90 results about "Alarm correlation" patented technology

The invention provides an operation and maintenance alarm processing method and system based on a knowledge graph enhanced large model, and relates to the technical field of knowledge graphs, and the method comprises the steps: collecting multi-source operation and maintenance alarm historical data, carrying out the adaptive hierarchical structured processing of the data, and constructing a space-time semantic multi-dimensional feature matrix; constructing a multi-dimensional alarm association network based on a dynamic weight fusion algorithm and forming a hierarchical knowledge graph; constructing a root cause inference rule base in combination with time sequence evolution, topology propagation and business dependence characteristics of the alarm; and when a real-time alarm is received, a diagnostic reasoning system is started to analyze a propagation link, a root cause is identified, and a processing suggestion is generated. The method can accurately identify the alarm root cause, improves the operation and maintenance efficiency, and reduces the false alarm rate.

The invention discloses a network threat multi-modal detection method based on a large model, and belongs to the technical field of network security, and the method comprises the steps: collecting three types of heterogeneous data of NetFlow flow of a network layer, a system call chain sequence of a host layer and a protocol load of an application layer, and carrying out the desensitization processing and feature coding to generate a unified tensor format; the method comprises the following steps: through network security threat intelligence and MITRE ATTamp; performing supervision fine tuning on the large model by using a CK attack chain sample, and constructing a network threat identification special model; cross-device behavior characteristics are extracted through a model self-attention mechanism, and a dynamic behavior map is constructed; and finally, comprehensively evaluating an attack mode matching degree, a node vulnerability mean value, historical alarm association and an attack path risk by adopting a weighted fusion algorithm, and triggering a high-confidence alarm when a comprehensive score exceeds 0.8. According to the method, through multi-modal data fusion and dynamic graph analysis, the detection precision and response efficiency of the complex attack chain are improved.

The invention relates to the technical field of micro-services, can be applied to the field of science and technology finance / digital medical treatment, and discloses a high-guarantee link automatic guarantee method and device, electronic equipment and a storage medium. The method comprises the following steps: performing series recording on a calling link of a service interface of a first micro-service through a proxy probe to obtain service interface calling link data; rendering a screening result of the calling link data as a high-guarantee link panorama; analyzing the high-guarantee link panorama to obtain a high-guarantee link dependency relationship, and calculating a micro-service dependency score of the first micro-service; screening the first micro-service according to the micro-service dependency score to obtain a second micro-service; when the service interface of the second micro-service generates an alarm, automatically collecting alarm associated data; and inputting alarm data to the high-insurance decision large model for analysis, and automatically issuing and executing an alarm self-healing strategy output by the high-insurance decision large model through the model context protocol server. According to the method, high-guarantee link automation and system stability are improved.

The embodiment of the invention provides a fault root cause positioning method, a fault root cause positioning device and a related product. The fault root cause positioning method comprises the following steps: acquiring network alarm data and corresponding network topology resource data; performing slicing processing on the network alarm data and the corresponding network topology resource data in a time dimension and a space dimension through a fault causal reasoning model to obtain an initial alarm associated data set; and carrying out screening and reasoning processing on the initial alarm associated data set through the fault causal reasoning model to obtain a fault root cause node and corresponding fault causal relationship data. According to the embodiment of the invention, the fault root cause can be quickly and accurately positioned, so that the fault processing efficiency is improved.

Embodiments of the present disclosure provide an alert analysis method, a server, and a storage medium. In the alert analysis method, once a target question associated with a target security alert of a user resource is obtained, a security model is used to perform action planning for the target question to obtain action planning information. On the basis of tool invocation information in the action planning information, a target alert handling tool is invoked to perform an alert information processing operation associated with the target question to obtain an alert information processing result. On the basis of the alert information processing result, the security model can output an alert analysis result corresponding to the target question. On the one hand, the security knowledge capability of the security model is used to realize automatic replies to security alert related questions; on the other hand, by using the action planning capability of the security model, an alert handling tool is invoked to process the alert information associated with the target question, so that security knowledge beyond the security model can be fully utilized, thereby effectively improving the accuracy of the alert analysis result corresponding to the target question.

