827 results about "Logfile" patented technology

The invention relates to a network attack tracing method, system and device based on a user portrait, and a medium. The attack tracing method comprises the following steps: collecting interactive behavior data of a user in a Web page; performing compression and serialization processing on the interaction behavior data to generate a structured interaction log file; collecting fingerprint data of the user terminal and performing standardization processing to generate a fingerprint feature vector; based on the structured interaction log file and the fingerprint feature vector, generating a user unique identifier containing a risk level tag; performing clustering analysis on the interaction behavior data according to the unique identifier of the user, identifying an abnormal behavior mode of the abnormal user and marking an attack type; acquiring a public network IP address of an abnormal user, and positioning a geographic position in combination with an IP database; and constructing an attack traceability report based on the user unique identifier of the abnormal user, the abnormal behavior mode, the public network IP address and the geographic position. According to the invention, the user portrait can be accurately constructed, and the network attack behavior can be effectively identified and traced.

The invention discloses a storage resource management method and system and a storage medium, and relates to the technical field of storage resource management, and the method comprises the steps: calculating the access popularity and popularity threshold of data according to a historical log file, and carrying out the popularity layering of the data in the historical log file according to the two; the data distribution mapping table is generated based on the historical log file and the access popularity, and finally multiple storage resources are allocated based on the layered data and the data distribution mapping table, so that the problems that application scenes of related technologies are limited, and the performance of a storage system is difficult to flexibly deal with complex and changeable business environments are solved; the technical problem that smooth expansion and rapid iteration of a storage system cannot be achieved is solved, and the technical effects of dynamic allocation, efficient utilization, flexible configuration and safety protection of storage resources and capability of leisurely coping with various complex application scenes are achieved.

Disclosed in the present invention is a fully automated log analysis and fault handling system and method based on an NLP large model. The method comprises: collecting a service state, an operating environment, and log files; extracting and generating log summaries from the log files, extracting key information from the log summaries to obtain state information of a service process; if an error is fed back, the NLP large model performing fault analysis on the error by using the service state and the operating environment as prior knowledge, and recording upstream and downstream key information involved in the error; on the basis of the upstream and downstream key information involved in the error, finding a fault cause and providing a fault handling action; and then executing a plan on the basis of the fault handling action. Log collection of the present invention is not limited to a prescribed format, and an NLP large model using a service state and an operating environment as prior knowledge is introduced for fault analysis.

A multi-node, multi-container cluster system that generates, aggregates, and manages log files from services and components to be used for audit logs and to debug and perform other serviceability tasks provided by a vendor of the cluster system. Logs are collected from all components of the system and aggregated into a consistent format for user analysis and debugging. Embodiments provide a comprehensive way to parse and index vast numbers of log files that can then be packaged and displayed to a user in a way that facilitates analysis and debugging and / or efficient input to appropriate debugging programs.

A method for identifying a log file for resolution of a fault of a medical imaging device, is provided. The method includes: obtaining log file browsing data describing one or more log files of the medical imaging device already viewed by a user to resolve the fault of the medical imaging device; obtaining problem data describing the fault of the medical imaging device; inputting the problem data and the log file browsing data to a machine learning algorithm, the machine learning algorithm being trained to predict, for each of a plurality of log files of the medical imaging device, and based on the browsing data, a resolution probability indicating a likelihood that the log file will assist in resolution of the fault of the medical imaging device; obtaining a prediction result from the machine learning algorithm in response to the inputting, the prediction result comprising a resolution probability for one or more of the plurality of log files of the medical imaging device; and identifying a log file for resolution of the fault of the medical imaging device based on the obtained prediction result.

A method for operating a first computer system is provided. The method includes: building a database comprising entries, each entry indicating a failure in one or more second computer systems and an action pattern to remedy the failure, the action pattern indicating log files to be accessed to remedy the failure. In response to detecting a failure in the first computer system, one or more entries of the database that match the detected failure may be identified. The one or more action patterns associated with the identified one or more entries may be used for extracting debugging data representing the detected failure. At least part of the debugging data may be sent to an external system. In response to the sending, instructions to remedy the detected failure may be received from the external system.

