231 results about "Alarm analysis" patented technology

The invention relates to an intelligent monitoring alarm method and system for a motor controller, and the method comprises the steps: obtaining an internal circuit signal and a body vibration signal of the motor controller, carrying out the combined noise analysis, and generating a dynamic noise baseline; performing component fault identification based on the dynamic noise baseline and the internal circuit signal to obtain a fault precursor current feature; performing component impact response separation based on the dynamic noise baseline and the body vibration signal to obtain hidden fault vibration characteristics; performing component association mapping on the fault precursor current feature and the hidden fault vibration feature to obtain an internal fault component identifier; and performing alarm analysis based on the fault component identifier, the fault precursor current feature and the hidden fault vibration feature to obtain a graded alarm decision. According to the invention, early fault features can be captured more sensitively, and false alarms and missing alarms are reduced.

The invention discloses an electric power optical communication network alarm analysis method and system based on a time-space diagram neural network. The method comprises the following steps: collecting data of an electric power optical communication network, preprocessing the collected data, and modeling the preprocessed data into a graph structure; extracting and learning the features of the nodes in the graph structure by using a space-time graph neural network, and generating space-time embedded representation of the nodes in combination with space feature extraction and time feature extraction; classifying the nodes based on the spatio-temporal embedding representation of the nodes to obtain prediction labels of devices corresponding to the nodes; and according to the prediction label of the equipment corresponding to the node, carrying out analysis and influence evaluation on the alarm condition in the power optical communication network. According to the method, the electric power optical communication network is represented as the graph structure containing the space-time information, the space-time dependence of the electric power optical communication network can be effectively captured based on the space-time graph neural network, the space relationship in the graph structure and the dynamic change in the time sequence are processed at the same time, and the accuracy and timeliness of alarm analysis are improved.

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 a remote control method and device for a security system, equipment and a storage medium, and the method comprises the steps: carrying out the feature extraction of security data of a fire alarm host, and obtaining a fire alarm scene vector; a preset linkage strategy library is matched according to the fire alarm scene vector, and a PTZ positioning instruction is obtained; controlling a camera to collect an alarm image based on the PTZ positioning instruction; based on a preset fire alarm analysis model, generating an alarm confirmation report according to the alarm condition image and the fire alarm scene vector; and notifying a plurality of preset user terminals to perform remote interaction according to the alarm confirmation report, and performing start-stop control on the fire-fighting equipment.

The invention relates to a power plant temperature control system safety protection device and system. The power plant temperature control system safety protection device comprises a redundant temperature acquisition module, a temperature control safety processing module, an actuator control protection module, an intelligent strategy recovery module and a communication and alarm module. Through multi-point temperature data acquisition, abnormal trend intelligent identification and local linkage control, the device realizes quick response and protection control of temperature control abnormity of a key area of a boiler thermodynamic system. The system supports data interconnection with a DCS platform and an alarm analysis platform, has local independent operation and remote centralized management capabilities, and is suitable for temperature control safety protection of high heat load areas such as boilers of coal-fired power plants.

The invention discloses an alarm information research and judgment noise reduction method. According to the method, multiple pieces of alarm information with internal relation are combined into a structured alarm cluster through deep mining and by utilizing context association information between alarm events, so that redundant information is greatly compressed, and the alarm signal-to-noise ratio is improved. And multi-dimensional and comprehensive evaluation is carried out on the alarm cluster by fusing three mechanisms of information entropy analysis, customization rule evaluation and semantic evaluation of a large model, so that high threat alarms can be identified more accurately. Wherein the information entropy analysis helps to identify potential unknown threats by quantifying the uncertainty of the alarm, and the large model semantic evaluation can deeply analyze the alarm content, so that the accuracy and efficiency of alarm analysis are improved.

