67 results about "Event level" patented technology

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 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 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.

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 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 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 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 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.

The application discloses a kind of processing method and processing system for community security emergency event, before the event of community security emergency event, event handling power is pre-configured access event processing system, event bursts after having timely receiving event discovery ability, and can timely and effectively classify event, and carry out effective event handling power resource matching, utilize the optimal event handling power after matching and carry out event emergency event processing, and form to each event handling feedback, the comprehensive matching of processing power and event level, cost factor in the community range of sudden security event, through the access of multidimensional security handling power, and multidimensional security event receiving response processing, realize the sustainable optimization processing method of community security event;It can be aimed at community production fire control, flood prevention and so on, through effective emergency handling of emergent event, improve community people's sense of security and happiness.

This invention discloses a multi-source sensing data fusion and event reasoning method based on edge computing and knowledge graphs, relating to the field of remote monitoring technology. The method includes the following steps: Step 1, synchronous acquisition of multi-source sensing data; Step 2, location semantic mapping and appliance operating status identification; Step 3, the edge gateway constructs a set of current observation facts based on the current functional area number, current area dwell time, appliance status register content, and the time interval to which the current timestamp belongs, determining the current event type and current event level; Step 4, the cloud server performs incremental updates to the home scene event knowledge graph based on event feedback records and distributes the updates to the edge gateway. This invention achieves three-dimensional fusion sensing of location information, electricity consumption behavior, and temporal context, possessing advantages such as strong privacy protection, good interpretability, and a response measure that matches the level of risk.

The invention relates to the technical field of cloud-side cooperation processing, and particularly discloses a scenic spot data analysis method and system based on cloud-side cooperation, a terminal and a medium, and the method comprises the steps that an edge node generates a preliminary event processing result through local processing logic, determines a data reporting strategy according to an event grade division result, and carries out data reporting; collaborative semantics representing a processing purpose are carried during data reporting. The cloud end portraits the to-be-processed tasks according to the task parameters and the current overall operation situation of the scenic area to obtain portraying features of the to-be-processed tasks; screening out a processing unit for executing the current to-be-processed task according to the portrait features in combination with the task priority, the data affinity and the cost effectiveness; the processing unit performs execution according to a pre-constructed scenic region knowledge graph to generate a processing result; and performing multi-level early warning according to a processing result, and generating and issuing a system control instruction. According to the invention, unnecessary data uploading is reduced, and end-to-end response time is shortened; and the accuracy and efficiency of model processing are improved.

The present disclosure relates to a method, an apparatus, a computer program product and a system for event identification and notification. The method includes identifying an event based on at least one of a video and audio temporally aligned with the video; allocating event levels for the events based on a preset grading mode; and determining a notification mode for the event based on the event level.

The invention discloses an unsupervised video general time sequence boundary detection method and device, and a storage medium, and the method comprises the steps: firstly, extracting the features of video frames through employing a convolutional neural network, then defining a boundary index which considers the inter-frame feature difference of a cross-boundary and the inter-frame feature similarity in the same segment at the same time, calculating the boundary index to evaluate the probability that each frame is a boundary, and carrying out the detection of the boundary. And the boundary index is smoothed in the time sequence dimension, then the local maximum value of the boundary index is taken as a candidate boundary, and finally, the candidate boundary is further screened by using a quantile threshold to obtain a final time sequence boundary. The method is suitable for time sequence boundaries of semantic levels such as a lens level, an event level and an action level, no training is needed, the boundary detection effect is good, the boundary detection granularity can be adjusted, and the method can be easily expanded to an online scene. The method can provide powerful support for long video understanding, and can be used as a basic preprocessing module for long video understanding.

The application discloses a kind of DevOps platform monitoring cycle intelligent regulation and control methods, first, system multidimensional index data stream is acquired, forms instant multidimensional vector by stream computing, window processing, feature combination etc., to judge system state. Then, the emergency degree is determined by quantifying event information using event level model. Then, the confidence vector is obtained by operating the preset rule base, the corresponding scheduling strategy is triggered, and the resources are allocated according to fuzzy logic and event emergency degree. At the same time, the event priority is determined by combining expert experience and fuzzy logic, and the flexible management plan is executed and the result is evaluated. Finally, the rule weight ratio is adjusted dynamically according to the rule base to optimize the judgment of system multidimensional index. The application realizes dynamic regulation and control of monitoring cycle, accurate evaluation of system and event, intelligent scheduling and disposal, and improves the stability, efficiency and adaptability of DevOps platform.

The application provides a kind of fusion size model coordination mechanism multi-modal sentiment data automatic labeling method, it is related to sentiment computing and multi-modal data processing technical field, including the following steps: multi-modal data preprocessing and event level segmentation: the original multi-modal sentiment data of input is scene segmentation, semantic screening, modality separation and time alignment, generates the standardized structured multi-modal sample;Small model is used to the respective fast sentiment recognition of structured multi-modal sample each mode, and the confidence of each mode prediction result is output;The application is scene segmentation and semantic screening to video by scene event consistency principle, effectively eliminates redundant fragments, simultaneously realizes the event level accurate alignment of each mode data, solves the problem of difficult cross-modal alignment and insufficient preprocessing accuracy in the prior art, provides high-quality structured sample for subsequent automatic labeling, and significantly improves the effect of multi-modal fusion.

The present invention discloses a method, device, equipment and storage medium for generating an emergency report, belonging to the field of data processing technology. The present invention obtains raw data corresponding to a target event and performs preprocessing to obtain first preprocessed data; the first preprocessed data is input into a preset event recognition model to identify the event type and event progress stage of the target event; based on the event type and the event progress stage, a corresponding target template is determined from a preset report template library; based on a preset data screening model, the contribution of different data in the first preprocessed data to the event level is calculated to determine key data; based on a natural language processing algorithm, the key data is converted into descriptive text, filled in the target template, and an emergency report is obtained. The present invention improves the readability of emergency reports as a whole and is applicable to the generation of reports for various emergency events.

The invention relates to the technical field of protection engineering, in particular to a facility system first-aid repair resource allocation method for a complex uncertain attack scene, which comprises the following steps of: S1, acquiring a facility system network structure and a facility number, and evaluating a facility initial function; s2, determining the level of an adverse event; s3, determining facility system first-aid repair resource preset planning constraint conditions, and constructing an improved facility system first-aid repair resource preset planning model; and S4, solving the first-aid repair resource preset planning model by adopting an adaptive random search algorithm (ARS-SVM) fused with a support vector machine to obtain an optimal solution of the first-aid repair resource configuration of the facility system. According to the method, the resource demand satisfaction degree expectation which maximally considers the conditional value-at-risk is taken as a target, the demand is regarded as a random variable, the probability distribution is obtained through historical data fitting, and the auxiliary variable is introduced to quantify the demand fluctuation, so that the urgent repair recovery accuracy can be improved, the cost effectiveness can be balanced, and the system adaptability can be enhanced.

The embodiment of the invention provides a self-adaptive fan variable pitch controller and a method thereof, which are used for carrying out dynamic self-adaptive adjustment on weights of targets such as power tracking and load suppression in a model predictive control cost function through a power grid event level and a unit load level which are monitored in real time. Through the method, the controller can autonomously switch and optimize the control priority according to the actual working condition, the power response is preferentially guaranteed when the power grid needs to be supported, the load is actively inhibited when the load is too large, and the emphasis is emphasized on generating capacity maximization under the conventional working condition; and finally, the optimal balance of the power generation efficiency, the structural safety and the power grid friendliness of the fan under the complex operation condition is realized, and the intelligent level and the comprehensive performance of the unit are remarkably improved.