Techniques for fast database recovery in a multi-volume database environment via transactional awareness are described. In the event of a failure associated with a first volume storing database page data, the first volume can be restored to a point in time and transactional metadata from a second volume storing logical change data can be obtained for a limited number of transactions occurring at / after that point in time, as opposed to analyzing extremely large change log files. These transactions can be checked to ensure that they have all been persisted, and if not, change data for those transactions can be obtained from the second volume and used to replay these transactions on the restored first volume.

The invention relates to the technical field of data processing, and provides a BMC fault recording method and device, computer equipment and a storage medium, and the method comprises the steps: responding to a kernel fault of a target operating system of a target BMC, and printing fault log information; obtaining a pre-configured memory technology device partition; transmitting the fault log information to an annular fault cache region to form cache log data; storing the cached log data in a memory technical equipment partition, and generating a fault log file; and in response to a user reading instruction, reading and outputting the fault log file of the memory technical equipment partition. According to the technical scheme provided by one or more embodiments of the invention, the fault log can be effectively recorded, maintenance personnel can timely and accurately know the fault position and the fault reason, and BMC fault diagnosis and repair can be efficiently completed.

The invention belongs to the technical field of log monitoring, and particularly relates to a real-time log monitoring alarm method, system and equipment based on event driving, and the method comprises the steps: dynamically obtaining a log file path needing to be monitored, monitoring a log file on a server, and sending incremental log data to a specified Topic of Kafka in real time; through an event-driven architecture, using a Kafka consumer group to consume log data in Kafka in real time, and using a kset data type of Redis to temporarily store the log data; performing real-time processing on the log data in the Redis; comparing the statistical result with a preset threshold value, and if the statistical result reaches the preset threshold value, sending alarm information according to a configured notification mode and a notifier; and storing the monitoring result in a relational database, and displaying the monitoring big disk through a visual page. And an event-driven architecture is adopted, so that the data processing flow is more efficient, and the overall performance and expandability of the system are improved.

The invention relates to the technical field of network security, and discloses a security vulnerability AI positioning method and system based on log files. The method comprises the following steps: firstly, analyzing user feedback describing open source component security vulnerabilities to extract fault key entity information, screening related log entries based on the fault key entity information, and constructing a vulnerability propagation graph; identifying a vulnerability triggering initial point set by using a graph traversal algorithm and a graph neural network, calling a component vulnerability case library to deduce a vulnerability utilization propagation chain and calculating a matching degree with a known open source component vulnerability utilization chain feature, and obtaining a maximum probability vulnerability utilization propagation chain and an associated vulnerability triggering initial point set; and generating a root cause positioning report, and updating the knowledge base or model training data according to a user verification result. According to the method, the initial vulnerability point and the complete vulnerability propagation chain of the security vulnerability of the open source component can be accurately and efficiently positioned, and the positioning accuracy and timeliness are improved.

Systems and methods are provided for generating metadata for an improved data structure that enables improvements to generative AI and LLM processing and use of the data. A system may receive a log file generated by an entity associated with a device and generate metadata of the log file that comprises origination data associated with the entity and location of the device. The system may store the metadata and content of the log file as a document object. A data structure may be generated using various data objects. For example, the system can generate a vector embedding object enriched with a temporal embedding, a positional embedding, or a hierarchy of the metadata. The system may initiate an action based on the vector embedding object.

The embodiment of the invention provides a log processing method, computing equipment, a storage medium and a program product. The method comprises the following steps: under the condition that a target transaction of a database is detected to meet a target condition, storing a temporary file generated by the target transaction to a storage node; the temporary file stores a binary log for the database; and in response to a transaction submission operation of the target transaction, processing the temporary file in the storage node to generate a binary log file. According to the technical scheme provided by the embodiment of the invention, the transaction submission efficiency is improved.

The techniques disclosed herein provide a system for condensing large log files to enable interoperability with transformer-based large language models in automated failure analysis. In various contexts such as enterprise cloud services, log files can be large oftentimes containing millions of lexical units that can incur significant processing time and resource consumption. As such, the disclosed techniques provide a system for producing a directional knowledge graph representing a condensed form of a log file for automated failure analysis. Firstly, the log file is preprocessed to filter information that is irrelevant to the failure that previously occurred. The filtered lexical units are then used to generate a directional knowledge graph comprising a plurality of vertices which correspond to individual lexical units, and which are connected by edges representing the semantic connection between the lexical units. In this way, the directional knowledge graph significantly reduces processing times and improves resource consumption.

