292 results about "Alarm management" patented technology

The invention relates to the technical field of fault monitoring, in particular to a power transmission line fault monitoring and positioning method, system and device and a medium. The method comprises the following steps: firstly, performing digital conversion on an original current signal to obtain time sequence data, and then identifying and classifying fault events through threshold detection and a feature extraction algorithm to obtain classification labels of fault types; then, based on the classification label, establishing a traveling wave propagation model and carrying out time difference calculation, carrying out clustering analysis on a plurality of wave head data in a preset time window, and judging whether the fault is a single-point fault or a multi-point fault through a clustering result; and finally, a positioning result is transmitted to the central server for comprehensive verification and alarm management, and a final fault response is formed. By introducing a time series data analysis and clustering judgment mechanism, the fault type can be accurately identified, single-point and multi-point fault conditions can be effectively distinguished, and meanwhile, the reliability of a positioning result is improved through comprehensive verification of the central server.

The invention relates to the technical field of large model agent application, and discloses a hierarchical management alarm analysis and prediction implementation method based on a large model agent, and the method comprises the steps: obtaining historical alarm logs and real-time operation parameters of a target system, and constructing a hierarchical management feature library; designing an alarm grading evaluation framework and determining a multi-source data acquisition scheme; collecting multi-dimensional alarm data, and carrying out alarm grade judgment and abnormal mode recognition through a large model agent; and transmitting an analysis and prediction result to a management decision module to optimize a grading strategy. According to the invention, through multi-modal data fusion, dynamic weight distribution and intelligent analysis, the problems of single data processing and rigid grade division in traditional alarm management are solved, accurate classification and prediction of alarms are realized, and the operation and maintenance efficiency and reliability of the system are improved.

The invention provides an electricity larceny prevention detection alarm method and device, electronic equipment and a medium, and relates to the technical field of power system monitoring, and the method comprises the steps: collecting the power utilization data of a target user through an intelligent electric meter, and building a historical power utilization data set; after obtaining a target user authentication result, performing multi-granularity feature extraction on the historical power consumption data set to generate a feature extraction result; behavior abnormity is judged based on the extraction result through the real-time monitoring data set, and a first abnormity score is established; performing transverse anomaly verification by combining the joint similarity factor with the real-time power consumption data of the joint user, and establishing a second anomaly score; establishing a joint verification feature set to discriminate anomaly, and generating a third anomaly score; and matching alarm levels according to the three types of abnormal scores, and executing alarm management. The technical problems of low electricity stealing detection accuracy and poor real-time performance caused by insufficient electricity stealing behavior detection sensitivity and single feature extraction of an existing power system under the interference of factors such as complex and changeable user electricity consumption behaviors, environment and time periods are solved.

The invention discloses a standard video-in-channel monitoring early warning model method and terminal, and the method specifically comprises the steps: creating a universal video access platform, and configuring an early warning model, specifically, the configuration of the early warning model comprises the steps: analyzing a video stream frame by frame, judging whether an image is abnormal or not, and immediately giving out an early warning by a system once the abnormality is detected, real-time playback or storage of related frames is triggered; the video corresponds to a static wide angle, video analysis is carried out through static background comparison and sound analysis, whether a change occurs is judged, and a change result is recorded; a video middle station realizes deep understanding and alarm management of video contents through an intelligent analysis technology and in combination with multi-dimensional information, an algorithm model is trained and optimized by constructing a positive sample set and an error sample set, configuration is adjusted through a visual interface, and the alarm accuracy and efficiency are improved; dynamic video electronic fence early warning is established, and through real-time monitoring and intelligent analysis, abnormity identification is realized, so that an alarm mechanism is triggered.

The invention relates to the technical field of data management, in particular to a data center abnormal behavior analysis system based on artificial intelligence, and the system comprises the steps: obtaining an operation efficiency index of each monitoring node in a data center environment; constructing a comprehensive evaluation and analysis architecture to analyze the operation efficiency indexes, wherein the comprehensive evaluation and analysis architecture comprises a deviation mode recognition model and an influence weight evaluation model; the deviation mode recognition model analyzes the operation efficiency index to obtain an operation deviation mode; the influence weight evaluation model analyzes the operation efficiency index and generates a deviation mode influence weight; constructing a risk probability calculation model to analyze an operation deviation mode and a deviation mode influence weight, generating a potential risk probability score, and determining an association influence confidence coefficient; and when the potential risk probability score and / or the association influence confidence coefficient meet a preset management attention triggering threshold value, obtaining a business influence description of the risk event, generating a management response strategy of an alarm, and further generating an operation risk management notification.

