272 results about "Event correlation" patented technology

The invention provides a user behavior tracking and portrait generation system, which is characterized in that multi-source original behavior data is acquired through a data acquisition module, and a standard behavior event stream is generated through cleaning and standardization; the behavior modeling module is used for carrying out cross-event association and self-defined time window combination modeling on a standard event flow based on a configurable rule engine to generate a complex event flow; the real-time processing and calculation module performs real-time aggregation calculation on the complex event stream, generates a real-time behavior index, a trigger signal and an incremental portrait snapshot in combination with a preset rule, and pushes a behavior trigger signal to a downstream service system; the user portrait management module dynamically updates user tag weights and values according to the real-time indexes, and generates a target user portrait tag library and a lightweight tag change event stream; the data storage and query module stores data of each link; and the visual configuration and operation and maintenance module issues a configuration instruction through a visual interface, and monitors full-link operation and task scheduling. And real-time accurate portrait construction and instant service response are realized.

The invention relates to the technical field of information security, in particular to a network security event association detection method based on big data analysis. Comprising the following steps: data acquisition; feature extraction and fusion; correlation detection is carried out, wherein an improved Apriori-Bayesian fusion algorithm is adopted, and discretization processing is carried out on the event feature vectors; mining a frequent item set by using an improved Apriori algorithm; and risk assessment and result output. According to the method, an improved Apriori-Bayesian fusion algorithm is adopted, discretization processing is carried out on event feature vectors according to types, meanwhile, a security event weight factor is introduced to calculate the item set weighted support degree, and a minimum support degree threshold value is dynamically adjusted to mine a frequent item set; the association confidence coefficient is calculated in combination with the Bayesian network, and the confidence coefficient is corrected through the space-time association coefficient, so that the association relationship between the network security events can be scientifically judged, the problems of limited association judgment accuracy and lack of quantitative correction in the traditional technology are solved, and the association false alarm and missing report probability is reduced.

The invention relates to the technical field of health information processing, and particularly discloses a user health data intelligent acquisition and management system based on data analysis, which performs event analysis and standardized coding on original health data to generate a user-level standardized event sequence; automatically constructing a causal structure network representing the causal relationship between the event types by analyzing the time delay dependence between the event types and executing a conditional independence test; further, instantiating a specific event into a node according to the causal network and a personal event sequence, establishing an edge with tense and causal composite attributes, and constructing a personalized event association network; performing data completion and prediction through multi-hop attribute propagation and multi-path evidence aggregation based on the network, and outputting a result with confidence evaluation; and dynamically optimizing the network by using a high-confidence result, and analyzing an influence propagation path of a key event to generate a personalized health analysis conclusion.

The invention relates to the technical field of vehicles, and discloses a fault diagnosis method and device, electronic equipment and a storage medium, the fault diagnosis method comprises the following steps: after a vehicle triggers a fault event, obtaining abnormal detection data and driving scene data associated with the fault event in the operation process of the vehicle; analyzing the correlation between the abnormal detection data and the driving scene data to obtain a correlation analysis result; fault diagnosis is conducted on the vehicle based on the correlation analysis result and historical associated data, a fault diagnosis result corresponding to the fault event is obtained, and the historical associated data is determined based on historical operation data of the vehicle and / or abnormal data of other vehicles when the fault event occurs. And potential safety hazards and maintenance cost of the vehicle are reduced.

The invention discloses a construction data management system and method for intelligent building mechanical and electrical installation, and relates to the technical field of intelligent construction, and the method comprises the steps: collecting the multi-source data of a construction site, carrying out the real-time analysis and matching, recognizing a disturbance event, and associating to a corresponding component of a BIM model and a construction plan target process; extracting resource information, calculating the influence of disturbance on subsequent processes, and generating a coupled digital twinborn model; progress and resource deviation analysis is carried out, the construction scheme change influence is simulated, and a comprehensive deviation report is output; calling a knowledge base historical scheme, generating a correction scheme in combination with a current resource state, simulating an implementation effect, and executing after examination and approval; and monitoring the execution of the correction scheme in real time, comparing the actual progress, updating the knowledge base and the BIM model, and forming reusable experience entries.