A data processing service uses minor compactions for committing transactions to a data table. The service may receive requests to commit transactions to a data table and write metadata for the transactions to log files, and generate a checkpoint file aggregating the transactions described in the log files to compute a data table state at a first time. The service may receive requests to commit a set of transactions and write metadata for the set of transactions to a set of log files. The service may determine that a number of log files in the set of log files reaches a threshold commit number, generate a minor compaction file aggregating the set of transactions, and generate a second checkpoint file aggregating the data table state at the first time with information from the minor compaction file to compute the data table state at a second time.

In some implementations, a health system may receive a documentation file associated with a software platform and may apply natural language processing to the documentation file to generate a first health indicator. The health system may receive a set of property indications associated with the software platform and may provide the set of property indications to a clustering model to receive a second health indicator. The health system may receive a log file associated with the software platform and may provide the log file to a machine learning model to receive a third health indicator. The health system may receive a set of notifications associated with failed builds, manual changes, and / or software incidents and may apply rules to the set of notifications to generate a suggested change to the software platform. The health system may output instructions for a user interface that includes the health indicators and the suggested change.

The invention relates to a server disk RAID state monitoring method and device and a readable storage medium. The method comprises the steps that the health state of a battery backup unit is judged according to RAID hardware information; obtaining SMART attribute information to judge the health state of a server disk; obtaining the state of a server disk RAID array, reconstruction progress information and availability information of a hot spare disk so as to judge the performance of the server disk; when the RAID array has the degradation state, a fault disk corresponding to the degradation state in the RAID array is determined, reconstruction of the RAID array is automatically triggered, replacement operation of the fault disk is executed, and alarm information is sent to an upper computer; the RAID hardware information, the SMART attribute information, the state of the RAID array, the reconstruction progress information, the availability information of the hot spare disk and the position information of the fault disk are uploaded to a log file; and encrypting the log file by using an AES algorithm. The accuracy of the method for monitoring the RAID state of the server disk and the safety and reliability of the log file are improved.

In the DevOps process, testing teams use automated testcases to test a product in a regression testing running daily, which generate thousands of log files per run, in various distributed environment with different formats. Solutions to errors depend on SMEs and the impact of solution leads to rework of defect / errors and high degree of heterogeneity. This leads to huge bottleneck in automation of DevOps process leading to loss of productivity and agility. Present disclosure addresses the challenges by providing systems and methods that auto capture the log files of different formats efficiently in a scalable, extendible, and plug-able way. The system then mines and parses the log files based on given identifiers to standardize and de-duplicate to create unique error records with detail description including cause, position, module, timestamp, etc. The system predicts the solutions leveraging the database containing solutions and errors by using a modified Smooth Inverse Frequency technique.

The invention relates to the technical field of artificial intelligence and data engineering, in particular to an intelligent data development treatment system and method based on a large model, the system is composed of a data access layer, a large model treatment engine, an intelligent feature factory, a compliance control layer and an application interface layer, and all the modules cooperate to achieve intelligent transformation of the whole data development process; the method has the beneficial effects that the potential mode of the multi-source heterogeneous data can be automatically identified, for example, transaction records, user behavior data and system monitoring information are distinguished from mixed log files, and manual rule pre-definition is not needed. And dynamically adjusting the protection intensity according to the data use scene.

Embodiments of the present disclosure provide a log storage method, a database system, a server, and a storage medium. In the log storage method, any database node in a distributed database can use a storage engine component to acquire a transaction log of a target transaction, and write the transaction log into a target online log file. The database node can use the storage engine to persistently store the transaction log in the target online log file into a corresponding target block device, and perform a transaction-level persistent storage operation on metadata information of the target online log file. In the implementation, on the basis of the online log file supporting a write operation at any location, the database node can directly store the transaction log in a block storage mode on the basis of the storage engine assembly of the database node without depending on a file system, thereby reducing resource overhead caused by incorporating the file system and a file storage mode.

