81 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.

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 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.

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 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.

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 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.

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.

A system is provided for determining an asset cleaning schedule. In some embodiments, the system may include a plurality of sensors tuned to monitor the asset, and a computing system, the computing system including at least one data processor and memory for storing instructions, the instructions causing the at least one data processor to perform an action when executed by the at least one data processor. In some embodiments, the actions performed by the processor may include receiving data from the plurality of sensors characterizing the asset's operational efficiency, determining the asset's operational efficiency, determining an operational efficiency threshold characterizing undesirable operational efficiency, determining an asset cleaning schedule based on the asset's operational efficiency and the operational efficiency threshold using an optimization algorithm, and providing the cleaning schedule.

The invention relates to a system for realizing transaction gateway distribution based on an asynchronous event model, and the system comprises an SDK access module which carries out the timeout judgment of a transaction request, and receives an asynchronous callback message response; the gateway access module is used for receiving the transaction request and decompressing the data; the service conversion module is used for loading service conversion rules from the service conversion management module and carrying out logic conversion and judgment on the request data; the data storage module is used for storing the business conversion rule and the effective time range thereof; and the service conversion management module provides addition, deletion, modification and check functions of service conversion rules. The system for realizing transaction gateway distribution based on the asynchronous event model has high availability, accesses the server through the SDK access module, realizes background service detection through the heartbeat detection module, realizes automatic switching when a fault is found, and ensures high availability of the service; according to the invention, the method is high in business rule taking-into-effect real-time performance, and has the obvious characteristics of high business rule taking-into-effect real-time performance, high expansibility, high availability and the like.

This application discloses a modeling method, apparatus, and equipment for a flight early warning center, relating to the field of civil aviation early warning technology, and is used to solve the technical problems of poor early warning accuracy, consistency, and centralized control capabilities in existing technologies. The method includes: constructing a rule management center using dynamic rule management services and dynamic rule loading services; the rule management center having standardized early warning event models and early warning rule distribution protocols; constructing a dynamic early warning rule library and an early warning processing center using the Drools rule engine; constructing an early warning calculation center using the Drools rule engine and the Disruptor asynchronous processing framework; and constructing a flight early warning center based on the dynamic early warning rule library, rule management center, early warning processing center, and early warning calculation center. Therefore, this application can effectively improve early warning accuracy and consistency through "Drools rule engine, Disruptor asynchronous processing framework, standardized early warning event models, and early warning rule distribution protocols."

The invention relates to the technical field of evaluation optimization, and discloses an advertisement delivery data evaluation system based on multi-channel information integration, and the system comprises a data standardization and fusion module which is responsible for receiving multi-source heterogeneous original data, carrying out the cleaning through a built-in data connector and an analysis engine, and carrying out the analysis of the multi-source heterogeneous original data; and forcibly converting all data into a unified atomic event model containing a unique device identifier, a standard timestamp and standard latitude and longitude coordinates. The technical effects of scientifically stripping interference factors and quantifying an online conversion net contribution value brought by offline advertisement putting in a causal inference mode are achieved by constructing an exposure group, generating a scientific control group by applying a tendency score matching algorithm and finally calculating a conversion improvement rate; the defect that the actual contribution of offline advertisements to online conversion cannot be accurately attributed and quantified due to the lack of reliable cross-screen tracking and scientific attribution methods is overcome.

An integrated energy information physical system modeling method considering event time-space attributes relates to the technical field of integrated energy system modeling, and establishes an integrated energy information physical system layered time-space modeling framework comprising a physical device layer, a fusion layer and a decision layer; the interaction relation between different hierarchies is described by defining space-time events of all the hierarchies; based on a mixed state machine, realizing fusion of space-time events of different levels, and finally considering characteristics of each level to establish a space-time event model of each level. Compared with the prior art, the hierarchical space-time modeling framework of the integrated energy cyber-physical system can effectively reflect the space-time characteristics of the integrated energy cyber-physical system and the state conversion process of equipment, improves the operation efficiency and stability of the integrated energy cyber-physical system, and further reduces the operation cost of the system.

