42 results about "Critical event" patented technology

Analytics dashboards for critical event management systems that include artificial-intelligence (AI) functionalities, and related software. AI functionalities disclosed include pattern recognition and predictive modelling. One or more pattern-recognition algorithms can be used, for example, to identified patterns or other groupings within stored critical events, which can then be used to improve response performance and / or to inform the generation of predictive models. One or more predictive-modeling algorithms can be used to generate one or more predictive models that can then be used, for example, to make predictions about newly arriving critical events that can then be used, among other things, to provide optimal response performance and allow users to efficiently and effectively manage responses critical events. These and other features are described in detail.

The embodiment of the invention relates to the technical field of communication, in particular to a beam report reporting method and a communication device, which can report beam reports associated with a plurality of events based on a priority rule, not only can avoid the problems of uplink resource competition and signaling conflict caused by simultaneous reporting, but also can improve the user experience. And the problem that the processing of the key event is delayed due to reporting according to a random sequence can also be avoided. The method comprises the following steps: detecting that a plurality of events are triggered, and reporting beam reports associated with the plurality of events according to a priority rule; wherein the priority rule is related to at least one of the following: the type of the triggered event; the triggered event corresponds to the type of the cell.

Analytics dashboards for critical event management systems that include artificial-intelligence (AI) functionalities, and related software. AI functionalities disclosed include pattern recognition and predictive modelling. One or more pattern-recognition algorithms can be used, for example, to identified patterns or other groupings within stored critical events, which can then be used to improve response performance and / or to inform the generation of predictive models. One or more predictive-modeling algorithms can be used to generate one or more predictive models that can then be used, for example, to make predictions about newly arriving critical events that can then be used, among other things, to provide optimal response performance and allow users to efficiently and effectively manage responses critical events. These and other features are described in detail.

Monitoring systems and methods for use in security, safety, and business process applications utilizing a correlation engine are disclosed. Sensory data from one or more sensors are captured and analyzed to detect one or more events in the sensory data. The events are correlated by a correlation engine, optionally by weighing the events based on attributes of the sensors that were used to detect the primitive events. The events are then monitored for an occurrence of one or more correlations of interest, or one or more critical events of interest. Finally, one or more actions are triggered based on a detection of one or more correlations of interest, one or more anomalous events, or one or more critical events of interest. A hierarchical storage manager, having access to a hierarchy of two or more data storage devices, is provided to store data from the one or more sensors.

The present disclosure provides live event systems having one or more processors coupled to memory. The live event system can access a plurality of messages associated with a live event broadcasted to a plurality of broadcast receiver devices via a broadcast provider system. The live event system can determine that at least one message of the messages satisfies a condition of a notification generation policy relating to the live event. The live event system can identify, from the broadcast receiver devices, a subset of broadcast receiver devices that satisfy a notification transmission policy relating to the live event. The live event system can transmit instructions to the broadcast provider system, instructions causing the broadcast provider system to communicate with each broadcast receiver device of the subset of broadcast receiver devices and cause each broadcast receiver device of the subset to display a notification identifying the live event.

The present disclosure provides a key event analysis method, device and electronic equipment, relates to the technical field of video event analysis method, and solves the problem that in the prior art, the corresponding key event can be marked only after the information to be analyzed is viewed one by one by the viewer when the key event is analyzed, resulting in low key event analysis efficiency. The method comprises the following steps: acquiring a first time sequence feature pool of a first video interval of a to-be-processed video and target data flow; acquiring a second time sequence feature pool in the target data flow based on the first time sequence feature pool; determining a second video interval based on the second time sequence feature pool; and determining a target video interval corresponding to a key event in the to-be-processed video based on the first video interval and the second video interval.

Methods and systems for improved impact detection in a device, such as a head-wearable device, are disclosed. The methods and systems relate to accurately detecting impact events such as falls or vehicular crashes to reduce false positives and enable a swift, appropriate emergency response. A method includes detecting use of the device by a user, for instance, confirming that a head-wearable device is being worn. The method further includes detecting a change in at least one of an orientation or a movement of the device within a threshold time period. An impact is then determined based on the confirmed use of the device and the detected change in its orientation or movement. This approach distinguishes genuine emergencies from non-critical events like dropping the device, thereby improving accuracy and conserving power by intelligently activating additional sensors only when a potential impact is detected.

A computer-implemented method is provided for reinforcement learning performed by a processor. The method includes obtaining, from an environment, a given experience that includes an action, a state and a reward. The method further includes storing the given experience in an experience buffer responsive to a value of the reward included in the given experience exceeding a first threshold. The method also includes responsive to obtaining another experience having another reward that less than or equal to the first threshold, searching the experience buffer for a candidate experience with a similar state to the other experience and copying the candidate experience into an event buffer. The method additionally includes during exploration, selecting an action to be taken to the environment from the event buffer with a predetermined probability.

