57 results about "Exception analysis" patented technology

The embodiment of the invention provides a code exception analysis method and device, a storage medium and a program product. In the embodiment of the invention, in the preset plug-in, the global knowledge graph is constructed through the logic syntax tree of the plurality of original business codes, so that intelligent anomaly analysis is carried out on the modified to-be-detected codes, multi-language and heterogeneous programming styles and complex dependency limitation are broken through, and cross-language and cross-file analysis is supported; change code snippets and associated influence code snippets thereof are accurately identified, and a data basis is tamped and analyzed; the analysis depth is deepened and the accuracy is improved through a multi-dimensional anomaly analysis large model and four-dimensional collaborative analysis in combination with an adaptive review rule and a knowledge graph; and finally, performing correction based on the generated repair scheme. Through the technical fusion of an MCP protocol, a multi-AI model, a syntax tree and a knowledge graph, the high efficiency, accuracy and real-time performance of code examination and repair are realized, and the service development quality and the system operation stability are remarkably improved.

The invention discloses an electric power communication analysis system and method based on big data, and the method comprises the steps: collecting multi-source heterogeneous data, carrying out the sensitivity grading, and generating labels; gathering the data to a big data platform, and completing storage, partitioning and index construction; an asynchronous main control chain mechanism is constructed, high-optimal data is dispatched for rapid anomaly judgment, and other data is processed in parallel; performing desensitization mapping on the high-sensitivity data to generate a short-time mapping bill and performing desensitization analysis; issuing a local task for the low-confidence exception, collecting backflow data, and verifying and determining a high-risk event; restoring the sensitive data in the authorization window, and destroying the bill and the secret key after abnormity analysis is completed; and triggering network regulation based on an analysis result, and recording the whole process to an audit log. By introducing an asynchronous scheduling and reversible desensitization mechanism, high-efficiency anomaly analysis and whole-process traceability of the electric power communication big data on the premise of ensuring safety and compliance are realized.

The embodiment of the invention provides an information processing method and device, and the method comprises the steps: obtaining an application exception index of a target application; extracting application exception information corresponding to the target application according to the application exception index, performing information processing on the application exception information in at least one exception analysis dimension, and determining an application exception type of the target application; creating an application repair strategy for the application exception type, and determining application code information corresponding to the target application; and inputting the application repair strategy and the application code information into a code repair model for processing to obtain an application repair code, and performing exception repair on the target application by using the application repair code.

The invention discloses an AI-based micro-service system exception handling method and system and a storage medium, and belongs to the technical field of software exception monitoring. The method comprises the following steps: in an exception real-time capturing stage, deploying a global exception intercepting component at a micro-service gateway or each service framework layer, and generating an exception report; in the AI anomaly analysis stage, the anomaly report is analyzed, and a solution thought or a code repair suggestion is generated; an active alarm and notification stage: notifying development, operation and maintenance personnel according to an AI analysis result; meanwhile, all abnormal information is pushed to a monitoring panel; in the automatic repairing and deploying stage, exception handling is carried out, and dynamic deploying and system self-healing of exceptional patches are achieved; in the feedback learning and iterative optimization stage, exception processing results are collected, and the diagnosis accuracy and the repair suggestion quality are improved by continuously learning new exception cases and processing modes. According to the invention, abnormal information from different services is collected in a unified manner through a global interception mechanism, and the omission risk caused by decentralized processing is eliminated.

The invention discloses a fault self-check recovery method and system based on a cloud deployment system, and the method comprises the steps: carrying out the multi-dimensional deep detection of the operation states of an operation system layer, a database layer and an application layer of a cloud platform through constructing an all-dimensional real-time monitoring system, and automatically triggering an adaptive recovery mechanism once an abnormality is found, and continuous and stable operation of the system is ensured. The matching tool integrates core modules of state acquisition, anomaly analysis, recovery execution, log recording and the like, collaboratively realizes real-time acquisition of operation data, accurate anomaly identification through an intelligent algorithm, rapid matching and implementation of a recovery scheme, and records logs in the whole process for troubleshooting and system optimization. According to the tool, server nodes, database states, service accessibility and webpage availability are checked in sequence, emergency processing is started immediately when abnormity is found, the stability and reliability of a cloud platform are remarkably improved, and the manual intervention cost is reduced.

