events into transition sets and removing noisy transitions sets according to a coefficient of variation. A relationship graph is generated (306) that determines relationships between pairs of nodes, where the nodes relate to respective data sources and where the relationships indicate a degree of correlation between nodes based on the de-noised categorical time-series data, using a Hawkes process that determines a likelihood of a category transition based on historical events. An anomaly threshold is determined (308) based on anomaly scores for a validation dataset using the relationship graph, wherein a likelihood output of the Hawkes process that exceeds the anomaly threshold indicates an anomaly.

The invention discloses a data burying point and analysis method and system for cross-platform dynamic configuration, and relates to the technical field of data burying point, a server determines a candidate parameter set of burying point configuration based on an event model, performs optimization through a swarm intelligence algorithm, generates dynamic burying point configuration including a target control topological neighborhood feature vector, and transmits the dynamic burying point configuration to a server; issuing to a terminal; the terminal calculates the geometric structure similarity between view tree nodes and topological neighborhood feature vectors, and binds the nodes exceeding a threshold value as a buried point trigger source; when a burying point trigger source is activated, obtaining an operation sequence of a current context and sampling strategy generation event data, injecting a logic precursor identifier and a statistical weight factor, and sending the data to a server through local caching and batch reporting; and the server performs logic causal rearrangement, constructs a statistical analysis model to identify a statistical weight factor, executes inverse probability weighted calculation and outputs an unbiased analysis result, so that the problem of burying point failure caused by version iteration is solved, and the robustness and adaptability of burying point configuration are improved.

The invention discloses an intelligent low-carbon service model and method based on block chain fusion, particularly relates to the technical field of low-carbon management, and is used for solving the problem that the credible recognition and settlement of carbon behaviors are not clear. According to the method, identity verification, time marking and caliber standardization are carried out on energy consumption and travel multi-source data through a trusted data gateway, a carbon behavior voucher containing a signature and an abstract is generated, minimum disclosure under an index upper chain and an original text chain is adopted, it is ensured that the source and the caliber are verifiable and cannot be tampered, and then merging is carried out through a unified event model; and performing conflict resolution and consistency verification to form a one-event one-key event, attaching a consistency label and a credibility index, performing calculation by a rule base and a scene parameter set, performing intelligent contract accounting, and providing rollback correction by using a unique identifier of the event, enabling the credibility index to participate in integral and right confirmation, performing low-credibility entering and re-verification, and suppressing abnormities. And the fusion accuracy and the auditing efficiency are improved.

An integrated artificial intelligence camera system provides multi-domain monitoring and enterprise automation from a single hardware endpoint. A camera assembly and auxiliary sensors, including at least one of depth, thermal, audio and environmental sensors, feed an edge compute module executing modular perception, identity, physiological estimation and domain-logic pipelines. The system detects and tracks persons, objects and activities; generates pseudonymous identity tokens for enrolled workers; estimates breathing rate and related wellness indicators from non-contact visual and thermal signals; and transforms these outputs into normalized event records annotated with security, inventory, workforce, payroll, physiological and compliance labels. A unified event model standardizes timestamps, device identifiers, site and zone identifiers, actor identifiers, metrics, confidence scores, policy tags and evidence references, enabling direct integration with payroll, inventory management, security incident and analytics platforms. A policy engine governs feature activation, anonymization and retention per jurisdiction and user role.

The invention discloses a multi-agent construction method for fault handling of a high-voltage converter station, and relates to the technical field of electric power automation, and the method comprises the following steps: S1, mapping establishment of in-station equipment and agents; s2, constructing a unified identifier and event model; s3, organization and credibility calibration of evidence information; s4, performing consistency arbitration and conflict grading processing; s5, arranging and executing a fault disposal process; s6, running experience learning and rule self-evolution; and S7, auditing and tracing the disposal process. According to the method, a multi-agent construction mechanism is introduced into the converter station, devices such as a direct current pole, a valve tower, protection, measurement and control, operation and the like are respectively mapped into independent agents, and cross-domain cooperation is realized under a unified identification and event model; each agent not only has an autonomous information processing capability, but also can realize rapid fusion and consistency judgment of multi-source information through an evidence chain and an arbitration mechanism, thereby forming explainable and traceable disposal suggestions when a fault occurs.

The invention relates to the technical field of smart power grids, and discloses a power distribution network fault positioning system and method, and the system comprises a causal generation module, an event inversion module, a dynamic pruning module, a causal collaborative verification module, and a meta-cognitive analysis module. The method comprises the following steps: iteratively calling a causal generation module through an event inversion module to reversely solve an optimal event parameter containing a fault position, and eliminating a physically unreasonable search space by utilizing a dynamic pruning module in iteration; then, the causal collaborative verification module verifies the result and calculates a diagnosis confidence coefficient, and when the confidence coefficient is lower than a threshold value, the event model is upgraded to reconstruct a complex fault; further, the system may generate a diagnosability map offline to identify a diagnostic blind spot. Through a generative inversion and model upgrading mechanism, accurate positioning of a fault and accurate reconstruction of a complex accident sequence are realized, a quantitative evaluation capability of a self-diagnosis capability is possessed, and an intelligent level of fault processing is substantially improved.