Systems, computer program products, and methods are described herein for determining and prioritizing interruption events to improve computer processing and performance in an electronic network. The present invention is configured to identify at least one interruption event; apply the at least one interruption event to a stochastic diffusion search (SDS) engine; determine, by the SDS engine, a number of computing agents; analyze the at least one interruption event by the number of computing agents; update, by the number of computing agents, at least one agent priority state for the at least one interruption event; apply the at least one agent priority state to a diagnostic inference model (DIM); and determine, by the DIM, whether the at least one interruption event is a critical event, a non-critical event, or a false event.

Aspects of the present disclosure configure a system component, such as memory sub-system controller, to capture debugging information in memory sub-system operations in response to a critical event. The memory sub-system controller receives critical event trigger data and determines whether the critical event trigger data corresponds to a fatal condition. The memory sub-system controller selects an error handling mode from a plurality of error handling modes based on determining whether the critical event trigger data corresponds to the fatal condition. A first of the plurality of error handling modes corresponds to storing a first set of debugging information associated with a memory sub-system. A second of the plurality of error handling modes corresponds to storing a second set of debugging information associated with the memory sub-system without interrupting a host. The second set can be a subset of the first set of debugging information.

A method for controlling a robotic apparatus. The method includes ascertaining a domain of observations made during subcritical events, from observations of controls from a control data database; ascertaining a criterion using which a limitation of the probability of occurrence of a critical event can be inferred from a limitation of the probability of occurrence of a subcritical event; ascertaining further observations when controlling the robotic apparatus, and ascertaining a relative frequency of those of the further observations that are in the ascertained domain; checking whether the ascertained relative frequency corresponds to a probability of occurrence of a subcritical event that is limited in such a way that a limitation of the probability of occurrence of a critical event can be inferred therefrom according to the criterion; and triggering a safety measure in response to the ascertained frequency not corresponding to a probability of occurrence of the subcritical event.

For deployed products containing Al models, environmental effects or failures may occur that cause the Al to detect an event that is not recognized. In these cases, it may be necessary to identify the nature of the event that triggered the Al process to output an unknown or anomalous event. This can be difficult as the product may be in operation and deployed for use in a residential or commercial setting. By identifying the nature of the event, and labeling it along with the associated data, the Al model can be retrained to allow it to properly recognize these events in the future. To facilitate this a smart phone application is disclosed that provides connectivity to critical event information, to event labeling, and to the model retraining process.

According to various embodiments, a method for controlling a robot device is described, comprising: determining, from observations of controllers from a control data database, a range of observations made during subcritical events; determining a criterion by means of which a limitation of the probability of occurrence of a subcritical event can be used to infer a limitation of the probability of occurrence of a critical event; determining further observations during the control of the robot device and determining a relative frequency of those of the further observations that lie within the determined range; and verifying whether the determined relative frequency corresponds to a probability of occurrence of a subcritical event that is limited in this way.that, according to the criterion, a limitation of the probability of occurrence of a critical event can be inferred from this, and triggering a safety measure in response to the fact that the determined frequency does not correspond to a probability of occurrence of a subcritical event that is limited in such a way that, according to the criterion, a limitation of the probability of occurrence of a critical event can be inferred from this.

This invention relates to a real-time early warning method and system for abnormal physiological states based on critical events during flight, belonging to the field of aviation medical diagnosis and identification. It addresses the problems of insufficient automated identification of critical events and the lack of real-time early warning of pilot physiological states in existing simulated flight training. The method includes acquiring flight parameter data and physiological parameter data of the pilot during simulated flight; acquiring the pilot's resting baseline physiological parameter data; calculating energy quantification indicators based on the flight parameter data during simulated flight; identifying the types of critical events during simulated flight based on the flight parameter data and energy quantification indicators; and identifying the pilot's physiological state based on the physiological parameter data, resting baseline physiological parameter data, and types of critical events during simulated flight, and issuing real-time early warnings if abnormalities are detected. This achieves real-time early warning of abnormal physiological states of pilots in the air.

This invention relates to the field of quality traceability technology in the chemical industry, providing a method and system for resolving emulsifier quality traceability identifiers throughout the entire process. The method includes: generating carrier identifiers resistant to physical corrosion for raw materials; real-time sensing of critical events in emulsifier phase transitions using a multi-mode sensor array deployed on production equipment; automatically triggering the generation of derived identifiers associated with the previous identifier when a preset phase transition condition is detected; dynamically binding key process parameters and quality inspection data from the production process to the identifiers of the current stage after aggregation through edge computing nodes; and storing summary information of the identifiers and their associated data from each stage in a cross-enterprise blockchain consortium network to form an immutable traceability chain. This invention ensures the continuity of identifiers throughout the physical evolution process and achieves reliable interaction and efficient resolution of cross-system data.