The invention relates to the technical field of fire alarm systems, in particular to a distributed intelligent smoke monitoring and alarm management system, which comprises a data acquisition and trend analysis module for analyzing a smoke concentration change trend and generating a preliminary early warning signal based on real-time data acquired by a smoke sensor; the method specifically comprises the steps that the smoke concentration value Cs (t) of a smoke sensor in the time sequence is obtained, the average value Cmean and the standard deviation Std of the smoke concentration values in the current period are calculated, the upper limit threshold value and the lower limit threshold value of the smoke concentration are determined according to the formulas that Threshold High = Cmean + K1 * Std and Threshold Low = Cmean-K2 * Std, K1 and K2 are adjustment coefficients and are used for weighing sensitivity, when Cs (t) exceeds Threshold High or is lower than Threshold Low, preliminary early warning is triggered, and when Cs (t) is larger than Threshold Low, the smoke concentration is not subjected to early warning. The system analyzes the smoke concentration trend in real time, filters interference, links environment variables to adjust threshold values, performs multi-stage filtering, performs self-learning optimization, performs intelligent hierarchical scheduling, and efficiently prevents and controls fire disasters.

The invention relates to the technical field of submarine cable fault positioning, in particular to a submarine cable fault positioning system based on underwater beacons. The method comprises the steps that an underwater acoustic beacon array unit monitors and collects submarine cable fault sound wave signals in real time based on underwater acoustic beacons; the fault signal processing analysis unit extracts submarine cable fault sound wave signal parameters based on a non-point source space sound source waveform analysis model, and generates multi-parameter fault sound source feature vectors; the sound source positioning analysis algorithm unit performs three-dimensional inversion positioning based on the multi-parameter fault sound source feature vector and the arrival time difference of the submarine cable fault sound wave signal to the underwater acoustic beacon, and obtains the spatial position information of the submarine cable fault point; and the data communication alarm management unit sends the spatial position information of the submarine cable fault point to a shore-based operation and maintenance system. The submarine cable fault positioning method is used for realizing a submarine cable fault positioning technology for quickly and accurately positioning and identifying spatial extension characteristics in a complex submarine environment.

The invention relates to the technical field of exception management, in particular to a digital twinning-based multi-level alarm management system and method, and the system comprises an information collection unit which is used for collecting the monitoring information of a target plant area; the digital twinborn body construction unit is used for performing bidirectional mapping on the geometric model, the physical model and the intelligent prediction model of the target plant area and constructing a digital twinborn body of the target plant area; the grid division unit is used for performing grid division on the digital twinborn body and establishing a position relation mapping table between the position of each object in the actual target factory area and the position of each object in the digital twinborn body; and the abnormality verification unit is used for judging the abnormal position of the actual target factory according to the abnormal information and the position of the grid where the abnormality occurs in the position relation mapping table, and performing verification according to the abnormal information and the real scene image where the abnormal position is located. According to the invention, the abnormal condition of the target factory can be monitored.

The invention discloses a monitoring video information intelligent processing system based on cloud computing, which relates to the technical field of monitoring video information processing and comprises a monitoring information processing platform. The monitoring information processing platform is in communication connection with a video data acquisition module, a data transmission module, a cloud data storage module, a data stream processing module, a target analysis module, an anomaly detection module and an intelligent alarm management module. Through training the deep neural network model and learning the features of normal and abnormal behaviors, the method achieves the precise recognition and tracking of a target object in a video, detects a dynamic target in the video, extracts the motion and appearance features of the dynamic target, recognizes the abnormal behaviors through behavior analysis, calculates an abnormal score, and generates alarm information in time. The accuracy of the monitoring system is improved, the situations of false alarm and missing alarm are reduced, and safety monitoring is more efficient and reliable.