The invention relates to a computing power anomaly detection method and device, computer equipment, a computer readable storage medium and a computer program product. The method comprises the following steps: acquiring abnormal heterogeneous computing power task data and heterogeneous computing power task detection data; determining abnormal computing power container information and abnormal hardware node information from the abnormal heterogeneous computing power task data; performing software exception analysis processing on the basis of the exception computing power container information to obtain software dimension exception information, performing hardware exception analysis processing on the basis of the exception hardware node information to obtain hardware dimension exception information, and comparing the heterogeneous computing power task detection data to obtain first exception detection information and second exception detection information; therefore, the calculation power anomaly detection rate is determined. According to the method, the detection rate of the heterogeneous computing power task detection data in the heterogeneous computing power task execution process can be determined, so that the stability of the computing power service is effectively improved through detection rate analysis.

The invention discloses an information security detection method and device, equipment, a medium and a program product. The method comprises the following steps: acquiring user access information and data access abnormal information in a to-be-detected data access period; performing anomaly analysis on the user access information to obtain a user access information analysis result; performing exception analysis on the data access exception information to obtain a data access exception information analysis result; and based on the user access information analysis result and the data access abnormal information analysis result, performing information security detection on the to-be-detected data access time period to obtain a target information security detection result. According to the method and the device, the abnormal access behavior of the user and the abnormal condition of data access can be effectively detected, so that the information security in the data access process is comprehensively detected.

The application belongs to the technical field of photovoltaic power generation equipment fault monitoring, and discloses a kind of distributed photovoltaic power generation equipment intelligent monitoring system, including light intensity equipment exception monitoring module, control equipment exception monitoring module, drive equipment exception monitoring module, tracking system exception analysis module, support structure data acquisition module, support structure exception analysis module and fault cause pointing identification module.The application analyzes the abnormal conditions of tracking system and support structure, and then comprehensively evaluates the health status of each automatic tracking photovoltaic power generation device.This analysis method takes into account the tracking system and the support structure, covering the key aspects of device operation and support, which greatly improves the accuracy of evaluation, accurately grasps the real situation of the equipment, discovers potential problems in time, formulates more targeted maintenance strategies, effectively ensures the stable operation of photovoltaic power generation devices, improves power generation efficiency and reduces equipment failure risk.

The invention relates to the technical field of computers, in particular to an application exception handling method, system and equipment based on agent arrangement, which can efficiently handle application exception problems. The application exception handling method based on agent arrangement comprises the steps that by means of agent arrangement, an exception handling workflow comprising a plurality of agent units capable of being arranged is formed in a visual mode for an application exception condition, and a flow engine of the exception handling workflow comprises a control node and a task node; for the captured application exception information, exception information features are extracted through exception analysis logic by using an exception recognition agent unit, and the influence range of the application exception is delimited through an entity relationship graph to form an exception analysis result to be output to a front-end visual interface or pushed to a collaborative agent unit; and establishing an agent co-processing system comprising a co-processing agent unit based on the exception analysis result, matching a processing scheme for the application exception information according to a co-processing strategy, and processing the exception condition.

The invention relates to the field of big data, and provides an anomaly detection and repair method and system for a big data task and a medium. The big data task anomaly detection and repair method comprises the steps that task information of a big data task with an abnormal task state is obtained, wherein the task information comprises a task log; obtaining an execution code of the big data task through a decompilation tool, and analyzing the execution code and the task log through an exception analysis module to identify an exception problem; in response to the fact that the exception problems are data source exception and code exception, exception code snippets, data source information and exception types are analyzed through a conjoint analysis module to determine exception root causes, the exception code snippets are recognized through an exception analysis module, the data source information is determined by executing codes, and the exception types are determined according to task logs; and triggering abnormal code repair and / or data processing optimization according to the abnormal root cause.