The invention relates to an enterprise information security management method, system and device and a medium. The method comprises the following steps: acquiring heterogeneous security data of a physical environment and a network environment, and performing standardized analysis and element extraction on the heterogeneous security data to obtain a physical event and a network event; based on a preset unified event model, carrying out cross-domain association analysis on the physical event and the network event, and calculating space-time proximity and semantic correlation between the physical event and the network event to obtain an association candidate pair set; inputting the associated candidate pair set into a pre-trained causal inference model to obtain the causal intensity of the influence of the physical event on the network event, and identifying the associated candidate pair of which the causal intensity exceeds a preset threshold value from the associated candidate pair set as a cross-domain event pair to obtain a visual attribution map; and triggering an enterprise dual prevention mechanism to perform linkage response based on the visual attribution map. By adopting the method, the overall capability of active defense and risk management and control of an enterprise can be improved.

The invention discloses a traceable graph construction method based on human-multi-agent collaborative software development, and relates to the technical field of software engineering, and the method specifically comprises the steps: defining a traceable graph framework used for representing the full life cycle of software development; collecting events generated by multiple agents and manual developers in the full life cycle of software development, and normalizing the events into a preset event model; analyzing the preset event model, and creating and updating a corresponding node in the traceable atlas; identifying an evolution relationship of a code entity of software development through static analysis on the basis of a scene, and adjusting the consistency of a tracing relationship between nodes and edges in the traceable atlas on the basis of an identification result; a structured graph query interface and a natural language query interface are provided through the traceable atlas, the problem that event semantics between heterogeneous systems are inconsistent can be solved, an enhanced updating mechanism is provided to guarantee real-time performance and accuracy of the atlas, and the risk of information lag is reduced.

The application relates to the field of evaluation optimization, and discloses an advertisement putting data evaluation system based on multi-channel information integration, which comprises a data standardization and fusion module, which is responsible for receiving multi-source heterogeneous original data, cleaning through an embedded data connector and an analysis engine, and forcibly converting all the data into a unified atomic event model containing a unique device identifier, a standard time stamp and a standard latitude and longitude coordinate. A scientific control group is generated by constructing an exposure group and using a propensity score matching algorithm, and finally, a conversion promotion rate is calculated, so that the technical effect of scientifically stripping interference factors and quantifying the online conversion net contribution value brought by offline advertisement putting in a cause-effect inference manner is achieved, and the problem that offline advertisement actual contribution to online conversion cannot be accurately attributed and quantified due to the lack of reliable cross-screen tracking and scientific attribution methods is solved.

The invention discloses an event-driven CPSPS distributed operation management and control method and system. The method comprises the following steps: establishing a process drawing model of a processing process; establishing an RFID event model, a sensor event model and a CPS node event model containing event triggering conditions; performing semantic annotation on the sensing stream data to obtain RDF stream data; performing continuous query and monitoring on the RDF stream data by adopting a sliding time window, performing mode matching on a continuously output query result and an event triggering condition by utilizing an improved RETE mode matching algorithm, and outputting an event instance; constructing an event processing mechanism according to the event instance; a distributed production interaction and autonomous cooperation method based on CPS node self-organization driving is adopted to complete process task distribution, and a global Gantt chart is formed; and executing the production task according to the global Gantt chart. The Gantt chart is generated in a multi-CPS node distributed production social mode, and event-driven CPS node distributed production social and autonomous cooperation are achieved.

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.

A method of providing a virtual door sensor for a transport unit is disclosed. The method includes monitoring operation of a transport climate control system for a climate controlled space to obtain transport climate control system operating data; transforming the transport climate control system operating data into door event model inputs; predicting a door event based on the obtained door event model inputs; and transmitting a notification according to the predicted door event.

The application discloses a method, device and server for dynamically monitoring a track. The method comprises: collecting position information of a mobile terminal in real time to obtain track information of the mobile terminal; obtaining electronic fence data sent by a management terminal to obtain dynamic electronic fence information; fusing the track information and the dynamic electronic fence information to obtain a data stream; inputting the data stream into a strict continuous event model to obtain an abnormality judgment result, the strict continuous event model comprising a condition for triggering an alarm event; and sending alarm information to the management terminal in the case that the abnormality judgment result is that the data stream satisfies the condition for triggering the alarm event. Thus, the transportation or after-sales service of the engineering machinery vehicle is monitored in real time, the construction process is effectively supervised, and the transportation efficiency or the after-sales service efficiency of the engineering machinery vehicle is improved.