The present invention discloses a standardized alarm process method, system, electronic device and storage medium, including obtaining alarm data indicators in alarm information; converting alarm data indicators into normalized indicators through a normalization algorithm; fusing normalized indicators with semi-structured data in corresponding alarm information to form a standardized alarm data structure; designing business tags based on systems or businesses, designing response tags based on notification methods and upgrade strategies, and adding business tags and response tags to standardized alarm data structures; receiving alarm event streams; filling business templates with data in alarm event streams based on tag matching rules; generating contextualized alarm data streams based on business templates. The present invention realizes a standardized alarm process method and system through multi-dimensional tool integration in multi-cloud and hybrid environments, and drives the deepening of alarm capabilities based on multi-algorithms, thereby improving the uniformity, efficiency and response speed of alarm management.

The invention discloses a unified management system and method for a virtual controller, and the system comprises a controller registration module which is used for initializing a controller registration array, checking whether the number of network IO registration reaches an upper limit or not, traversing the controller registration array, searching vacancies, and recording new instance information; the instance mapping module is used for mapping the successfully registered controller indexes into a controller information array and updating the registration number; the multi-thread security module is used for realizing concurrent security access in a registration process through a mutual exclusion lock; and the communication management module is used for creating a read management module, a write management module and an alarm management module, processing different types of data and carrying out heartbeat monitoring and data forwarding through a port. The technical problems that when a plurality of virtual controllers work cooperatively, control tasks of the virtual controllers are difficult to coordinate efficiently, so that the system performance is reduced, and even control conflicts or data loss may occur are solved.

The invention discloses an electrical fire alarm management method and system under multi-modal information fusion, and relates to the technical field of electrical fire alarm, and the method comprises the steps: carrying out the multi-modal information collection of electrical equipment and lines in a production region, and obtaining multi-modal heterogeneous data; carrying out real-time monitoring on the multi-mode heterogeneous data of each electrical device and line, and judging whether an electrical fire alarm signal is generated or not according to a monitoring result; performing dynamic association analysis on different types of electrical fire alarm signals generated by each electrical device and line in a plurality of historical acquisition periods to obtain a dynamic association network corresponding to each type of electrical fire alarm signal; according to the dynamic association network corresponding to each type of electrical fire alarm signals, an electrical fire early warning model is constructed, and real-time early warning monitoring is performed on electrical equipment and lines which do not generate the electrical fire alarm signals, so that the comprehensiveness and accuracy of electrical fire alarm are remarkably improved.

The invention discloses a power distribution network switching station on-line comprehensive monitoring method based on the Internet of Things, and aims to solve the problems that a traditional switching station is poor in monitoring real-time performance, insufficient in fault early warning capability and the like. The method is realized based on an intelligent monitoring and coping system of an Internet of Things architecture, and through cooperation of multiple modules of equipment operation state monitoring, environment monitoring, security monitoring, intelligent linkage control, alarm management, historical data analysis and the like, an alarm linkage intelligent control closed-loop cooperation algorithm and a time sequence analysis algorithm are combined. And comprehensive perception and intelligent analysis of the operation state, environmental parameters and the like of the switching station equipment are realized. The system can monitor key parameters such as cable head temperature and partial discharge in real time, supports intelligent linkage processing of abnormal scenes such as harmful gas exceeding and water immersion, and predicts the equipment trend through historical data mining. The technology improves the equipment operation safety and operation and maintenance efficiency, reduces the line loss, enhances the power supply reliability, lays a foundation for constructing a novel power system, and promotes the digital transformation of a power distribution network.

The invention discloses a multipath cascade coil assembly power-on aging intelligent detection device and a test method, relates to the technical field of hydraulic servo valve detection, adopts a cascade expansion mode, and can meet the requirement of the actual production coil aging number according to the number of field actual aging coil loads and the number of cascade splicing coil aging test boards. Through matched testing software, the actual current magnitude and the aging time duration of a coil assembly needing to be aged are independently set, the actual coil current value and the coil resistance value in the aging process are monitored in real time, the abnormal coil assembly is subjected to color distinguishing alarm management, and the fault time and state are recorded; and the actual production process requirements of the field coil assembly can be well met, and the convenience and operability are greatly improved.

