89 results about "Event level" patented technology

An intelligent regulation and control method for a DevOps platform monitoring period comprises the following steps: firstly, acquiring a multi-dimensional index data stream of a system, and forming an instant multi-dimensional vector through stream-oriented calculation, window processing, feature combination and the like so as to judge the state of the system; and quantifying event information by using an event level model to determine an emergency degree. A confidence vector is obtained through operation of a preset rule base, a corresponding scheduling strategy is triggered, and resources are allocated according to the fuzzy logic and the event emergency degree. Meanwhile, the event priority is determined in combination with expert experience and fuzzy logic, and a flexible management plan is executed and a result is evaluated accordingly. And finally, according to the rule base evaluation, dynamically adjusting the rule weight ratio, and optimizing the judgment on the multi-dimensional indexes of the system. According to the invention, dynamic regulation and control of a monitoring period, accurate evaluation of systems and events, intelligent scheduling and handling are realized, and the stability, high efficiency and self-adaptability of a DevOps platform are improved.

The invention relates to the technical field of intelligent decision making, can be applied to business scenes of financial science and technology, medical health and the like, and discloses a multi-modal data-based event analysis method, which comprises the following steps of: obtaining attribute data, monitoring data, environment data and biological characteristic data of a user, and performing standardization processing on the attribute data, the monitoring data, the environment data and the biological characteristic data; combining semantic analysis and structured fusion of the text records to form a unified input data set; and performing multi-modal fusion modeling on the unified input data set by using an intelligent decision model, generating a potential event analysis result of an individual state, generating a visual report containing potential event levels, main influence factors and intervention suggestions based on the result, and finally sending the report to a user terminal. According to the method, the multi-source structured data and the multi-source unstructured data are integrated, and the intelligent decision-making model is introduced to realize feature level fusion and semantic enhancement modeling, so that the accuracy and timeliness of individual state analysis are effectively improved, and the personalized expression of an analysis result is enhanced.

A system comprising security devices and a computing device. The security devices may each be configured to detect an event in response to video frames and audio and communicate the video frames, the audio and a notification of the event. The computing device may be configured to execute one or more steps, the steps comprising performing a classification of the notifications of the events into a plurality of event levels and displaying a list of the notifications of the events according to the classification. Each of the plurality of event levels determined to be high priority events are added to a top of the list as the high priority events are detected. Each of the plurality of event levels determined to be a lower priority than the high priority events are added to the list below the high priority events as the events with the lower priority are detected.

The invention discloses a rumor detection method and system based on multi-modal information in a low-resource environment. The method comprises the following steps: collecting multi-modal data of each post in a first field; performing feature extraction on each piece of modal data in the multi-modal data by using a corresponding preset model to obtain a modal feature of each piece of modal data; the modal features of all the modal data are fused by using a multi-modal attention mechanism to obtain multi-modal fusion features; constructing a propagation graph structure for all posts in the first field, and performing convolution on the propagation graph structure by using a graph convolution network to obtain an event-level structure representation representing a propagation relationship of each node in the propagation graph structure, and determining a rumor detection node of the post corresponding to each node by using the event-level structure representation of each node. According to the scheme provided by the invention, the rumor detection accuracy can be effectively improved in a low-resource scene.

The invention discloses a converter station important event diagnosis method and system based on correlation causality. According to the method, historical messages and equipment information of the converter station are collected and preprocessed, and SER event modeling is carried out; recording sequence events in the database, namely SER events occurring according to a time sequence, and mining association rules according to event levels by utilizing an Apriori method; constructing an event task atlas based on an SER model in combination with an association rule event set and a converter station sequential control operation task process; converting the event atlas into a causal model by utilizing a causal framework based on a graph model; and carrying out real-time analysis on data in the converter station message system by utilizing a causal atlas model, and judging the occurrence probability of an emergency event after a non-emergency event occurs. According to the method, the important events of the converter station are diagnosed based on the correlation causal method, and the method is small in data demand and high in diagnosis accuracy.

The invention discloses an emergency event handling auxiliary method and system for urban rail transit driving, and the method comprises the steps: obtaining the operation monitoring information of a target station, carrying out the time alignment and space alignment of the operation monitoring information of the target station, and obtaining the operation monitoring information of the aligned target station; event feature detection and analysis are carried out based on the aligned target station operation monitoring information, and multi-modal fusion features of the target station operation monitoring information are constructed; establishing a fuzzy rule database, and performing event level analysis and event influence analysis on the current event according to the multi-modal fusion features of the operation monitoring information of the target site to obtain event level analysis information and emergency event influence analysis information; and constructing an emergency event disposal auxiliary model, and inputting the emergency event influence analysis information and the event level analysis information into the emergency event disposal auxiliary model to generate an emergency event disposal scheme for recommendation. And the real-time performance and reliability of emergency event handling are improved.