The invention relates to a geomechanical data visualization method, device and equipment and a medium thereof. The method comprises the following steps: acquiring original data containing rock mass structural characteristics and temperature and humidity environmental parameters, and completing grid division and load and displacement constraint boundary condition setting by adopting finite element analysis to obtain a rock mass stress distribution model; core mechanical parameter evolution of rock mass at different time nodes is simulated based on the model, and a dynamic stress distribution sequence is generated; identifying and marking stress overrun potential event association points through a threshold value, extracting a key time-space change trend, and inputting the key time-space change trend into the time-space dynamic model to obtain a risk prediction distribution diagram containing a risk level, a coverage range and an occurrence probability; and constructing a three-dimensional dynamic view by adopting visual rendering, analyzing a mechanical parameter full-cycle evolution path and geological event association mode, and outputting a three-dimensional dynamic result integrating multi-dimensional information. According to the method, the problems of static data presentation and event association deficiency in the traditional technology can be solved, and the scientificity and the high efficiency of geological engineering analysis are improved.

The invention provides an intelligent Internet of Things safety monitoring method and system, and the method comprises the steps: obtaining an equipment operation record and a behavior operation record, carrying out the event serialization coding of the equipment operation record, obtaining a sensor event sequence vector, carrying out the behavior mode vectorization processing of the behavior operation record, obtaining a behavior event sequence vector, and carrying out the monitoring of the behavior event sequence vector. Inputting the sensor event sequence vector and the behavior event sequence vector into a pre-trained association analysis model, carrying out cross-sequence causal relationship mining processing, generating an abnormal event association graph, and based on event node attributes and association edge weight values in the abnormal event association graph, executing risk conduction path analysis to obtain a risk conduction path; and obtaining a key conduction path set and a risk accumulation intensity value of the house safety risk, and generating a safety monitoring result containing the risk level identifier and the risk source positioning information according to the key conduction path set and the risk accumulation intensity value. According to the invention, the accuracy and comprehensiveness of intelligent internet-of-things safety monitoring are effectively improved.

The invention relates to the technical field of artificial intelligence and music information processing, in particular to a multi-modal signal fusion music playing accuracy evaluation system. The system comprises a multi-modal signal synchronous acquisition module, a signal preprocessing and feature extraction module, a time domain alignment and event association engine, a hierarchical collaborative evaluation module and a comprehensive evaluation report generation module. Through high-precision synchronous acquisition of an MIDI instruction, an acoustic signal and a structural vibration signal, a nonlinear time domain alignment mechanism taking dynamic time warping as a core is constructed, accurate matching of a symbol event and a physical response is realized, and an alignment multi-modal event frame is formed; and collaborative quantitative evaluation of basic accuracy, technical skill and music expressive force is carried out through the hierarchical model. By adopting the technical scheme, full-link high-fidelity analysis from playing intention to physical presentation can be realized, and the accuracy, robustness and interpretability of evaluation are remarkably improved.

The invention discloses a linkage command decision-making and early warning method fusing grid patrol events and artificial intelligence, and belongs to the technical field of crossing of smart cities and artificial intelligence. The method aims at solving the problems of low efficiency and insufficient early warning caused by the fact that multi-source data are difficult to fuse and event association depends on manpower in existing urban governance. According to the scheme, multi-source data such as grid patrol, Internet of Things monitoring and hotline are accessed and standardized to form a standardized event in a unified format; performing time-space and semantic fusion analysis on the events by using an artificial intelligence analysis engine, intelligently merging associated events and identifying high-risk events; early warning is automatically generated based on a preset rule base, and a processing task is distributed to a corresponding service system; and finally, the command system carries out unified scheduling. According to the method, intelligent association analysis, active early warning and cooperative command of urban governance events can be realized, and the management efficiency is improved.

The invention discloses an epidemic disease investigation information processing method and system fused with event recognition. The method comprises the following steps: constructing a dynamic knowledge base; deploying an event recognition model in a layered manner; designing an investigation information co-processing rule; performing event offline preliminary identification on the acquired epidemic survey information by using a lightweight model to obtain a preliminary judgment result, and binding the preliminary judgment result with an event association identifier to generate an offline event data packet; receiving the offline event data packet by using the investigation information management end, and querying associated data of the epidemic investigation information through the event association identifier; performing feature extraction on the associated data of the offline event data packet and the epidemic survey information; carrying out feature matching in advance and obtaining a recognition result by combining storage contents of the dynamic knowledge base; and for unmatched features, event depth identification is carried out by using the complete model, an identification result is obtained, and a report is generated. The method can achieve the intelligent circulation of the related data of the survey information, and improves the timeliness and accuracy of event recognition.