A smart sensor early warning notification system. Certain aspects of the present disclosure provide for a smart sensor early warning notification system comprising a smart sensor coupled to a door and configured to detect one or more unauthorized access events, including sawing, blunt force and other atypical vibrations to the door. A smart sensor may be communicably engaged with a local alarm to communicate sensor data received at the smart sensor. The local alarm may be configured to process the sensor data received from the smart sensor and trigger an alarm event and / or notification or pre-event detection alert in response to the sensor data. A door controller may be configured to send different pulses or messages to indicate the type of alarm event to an alarm management server and / or access control servers via public / private cloud.

The invention provides a unit vehicle use monitoring system, method and device based on multi-source information fusion, and relates to the field of vehicle management. The system comprises an information acquisition module used for acquiring various detection data through a vehicle-mounted multi-source information acquisition terminal of a unit vehicle to obtain a first data set; the data preprocessing module is used for classifying and cleaning various detection data in the first data set to obtain a second data set; the data fusion module is used for carrying out fusion processing on various detection data in the second data set, and determining the comprehensive state and behavior information of the unit vehicle according to the fused data; and the result application module is used for carrying out real-time monitoring, alarm management, data analysis and management optimization processing on the fused data. The system can realize omnibearing and multi-angle monitoring of vehicle usage of a unit, improves the management efficiency, guarantees the life and property safety of employees, and meets increasingly strict supervision requirements.

The invention discloses a fault alarm management method and device. The method comprises the following steps: acquiring a plurality of pieces of alarm data in a target time period, and determining feature vectors related to space and fault professional dimensions and corresponding to each piece of alarm data; calculating a first text similarity emphasizing on a spatial dimension and a second text similarity emphasizing on a fault professional dimension between every two pieces of alarm data according to the feature vectors; clustering all the alarm data based on the first text similarity to obtain a plurality of clusters, determining the cluster with the number of the alarm data exceeding a first threshold as a first target cluster, and generating regional large-scale alarm prompt information; and clustering the alarm data in the first target cluster based on the second text similarity to obtain a plurality of clusters, determining the cluster with the number of the alarm data exceeding a second threshold as a second target cluster, and generating single-specialty large-scale alarm prompt information. According to the invention, the technical problems that a related alarm processing scheme is difficult to accurately identify and merge large-scale alarm events and the processing efficiency is low are solved.

The invention discloses a coal loading and unloading equipment operation detection system based on visual identification, and relates to the technical field of visual identification. The system comprises a hardware architecture and a software architecture, the hardware architecture comprises an image acquisition unit, a data processing unit and a control feedback unit; the software architecture comprises an image processing layer, an intelligent analysis layer and a decision application layer; the image processing layer comprises an image acquisition subsystem, a preprocessing algorithm library and a feature extraction module; the intelligent analysis layer comprises a target detection and identification module, a three-dimensional reconstruction module and an abnormal behavior identification module; and the decision application layer comprises a rule engine, an alarm management subsystem and a data visualization platform. By deploying sensor equipment such as a multi-angle industrial camera and a laser radar and combining a deep learning algorithm and a computer vision technology, various illegal operations and abnormal conditions in the loading and unloading process are automatically identified, total-factor digital monitoring of the loading and unloading process is realized, the detection efficiency is improved, and the loading and unloading risk is reduced.

Systems, methods and apparatus for automatic alarm management in a radio-frequency (RF) environment are disclosed. An apparatus calculates a power distribution by frequency of the RF environment in real time or near real time, including a first derivative and a second derivative of FFT data of the RF environment. The apparatus then creates a baseline based on the power distribution by frequency of the RF environment in a period of time, identifies at least one alarm situation based on a multiplicity of alarm triggering conditions by comparing the power distribution in real time or near real time to the baseline of the RF environment, identifies at least one signal based on the first derivative and the second derivative of FFT data in the at least one alarm situation, and sends at least one alarm comprising details of the at least one signal identified in the at least one alarm situation.