The invention relates to the technical field of community emergency management, and discloses a community multi-mode emergency event grading response system. The multi-modal data acquisition module acquires and divides community multi-modal sensing data, the event feature extraction module respectively analyzes and generates event feature sets, the grading evaluation module fuses features to match emergency event grades, the response decision module calls a target response scheme according to the grades, and the central processing unit verifies related parameters to generate a final response instruction. The system is further provided with a resource scheduling module for optimizing emergency resource allocation, an instruction feedback module for triggering equipment response action, and a data archiving module for storing data to form an event record chain. According to the system, multi-modal data deep processing, accurate grading evaluation and intelligent response decision making are realized, the community emergency management efficiency and the intelligent level are improved, and the community safety is guaranteed.

The invention belongs to the technical field of aviation flight training, particularly relates to a multi-module collaborative flight simulator control panel and method, and solves the problems that an existing system is low in interaction efficiency, subjective in evaluation, lagged in early warning and time-consuming in comment. The system depends on traditional IOS hardware, and is provided with an intelligent voice interaction module which recognizes teacher voice through ASR, generates an instruction through intention recognition and three-level slot completion, and cooperates with double-layer flight stage recognition logic to guarantee interaction accuracy; the flight quality monitoring module operates a rule engine and an enhanced LSTM neural network in parallel, and outputs a comprehensive flight quality score in combination with an adaptive fusion algorithm; the real-time reminding module dynamically adjusts the early warning priority according to the event level and the flight stage, and multi-mode early warning is provided; and the intelligent comment module fuses the multi-source data and generates a personalized structured report according to the priority of the template. According to the invention, intelligent interaction, quantitative evaluation and automatic commenting of training are realized, and the training efficiency and evaluation objectivity are improved.

The invention discloses a production abnormal event monitoring method and system and a readable storage medium, and the method is characterized in that the method comprises the steps: extracting event features corresponding to monitoring tasks according to the event level of each monitoring task in a monitoring queue; under the condition that an operation of a user for a target monitoring service is received, a target monitoring task is added into the monitoring queue, and the event level of the target monitoring task is controlled to be higher than at least one event level in the monitoring queue; when the event feature extraction of the target monitoring task is completed, calculating the event risk degree of the target monitoring service, and generating a risk characterization result; and displaying a risk monitoring page of the target monitoring service based on the risk representation result. According to the invention, by adding control means for the monitoring queue, the response degree of the monitoring event is improved, and meanwhile, the accuracy of the monitoring task is improved by utilizing the multi-dimensional event characteristics.

The invention relates to the technical field of flight control task planning, and particularly discloses a flight control task hierarchical planning method and system based on software, and the method comprises the steps: generating a monthly event set based on the flight control task demands proposed by multiple parties for a target flight control task, and carrying out the flight control task hierarchical planning according to the flight control task behavior logic; dividing each monthly event in the monthly event set into at least one initial flight control event; and determining and adjusting an initial flight control event of the flight control instruction to be adjusted based on an instruction constraint condition between the flight control instructions, and determining the flight control instruction corresponding to the adjusted initial flight control event and the flight control instruction corresponding to the initial flight control event which does not need to be adjusted as a planning result of the target flight control task according to the flight control event level. According to the method, efficient preparation, high-reliability and high-timeliness accurate planning and rapid emergency response of flight control implementation can be realized through a monthly event-flight control event-instruction layered planning mode, and powerful support is provided for long-term reliable and safe operation of spacecrafts.

The invention discloses a digital platform operation monitoring optimization system and method based on a simulation model. The method comprises the following steps: S1, constructing an operation monitoring set; s2, generating a preprocessing operation monitoring set; s3, simulating the operation state of the digital platform in real time; s4, generating macroscopic trend data of platform resource requirements; s5, event level data of the discrete event simulation model and macroscopic trend data of the system dynamics simulation model are updated in real time; s6, generating comprehensive operation strategy effect prediction data by combining the prediction results of the two; and S7, based on the operation strategy effect prediction data and the real-time operation monitoring set, carrying out adaptive adjustment on the current operation strategy, and generating a corresponding strategy optimization scheme. According to the method, the system bottleneck problem caused by insufficient resources can be effectively avoided in a high-concurrency environment, and the stability and expansibility of the platform are guaranteed.