The invention provides a transportation strategy optimization method and system applied to cold-chain transportation, and the method comprises the steps: firstly obtaining a transportation data set composed of equipment operation parameters and transportation environment data of transportation equipment in a transportation period in a cold-chain transportation process, and then carrying out the feature extraction of the transportation data set; the equipment feature set and the environment feature set are input into a preset exception analysis model for joint exception analysis, an exception analysis result containing an equipment exception type and an environment exception type is generated, and the equipment exception type and the environment exception type are generated according to the exception analysis result and a preset report generation rule. According to the method, a transportation abnormality analysis report including abnormal condition description, occurrence time periods and influence evaluation is generated, and finally, a transportation optimization strategy for transportation equipment maintenance and transportation environment regulation and control is generated based on the report and historical transportation data and is fed back to a cold chain transportation management system, so that the cold chain transportation quality and efficiency can be improved, and the cargo safety is guaranteed.

The invention provides an exception analysis method and device, electronic equipment and a storage medium, and the method comprises the steps: responding to an exception alarm event of an independent station, and determining a time window of the occurrence of the exception alarm event and an exception service corresponding to the exception alarm event; obtaining service data of the abnormal service in the time window, wherein the service data indicates a change trend of a working condition of the abnormal service in the time window; acquiring a log and a topological structure of the abnormal service; performing structured processing on the service data based on the topological structure and the log to obtain a knowledge graph corresponding to the abnormal alarm event; and outputting root cause information of the abnormal alarm event according to a preset generative model based on a preset prompt word and the knowledge graph. According to the invention, the accuracy of analyzing the fault root cause of the independent station can be improved.

The embodiment of the invention provides a database abnormal root cause positioning method and device, equipment, a medium and a program product, and relates to the field of financial science and technology or other related fields. The method comprises the steps of collecting performance data of at least one dimension of a database according to a set period; for multiple items of data of the same index in the performance data, determining alternative abnormal data based on values of the multiple items of data; clustering the alternative performance data according to at least one dimension, and determining candidate performance data based on a clustering result; determining abnormal performance data from the candidate performance data through threshold comparison and / or mode matching, and determining an abnormal dimension according to the abnormal performance data; and generating and feeding back an exception analysis result of the database based on the exception performance data and the exception dimension thereof. By analyzing the performance data of at least one dimension of the database, automatic database exception analysis and exception positioning are realized, and the efficiency and accuracy of exception analysis are improved.

The invention provides an exception analysis method applied to programming and a related device, and the exception analysis method applied to programming comprises the steps: analyzing a currently written code, and determining whether the code is abnormal or not; and in response to the exception, analyzing the exception code, and explaining an exception reason of the exception code by utilizing a target language. According to the abnormality analysis method applied to programming, the encoding abnormality can be analyzed in real time in the programming process, and the timeliness is high; exception codes are explained and analyzed through the target language, high-precision answers aiming at the exceptions are generated, and understanding is easy.

The embodiment of the application provides a kind of based on digital model's converter station unmanned plane cruise route collaborative processing method and system, obtain network knowledge learning data sequence including multiple template collaborative control data and each prior abnormality mark corresponding to multiple template collaborative control data;Load basic exception label diagnostic network, and the basic exception label diagnostic network is the exception label diagnostic network generated by knowledge learning long short-term memory network model based on initialization weight parameter;Based on knowledge domain self-adaptability training, the prediction unit of the basic exception label diagnostic network is learned by network knowledge learning data sequence, and the exception analysis network is obtained;Prediction unit includes one or more multilayer perceptron;According to exception analysis network, abnormal node positioning is carried out, and the high-performance exception analysis network is generated using the technical solution, so that subsequent abnormal node positioning accurate, fast can be realized.