The invention belongs to the field of intelligent monitoring systems, and particularly relates to a catering employee multi-mode behavior specification intelligent monitoring system based on deep learning, which comprises an active video inspection module, an image preprocessing module, an employee target element detection module, a multi-mode abnormal behavior analysis module and an abnormal information alarm management module. According to the invention, the video inspection management platform, the data processing platform, the target detection model and the modeled behavior analysis algorithm are fused, and the capability of detecting the abnormal behaviors of the store employees under the conditions of poor network environment, poor hardware facilities, poor monitoring environment and complex scenes is greatly improved. And intelligent identification of nonstandard abnormal behaviors of catering store employees in various scenes and different modes is realized with low computing power cost. And the operation efficiency and the management level of the store are further improved, a solid foundation is laid for intelligent transformation of the catering industry, and the method has relatively high technical replicability and generalization performance.

The invention provides a configurable helicopter unit alarm management system and method. Belongs to the technical field of computer software, and is characterized in that a GPU (Graphics Processing Unit) reads an alarm configuration XML (Extensible Markup Language) file and converts the file into a local alarm information database, and after receiving fault data reported by each subsystem, the GPU updates existence information of all faults according to interface control file ICD (Interface Control Document) information in the alarm information database; for the data needing to be displayed in the fault list, the GPU performs unified sorting on the data and displays the data through the IDU; if the fault that the warning lamp needs to be triggered exists, the GPU lights up the warning lamp to give an alarm; according to the method and the device, the software change workload caused by design change can be effectively reduced, the reusability of a software module is improved, and the airborne software verification and airworthiness cost is reduced.

In some aspects, the techniques described herein relate to a method including: receiving, at an alarm management service, a plurality of alarms, wherein each of the plurality of alarms includes respective alarm data; clustering the plurality of alarms into an alarm cluster group; generating a plurality of binary time sequences, wherein each of the plurality of binary time sequences corresponds to one of the plurality of alarms; generating an initial alarm graph based on the alarm cluster group and the plurality of binary time sequences; providing, as input to a causal inference process, the initial alarm graph and the plurality of binary time sequences; and generating, by the causal inference process, a causal alarm graph, wherein the causal alarm graph is a partially connected and directed graph.

According to one example aspect of the present disclosure, there is provided an apparatus configured to: store a set of parameters of a machine-learned classifier configured to predict a networking alarm, the set of parameters including at least one maximum time interval; processing a first sequence of alarm signals originating from a networked environment, continuous alarms being contained in said first sequence of alarm signals occurring at a maximum time interval, said maximum time interval being contained in said at least one maximum time interval between each other; and predicting at least one second alarm signal to occur during the first time interval based on the first alarm signal sequence using the set of parameters of the machine learning classifier and the machine learning classifier.

The invention provides a data monitoring method and platform, computing equipment and a computer readable storage medium. The method comprises the following steps: for a target application program accessed to the data monitoring platform, acquiring service data of the target application program by using a data acquisition service; wherein the service data comprises data of service indexes, data of service logs and link tracking data; based on a data monitoring demand corresponding to the target application program, processing the service data; displaying a processing result of the service data in a chart form by using a data display service; and when determining that the service data satisfies a target alarm condition based on the processing result, sending alarm information in an information notification mode corresponding to the target alarm condition by using an alarm management service. The method can improve the data monitoring effect.

The invention relates to the technical field of marine transportation safety, and discloses an AI video-based cabin smoke and open fire identification alarm system, which comprises a shipborne system and a shore-based system, the shipborne system comprises an information acquisition unit used for acquiring video, temperature difference, audio and other information of a control area; the smoke open fire and temperature difference identification unit is used for identifying smoke open fire and temperature difference appearing in the control and management area; the fire development situation evaluation unit is used for evaluating the fire development situation of the control and management area based on a fire situation development evaluation model; the shore-based system and the information acquisition unit are used for training a ship smoke open fire identification standard model and a ship fire situation development evaluation standard model; and alarm management: generating a coping decision based on the shipborne fire asking-for-help information. The defect that an existing smoke and fire sensor is low in sensitivity can be overcome, fire hazards can be found as soon as possible without dead angles, ship-shore linkage scientific rescue can be carried out in time, and ship fire prevention and shore-based rescue efficiency is improved.