The invention discloses a security event detection method and system, electronic equipment and a storage medium, which are used for solving the problems of waste of computing resources and poorer timeliness of detection due to large calculation amount of the existing security event detection method, and the method comprises the following steps: obtaining model mark information of a security event model before a current time point, the model mark information comprises model calculation time, a model mark and a field value, the security event model is an atomic security event rule set formed by combining a plurality of atomic security event rules according to a preset association mode, and the field value is a field value matched with the atomic security event rules in the weblog; obtaining an atomic security event rule of the security event model according to the model mark; calculating an aggregation result of the atomic security event rules according to the atomic security event rules, the corresponding field values and the model calculation time; and judging whether the weblog in the current statistical period hits a security event corresponding to the security event model or not according to an association calculation condition of the aggregation result and the security event model.

The invention relates to a data-driven event automatic modeling and dynamic display method and device. The method comprises the following steps: constructing an event model driven based on time sequence data, wherein the event model comprises key time nodes, events and an object set which are mutually associated; generating a dynamic visual chart according to the event model, displaying the state change of an object set in the event model by configuring dynamic display parameters of the visual chart, and in the display process, displaying the state change of the object set based on the updating of the time sequence data. By adopting a dynamic position adjustment algorithm, an importance scoring algorithm, an intelligent layout algorithm and a multi-view synchronous display algorithm, the display of an object set in a visual chart is synchronously and dynamically adjusted. By adopting the method, various events in the time sequence data can be dynamically displayed and effectively tracked and reset, and the decision-making efficiency and accuracy in a confrontation scene are improved.

The disclosure relates to a system (100) for collision and intrusion detection of a door (1), the system (100) comprises a movement sensor (10a, 10b) configured to be arranged at the door (1) to detect movement of the door (1), a processing circuitry (102a, 102b, 102c) configured to cause the system (100) to obtain, by the movement sensor (10a, 10b), movement data indicative of a movement of the door (1), analyze the movement of the door (1) during a predefined time period based on the obtained movement data, and utilize a movement-to-event model to determine a probable event that caused the movement of the door (1). The disclosure further relates to a method for collision and intrusion detection of a door (1) and a computer program product (500).

A model management system for validating models for predicting a client behavior event includes a model validation system configured to execute each of multiple stages of a selected model, which includes calculating a respective output based on a respective input of each of the stages. The model validation system is configured to separately verify, based on the respective outputs, each of the stages. The model validation system is configured to generate and store model data corresponding to the respective outputs of each of the stages. The model data includes a first set of model data and a second set of model data different from the first set of model data. A user interface module is configured to receive an indication of credentials of a user and selectively retrieve and display one of the first set of model data and the second set of model data based on the indication.

The invention belongs to the technical field of intelligent control, and particularly relates to a self-adaptive real-time data acquisition system and an acquisition method thereof. The system comprises an access control module used for controlling access of a target data source and a target database; the protocol adaptation module is used for being connected with the target data source based on the protocol configuration information of the target data source; the data analysis module is used for analyzing the data information of the target data source and constructing a unified data model; the event model module is used for responding to the event triggering condition and generating a real-time event model; the self-adaptive strategy module is used for generating a self-adaptive acquisition strategy based on the unified data model and the real-time event model; the acquisition scheduling module is used for generating an acquisition task based on the acquisition configuration information and the point location configuration information of the target data source; and based on a preset acquisition strategy or / and an adaptive strategy, executing the acquisition task to acquire data information of the target data source. According to the invention, automatic configuration can be realized, and the flexibility and adaptivity of data acquisition are improved, so that the data acquisition efficiency is improved.

Systems and methods include converting historical data into categorical time series data and de-noising the categorical time series data by removing noisy transitions sets according to a coefficient of variation. A likelihood of a category transition is determined based on historical events using a Hawkes process to generate a relationship graph. Relationships between pairs of nodes are determined using the relationship graph, where the relationships indicate a degree of correlation between the nodes based on de-noised categorical time-series data. An anomaly threshold is determined based on anomaly scores for a validation dataset using the relationship graph, wherein a likelihood output of the Hawkes process that exceeds the anomaly threshold indicates an anomaly.

The invention belongs to the cross technical field of a computer technology and a geographic information system (GIS), and particularly relates to a multi-level interaction system fusing space-time constraints, which comprises a user management module used for identity authentication, authority distribution and operation auditing; the virtual earth module is used for constructing a virtual space and superposing multiple layers; the multi-dimensional event modeling module is used for standardizing multi-modal event data; the multi-timeline space-time synchronization engine module is used for managing timelines and event synchronization; the intelligent space-time publishing module is used for dynamically distributing event data; the space-time constraint interaction engine is used for checking the space-time compliance of user operation; the instant messaging module is used for supporting multi-mode instant messaging; and the interactive visualization module is used for rendering spatio-temporal data and user interaction.

A method of identifying events within a wellbore comprises obtaining a first set of measurements of a first signal within a wellbore, identifying one or more events within the wellbore using the first set of measurements, obtaining a second set of measurements of a second signal within the wellbore, wherein the first signal and the second signal represent different physical measurements, training one or more event models using the second set of measurements and the identification of the one or more events as inputs, and using the one or more event models to identify at least one additional event within the wellbore.