The invention discloses a fault root cause positioning method and device for certificate management, equipment, a medium and a product. The method can be applied to financial science. The method comprises the following steps: acquiring server state data and certificate operation related data of a certificate management server, acquiring certificate transmission related data of certificate transmission link network equipment, and acquiring certificate calling related data of certificate access application equipment in a current detection time period; performing data exception analysis on the server state data, the certificate operation related data, the certificate transmission related data and the certificate calling related data, and determining a certificate exception node, a certificate exception index and exception associated data; determining a node exception propagation path of the certificate exception node; the node exception propagation path comprises a plurality of exception nodes influenced by the certificate exception node; and generating a fault root cause and a disposal scheme in the certificate management process according to the certificate exception index, the exception associated data and each exception node in the exception propagation path.

The invention discloses an exception analysis method and device, equipment, a storage medium and a program product, and relates to the technical field of computers. The method comprises the following steps: acquiring account log data corresponding to a first account; extracting a sequence semantic feature representation corresponding to the account log data, and obtaining a directed graph network corresponding to the account log data; obtaining graph network semantic feature representation corresponding to the directed graph network; based on the sequence semantic feature representation and the graph network semantic feature representation, account semantic feature representation corresponding to the first account is obtained, and the account semantic feature representation is used for analyzing the abnormal condition of the first account. According to the method, the sequence semantic feature representation and the graph network semantic feature representation can be combined, the link information among the multiple execution events is combined under the condition that the time sequence information is not ignored, more accurate account semantic feature representation is obtained, and the analysis accuracy of the abnormal condition of the first account can be improved. The method and the device can be applied to various scenes such as user management scenes and security detection scenes.

The application discloses an FTTR network data exception analysis method, system and equipment, which is applied to an FTTR-B network and comprises the following steps: acquiring local area network history behaviors and Internet history behaviors of a target terminal device in a target FTTR network; obtaining a target terminal device portrait according to the local area network history behaviors and the Internet history behaviors; analyzing real-time data in the target FTTR network to obtain a target behavior of the target terminal device; and judging whether the target behavior is abnormal according to the target behavior and the target terminal device portrait. At least, the problem that many enterprises access the Internet through FTTR-B while carrying out local area network networking, and it is difficult to discover and locate data exceptions across networks in time and to flexibly adjust monitoring strategies to adapt to business dynamic changes if only relying on traditional monitoring mechanisms is solved.

The invention relates to an aircraft component test report analysis system based on an anomaly inference model, and relates to the technical field of component testing, and the aircraft component test report analysis system comprises a data analysis module which is configured with a file format analysis strategy to perform file format feature extraction on a test file, carrying out aircraft part test information analysis on the test file by matching a corresponding analysis step in the format analysis database so as to generate a test report; the file caching module is configured with a multi-stage data caching strategy, reads, writes and pre-caches the test file through a pre-constructed memory, file and database caching architecture, and keeps the data read-write efficiency and the system response speed within a preset good threshold range; and the exception analysis module is configured with an exception report identification model and is used for identifying and analyzing the test report through similar fault identification logic and a fault analysis engine so as to determine exception test information in the test report. The method has the effect of improving the analysis efficiency and accuracy of the test report of the aircraft component.

The embodiment of the application belongs to the technical field of process optimization, is applied to the field of intelligent reporting of supervision data, and relates to a supervision data pushing and abnormal self-checking method, device and equipment and a storage medium thereof, and comprises receiving supervision request data pushed and reported by each supervised party. Through repeated abnormal self-checking and abnormal automatic repairing, supervision request data examination is performed, the normativity of supervision data reporting is further ensured, abnormal data is avoided from being transmitted to a supervision department, meanwhile, an automatic mode is adopted, abnormality can be automatically detected and repaired, manual abnormality analysis and repair are not needed, the workload of manual abnormality screening is reduced, manual consumption is saved, and supervision reporting efficiency and accuracy are improved.