The invention relates to a network detection method and device, computer equipment, a computer readable storage medium and a computer program product. The method comprises the following steps: acquiring preset topology resource association information, flow information and first alarm information of a plurality of alarm types of a target network; calculating a target anomaly score corresponding to each piece of first alarm information, and performing preliminary screening on the first alarm information based on the target anomaly score to obtain second alarm information; performing root cause alarm identification on the second alarm information based on the preset topological resource association information and the target abnormal score corresponding to the second alarm information, and extracting to obtain the target alarm information and a dependency relationship corresponding to the target alarm information; and performing root cause alarm analysis processing on the traffic information, the target alarm information and the dependency relationship according to a preset large language model to obtain a network detection result. By adopting the method, the accuracy of a network detection result can be improved.

The invention discloses an alarm analysis method, device and equipment, and the method comprises the steps: determining a target DAG matched with a target alarm analysis scene from a preset directed acyclic graph DAG; the DAG comprises a plurality of rule judgment nodes; the rule judgment nodes comprise at least one natural language rule judgment node, and each natural language rule judgment node is used for calling a large language model based on a preset prompt text to perform binary classification judgment on the input alarm data; inputting the alarm data into the target DAG, and generating a node hit track of the alarm data; the node hit track is used for recording a rule judgment node triggered by the alarm data and a corresponding judgment result; and obtaining an alarm analysis result based on the node hit trajectory. According to the invention, the automatic, interpretable and traceable research and judgment process of the alarm can be realized, and the accuracy, interpretability and maintenance convenience of alarm analysis are improved.

The invention provides an alarm event processing method and device, equipment, a medium and a product, and the method comprises the steps: responding to a received first alarm event, and determining at least one piece of first alarm information related to the first alarm event and a first aggregation rule corresponding to the first alarm event; sending the at least one piece of first alarm information and a prompt word corresponding to the first aggregation rule to a language model, and receiving alarm event analysis information output by the language model; and displaying the alarm event analysis information. In the method, the same aggregation rule corresponds to the same analysis thought, alarm analysis is carried out in combination with the characteristics of the aggregation rule, and meanwhile, targeted analysis is carried out on alarm information related to an alarm event, so that alarm event analysis information for accurately describing the alarm event can be generated; the accuracy and pertinence of alarm event analysis information are improved, and then the efficiency of subsequent alarm processing is improved.

The invention provides a multi-source log fusion driven database monitoring method and system, and relates to the technical field of computers.The method comprises the steps that format analysis processing is conducted on multi-source heterogeneous logs of a to-be-monitored database on the basis of a dynamic analysis template library, and initial database logs are obtained; performing data preprocessing on the initial database log based on a rule engine to obtain a database log; and performing streaming processing on the database log to obtain a log event stream, and sending the log event stream to the server side, so that the server side detects the log event stream based on the log anomaly detection model and the associated alarm analysis engine, and generates a monitoring analysis result of the to-be-monitored database. By means of the mode, the technical bottleneck that heterogeneous database logs are difficult to analyze is broken through, the problem that the terminal is poor in database log format compatibility can be solved, invalid data transmission between the terminal and the server side is effectively reduced, and the database monitoring effect is improved.

The invention relates to an alarm analysis system and method based on chemical industry safety production, and the system comprises an alarm basic ledger, an alarm analysis general collection, and a technology alarm analysis, and specifically comprises the steps: the alarm basic ledger, a terminal device detects the real-time data of a detection point on the device, carries out the recognition and alarm through setting a threshold value, reduces the maintenance workload of the production, and achieves the alarm analysis. Providing data support; an alarm analysis general cable: through analyzing and calculating indexes of peak value alarm, average alarm and disturbance rate, checking evaluation conditions of all alarm devices in a factory, and knowing evaluation change conditions; process alarm analysis is an important link for ensuring production safety and efficient operation in the chemical industry, accidents are prevented, and the production process is optimized. Through real-time monitoring and alarming, abnormal conditions are found and handled in time, accidents are prevented, and property loss and casualties caused by the accidents are reduced.