A multi-node, multi-container cluster system that generates, aggregates, and manages log files from services and components includes a serviceability suite of functions for the system as it performs its tasks of supporting applications for a user. A logging function records events and states of nodes as the applications are run, an audit log processing function processes and compiles the logs for audit purposes, and the relevant log data for is provided in the form of uniform core files. Failure processing functions and debugging tools are provided to help analyze, debug, and correct issues that may be encountered. A support bundle processing function is provided to generate and process the log, debugging, failure, and other information for transfer to vendors or other third parties tasked with supporting the cluster network.

The invention discloses a debugging method and device for a network-on-chip, electronic equipment and a readable storage medium, and relates to the technical field of the network-on-chip, and the method comprises the steps: automatically carrying out the function verification and performance debugging of the network-on-chip through a log file and a signal waveform file, finding out the reasons of function errors and performance substandard, and carrying out the debugging of the performance of the network-on-chip. Therefore, a user can be helped to quickly and efficiently complete the function debugging and performance analysis of the network-on-chip, so that the verification efficiency of verifying the network-on-chip can be improved, and the verification period can be shortened; therefore, the problem of low efficiency of function debugging and performance analysis of the network-on-chip in the prior art is solved.

The invention discloses a log processing method and device, electronic equipment and a computer program product. Relates to the field of financial science and technology, and comprises the following steps: receiving a log query request, and extracting log generation time and a log tag of a target log file from the log query request, the log tag being used for indicating a file type of the target log file; generating a query condition according to the log generation time and the log tag, and obtaining a storage path of the target log file from a query index associated with the object storage system according to the query condition to obtain a target storage path; and extracting a target log file from the object storage system based on the target storage path, and sending the target log file to the client. Through the method and the device, the technical problem that the query efficiency is low when the target log file is acquired from the computing cluster in related technologies is solved.

A distributed operation and maintenance data management method based on a multi-modal log aggregation framework relates to the field of electrical digital data processing, and comprises the following steps: calculating a node load state index; determining node acquisition parameters including an acquisition period and an acquisition trigger condition; when the log file change amount of the target node is larger than the collection triggering condition, event collection is executed, and event collection data flow is obtained; when the log file change amount is smaller than or equal to the collection triggering condition, polling collection is executed according to the collection period, and a periodic collection data stream is obtained; generating a data compensation instruction and executing incremental acquisition to obtain a compensation data stream; merging to generate complete log data, and carrying out type distinguishing to generate a multi-modal data packet; and allocating the data to corresponding storage engines to obtain multi-modal log aggregation data. By implementing the method, the integrity of log data acquisition in a large-scale distributed system can be improved.

Abnormal behavior detection for industrial machines component(s) using large language models based on sequence(s) of log file messages recorded by logger(s) at initial time(s). The log file message sequence may be transformed into an anomaly severity sequence comprising log tokens encoding, times, and / or associated anomaly severity levels. The anomaly severity sequence may be input into the large language model. The large language model may output an anomaly severity histogram predicting M probabilities that the logger(s) will record log file messages encoded by log keys associated with M respective anomaly severity levels at the subsequent time(s). Abnormal behavior may be predicted based on the M probability patterns of the anomaly severity histogram. in the one or more components of the industrial machine logged by the one or more loggers at the one or more subsequent times. A control command may trigger automatically altering the component(s) operation to prevent the abnormal behavior.

The invention relates to the technical field of data storage, and discloses a solid state disk compatibility testing method which comprises the following steps: after a solid state disk is started, automatically collecting basic performance information of the solid state disk based on a Python script and generating a standardized log file; running performance operation of the solid state disk in a preset time period, and simultaneously executing multi-user or multi-task concurrent test operation based on the automated script; a script is used for calling a system startup command or a shutdown command, the next startup process is automatically triggered after shutdown, a startup and shutdown cycle test mechanism is formed, and the startup time, the initialization duration and the abnormal interruption record of the solid state disk are synchronously collected in each startup and shutdown process; when the system runs, the Python script is used for safely removing and reinserting the solid state disk. The problem that an existing test method is narrow in platform coverage range is solved.