The invention relates to the technical field of operation and maintenance, and discloses an equipment fault processing method and system. The method comprises the following steps: acquiring alarm messages of a plurality of devices in real time, identifying associated alarm messages caused by the same fault according to an alarm association rule, and compressing the associated alarm messages and an existing fault group to obtain new alarm messages; determining the work order type and the circulation process of the new work order according to the alarm type of the newly sent alarm message, and carrying out order dispatching priority configuration on each process link based on the alarm level of the newly sent alarm message and the pre-configured authority information of each process link; and calling a corresponding work order template based on the work order type, configuring the work order template to generate a new work order, monitoring the full life cycle of the new work order, and synchronously updating the processing state of the new work order. According to the method, the fault processing efficiency of the machine room intelligent connection equipment is effectively improved, resource configuration is optimized through real-time monitoring and data analysis, and the method can meet the fault troubleshooting requirements of equipment of different scales and types.

The invention relates to a log alarm data processing method and device, a storage medium and electronic equipment, and relates to the technical field of big data processing.The method comprises the steps that the data source category of original log alarm data is determined, and a key information extraction model corresponding to the data source category is distributed to the original log alarm data; performing semantic analysis on the original log alarm data based on the key information extraction model to obtain alarm key information in the original log alarm data corresponding to the data source category; performing multi-dimensional correlation analysis on the alarm key information of the different data source categories based on a preset correlation analysis model to obtain a correlation relationship between the alarm key information of the different data source categories; and performing alarm root cause reasoning on the association relationship between the alarm key information based on a preset fault reasoning model to obtain an alarm association analysis result. According to the invention, the accuracy of the alarm association analysis result is improved.

The invention discloses an alarm association rule generation method and device based on clustering, equipment and a medium. The method comprises the following steps: collecting alarm logs in response to an alarm log information collection instruction, and sorting the alarm logs according to a timestamp sequence; performing clustering processing on the sorted alarm logs based on an elbow rule to obtain a plurality of alarm clustering groups; dividing each alarm cluster group into at least one transaction according to a time window, and generating an item set; and performing frequent item set mining on each item set by adopting an association rule learning algorithm, generating an alarm association rule for describing an attribution relationship among the fault description information, and storing the alarm association rule in the graph database. Different from the prior art which only depends on timestamp sorting, the embodiment of the invention creatively puts forward that alarm logs in a time window are grouped by utilizing an elbow rule to form a high-cohesion alarm clustering group, so that subsequent association rule mining focuses on an alarm combination with strong space-time association, and the rule confidence is remarkably improved.

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 relates to an attention attack traceability blocking method and device for a multi-mode graph of a power system. The method comprises the following steps: cooperatively matching and screening abnormal flow data by utilizing alarm data and flow data; secondly, real attack traffic data are screened out through an undirected abnormal flow graph based on an aggregation and clustering mechanism, and the screening accuracy can be improved through an attention mechanism; then constructing a directed alarm graph associated with the alarm, generating an initial attack chain based on the directed alarm graph, and realizing association and traceability of fine-grained multi-step attack behaviors; nodes in the initial attack chain are pruned based on negative causal association to obtain a final attack chain, so that attack blocking is carried out; according to the method, through multi-mode collaborative analysis between the undirected abnormal flow graph and the directed alarm graph, fine-grained attack behavior traceability is realized, attack chain analysis is carried out by using a non-causal reasoning method, causal relevance of alarm types can be ensured, and dependence on expert knowledge, attack marks and simulation work can be reduced to the greatest extent.

The invention relates to the technical field of plant-level monitoring systems, in particular to a multi-source heterogeneous system and method for plant-level monitoring based on liquid air energy storage. According to the technical scheme, the method comprises the following steps of S1, collecting and preprocessing multi-source heterogeneous data in real time; s2, constructing a cross-system equipment graph; s3, formulating a hierarchical response intelligent strategy; s31, making a topological graph; s32, according to a hierarchical response strategy, dynamic adjustment is carried out according to the severity degree and the propagation range of the alarm; s33, calculating a weight; and S34, dividing response levels according to the weights. Multi-source heterogeneous data real-time acquisition and preprocessing can align millisecond-level and second-level data streams and eliminate time sequence errors, so that the system alarm association accuracy is improved, six system data formats are unified through JSON structured conversion, the analysis error rate can be reduced, and a basis is provided for large-scale monitoring; a three-flow topology model is established, a key equipment network is constructed, the root cause positioning time is shortened, and the false alarm rate is reduced through a topology weight optimization mechanism.