A computer system receives, by an interface agent and via a web user interface, a first user query from a user that defines a request to summarize historical data corresponding to a critical event associated with a plurality of computing incidents. In response to receiving the first user query, the computer system instantiates, by an executing agent, an event report associated with the critical event. The computer system displays the event report to the user. The computer system receives, by the interface agent and via the web user interface, a second user query from the user that defines a request to reduce a mean time to repair (MTTR) for resolving the critical event. In response to receiving the second user query, the computer system instantiates, by the executing agent, an enhanced standard operating procedure (SOP) configured to reduce the MTTR. The computer system displays the enhanced SOP to the user.

Systems and methods are disclosure for using sensors to deliver educational content to vehicle users during critical events. One method comprises: receiving, by a first computing device having at least one processor and from a user device of a vehicle user via a wireless data connection, a notification of a critical event for a vehicle of the vehicle user and a vehicle identification of the vehicle; receiving, from the user device via the first wireless data connection, user input soliciting educational content to remedy the critical event; determining, based on the received user input, a first set of search parameters; for each of the search parameters in the first set of search parameters, selecting educational content for a first list of educational content from a second list of educational content; and displaying, on the user device, the first list of educational content based on the first set of search parameters.

Various embodiments described herein relate to providing and / or employing a system and a method for tracking incoming events in a facility. In this regard, event data associated with a plurality of events is collected from at least one data source of a plurality of data sources. As a result, duplicate events are identified and filtered out from the plurality of events. Further, a set of anomalous events from the plurality of events is identified after filtering out the duplicate events. In this regard, at least one throttling parameter is adjusted in real-time based on the identification of the set of anomalous events and a current load on the system. Accordingly, processing of critical events from the set of anomalous events is prioritized based on the adjusted at least one throttling parameter.

The present disclosure provides live event systems having one or more processors coupled to memory. The live event system can access a plurality of messages associated with a live event broadcasted to a plurality of broadcast receiver devices via a broadcast provider system. The live event system can determine that at least one message of the messages satisfies a condition of a notification generation policy relating to the live event. The live event system can identify, from the broadcast receiver devices, a subset of broadcast receiver devices that satisfy a notification transmission policy relating to the live event. The live event system can transmit instructions to the broadcast provider system, instructions causing the broadcast provider system to communicate with each broadcast receiver device of the subset of broadcast receiver devices and cause each broadcast receiver device of the subset to display a notification identifying the live event.

The present invention relates to a method for identifying pipeline failures that enables the detection of specific failures in critical events, since it presents an optimized search space for obtaining a precise and adaptable trained model, based on the use of a hybrid database comprising operational data and simulated data, and feedback from performance metrics combining variable data in an optimizer, generating machine learning models with greater specialization capacity and consequently allowing an increased capacity for classifying critical events.

The embodiment of the invention provides an interaction method and related equipment / products, and relates to the technical field of artificial intelligence. The interaction method comprises the following steps: determining whether a moving object exists in a target environment at the current moment; under the condition that the moving object exists, an information event at the current moment is obtained, and an interaction event of the moving object for the target environment at the current moment is determined; determining respective effective information amounts of the information event and the interaction event according to the moving object, and determining the information event or the interaction event as a target event based on the effective information amounts; and determining target interaction content based on the target event, and presenting the target interaction content to the moving object. According to the technical scheme, the event most critical to the moving object in a complex scene can be accurately recognized, and the interaction content matched with the requirement of the moving object is selected and presented according to the key event, so that the man-machine interaction effect is remarkably improved.

The application provides a drought-flood sudden change key factor prediction method and system based on causal inference, relates to the drought-flood sudden change prediction technical field, and comprises the following steps: acquiring critical state data of a drought-flood system when the drought-flood system transits from a metastable state to a sudden change state; determining a critical starting point of drought-flood sudden change; screening a precursor factor of an order parameter capable of driving the system to transit from a metastable state to a critical state; excluding a pseudo factor that only accompanies a critical state but has no driving effect; verifying the universality of each factor in a candidate factor set in triggering a critical phase change of the system; screening a factor with cross-system domain universal driving performance and determining the factor as a drought-flood sudden change key factor; constructing a critical phase change deduction model; predicting whether the system will have a critical transition in the future 1-15 days and generating a prediction report. By means of a critical state fingerprint matching method, multi-dimensional core fingerprint characteristics are extracted, the critical starting point of drought-flood sudden change can be accurately determined, a complete critical state event list associated with geographical environment information is generated, and the accuracy of critical event identification is improved.