The invention provides a network service application operation and maintenance alarm system, which relates to the technical field of network operation and maintenance and comprises a comprehensive monitoring module, a service management module, an alarm management module, an alarm analysis module, a service topology management module, a report management module and a user management module. According to the method, the overall operation state of the network service application can be monitored in real time, when a fault or abnormity is monitored, the position of the fault or abnormity is accurately positioned through the fault analysis model, and potential risks and influence ranges are analyzed, studied and judged. And an accurate conclusion is obtained through the fault analysis model, and an alarm notification is sent to operation and maintenance personnel in time, so that the operation and maintenance management personnel are helped to perform better management and efficient and stable operation of business applications is ensured.

The invention provides a lithium iron phosphate battery monitoring system, and relates to the technical field of lithium iron phosphate battery monitoring. Comprising a battery management system, a data acquisition and processing subsystem, a communication and remote control subsystem, a temperature monitoring and control subsystem, a voltage monitoring and protection subsystem, a current monitoring and protection subsystem, a power monitoring subsystem, a test and diagnosis subsystem, an alarm management subsystem, a visual interface subsystem and an enhancement subsystem, the enhancement subsystem comprises an SOC estimation subsystem, a health state evaluation subsystem, a temperature distribution monitoring subsystem, an external environment monitoring subsystem, an intelligent control and optimization subsystem and a predictive maintenance subsystem, and the SOC estimation subsystem is arranged, so that the residual capacity of a battery pack can be estimated in real time, accurate battery charge and discharge state information is provided, and the battery charge and discharge state of the battery pack is improved. A user is helped to reasonably use the battery pack capacity, overcharge or discharge is avoided, and the battery life is prolonged.

The invention discloses a fault alarm management system based on a novel electric power system, and particularly relates to the field of electric power, which comprises a system operation database, a system central processing module, a user information end, a data acquisition time marking module, an electric power data acquisition module, an electric power data analysis module, an anomaly analysis module and a fault judgment module. According to the invention, various data of the power system can be comprehensively collected, advanced data analysis models and algorithms can be applied, the system operation state can be accurately evaluated, the accuracy and timeliness of fault judgment can be improved, in addition, an automatic alarm mechanism is beneficial for management personnel to quickly respond and process faults, and the fault diagnosis efficiency can be improved. The safety and the operation efficiency of the power system are remarkably improved, and a powerful guarantee is provided for stable operation of the novel power system.

The invention relates to the field of factory management, and provides an intelligent management system for a smart factory, and the system comprises a smoke foreground and background distinguishing module, a smoke detection module, a smoke region heating power recognition module and an alarm management module which are carried on an MES platform. The foreground and background distinguishing module is used for carrying out smoke foreground and workshop background distinguishing on workshop video images collected by the visible light camera; the smoke detection module generates a smoke recognition frame from the smoke foreground image through a smoke detection model based on improved YOLOv5, the smoke detection model is combined with an attention mechanism, and an improved loss function combined with an improved frame difference method is adopted; the smog area thermodynamic identification module generates a smog thermodynamic diagram representing smog temperature distribution from the smog identification frame through a smog segmentation network based on UNet; and the alarm management module displays the smoke thermodynamic diagram and manages fire alarm areas and grades according to the smoke thermodynamic diagram. According to the invention, visual intelligent management of the fire smoldering stage of the smart factory is realized.

The embodiment of the invention provides a safety production digital management system. The system comprises at least one of a risk and hidden danger map management module, a dangerous workshop fixed-staff management module, a major danger source and dangerous point management module, a risk management and control unit module, an equipment safety management module, an abnormal alarm management module, a safety portrait comprehensive situation module, a safety organization structure module and a hidden danger investigation and treatment module. According to the embodiment of the invention, the safety management content is clarified, the safety related production operation and process data of the equipment manufacturing enterprise do not need to be manually collected, recorded and analyzed, the safety management can be realized through an informatization means, the safety production management and control means are rich, the system is strong, and the management is transparent and refined; and the safety monitoring and early warning means are sufficient, and the systematic strengthening safety production supervision capability is high.