The invention provides an alarm processing method and device, equipment, a storage medium and a program product, and relates to the field of artificial intelligence. The method comprises the following steps: extracting a plurality of alarms in a historical time period based on a preset time window size to obtain a historical alarm transaction corresponding to each time window in a plurality of time windows; wherein the alarms comprise event type alarms and business index type alarms, and the event type alarms and the business index type alarms respectively comprise a plurality of alarm types; determining a frequent item set combination according to the historical alarm transaction corresponding to each time window in the plurality of time windows, and constructing an alarm association knowledge graph; wherein the alarm association knowledge graph comprises a plurality of nodes, and each node corresponds to one alarm type; according to the to-be-queried current event type alarm set, the alarm association knowledge graph is queried, and the business index type alarm corresponding to each event type alarm in the current event type alarm set is determined, so that the business index influenced by the system fault is accurately determined.

The invention discloses a security alarm processing method and device, equipment and a storage medium, and relates to the technical field of network security, and the method comprises the steps: carrying out the data preprocessing of multi-source heterogeneous data, and obtaining security alarm data; identifying the security alarm data through an alarm identification model, and determining an alarm type and a threat level; performing graph construction on the security alarm data through a graph database to generate a security knowledge graph; performing association analysis on the security knowledge graph to obtain attack chain information; and carrying out risk research and judgment on the attack chain information according to the alarm type and the threat level to obtain a comprehensive risk level. According to the method, the security alarm data is identified through the alarm identification model, so that the detection capability of novel attacks can be improved; and meanwhile, alarm correlation analysis is carried out in combination with the constructed security knowledge graph, and potential attack chains and complex attack behaviors are effectively identified, so that the alarm can be deeply analyzed, and the hazard degree of the alarm can be accurately judged.

The invention discloses a fault positioning method, system and device and a storage medium, and relates to the technical field of data processing, and the method comprises the steps: collecting alarm data, carrying out the entity relation extraction and link traceability tracking of the alarm data, and obtaining a corresponding text entity relation triple and a potential fault propagation path; performing multi-dimensional alarm association aggregation on the alarm data to generate a corresponding alarm clustering set; and in combination with variable information and multi-dimensional feature values in the text entity relationship triple, performing deep aggregation and association degree evaluation on the alarm clustering set to obtain a target fault set and a corresponding association degree score, and in combination with the association degree score and a potential fault propagation path, positioning a target root cause fault. According to the method, identification and convergence of alarm storm are realized through multi-dimensional alarm association aggregation, and root cause faults are quickly positioned in combination with a fault propagation path and a text entity relationship triple.

The invention discloses an automobile charging pile large screen real-time monitoring system and method, and relates to the technical field of screen monitoring. The large-screen real-time monitoring system for the automobile charging pile is established and comprises a connection module, a data acquisition module, a data processing module, a large-screen visualization module and an alarm notification module, and the monitoring process comprises the steps that 1, the charging pile and a server side establish long connection through the connection module by means of tcp, 2, the charging pile reports pile information to the data acquisition module in real time, and 3, the data acquisition module sends the pile information to the server side; the method comprises the following steps of 1, sending data to a data acquisition module, 2, sending collected information to a data processing module through a data acquisition module, 4, carrying out data processing through the data processing module, 5, selecting or developing a new component through a large-screen visualization module, and displaying summarized information according to needs, and 6, receiving abnormal information through an alarm notification module. And sending an alarm notice: notifying a processor associated with the alarm.

The invention relates to the technical field of mobile communication network operation and maintenance, and discloses an automatic fault monitoring method and system for base station operation and maintenance. The method comprises the following steps: acquiring operation indexes of a base station and performing time sequence analysis to obtain a drift vector; according to the drift vector, track clustering is carried out by adopting a K-means algorithm, and an initial performance baseline is determined to construct a baseline model; deviation is calculated and filtering processing is carried out to obtain a pure residual error sequence; micro degradation modes in the pure residual sequence are identified and aggregated, and an early signal list is obtained; constructing a correlation graph in combination with historical alarm logs, and mining a dependency relationship to obtain a correlation alarm cluster; performing root cause positioning rule fusion and feature aggregation on the cluster to obtain a labeled fault description; and finally, matching the early warning template and determining the priority to obtain final early warning output. According to the method, tiny hidden dangers of base station performance can be effectively identified, the problem that massive alarms are difficult to associate is solved, and early-stage accurate early warning and root cause positioning of faults are achieved.