The invention discloses a statement analysis method and device, electronic equipment, a storage medium and a program product. Obtaining a to-be-analyzed vector corresponding to the to-be-analyzed statement; determining a query tree vector corresponding to the query tree; splicing the vector to be analyzed and the query tree vector to obtain a spliced vector; and inputting the splicing vector into an exception analysis model, and outputting a root cause which causes the execution time exception of the to-be-analyzed statement. According to the method, the features of the to-be-analyzed statement are captured more accurately and more comprehensively, the dimensionality of the features of the to-be-analyzed statement is enriched, statement analysis focuses on the to-be-analyzed statement, end-to-end statement analysis is achieved, the root cause causing the execution time abnormity of the to-be-analyzed statement is automatically output, and statement analysis is more efficient.

The application relates to a service exception analysis method and device, equipment, a storage medium and a program product. The method comprises the following steps: in the case that a target service appears an exception, determining a task flow corresponding to the target service and an exception time period, and acquiring an exception log obtained by executing the task flow in the exception time period; executing the task flow in a test environment corresponding to the target service to obtain a test log; and determining an exception type of the target service according to the exception log and the test log. The method can quickly and accurately locate the exception type when the service is abnormal.

The invention discloses an information anomaly analysis method and device based on data and knowledge fusion bidirectional driving. The method comprises the steps of obtaining to-be-analyzed data information and standard data; extracting a core entity from the standard data and constructing a primary anomaly analysis model; the data information to be analyzed is analyzed and judged through the primary anomaly analysis model, and a primary analysis result is given; performing semantic analysis on the to-be-analyzed data information to obtain a unified analysis vector; and fusing the primary analysis result and the unified analysis vector, and performing bidirectional driving anomaly analysis on the to-be-analyzed data information through a multi-layer graph neural network model to obtain an information anomaly probability. Through a three-layer heterogeneous architecture of a multilayer graph neural network model, a primary analysis result directly reaches a deep layer through interlayer bidirectional modulation and progressive fusion of data driving and knowledge driving, knowledge signal attenuation interlayer function decoupling is prevented, an abnormal source can be traced, and accurate analysis of information abnormity is realized.

The application relates to the computer technical field and provides a metered material accounting exception identification method and system based on a knowledge graph and a rule center, which comprises the following steps: determining state change information based on entity state transition tracks of an accounting process of metered materials in a knowledge graph, determining a target accounting node based on state change information of an accounting stage; performing rule conflict analysis based on node state information of the target accounting node and associated business rules of the target accounting node in a rule topology network, obtaining an abnormal accounting node; obtaining a candidate accounting node based on a state transition path before the abnormal accounting node according to the entity state transition tracks, performing accounting exception analysis based on node states between the abnormal accounting node and the candidate accounting node and between the candidate accounting nodes, and determining a potential starting node; and determining an accounting exception reason of the metered materials based on an abnormal operation mode of the potential starting node. The application improves the accuracy and reliability of accounting exception identification of the metered materials.

The application discloses a log exception automatic analysis system and relates to the technical field of exception analysis. The system comprises a collection module, an analysis module, a big data storage module, a console module and an exception analysis module. The collection module is used for collecting relevant type data of a host end. The relevant type data comprises security product data and host security event data. The analysis module is used for standardizing analysis of the collected data. The application helps users solve the problems of security data collection, data mining, event backtracking and security capability integration based on an ATT&CK framework. The application directly mines data in depth to discover potential threats. The application realizes cross-node query operation through deep integration of original data, and can uniformly query, count and analyze heterogeneous data through embedded database data retrieval and threat hunting syntax. The application fully utilizes the data advantages of enterprises and truly hunts threats that cannot be discovered by conventional means.

