25 results about "Severity level" patented technology

In some implementations, a compliance system may receive, from a tracking system, a set of data structures representing a set of security vulnerabilities and indicating a corresponding set of severity levels. The compliance system may determine a set of levels of effort, corresponding to remediating the set of security vulnerabilities. The compliance system may provide the set of levels of effort to a machine learning model, in order to generate a proposed change to a set of users that are responsible for remediation, and may output an indication of the proposed change. The compliance system may provide the corresponding set of severity levels and the set of levels of effort to the machine learning model in order to generate clusters for the set of security vulnerabilities. The compliance system may output, based on the clusters, an indication of which users are assigned to which security vulnerabilities.

A method of initiating actions based on analyzing automated fix actions in a telecommunication network management system, the method comprising obtaining, by an incident management application executing on a computer system, prior incident reports associated with a plurality of historical incidents from a database, wherein the prior incident reports comprise one or more recommended corrective actions taken or to be taken to resolve the historical incidents; evaluating, by the incident management application, the prior incident reports to determine a severity level of the prior incident reports and / or an effectiveness value of the one or more recommended corrective actions; prioritizing actions to be taken automatically or by NOC personnel based on the severity level and / or the effectiveness value; and initiating a corrective action to be taken automatically or by the NOC personnel to resolve an active incident report based on the prioritization.

The invention belongs to the technical field of flight quality evaluation, and discloses a time sequence fusion model-based flight quality comprehensive evaluation method, which comprises the following steps of: obtaining a flight subject completion degree through a ratio of an actual flight subject completion content item to a flight subject action element; establishing a key evaluation index of each flight action according to the flight subject, and obtaining the flight quality accuracy of each evaluation index based on the improved process capability index model; setting an evaluation index influence factor according to the severity level of the flight subject evaluation index to the flight; obtaining evaluation scores of all flight actions according to the flight quality accuracy and the influence factors, and obtaining a single-sortie flight quality evaluation score in combination with the flight subject completion degree; and according to the historical sortie evaluation score of the pilots of the fleet, based on a time sequence influence attenuation fusion formula, obtaining the comprehensive evaluation score of the flight quality of the pilots and the fleet.

Techniques are described that enable an IT and security operations application to prioritize the processing of selected events for a defined period of time. Data is obtained reflecting activity within an IT environment, wherein the data includes a plurality of events each representing an occurrence of activity within the IT environment. A severity level is assigned to each event of the plurality of events, where the events are processed by the IT and security operations application in an order that is based at least in part on the severity level assigned to each event. Input is received identifying at least one event of the plurality of events for expedited processing to obtain a set of expedited events, and the identified events are processed by the IT and security operations application before processing events that are not in the set of expedited events.

A method of monitoring operation of an aircraft engine includes, during operation of the aircraft engine, monitoring a parameter associated with the aircraft engine using one or more sensors. In response to determining that one or more values of the parameter are indicative of a foreign object damage (FOD) event of a first severity level to the aircraft engine, communicating data pertaining to the FOD event of the first severity level in accordance with a first communication scheme assigned to the first severity level. In response to determining that one or more values of the parameter are indicative of FOD event of a second severity level to the aircraft engine, the second severity level being higher than the first severity level, communicating data pertaining to the FOD event of the second severity level in accordance with a second communication scheme assigned to the second severity level.

In some implementations, a compliance system may receive, from a pipeline system, a set of properties associated with configuration of a cloud infrastructure. Additionally, the compliance system may receive, from a code repository, a set of computer code associated with the cloud infrastructure. The compliance system may provide the set of properties and the set of computer code to a machine learning model to receive a set of compliance indicators and a set of severity levels. Each compliance indicator in the set of compliance indicators being associated with a corresponding severity level in the set of severity levels. The compliance system may selectively deploy the cloud infrastructure in response to receiving the set of severity levels.

The present invention relates to a system (108) and a method (600) for predicting the one or more future alarms The method (600) includes step of retrieving, at least one of, historic network performance data from one or more data sources (110). Further, determining, an anomaly when one or more metrics of the network performance data exceed a threshold. The method (600) further includes step of categorizing one or more alarms generated into severity levels based on one or more alarm parameters. Furthermore, training a model (220) with at least one of, the historic network performance data and the categorized one or more alarms. The method (600) further includes step of predicting, utilizing the trained model (220), the one or more future alarms.

The invention discloses a safety operation domain monitoring method for a virtual marshalling train and a related device, and relates to the technical field of rail transit train operation control. Comprising vehicle system related data, signal system data, comprehensive monitoring system data, intelligent operation and maintenance system data and environment sensing data; inputting the safe operation domain state data into the trained safe operation domain situation prediction model to obtain a safe operation domain change trend; inputting the change trend of the safe operation domain into a driving risk propagation model to obtain the probability of occurrence of a driving accident; the driving risk propagation model represents an association relationship between a monitoring index of a safe operation domain and a driving accident; determining a driving risk level according to the occurrence probability of the driving accident and the driving accident consequence severity level; and generating a risk relief strategy according to the driving risk level. Real-time decision support can be provided for the virtual marshalling train, and safe and reliable operation of the virtual marshalling train is ensured.