The invention discloses a risk assessment and scheduling method and system based on a main distribution network, and the method comprises the steps: recognizing a fault of the main distribution network, and transmitting a first alarm signal based on the recognized fault; setting an observation period, obtaining all alarm signal fault types in the observation period, and constructing a fault-weight corresponding table; based on the fault-weight corresponding table, calculating a comprehensive risk coefficient of all fault types in the observation period; judging the comprehensive risk coefficient, and sending out a second alarm signal based on a first judgment result; and constructing a scheduling objective function based on the operation and maintenance cost, and solving the scheduling objective function in combination with the operation and maintenance information of the first alarm signal at the current moment. According to the method, risk analysis is associated through real-time alarm, so that the risk conditions of the same week based on different areas can be compared, or the risk conditions of different periods of the same area can be compared; the target function is constructed through the matching cost, the labor cost and the transportation cost, the resource configuration is optimized, and the transportation cost is reduced.

The invention provides an alarm analysis method and device, computer equipment and a storage medium. The method comprises the following steps: acquiring alarm information, wherein the alarm information comprises alarm time period information, alarm range information and alarm scene information; generating alarm query data corresponding to the plurality of alarm associated data sources according to the alarm time period information and the alarm range information, and sending the corresponding alarm query data to each alarm associated data source; when a query result returned by any alarm association data source based on the corresponding alarm query data is received, determining an association degree corresponding to at least one received query result according to the alarm scene information; calculating the confidence coefficient of the analysis result according to the correlation degree; and when the analysis result confidence is greater than a preset confidence threshold, analyzing the alarm root cause corresponding to the alarm information according to the at least one query result. According to the method, the alarm analysis efficiency can be improved.

A software defined network (SDN) can add network repository functions (NRF) into a configurations database to enable NF discovery. The SDN can subscribe to NRF notifications to receive new cloud native functions (CNF), registrations, or any other update to the CNF status in 5G system. In addition to listening to NRF notifications, the SDN can implement CNF pooling processes to periodically retrieve CNF from an NRF repository and stay in sync with 5G systems. Thus, a deep service path discovery can be developed from network service configurations and container call flows to enable an accurate alarm correlation and troubleshooting for the operations. This service path deep discovery can be designed and implemented as a standalone system or in an SDN framework with integration of a container management framework such as K8 kubernetes.

The invention provides an alarm automatic processing method and system based on threat mode recommendation, and a related device. The method comprises the following steps: step 1, constructing an alarm association diagram by using obtained alarm data; 2, performing community detection on the alarm association graph by adopting a graph division algorithm to obtain a plurality of security events; 3, matching the obtained multiple security events with a pre-constructed mode knowledge base to obtain a risk level and disposal information corresponding to the alarm data, and completing alarm automatic disposal; according to the method, the alarm association efficiency, the threat identification accuracy and the automatic disposal capability are remarkably improved, the problems of weak alarm association, rigid disposal strategy and high manual intervention cost in a traditional method are effectively solved, and the method is suitable for enterprise-level network security operation scenes.

This invention discloses an online maintenance and overload control method for multi-source IDS alarm correlation tracing graphs in vehicles. Rule-based intrusion detection systems (IDS) are deployed in the vehicle host domain, vehicle Ethernet domain, and Controller Area Network (CAN) to generate alarms. At the vehicle gateway electronic control unit (ECU) side, multi-source alarms undergo event standardization, sliding time window buffering and waterline out-of-order tolerance, deduplication aggregation, and indexed candidate retrieval. Under the condition of satisfying alarm correlation judgment rules, a directed alarm tracing graph is incrementally constructed. By suppressing forks and overloads through in / out-degree limits, node / edge budgets, and priority pruning, combined with two-stage expiration recycling and tracking record solidification, stable alarm correlation and tracing are achieved under resource-constrained conditions. This method can be used for real-time tracing and offline forensic analysis on the vehicle side.