The invention discloses a direct current B code wave recording alarm analysis device and method based on an FPGA, and relates to the technical field of power system automation and signal analysis, and the device comprises an FPGA collection module which is used for collecting a TTL level signal of a direct current B code in real time at a preset sampling frequency, recording a high-precision timestamp of each sampling point, and carrying out the subsequent pulse parameter calculation; the pulse width measurement module is connected with the FPGA acquisition module, is used for calculating the pulse width of the current pulse based on the rising edge timestamp and the falling edge timestamp of the signal, triggering a wave recording module, and is used for starting wave recording operation when the pulse width is detected to exceed a preset threshold range or abnormity occurs for a plurality of times continuously; collecting complete waveform data before and after the fault; the DC B code wave recording alarm analysis device and method based on the FPGA improve the intelligent level, response speed and fault visualization capability of a B code monitoring system, and are especially suitable for high-reliability monitoring and management scenes of key synchronization signals in a DC system.

The method comprises the following steps: constructing an intelligent research and judgment model, carrying out environment modeling on the intelligent research and judgment model, constructing a network attack path according to the intelligent research and judgment model, and identifying a network attack by using a known signature of the network attack or a rule-based detection technology; classifying and marking the data of the network security equipment by using an SVM classification algorithm, training an intelligent study and judgment model, establishing a threat identification strategy of the network attack, and identifying the features of the network attack; performing context learning and state prediction by using the intelligent study and judgment model, and optimizing a threat identification strategy of network attacks; a data field of a normal HTTP request is slightly disturbed, an adversarial sample is generated by using a threat of a known network attack, adversarial training is performed in combination with a forged HTTP request, and a deceptive network attack is identified. According to the method, cross validation and correlation analysis of high-quality and multi-source threat intelligence are realized, and the attack defense capability is improved.

The invention discloses an intelligent alarm analysis method for an operation and maintenance scene, a computer device and a medium. The intelligent alarm analysis method for the operation and maintenance scene comprises the following steps: analyzing alarm information to obtain target information; determining a plurality of processing strategies associated with the target information from a preset knowledge base, and obtaining a plurality of association indexes of the target information and each processing strategy; based on a plurality of relevance indexes and a preset weight of each relevance index, calculating a relevance score of the target information and each processing strategy; the relevance scores are ranked from high to low, if the highest relevance score is larger than a preset threshold value, the processing strategy corresponding to the highest relevance score serves as a target strategy, and alarm information is automatically processed based on the target strategy; and if the highest relevance score is not greater than a preset threshold value, pushing the alarm information to the operation and maintenance personnel, receiving a processing strategy of the operation and maintenance personnel, and updating the processing strategy to a preset knowledge base. According to the method, the alarm information can be quickly and effectively processed.

The invention relates to an intelligent alarm analysis method and device, and a medium. The method comprises the following steps: receiving aggregated alarm data; matching alarm rules in the aggregated alarm data with strong rules in an intelligent rule base; if the strong rule is hit, performing attack judgment based on the strong rule; if the strong rule is not hit, preliminarily analyzing the aggregated alarm data; based on a preliminary analysis result, issuing a traffic acquisition task to acquire associated traffic data; analyzing the associated flow data by using an AI alarm analysis model, and outputting an attack judgment result; generating a security event report based on the attack judgment result; combining the security event report with the corresponding aggregated alarm data and the attack judgment result into a security event; and outputting the security event to a security event early warning notification module. By constructing an automatic and multi-level analysis decision system, various problems confronted by traditional manual analysis are effectively solved.

The invention provides a photovoltaic station virtual monitoring method and system based on a digital twinning technology, and virtual monitoring and alarming of a photovoltaic station are realized by using the digital twinning technology. Through the virtual monitoring platform, operation and maintenance personnel can grasp the operation state of the power station in real time, discover and process abnormal conditions in time, and improve the operation and maintenance efficiency. Through virtual monitoring and alarming, the on-site inspection frequency and manual intervention can be reduced, and the operation and maintenance cost is reduced. Momo AI is used for monitoring alarm analysis management of the virtual model, comprehensive and real-time monitoring of various elements and behaviors in the virtual environment can be achieved, and an alarm is given out in time when abnormity or potential risks are detected. Momo AI is used for monitoring and alarming analysis management of the virtual model, efficient, accurate and real-time monitoring and alarming functions can be achieved, powerful guarantee is provided for safety and stability of the virtual environment, and intelligent, virtualized and unmanned monitoring of the photovoltaic power station is achieved.