The application discloses a kind of power communication analysis system and method based on big data, comprising: collecting multi-source heterogeneous data, sensitivity grading and generating label;Data is gathered to big data platform, storage, partition and index construction are completed;Asynchronous master chain mechanism is built, high-optimal data is scheduled to quickly determine exception, the rest data is processed in parallel;Sensitive mapping is executed to high-sensitive data, short-time mapping ticket is generated and desensitization analysis is carried out;Local task is issued to low-confidence exception, and high-risk event is determined by collecting backflow data and checking;Sensitive data is restored in authorization window, ticket and key are destroyed after completing exception analysis;Network regulation is triggered based on analysis result, and whole process is recorded to audit log.The application realizes efficient exception analysis and whole-process traceability of power communication big data under the premise of guaranteeing safety compliance by introducing asynchronous scheduling and reversible desensitization mechanism.

The invention discloses a page exception processing method and device, a medium and a program product, and relates to the field of artificial intelligence, and the method comprises the steps: obtaining an exception recognition feature vector; inputting the anomaly identification feature vector into a pre-trained feature encoder to obtain anomaly potential space representation output by the feature encoder; inputting the abnormal potential space representation into a pre-trained page exception analysis model to obtain a page exception detection result and exception category information corresponding to the abnormal potential space representation output by the page exception analysis model; and determining an exception handling mode corresponding to the exception category information according to a preset mapping relationship between the exception category information and the exception handling mode and the exception category information, and handling the page exception according to the exception handling mode. According to the scheme, the time consumption of exception handling is reduced, the exception handling efficiency is improved, the accuracy of the page exception analysis result is improved, and then the use experience of a user on the financial system page is improved.

The embodiment of the application provides an address conversion exception analysis method, system and storage medium, and belongs to the technical field of data analysis.The method comprises the following steps: executing a firmware log analysis thread, extracting request parameter information and response data information of any admin command in a firmware log of the NVMe acceleration card; executing a PCIe message analysis thread, extracting any PCIe message generated by a PCIe protocol analyzer between the host and the NVMe acceleration card, wherein the PCIe message represents request data or response data of any admin command or request data or response data of any IO command; and determining whether the NVMe acceleration card has address abnormal conversion for any admin command and any IO command according to the request parameter information and the response data information of any admin command extracted from the firmware log and any PCIe message extracted. The embodiment of the application aims to improve the analysis efficiency of address conversion errors.

The invention particularly relates to a micro-service interface abnormal chain tracking and root cause positioning method. According to the micro-service interface abnormal chain tracking and root cause positioning method, an abnormal tracking context is transmitted in a micro-service call chain by adopting a call chain context transparent transmission technology; when an exception is captured, dynamically obtaining a method parameter snapshot and call stack information through a Java reflection mechanism; aggregating according to the tracking ID through a centralized analysis engine, and constructing an anomaly propagation map; and the exception analysis engine matches the exception chain according to the exception propagation path and a preset exception mode rule base, identifies an exception root cause and outputs a positioning suggestion. According to the micro-service interface abnormal chain tracking and root cause positioning method, the troubleshooting efficiency under the micro-service architecture is remarkably improved, the diagnosis accuracy is enhanced, the operation and maintenance cost is reduced, the compatibility is high, the expansibility is good, and the resource overhead is low.

The present application relates to the field of big data, and provides a big data task exception detection and repair method, system and medium.The big data task exception detection and repair method comprises the following steps: obtaining task information of a big data task with an abnormal task state, wherein the task information comprises a task log; obtaining execution code of the big data task via a decompilation tool, and analyzing the execution code and the task log via an exception analysis module to identify an exception problem; in response to the exception problem being a data source exception and a code exception, analyzing an abnormal code segment, data source information and an exception type via a joint analysis module to determine an exception root cause, wherein the abnormal code segment is identified by the exception analysis module, the data source information is determined by the execution code, and the exception type is determined according to the task log; and triggering abnormal code repair and / or data processing optimization according to the exception root cause.