The invention is suitable for the field of data security assessment, and provides a data center security assessment method and system, and the system comprises a scene library construction module, an event triggering and real-time monitoring module, an event level division recording module, and a summary report generation module. By establishing a scene library, various possible security events and disaster scenes are covered, and the comprehensiveness and systematicness of evaluation are ensured. And each scene is defined and classified in detail, so that the emergency response capability of the data center under different conditions can be comprehensively tested. And the scientificity and objectivity of the evaluation process are ensured by presetting evaluation indexes and result evaluation standards. Each security event and disaster scene has a clear evaluation standard, the evaluation result can be quantified, and the accuracy and repeatability of evaluation are ensured.

Disclosed techniques relate to using one or more of racing event statistics, textual insights, predictions (e.g., team and player at the event level, and team at the season level), graphics, video overlays, and player and ball tracking data. Tracking data may be generated using an in-venue feed or a broadcast feed. The tracking data may be supplemented with event data which may be provided by an operator or an automated system based on the events related to a given sport within a venue or via a broadcast feed. The tracking data and / or event data may be used to generate insights such as event statistics, textual insights, predictions, graphics, video overlays, and or the like. Accordingly, the tracking data and insights generated in accordance with the subject matter disclosed herein may be specific to a given sporting event and / or the sport associated with the sporting event.

The invention discloses a hydropower station production and emergency coordination method and platform based on a communication system, and the method comprises the steps: a power plant duty room receives event information, and an on-duty person immediately calls the hydropower station production and emergency coordination platform; corresponding event important information is clicked or supplemented on the basis of the current template, and disposal prompt information pointing to corresponding event levels of all departments is automatically activated; checking the default notification object of the current event and confirming the accuracy; processing prompt information is automatically sent according to a pointed department; a receiver signs for receiving; the receiver carries out disposal according to the event information or the related regulations of the emergency response level, and the disposal process is fed back to the platform publishing page in real time; and if the event is completely handled, final reporting can be carried out, and a corresponding event flow is closed. The method can replace a current one-to-one telephone notification coordination mode in hydropower station production operation event disposal, greatly improves the event disposal efficiency, reduces human error factors, and improves the event disposal accuracy and the operation management level.

The invention discloses an emergency report generation method and device, equipment and a storage medium, and belongs to the technical field of data processing. The method comprises the following steps: acquiring and preprocessing original data corresponding to a target event to obtain first preprocessed data; inputting the first preprocessed data into a preset event recognition model, and recognizing to obtain an event type and an event progress stage of the target event; based on the event type and the event progress stage, determining a corresponding target template from a preset report template library; calculating contribution degrees of different data in the first preprocessed data to event levels based on a preset data screening model, and determining key data; and based on a natural language processing algorithm, converting the key data into a descriptive text, and filling the target template to obtain an emergency report. According to the method, the readability of the emergency report is improved on the whole, and the method is suitable for report generation of various emergencies.

The invention provides an event processing method and device, equipment and a storage medium, and the method comprises the steps: carrying out the information extraction of a to-be-processed event instance based on the information carried by an event, obtaining the event identification information, carrying out the grading of the to-be-processed event instance based on the event identification information, obtaining the event grade information, and carrying out the grading of the to-be-processed event instance. And finally, performing event collection on the to-be-processed event instances based on the event level information to obtain an associated event set of the to-be-processed event instances, and processing the to-be-processed event instances based on the associated event set. According to the method, the events can be collected, so that the handling flexibility and practicability of repeated events, range events and major events are improved, and the event handling efficiency is improved.

The embodiment of the application discloses a kind of traffic network scheduling control method, system based on space-time big data, electronic equipment, belong to the technical field of electronic data processing, method steps include: the following steps are executed in edge side: receiving and analyzing terminal raw data, filtering and trajectory generation using the rules issued by cloud end;Call fusion engine to perform space-time alignment and matching on multi-source trajectory, generate fusion trajectory;Based on fusion trajectory, traffic flow, average speed are calculated synchronously, and input to event risk assessment model to output risk probability value;After comparing risk probability value with threshold, event level is generated and corresponding alarm is triggered.In cloud side, server converges multi-dimensional information uploaded by each node for macro analysis and model iteration optimization, and periodically issues updated strategy and configuration to edge node to dynamically adjust its processing logic, so as to realize intelligent traffic control from cloud end global optimization to edge real-time execution.