The invention discloses a video event association reasoning method based on a knowledge graph. The method comprises the following steps: S1, collecting video data and preprocessing the video data; s2, constructing an event graph according to a preprocessing result, and generating a state representation sequence through a graph convolutional network; s3, constructing a DreamerV3 world model, inputting a state representation sequence, and generating an event trajectory set; s4, performing trajectory value evaluation operation on the event trajectory set, and constructing a target path based on an evaluation result; s5, executing a structure alignment operation in the event graph according to the target path, and generating a reasoning knowledge graph; and S6, based on the inference knowledge graph, outputting a video event association result according to a time sequence. According to the method, video identification, map modeling and trajectory prediction methods are fully fused, and an efficient closed loop of key event extraction and map-level causal reasoning is realized.

Examples relate to systems and methods for generating icons based on events. The systems and methods receive a content item from a first user system comprising a digital effect associated with an event and determine whether an event condition is satisfied. The systems and methods, based on determining whether the event condition is satisfied, automatically modify an icon that notifies a second user system about the content item to include an event indicator. The systems and methods, in response to receiving input from the second user system that selects the icon, present the content item comprising the digital effect associated with the event on the second user system.

The invention discloses a visual asynchronous event triggering and calling method based on a medical information system, which comprises the following steps of: asynchronously acquiring multi-source data streams such as clinical events, equipment monitoring, doctor advice execution and patient states in the medical information system to form a multi-source asynchronous medical data stream set; generating a system event triggering association group based on the set and creating an asynchronous event scheduling strategy table; performing system event association degree calculation on the data stream to obtain a multi-dimensional asynchronous event association matrix; and carrying out system asynchronous time delay compensation and event chain visualization processing on the incidence matrix based on the scheduling strategy table, and finally generating a dynamic asynchronous event correlation topological graph. According to the invention, by constructing a system event detection operator set, establishing cross-flow system relevance calculation and establishing a priority-based asynchronous scheduling optimization mechanism, intelligent triggering, calling and early warning functions of medical system events are realized.

In various embodiments, a live event caching application caches segments that are associated with live events. At a first point-in-time, the live event caching application stores a segment of a downloadable that is associated with a live event in a high priority list. At a second point-in-time, the live event caching application determines that the segment is at a tail of the high priority list, where the second point-in-time is subsequent to the first point-in-time. Upon determining that the segment is at the tail of the high priority list, further determining that an age of the segment is greater than a cutoff threshold. In response to determining that the age of the segment is greater than the cutoff threshold, moving the segment from the high priority list to a low priority list.

The application discloses a kind of point-by-point space-time variable three-dimensional closure difference correction method, system, terminal and medium, comprising: based on two original seismic data are spliced, obtain splicing data;To poststack event correlation enhancement processing is carried out to splicing data, obtain model data;Splicing data and model data are matched, and reference time difference displacement field is obtained;Reference time difference displacement field is handled, and the time difference displacement field after optimization is obtained;Splicing data is applied to the time difference displacement field after optimization, and the splicing data after correction is obtained;Whether the splicing effect of the splicing data after correction meets production demand is judged, until meeting production demand.The application can eliminate the time difference of each point from shallow to deep in splicing area, can guarantee that the data outside splicing area remains original appearance, and then can better improve the fidelity of splicing seismic data, and brings great convenience to subsequent seismic prestack attribute analysis.

This invention relates to a closed-loop data system and method for embodied intelligent robots. The system includes: a data acquisition module for acquiring multimodal operational data of the embodied intelligent robot during task execution and constructing multiple embodied state nodes based on the multimodal operational data; a construction module for constructing event correlation relationships between embodied state nodes based on temporal and physical constraints, forming an embodied state event graph; an anomaly identification module for identifying anomalous embodied state nodes in the embodied state event graph and marking them as failure source nodes; and a propagation analysis module for determining failure propagation paths forward or backward from the failure source nodes based on event correlation relationships. Using the above scheme, the operational data during the task execution of the embodied intelligent robot can be effectively analyzed, anomalous states can be accurately identified, and data-driven closed-loop optimization can be achieved.