The invention discloses a micro-service cloud platform transverse network performance real-time monitoring operation and maintenance method and system, and relates to the technical field of operation and maintenance, and the method comprises the steps: collecting flow information among a container Pod, a node Node and a host in a micro-service cloud platform, and constructing an application service topological graph, a node topological graph and a host topological graph; based on the collected data, the system carries out network flow index analysis and network fault alarm analysis to help to identify abnormal links and network load abnormalities; network flow information is acquired in real time through a plurality of acquisition components of a virtual machine and a container environment, and link faults are automatically identified and alarmed. According to the method, the accuracy of network fault diagnosis is improved, data support is provided for network optimization and resource allocation, the fault processing time is shortened, the service interruption risk caused by network problems is reduced, the fault response speed is improved, and stable operation and service continuity of a micro-service cloud platform are guaranteed.

The invention provides an intelligent alarm analysis and diagnosis method and device based on an AI algorithm, and the method comprises the steps: obtaining historical alarm data, and generating an intelligent alarm analysis and diagnosis large model according to the historical alarm data and historical disposal data; acquiring all real-time alarm data in a preset time window, inputting all the real-time alarm data into the intelligent alarm analysis and diagnosis large model, and associating all the real-time alarm data into a plurality of combined alarm records through an alarm association engine, according to the type confidence degree distribution of each combined alarm record by the trend classification model and the component probability distribution of each combined alarm record by the component positioning model, the fault type and the specific fault position are obtained; and generating an alarm disposal suggestion for each combined alarm record according to the fault type and the specific fault position by the matching disposal rule, and then distributing the alarm disposal suggestion. According to the invention, the problems of difficult alarm problem tracing, difficult problem root cause positioning and long fault recovery time are solved.

The invention discloses an elevator monitoring system based on Beidou satellite positioning, and the system comprises an elevator person trapping event recognition module which is used for judging whether a person trapping event occurs in an elevator or not; the elevator operation characteristic data acquisition module is used for acquiring elevator operation characteristic data; the elevator fault recognition module is used for constructing an elevator fault recognition model and recognizing the elevator fault type; the active alarm analysis module is used for judging whether emergency alarm is needed or not according to the elevator operation characteristic data; the automatic alarm module is used for sending rescue data of people trapped in the elevator through short message services of the rescue unit in the elevator car and the Beidou satellite navigation system; the elevator posture monitoring module is used for continuously monitoring elevator posture data and recognizing the abnormal state of the elevator posture; and the elevator fault maintenance module is used for generating a maintenance work order of the equipment and synchronously updating the maintenance execution state of the fault elevator. According to the method, the comprehensive management level of all parties on elevator equipment is improved, the elevator operation management cost is reduced, and the elevator operation economic benefits are improved.

The invention belongs to the field of power distribution cabinet supervision, relates to a data analysis technology, is used for solving the problem that the maintenance necessity and maintenance influence factors of an outdoor power distribution cabinet cannot be analyzed according to electrical monitoring data in the prior art, and particularly relates to an outdoor power distribution cabinet operation safety linkage supervision control system. Comprising an electrical monitoring module, an alarm analysis module, an operation and maintenance management module, a maintenance analysis module and a database, the electrical monitoring module, the alarm analysis module, the operation and maintenance management module and the maintenance analysis module are sequentially in communication connection, and the alarm analysis module, the operation and maintenance management module and the maintenance analysis module are all in communication connection with the database; according to the method, operation and maintenance management analysis can be carried out on the outdoor power distribution cabinet, the distribution condition of the electrical coefficients of the outdoor power distribution cabinet in the analysis period is analyzed to obtain the operation and maintenance coefficients, and the operation and maintenance necessity of the outdoor power distribution cabinet is fed back through the operation and maintenance coefficients, so that the outdoor power distribution cabinet can be maintained in time, and the fault probability of the outdoor power distribution cabinet is reduced.