The invention relates to the technical field of oil and gas production, in particular to a system event early warning analysis method based on a graph database, which comprises the following steps of: acquiring data to form a system operation and maintenance relation model based on the graph database; setting a specific weight value for a node label according to the importance degree of the node label in the system operation and maintenance relation model in an operation and maintenance system, and constructing a node weight library; early warning levels are divided according to the severity of the system events, and a system event early warning rule base is constructed; carrying out system event influence surface analysis, and carrying out accumulation calculation according to node weights to obtain a system event influence degree value; judging system event levels according to the system event influence degree values, and sending message notifications to related cadres according to different system event levels. According to the method, the system event can be analyzed quickly, flexibly and comprehensively, the influence surface of the system event is quantified, the analysis efficiency of the system event is improved, and the method has the value of comprehensive popularization in the field of system operation and maintenance.

The invention relates to a method, a device and a system for processing computer network intrusion data. The method comprises the following steps: in response to a detected network intrusion event, obtaining event information associated with the network intrusion event, and based on the event information and a preset first mapping relation table, determining whether an event level matched with an event type exists in the first mapping relation table, and under the condition that the event level matched with the event type does not exist in the first mapping relation table, determining an initial event level and an initial root cause of the network intrusion event based on the event information, and in response to monitoring that the repair operation of the vulnerability associated with the network intrusion event is ended, retesting the network intrusion event, and when the retest result is that the vulnerability is successfully repaired, labeling the network intrusion event based on the initial event level. According to the scheme, the solving efficiency of the root problem of the network intrusion event is improved.

This application provides a vulnerability prediction method, apparatus, electronic device, and storage medium, relating to the field of network security technology, and capable of solving the problem of low security and stability in data centers. The method includes: acquiring log data from the data center, the log data carrying event content and the event level corresponding to the event content; determining vulnerability log data based on a vulnerability database and the log data, the event level corresponding to the vulnerability event content in the vulnerability log data being the vulnerability level; inputting the vulnerability event content from the vulnerability log data into a vulnerability prediction model, outputting first predicted event content, the first predicted event content having a correlation with the vulnerability event content, the event level corresponding to the first predicted event content being the vulnerability level; and the vulnerability prediction model being trained based on the vulnerability event content and the predicted event content. This application improves the efficiency and accuracy of vulnerability detection in data centers.

The disclosure generally describes methods, software, and systems for integration of attribution reports and auxiliary data sources. Data including aggregated summary reports and event level reports is received from a first system. Additional raw affirmative action data related to the aggregated summary reports and event level reports is received from a second system. A matrix is created with rows for interaction events and columns for raw affirmative action data. Denoised aggregated counts and denoised event counts are determined from the received reports. The matrix fields are optimized by resolving conflicts between raw counts, denoised aggregated counts, and denoised event counts.

An event prediction method according to an embodiment of the present disclosure includes: receiving one or more observation data related to a prediction event, and analyzing the one or more observation data based on an event prediction model and outputting an event level at which the prediction event occurs, wherein the event prediction model includes a hierarchical analysis network, the hierarchical analysis network includes multiple types of prior events, each type of prior event includes one or more characterizing events, and each type of prior event corresponds to an event level at which the prediction event occurs. An information processing device for event prediction according to an embodiment of the present disclosure further includes a processor configured to execute the above event prediction method.

The present application relates to the technical field of data processing. Disclosed are a data-driven safety production management method and system, and a device and a storage medium. The method comprises: on the basis of an event level of accident data and a preset indicator of each event level, obtaining an accident safety indicator achievement result of an enterprise; analyzing the accident safety indicator achievement result in at least one preset dimension, so as to obtain accident distribution information; and generating action item tracking information on the basis of the accident distribution information, wherein the action item tracking information is used for tracking a handling progress of an accident. The present application can fully tap into the value of enterprise production data.

The invention discloses an intelligent test support software platform and a resource scheduling method. The resource scheduling method comprises the following steps: detecting abnormal events of an infrastructure as a service layer, a joint test platform as a service layer and / or a joint test service as a service layer; the event level of the abnormal event is obtained, and the event level comprises a first-level abnormal event, a second-level abnormal event and a third-level abnormal event; the first-level abnormal event is solved through resource scheduling, the second-level abnormal event is solved through resource scheduling and joint test parameter adjustment, and the third-level abnormal event is solved through joint test parameter adjustment.