The invention discloses a large model question and answer generation system and method, computer equipment and a storage medium. According to the scheme, firstly, a life semantic network of a target person is constructed, and life events, associated persons, works, backgrounds, places and other entities and mutual relations of the target person are integrated in a structured mode; when a user question is received, the system parses its semantics and retrieves related knowledge fragments based on the semantic network. Then, inputting the retrieved knowledge fragments and logic rules in the network into a large model together to constrain the reasoning and generating process of the large model so as to obtain a preliminary answer; and finally, performing fact verification on the preliminary answer to ensure that the preliminary answer is consistent with the fact recorded in the semantic network, thereby outputting an accurate and reliable final answer. According to the invention, the accuracy and reliability of character theme question answering are effectively improved.

This invention discloses a multi-condition access, dynamic hierarchical switching, and event-related metering method and system for low-voltage emergency power supply. The method first collects data on the user-side interface residual voltage, original power supply isolation status, grounding status, emergency power supply output parameters, and load operating status, and then performs a multi-condition access determination. After successful access, a test load is connected to estimate the available power supply capacity and start-up impact margin under the current operating conditions. Next, loads to be restored are grouped according to load priority and electrical impact severity, and reserve margin verification and dynamic connection are performed for each group. After mains power is restored, loads are decoupled in reverse order and regular power supply is restored, outputting the event-related metering traceability results. This invention can improve the safety access capability, capacity utilization efficiency, and post-event traceability capability of low-voltage emergency power supply.

The invention relates to the technical field of tax data processing, in particular to a blockchain-based tax audit management system, which comprises a semantic business activity acquisition module for capturing business data from an operating system of a factoring business participant, converting the business data into a semantic unit and storing the semantic unit in a blockchain; the dynamic tax rule evolution module optimizes a tax payment obligation identification model through a federated learning framework; the tax payment event association module is integrated with an oracle machine network to construct a tax payment event atlas; the cash flow tracing and sharing module adopts an ant colony optimization algorithm to generate a cash flow incidence matrix; the auditing deduction and feedback module generates a calibration signal through simulation deduction and feeds the calibration signal back to the rule evolution module. All the modules form a closed-loop system through a data flow and a feedback mechanism, dynamic association and automatic matching of multi-subject sub-link tax payment obligations and cash flow paths are achieved, and the tax auditing efficiency is improved.

A system receives video data from a first device, audio data from a second device, and activity data indicative of events on a user device. The system detects at least one violation of user activity occurring during a time period by applying, on one of the video data, the audio data, and the activity data, at least one rule for controlling user interactions with critical data on the user device. The system stores, in the at least one memory, the at least one violation in association with time-synchronized video, audio, and activity events captured during the time period. The system terminates, on the user device, access to the critical data based on the at least one violation.

The invention discloses a network traffic attack chain reasoning method based on deep learning. The method comprises the following steps: collecting and preprocessing original network traffic data; executing session recombination and protocol analysis, generating a unified feature vector, and forming an alignment index table; constructing a multi-granularity graph structure; inputting an improved NeuroSAT model for modeling, introducing a hypergraph message passing mechanism, and outputting a stage score and a directed association score; constructing a training sample set; multiple rounds of message passing and reading are executed, and a trained improved NeuroSAT model is obtained; generating a candidate attack chain topology set through phase probability distribution and directed association probability in combination with predefined topology constraints; an effective attack chain topology set is obtained, end-to-end recognition and chain reconstruction of complex multi-stage and multi-entity network attacks are achieved, and the accuracy of attack stage recognition and event association reasoning is improved.

The application provides a data sampling method and device based on multi-dimensional load adaptation, the method comprising: acquiring system load data, target process load data and collector load data, and calculating a comprehensive load factor; storing captured multi-dimensional events into a per-CPU ring event stack in a kernel state, performing time sequence alignment on events of the same tracking identifier based on a time wheel time sequence index structure to generate composite data; and dynamically adjusting a time wheel granularity and a maximum waiting window according to the comprehensive load factor, and reporting the composite data to a user state for data sampling. The application can reduce inherent scheduling delay and data copying overhead associated with the user state, achieve cross-CPU event association while ensuring write performance, achieve dynamic balance between alignment accuracy and processing efficiency, and achieve adaptive change of kernel state processing overhead with system load.