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 an intelligent launching pad management system based on AI visual auxiliary supervision. The system carries out target identification through a YOLO model, carries out behavior sequence analysis in combination with an LSTM model, and carries out cross-modal dynamic feature fusion through spatio-temporal feature alignment, time sequence and spatial feature enhancement and a graph convolution network and graph attention mechanism to generate globally optimized feature representation. And further utilizing a knowledge reasoning engine to combine with the case database and the equipment knowledge graph to generate an intelligent emergency processing scheme. And finally, generating a preliminary potential safety hazard analysis report based on a Transform technology, and performing semantic calibration in combination with the central control module and the domain knowledge base to generate an accurate and normalized alarm analysis report. The system also has the functions of adaptive algorithm optimization, multi-modal data fusion processing and real-time alarm and feedback, realizes the intelligent and automatic management of the launching pad, improves the safety supervision efficiency and fault processing capability, and effectively improves the intelligent level of the safety management of the launching pad.

The invention relates to the technical field of data analysis, in particular to an alarm data analysis method and system of a power plant. The method comprises the following steps: collecting original alarm signals, generating a characteristic matrix of the alarm signals through frequency calculation and time sequence characteristic analysis, carrying out visualization processing, positioning repeated alarm points, carrying out clustering analysis, generating classified alarm data, carrying out inter-class correlation analysis, and generating deduced alarm optimization data. Performing pattern recognition on the feature matrix based on an alarm historical record, extracting an alarm pattern sequence, predicting an alarm flooding situation, performing response evaluation on the classified alarm data, optimizing and adjusting the deduction data in combination with a flooding situation prediction result to generate pre-adjusted alarm optimization data, and outputting the pre-adjusted alarm optimization data. And integrating and summarizing the classification data, the response evaluation data and the optimization data to form an alarm analysis report. According to the invention, a safer and more efficient alarm data analysis method of the power plant is realized.

The invention discloses a fault analysis method, device and equipment for power distribution equipment and a storage medium, and the method comprises the steps: obtaining historical alarm data and geographical environment data of the power distribution equipment, and determining an environment interference change trend based on the historical alarm data and the geographical environment data; wherein the environment interference change trend is used for representing a dynamic influence trend of each geographical environment factor on power distribution equipment alarm generation; performing feature extraction on the historical alarm data by adopting a convolutional neural network according to the environmental interference change trend to obtain comprehensive feature representation; training the convolutional neural network by adopting comprehensive feature representation to obtain a trained fault prediction model; acquiring real-time alarm data, inputting the real-time alarm data into the fault prediction model, and outputting a preliminary fault reason; performing time sequence analysis on the preliminary fault reason by adopting a long short-term memory network to obtain a fault diagnosis result; according to the invention, the accuracy of fault reason identification and the intelligent level of alarm analysis can be improved, and reliable operation of a power system is ensured.

The invention discloses an interpretable correlation analysis method for alarm noise reduction, and relates to the field of network security and alarm analysis. The problems that a traditional alarm event interpretation and analysis method is insufficient in consideration of dimensionality and insufficient in interpretability are solved. According to the method, the space-time similarity and the semantic similarity are combined, the natural language processing capacity of large language reasoning is utilized, a novel alarm noise reduction and correlation analysis method is constructed, the false alarm rate can be reduced, efficient, accurate and interpretable alarm analysis support is provided, the complex attack detection capacity is improved, and the network security automatic early warning capacity is enhanced. The method is mainly applied to network security alarm analysis.

The invention discloses a root cause alarm analysis method and system based on time sequence association and multi-modal fusion, and the method comprises the steps: taking digital twin dynamic threshold cleaning as a core, fusing multi-source data time sequence alignment verification, and forming a multi-source data set of time sequence alignment and preliminary restoration through exception handling and cross-system time consistency verification; a cleaning threshold is optimized in combination with a working condition to filter noise, a label confidence weighting method is fused, confidence is given to a label through twin simulation, and a multi-modal data set with a multi-dimensional label is constructed. A learning model is constructed and trained, exclusive feature extraction branches are designed for multi-source data, a twin working condition adaptation enhancement module is introduced to strengthen core modal extraction, and the proportion of abnormal modals in fusion is reduced by means of a dynamic attention filtering mechanism. Based on the model and a twinning system, root cause penetration type positioning is completed through dynamic sequencing of root cause confidence in combination with a twinning reduction time sequence. And the root cause analysis efficiency and accuracy are improved.