Techniques are described that enable an IT and security operations application to prioritize the processing of selected events for a defined period of time. Data is obtained reflecting activity within an IT environment, wherein the data includes a plurality of events each representing an occurrence of activity within the IT environment. A severity level is assigned to each event of the plurality of events, where the events are processed by the IT and security operations application in an order that is based at least in part on the severity level assigned to each event. Input is received identifying at least one event of the plurality of events for expedited processing to obtain a set of expedited events, and the identified events are processed by the IT and security operations application before processing events that are not in the set of expedited events.

In some implementations, a compliance system may receive, from a pipeline system, a set of properties associated with configuration of a cloud infrastructure. Additionally, the compliance system may receive, from a code repository, a set of computer code associated with the cloud infrastructure. The compliance system may provide the set of properties and the set of computer code to a machine learning model to receive a set of compliance indicators and a set of severity levels. Each compliance indicator in the set of compliance indicators being associated with a corresponding severity level in the set of severity levels. The compliance system may selectively deploy the cloud infrastructure in response to receiving the set of severity levels.

The present invention discloses a method for prioritizing test cases of a train autonomous operation system. This method simultaneously covers the functional logic transfer and severity changes of the system; generates sorted test cases based on the severity level, while ensuring the comprehensiveness of the train autonomous operation system test, adds the attribute of priority to the test cases, ensures that high-security functional modules are tested first, avoids the problem that the execution order of current test cases depends on the experience of testers, with large subjectivity and randomness, discovers problems in high-security functional modules such as complex logic and core business in the system at the initial stage of testing, advances the timing of discovering defects in high-security functional modules, helps to better predict project risks, and improves and optimizes the management of the train autonomous operation system life cycle.

The invention discloses a lifeline disability grade division method, device and system, and a storage medium. The method comprises the following steps: S1, acquiring group psychological state influence factors of an emergency; s2, acquiring influence factors of the sudden electric power disaster event; and S3, associating the group psychological state influence factors with the sudden electric power disaster event influence factors, and performing sudden electric power disaster event severity evaluation through a comprehensive fuzzy evaluation method. By adopting the technical scheme of the invention, the severity levels of different sudden electric power disaster events can be given according to the actual conditions of the events.

In some implementations, a device may provide, as input to a model, input data indicating current value(s) corresponding to key performance indicator(s) (KPI) associated with current calls to a contact center, enterprise event(s), and stability event(s) associated with the contact center. The device may receive, as an output from the machine learning model, predicted value(s) corresponding to the KPI(s). The device may determine predicted rate(s) of change of the KPI(s) based on a comparison of the current and predicted value(s). The device may assign severity level(s) corresponding to the KPI(s) based on the predicted rate(s) of change. The device may determine a service level impact based on the predicted value(s). The device may determine a performance degradation score based on the severity level(s) and the service level impact. The device may determine and automatically apply a strategy based on the performance degradation score.

A system for enhancing personnel safety on a railroad track is presented. The system can receive data from sensors and / or a PTC system to determine positions of clients / worker and / or vehicles, and further utilize such data to determine if a vehicle is within a certain proximity of the client / worker location. The system can further generate geofences around clients / vehicles to and determine when such geofences intersect one another. Additionally, the present disclosure can assign severity levels and generate alerts with the assigned severity levels, and such severity levels can indicate how close a vehicle is to a particular location. It is an object of the invention to provide a system for generating alerts to notify personnel when they much retreat to a place of safety.

In some implementations, a compliance system may receive, from a pipeline system, a set of properties associated with configuration of a cloud infrastructure. Additionally, the compliance system may receive, from a code repository, a set of computer code associated with the cloud infrastructure. The compliance system may provide the set of properties and the set of computer code to a machine learning model to receive a set of compliance indicators and a set of severity levels. Each compliance indicator in the set of compliance indicators being associated with a corresponding severity level in the set of severity levels. The compliance system may selectively deploy the cloud infrastructure in response to receiving the set of severity levels.

The invention relates to an operator station server (OS1.1, OS1.2) for a control system (1) of a technical installation, in particular a manufacturing or process installation, which operator station server is designed to operate and monitor the technical installation and on which at least one software component is implemented, wherein the at least one software component is designed to generate, for the duration of an error occurring in the software component, an alarm having a specified severity level, the severity level of the alarm reflecting the severity level of the error. The operator station server (OS1.1, OS1.2) is characterised in that it is designed to calculate its own health index which depends on the duration and the severity level of the generated alarm, and to transmit this health index to components of the control system (1) that are connected to the operator station server (OS1.1, OS1.2).