This application provides a multimodal data fusion method, computer equipment, and program product for subway stations. It performs intramodal event detection, cross-modal event correlation calculation, and structured aggregation processing of station operation status description data centered on station event entities obtained from cross-modal event correlation on data from various devices within the subway station. This results in structured event representation data that accurately reflects the real operation status of the subway station. With the help of the obtained event representation data, the station operation status can be comprehensively, accurately, and realistically depicted, improving the station's ability to identify abnormal events and operational management efficiency, and providing a reliable data foundation for station operation analysis, anomaly monitoring, and operational decision-making.

The invention provides a software test failure root cause positioning method and device, electronic equipment and a storage medium, and the method comprises the steps: obtaining multi-modal data associated with a software test failure event, the multi-modal data comprising test log data, test scene data and system performance index data; performing preprocessing and feature extraction on the multi-modal data to obtain a test log feature vector, a test scene feature vector and a system performance feature vector; inputting the test log feature vector, the test scene feature vector and the system performance feature vector into a root cause positioning model, performing root cause positioning by combining the root cause positioning model with a pre-constructed test failure root cause knowledge graph, and outputting a root cause positioning result of a software test failure event; the root cause positioning model is obtained by training based on a multi-modal feature vector sample and a root cause positioning result label. According to the invention, root cause positioning is carried out by adopting the root cause positioning model and combining the multi-modal features, so that the efficiency and accuracy of root cause positioning are improved.

The application relates to a time sequence feature calculation method and system, computer equipment and a readable storage medium. Task information corresponding to a target time sequence feature task and source data flow are acquired; a time sequence feature calculation model is acquired; based on a stateless conversion statement, the source data flow is subjected to stateless conversion to obtain corresponding stateless conversion data flow; based on a time sequence aggregation statement, associated data flow matched with a main event associated entity is screened out from the stateless conversion data flow, and time sequence aggregation calculation is performed on the associated data flow under time window constraints to obtain corresponding time sequence aggregation features; based on a feature splicing statement, the time sequence aggregation features are subjected to feature splicing with the main event time as a time sequence alignment benchmark to generate time sequence feature results corresponding to the target time sequence feature task; the complexity of the overall calculation logic can be effectively reduced, and the readability and accuracy of the time sequence feature calculation process are improved.

The application discloses a kind of perfect preset area history flood disaster database method and system, belong to flood disaster information science and big data technology field;Including the following steps: obtaining preset area history hourly precipitation grid data, generates hourly grade grid map according to rainstorm threshold classification, connects the rainstorm area of corresponding moment of adjacent same grade grid;Combined with the process of spatial and temporal splitting and merging, complete rainstorm process is aggregated;Matching the corresponding city of preset area rainstorm flood related social media data, associate rainstorm process with city, extract corresponding moment data to generate pseudo document to determine city flood event;Linkage disaster city, build cross-city transmission chain;Based on flood propagation chain, flood event number caused by rainstorm, the number of affected cities, the start and end time of each city flood and cross-city chain are written into history flood disaster event database;The application can accurately identify and track flood events in space and time.

The invention discloses a low-voltage power distribution network user-transformer relation identification method and system based on multi-dimensional index fusion, and belongs to the technical field of power system data processing and analysis. Sequentially analyzing the synchronism of the user and the distribution transformer on the power abrupt change event through the power abrupt change event, calculating the linear correlation degree, quantifying the load contribution degree of the user to the distribution transformer, and respectively obtaining the event correlation degree, the event coincidence degree, the power reconstruction degree, the global correlation degree and the energy distribution degree; and performing weighted calculation on the five indexes according to a preset index weight to obtain a matching score of the user and the distribution transformer, and identifying the user-transformer relationship between the user and the distribution transformer through the matching score. Therefore, by implementing the application, the problems that the identification precision of the user change relation is easily interfered by the external environment and the user change relation is difficult to adapt to various scenes due to excessive dependence on single electrical data in the prior art can be solved.

A system for construction management including a device having at least one processor and a memory storing at least one program for execution by the at least one processor, the at least one program including instructions, when, executed by the at least one processor cause the at least one processor to perform operations. The operations include receiving as input an electronic version of a document; identifying at least one string of data from the electronic version of the document; mapping the identified at least one string of data to a predetermined unitary database including at least one key string of data associated with at least one key event; tagging the mapped and identified at least one string of data with a tag associated with the at least one key string of data associated with the at least one key event; and transmitting a signal based on the tagged, mapped, and identified at least one string of data. Related methods, devices, apparatuses, systems, techniques and articles are also described.