The invention discloses a method and system for routing inspection of a cable channel by an unmanned aerial vehicle, and the method comprises the steps: carrying out the routing inspection of the cable channel in construction through the unmanned aerial vehicle at a channel construction stage, and carrying out the geographic position calculation and modeling of the cable channel based on a language prompt word coding module and closed operation; planning a cable channel inspection route based on the established cable channel model; in the inspection stage, the unmanned aerial vehicle autonomously flies according to a planned route and shoots real-time images, and hidden danger target recognition and effective alarm analysis of the position and the moving direction are carried out on each frame of image. The route planning efficiency is high, and the polling effect and timeliness are improved.

The invention discloses a monitoring, alarming and early warning method and system based on numerical analysis and coupling analysis. The method comprises the following steps: acquiring monitoring data of each industry based on urban safety development evaluation from a plurality of data sources and preprocessing the monitoring data; calculating the preprocessed monitoring data by using an alarm logic formula based on static numerical analysis and dynamic numerical analysis, and determining whether to give an alarm or not according to a calculation result; according to an alarm logic formula, determining an association relationship and an interaction factor of multi-source alarm, and according to the association relationship and the interaction factor, performing association coupling early warning analysis and spatial coupling early warning analysis on each monitoring data; through the preset big data analysis model, urban safety event prediction and early warning prompt are carried out based on the coupling early warning analysis result, and through an alarm analysis mechanism and an early warning analysis mechanism which are widely applied and fully verified, the alarm accuracy and the early warning effectiveness in a cross-industry, cross-organization and cross-region monitoring scene are effectively improved.

An operation and maintenance alarm method and apparatus, and a storage medium and an electronic device. The method comprises: acquiring an operation and maintenance data set to be processed, and by means of a target algorithm, performing data cleaning processing on operation and maintenance data in the operation and maintenance data set to be processed, so as to obtain a processed operation and maintenance data set (S101); performing a stability test on a time sequence of the operation and maintenance data in the processed operation and maintenance data set, so as to obtain a test result, and determining a first operation and maintenance data set from the processed operation and maintenance data set on the basis of the test result (S102); performing feature extraction processing on the first operation and maintenance data set by means of a principal component analysis method, so as to obtain a plurality of operation and maintenance data features, and performing feature selection processing on the plurality of operation and maintenance data features by means of a K-means clustering algorithm, so as to obtain a target operation and maintenance data set (S103); and performing alarm analysis on the basis of the target operation and maintenance data set, so as to obtain an alarm analysis result (S104). The technical problem in the prior art of the accuracy of an operation and maintenance alarm being relatively low due to the fact that the accuracy of data pre-processing performed on the basis of a fixed rule is low is solved.

The invention relates to the technical field of meteorological and traffic data processing, and discloses a distributed meteorological data transmission and processing method for regional meteorological alarm, which comprises the following steps: acquiring distributed meteorological data of a target region, constructing a meteorological alarm analysis reference feature set, and predicting meteorological information of each position coordinate in the target region in a target time period; according to the path information of the target highway and the predicted meteorological information, regional meteorological alarm information is generated, then a highway traffic flow prediction database is utilized, the maximum traffic flow value and the maximum driving speed of the regional alarm range in the corresponding regional alarm time period are considered, and a speed limiting setting strategy is generated; dynamic speed limiting setting of different unit periods is executed in different sections of the target expressway, and the traffic flow and the vehicle running speed in different sections of the expressway are regulated and controlled by utilizing reasonable setting of the speed limiting, so that the running safety and the passing efficiency of the running vehicle in the corresponding meteorological environment of the expressway are improved.