The invention discloses a project risk level evaluation method, device and equipment and a storage medium, and the method comprises the steps: inputting obtained project basic information into a prediction model, so as to obtain a risk severity level of a target engineering project; determining a target risk event occurring in the target engineering project from a fault tree, and obtaining a risk coefficient of the target risk event from the fault tree; fusing the risk severity level and the risk coefficients of all the target risk events to obtain a comprehensive risk level of the target engineering project, and performing early warning; according to the invention, the objectivity of the overall risk assessment of the target engineering project can be improved, and the overall risk condition of the target engineering project can be early warned more accurately.

Methods for assessing traffic accidents, characterized by the following features: - Past traffic accidents are recorded, each with a known severity level, and - A model is trained by pairwise differential learning based on past traffic accidents and their respective severity levels to determine the probability with which a simulated traffic accident reaches that severity level for each severity level.

In some implementations, a compliance system may receive, from a tracking system, a set of data structures representing a set of security vulnerabilities and indicating a corresponding set of severity levels. The compliance system may determine a set of levels of effort, corresponding to remediating the set of security vulnerabilities. The compliance system may provide the set of levels of effort to a machine learning model, in order to generate a proposed change to a set of users that are responsible for remediation, and may output an indication of the proposed change. The compliance system may provide the corresponding set of severity levels and the set of levels of effort to the machine learning model in order to generate clusters for the set of security vulnerabilities. The compliance system may output, based on the clusters, an indication of which users are assigned to which security vulnerabilities.

Systems and methods for screening and scoring outputs generated by artificial intelligence language models, including receiving a generated output generated by an artificial intelligence language model in response to a user query, analyzing the output using detection engines, each detection engine evaluating different error dimensions, classifying each detected error into a severity level, generating color-coded visual indicators that correspond to a highest severity level among the one or more detected errors, generating remediation guidance configured to remediate at least one detected error of the one or more detected errors, and presenting screening results including the color-coded visual indicator a list of the one or more detected errors with their respective severity levels and locations, and the remediation guidance.

A method for operating a security system of a facility includes receiving video from a video camera of the security system of the facility. An event of interest occurring in the video is identified via video analysis, and the event of interest is classified into one of a plurality of event severity levels. When the event of interest is classified as having a first event severity level of the plurality of event severity levels, an alert is automatically generated and sent to an operator of the security system. When the event of interest is classified as having a second event severity level of the plurality of event severity levels, an alert is automatically generated and sent to the first responder.

The invention belongs to the technical field of heavy haul railway transportation safety, and provides a heavy haul railway group control risk assessment method and system, a storage medium and equipment. The method comprises the following steps: identifying potential risks based on a heavy haul railway group control center structure, and defining risk grade division; defining a membership function for the risk factors of the potential risk based on a fuzzy set theory, and determining a risk occurrence frequency level and a consequence severity level; and according to the risk occurrence frequency grade and the consequence severity grade, combining the risk grade division to determine a safety risk grade. According to the heavy haul railway group control risk assessment method, the running safety of the heavy haul railway is improved.

A system, method, and communication protocol for providing warning indicator severity diagnoses and alerts. The system captures data associated with operations of the vehicle using the plurality of system sensors. The system further detects a service trigger event based on the data that is captured. The system further identifies one or more vehicle components associated with the service trigger event. The system further estimates one or more severity levels of the service trigger event based on the one or more vehicle components that are identified. The system further transmits one or more service alerts to one or more target recipients based on the one or more severity levels for each vehicle component of the one or more vehicle components that is identified in response to at least one severity level of the one or more severity levels meeting a predetermined severity threshold.

The invention discloses a train autonomous operation system test case priority ranking method. According to the method, function logic transfer and severity change conditions of a system are covered at the same time; the sequenced test cases are generated based on the severity level, while the test comprehensiveness of the train autonomous operation system is guaranteed, the priority attribute is newly added to the test cases, it is guaranteed that high-safety function modules are tested preferentially, the situation that the execution sequence of the test cases depends on the experience of testers at the present stage is avoided, and the test efficiency is improved. According to the method, the problems of high security and high subjectivity and randomness in the prior art are solved, the problem of high-security function modules such as complex logic and core services in the system is preferentially found in the initial test stage, the opportunity of defects of the high-security function modules is found in advance, project risks can be better predicted, and management of the life cycle of the train autonomous operation system is improved and optimized.

The present invention comprises: an identification unit that receives input of protocol specification data pertaining to a protocol which includes at least one item and that identifies items included in the protocol specification data; a hazard index allocation unit that allocates, to each item, a hazard index indicating an index of vulnerability; a severity level calculation unit that imparts, to each item, an individual severity level, which is set according to the severity of the allocated hazard index, that performs ranking, and that identifies a prescribed item having vulnerability from among the items and calculates a total severity level of each prescribed item for the protocol; and a report presentation unit that externally presents, for the protocol, a prescribed description item for which the total severity level is higher than a prescribed threshold value.