The invention discloses an alarm correlation analysis method, device and equipment and a storage medium, and relates to the technical field of data processing, and the method comprises the steps: carrying out the feature extraction of target alarm data, and obtaining the multi-mode alarm feature information in the target alarm data; performing alarm data association analysis based on the target alarm data and the multi-modal alarm feature information through a preset alarm association model to generate alarm data association representation; performing clustering analysis on the alarm data association representation, and generating an alarm association group and an alarm attack chain identification result according to a clustering analysis result; and generating an alarm association analysis result based on the alarm association group and the alarm attack chain identification result. According to the technical scheme, the technical problem that in the prior art, a rule-based alarm correlation analysis method depends on manual definition and maintenance of rules, and consequently the efficiency and flexibility of alarm analysis are not high is solved.

The application discloses a kind of correlation analysis methods, device and equipment of warning event, the method includes: based on the matching of the obtained warning label in the preset warning correlation analysis rule to warning label group, obtain matching result;According to the matching result, determine warning label set;Based on the warning label set, generate the prompt warning event corresponding to the warning label group of the warning label group.It can be seen that, in the method, a variety of warning events are generated into uniform warning labels, and the warning correlation analysis rules suitable for the uniform format of the warning labels are set to realize the correlation analysis of the warning events generated by various security protection products, output the prompt warning events that have an impact on network security, effectively reduce the number of alarms, improve the accuracy of alarm, and provide reliable basis for the analysis and research of network security by staff.

The application discloses an alarm correlation method, device and equipment and a computer storage medium, the alarm correlation method comprises the following steps: constructing a training set according to obtained original data, and determining characteristic data of alarm data pairs in the training set; determining all network elements according to the characteristic data, and constructing a network topology graph according to the network elements; determining all network element sequences in the network topology graph, and constructing an alarm correlation model according to network element vectors corresponding to the network element sequences; if an input test alarm data pair is received, performing alarm correlation prediction on the test alarm data pair according to the alarm correlation model, so as to determine an alarm correlation relationship of the test alarm data pair. The application realizes deep mining of alarm data, and improves the correlation effect between correlated alarm data.

The application relates to a power system multimodal graph attention attack tracing and blocking method and device. The method uses alarm data and traffic data to cooperatively match and screen abnormal traffic data. Then, based on an aggregation and clustering mechanism, real attack traffic data is screened out through a non-directional abnormal flow graph, and the accuracy of screening is improved through an attention mechanism. Then, a directional alarm graph associated with alarms is constructed, an initial attack chain is generated based on the directional alarm graph, and the correlation and tracing of fine-grained multi-step attack behaviors are realized. Based on negative causal correlation, nodes in the initial attack chain are pruned to obtain a final attack chain, so that attack blocking is realized. Through multimodal cooperative analysis between the non-directional abnormal flow graph and the directional alarm graph, fine-grained attack behavior tracing is realized, attack chain analysis is performed using a non-causal reasoning method, the causal correlation of alarm types can be ensured, and the dependence on expert knowledge, attack markers and simulation work can be maximally reduced.

The present invention discloses a method, device, equipment and storage medium for determining an alarm mining model, and belongs to the technical field of data analysis and processing. The method comprises: mining training data according to initial full mining parameters to obtain a set of all alarm association relationships, and then marking a specified alarm according to a rule base and a set of all alarm association relationships, and performing specified alarm mining according to test data and the marked specified alarms, and generating an alarm mining model when the mining results meet the convergence conditions. In this way, on the basis of full mining, by marking the specified alarm mining, and analyzing and iterating on this basis, the certainty of the alarm association relationship is continuously enhanced, thereby scientifically obtaining an alarm mining model.

The invention provides an optical transmission network alarm prediction method and device, and relates to the technical field of data processing, and the method comprises the steps: obtaining the performance monitoring data of optical transmission network equipment and reported alarm information; performing alarm association processing and alarm label extraction according to the alarm data to obtain an alarm label vector; and after the performance data and the alarm label vector are aligned, alarm prediction is carried out through a random forest.

The present invention discloses a method for tracing the source of network attacks, including: obtaining threat intelligence of a specified type and caching it in a local threat intelligence database; obtaining current alarm information and the local threat intelligence to determine the attack source of the alarm; correlating the alarms based on the device identifiers of the attack source and / or the attack target, determining the attack stage of the device and determining the device vulnerability level based on the attack stage; obtaining the asset information of the device and determining the impact range of the attack based on the alarm correlation results. This method achieves effective tracing and analysis of network attacks, obtains multiple tracing results, and updates the tracing data each time the tracing is performed, which provides a basis for network operation and maintenance personnel to handle problems and strengthen security policies.