The invention relates to a method and a system for the automated detection of the risk of fraud in a monitored system, based on data streams generated by said monitored system and characterizing events performed or generated by operators in said monitored system. The method includes: a pre-processing (30-38) of at least one set of data recorded over a period of time, so as to obtain a subset of critical events associated with an operator; the iterative application of a first parameterized estimation process (52,56) for a risk of fraud, so as to obtain a first legitimacy score and a first associated probability of occurrence; the iterative application of a second parameterized estimation process (54,58) for a risk of fraud, so as to obtain a second legitimacy score and a second associated probability of occurrence, and the comparison (60) of the results of said first and second processes, for determining (64) whether said operator is a legitimate operator or a fraudulent operator.

Methods, systems, and devices for debug reporting by a memory system are described. A memory system may implement a forward tracking debug solution (FSDS) for reporting information to a host system. According to the FSDS, the host system may perform periodic requests to request firmware log data after a duration from a previous request. The firmware log data may include one or more counters corresponding to occurrences of one or more critical events, such as read retry events and error handler events, among other examples. The memory system may maintain the one or more counters associated with the critical events and may adjust the counters in response to identifying the occurrence of a critical event. The memory system may transmit the values of the counters to the host system in response to receiving a checkpoint request, and may reset the counters after transmitting the counter values.

The present disclosure relates to a method for managing a critical event in an underground environment (1), the method being performed by a system (100) comprising a central server (2) connected to at least one sensor (3). The method comprising: • obtaining (301) data from the at least one sensor (2) indicating an ongoing critical event associated with generation of fume, • obtaining (302) a location of the ongoing critical event, • obtaining (303) a ventilation model of the underground environment (1), and • predicting (304) a fume motion in the underground environment (1) based on the location of the ongoing critical event and the ventilation model. The at least one sensor (3) is positioned in the underground environment (1).

Analytics dashboards for critical event management systems that include artificial-intelligence (AI) functionalities, and related software. AI functionalities disclosed include pattern recognition and predictive modelling. One or more pattern-recognition algorithms can be used, for example, to identified patterns or other groupings within stored critical events, which can then be used to improve response performance and / or to inform the generation of predictive models. One or more predictive-modeling algorithms can be used to generate one or more predictive models that can then be used, for example, to make predictions about newly arriving critical events that can then be used, among other things, to provide optimal response performance and allow users to efficiently and effectively manage responses critical events. These and other features are described in detail.

Systems and methods to improve resource utilization in ambulatory patient monitoring are disclosed, including detecting a critical event using the physiologic information, transitioning the ambulatory medical device from a long-term ambulatory monitoring mode to a critical response mode, and controlling, in the critical response mode, transmission of an advertising packet and information about the detected critical event to a remote device in a continuous listening mode or transition of a communication circuit of the ambulatory medical device to a listening mode to receive an advertising packet of the remote device in a continuous advertising mode to transmit a connection response and information about the detected critical event to the remote device to improve resource utilization of the ambulatory patient monitoring.

Examples of systems and methods for monitoring and optimizing battery performance for a site are disclosed. In an example, sensor data pertaining to the site may be obtained. Based on the sensor data and historical data of the site, one or more anomaly events is predicted by a first machine learning model (126). The one or more anomaly events may further be classified into critical event and non-critical event. In case of the critical events, one or more commands may be sent to an edge device (102) installed at the site to perform corrective actions.

The invention discloses an emergency drill intelligent evaluation and report generation method and related equipment, and relates to the technical field of emergency management, and the method comprises the steps: collecting multi-modal operation data generated in a drill process, carrying out the standardization processing of the multi-modal operation data into an atomic event sequence with a timestamp and a semantic identifier, and carrying out the calculation of the atomic event sequence; and performing quantitative calculation on drill performance under the hierarchical evaluation index system. When key events such as casualties and equipment faults are identified in the drilling process, the system can dynamically adjust the weight of related evaluation indexes, so that the evaluation result focuses on the key disposal capability. Furthermore, based on the structured rule knowledge base, the evaluation result is diagnosed and analyzed, a diagnosis conclusion and an improvement suggestion are generated, a natural language generation model is called under rule constraints to form professional comments, and finally a structured emergency drill evaluation report is automatically generated. According to the invention, the dynamic, intelligent and automatic drill evaluation is realized, the objectivity and interpretability of the evaluation result are improved, and the guidance value is improved.

The present technology is directed to training and using a machine learning model to predict a likelihood of a counterfactual safety critical event in autonomous vehicle (AV) driving in a projected scenario occurring after a human takes over control of an AV. An AV management system can identify driving data collected from periods around an occurrence of a human take over event where a human takes over control of an AV. The AV management system can project a scenario that would have resulted if the human did not take over control of the AV based on the driving data and output a counterfactual safety score for the projected scenario. The counterfactual safety score can indicate a probability of a counterfactual collision between the AV and the at least one object in the projected scenario.