The invention relates to the technical field of digital integrated circuit verification, in particular to a large-scale chip post-simulation log file intelligent analysis method and device and a medium. The method comprises the following steps: performing data cleaning on a post-simulation log file; dividing the cleaned log file into a plurality of sub-file blocks according to timestamps, starting multiple threads to process each sub-file block in parallel, counting the types and quantity of time sequence violations, extracting related logs and generating a time sequence violation file; classifying and analyzing the time sequence violation file by utilizing a machine learning algorithm; dynamically filtering the time sequence violation file according to a preset rule, screening key violation information, and repairing time sequence violation caused by signal delay; and in combination with a chip circuit structure and a signal transmission path, positioning a root module or node of a time sequence violation, formulating an optimization scheme and verifying a repair effect. According to the method, efficient and accurate analysis of the post-simulation log file of the large-scale chip is realized, and the efficiency and accuracy of chip verification are improved.

The invention provides a DFT process monitoring method and device, electronic equipment and a storage medium, and belongs to the technical field of DFTs. The DFT process monitoring method comprises the steps that in response to monitoring that execution of a current test task is finished, a process state and a current log file generated in the process of executing the current test task are obtained; determining a target predefined analysis rule corresponding to the type of the EDA tool adopted for executing the current test task through a mapping relation between the type of the EDA tool and the predefined analysis rule; analyzing the current log file through a target predefined analysis rule to obtain semantic keywords; and determining a final execution state of the current test task based on the process state and the semantic keyword. According to the scheme, massive unstructured log files generated by different EDA tools can be analyzed, and the final execution state of each test task can be determined.

The invention provides a performance diagnosis method and device for a network transmission protocol, electronic equipment and a storage medium, and the method comprises the steps: obtaining a log file of the network transmission protocol through a sliding window, wherein the log file is in a target log format; obtaining multi-dimensional time series data based on the log file; the multi-dimensional time sequence data comprises a plurality of fields representing the performance of the network transmission protocol; performing multi-dimensional index calculation on one or more fields in the multi-dimensional time sequence data, and obtaining an anomaly detection result of the network transmission protocol according to a multi-dimensional index calculation result; and under the condition that the anomaly detection result is abnormal, determining the performance anomaly type of the network transmission protocol according to the anomaly detection result based on a pre-trained anomaly positioning model. The method not only can meet the anomaly positioning timeliness of the network transmission protocol, but also can fully cope with a complex and changeable protocol stack core algorithm anomaly scene, and can accurately position the anomaly without learning a specific protocol stack core algorithm anomaly scene.

The invention discloses a data recovery method, device and equipment, a medium and a program product, and relates to the technical field of computers. The data recovery method comprises the following steps: analyzing a pre-write log WAL file in a database to obtain a log frame in the WAL file; the log frame comprises modification information, a page number, transaction metadata and page mapping information for a database page; according to the transaction metadata in the log frame, performing boundary identification on the modification information of the database page to obtain at least one item of transaction information; and according to the transaction information, the page number, the page mapping information and a data structure of the database, performing data recovery in the database to obtain a recovered database. According to the technical scheme provided by the embodiment of the invention, the problem of data loss easily occurring during data recovery can be solved by utilizing the characteristic that the WAL file is independently stored and is not easy to lose.

The invention discloses an artificial intelligence large model training process heterogeneous resource index time sequence visualization method and system based on meteorological supercomputing, and the method comprises the following steps: P1, collecting supercomputing architecture intelligent computing resource indexes, and generating a log file in a standard text format; step P2, analyzing the log and storing the log in a database, and storing the log in a distributed retrieval and analysis engine; p3, acquiring training task operation information, and storing the training task operation information in a distributed retrieval and analysis engine; step P4, performing index information association and aggregation to form resource index time sequence information of the computing nodes during operation of the large-model training task, and issuing the resource index time sequence information as a standard API retrieval interface; and step P5, visually displaying the resource index time sequence. According to the method, the problems that the resource indexes of the large model training task based on supercomputing are difficult to monitor and the resource indexes and the operation are dynamically associated can be solved.