The invention discloses an event-driven adaptive sampling and hierarchical transmission method based on a data change rate. The event-driven adaptive sampling and hierarchical transmission method specifically comprises the steps that S1, an equipment end collects industrial equipment operation data at a fixed frequency and generates a data frame; s2, the server side calculates the data change rate of adjacent data frames from the same equipment, and generates an abnormal score based on the change rate; s3, the server side divides the data frame into event levels at least including a heartbeat level, a conventional level, an abnormal level and an accident snapshot level according to the abnormal score Score; s4, the server performs adaptive adjustment on an issuing strategy to the client according to the event level; s5, the server triggers an accident snapshot level event, and reliably transmits and issues the accident snapshot level event to the client through a TCP; and S6, the server side enables the data of different event levels to enter different priority queues and dispatches and issues the data according to the priorities. According to the invention, through event grading, sampling self-adaption and grading transmission, priority arrival of key abnormal data and complete reservation of accident data are realized.

This application discloses a competition rights management system, method, apparatus, and related equipment. The system includes: a blockchain network consisting of provincial nodes deployed in various provincial-level administrative regions and core nodes deployed in the Chinese Athletics Association. The provincial nodes employ the Hyperledger Fabric framework, and the nodes interact using the Practical Byzantine Fault-Tolerant (PBFT) consensus algorithm; edge computing nodes deployed at the event site, communicating with the blockchain network, for real-time collection of athlete performance, event level, and regional difficulty coefficient data; a smart contract module deployed in the blockchain network, for determining a comprehensive score based on athlete performance, event level, and regional difficulty coefficient data, and dynamically adjusting competition rights based on the comprehensive score and preset conditions; and a competition rights allocation engine coupled to the smart contract module, for generating a competition rights allocation scheme based on the comprehensive score, host city requirements, and athlete influence data.

The application discloses a data storage method of an electronic speed regulator and the electronic speed regulator, relates to the technical field of electronic speed regulator control, and discloses the data storage method of the electronic speed regulator and the electronic speed regulator. The data storage method of the electronic speed regulator and the electronic speed regulator disclosed realize differentiated storage of data of different importance degrees by dynamically selecting a storage area and a sampling frequency based on an event level, solve the problems that storage resources are not reasonably allocated and key data is prone to loss in a traditional scheme, have the advantages that a storage strategy can be dynamically adjusted according to an event level, storage resource configuration can be optimized, and the saving integrity of key data and fault diagnosis accuracy are improved.

1. The name of the design product: monitoring and inducing integrated machine. 2. The use of the design product: for monitoring abnormal events in the covered area, and actively controlling and driving inducing the area by using event level. 3. The design points of the design product: in shape. 4. The picture or photo that best indicates the design points: perspective view.

The application provides a network public opinion video content analysis method based on multi-modal fusion, and relates to the technical field of network information processing.The method comprises the following steps: by splitting the public opinion video into an audio stream and an image frame sequence, structurally analyzing picture text and speech content respectively, and completing semantic embedding and cross-modal alignment under a unified time axis, introducing a bidirectional cross-modal attention and a gating fusion mechanism to realize deep fusion of multi-modal information, and further combining multi-role consistency, semantic superposition effect and emotion and fact collaborative features for joint modeling at an event level scale, so as to realize overall understanding of network public opinion video content and accurate determination of public opinion risk level.The application can fuse multi-modal information as a whole and realize joint modeling to analyze network public opinion video content.

This invention relates to the field of intelligent alarms and discloses a store intelligent alarm and voice reminder method based on event classification, comprising the following steps: collecting and preprocessing multi-source data from stores through a full-domain sensing terminal to construct a core data feature library; constructing an event classification scoring model based on the feature library, completing dimension weight initialization and risk quantification classification; dynamically scoring real-time events and determining whether to trigger proactive reminders, generating standardized alarm information; classifying reminder objects according to event levels and collaboratively pushing text and voice reminders; simultaneously implementing a duplicate alarm suppression and timeout escalation mechanism to avoid alarm proliferation and response delays; and finally iteratively optimizing the classification model based on the handling data. This invention achieves accurate event classification, differentiated proactive reminder push, and intelligent linkage between voice and text, effectively solving the problems of frequent false alarms, chaotic reminders, untimely responses, and lack of closed-loop optimization in traditional store alarms, thereby improving alarm effectiveness and handling efficiency.