16 results about "Server log" patented technology

The application relates to the technical field of electrical equipment testing, in particular to an IDC equipment intelligent detection and classification management method and system, which comprises the following steps: collecting the current, voltage, power consumption and temperature of a server in an IDC cabinet, synchronously recording the CPU occupancy rate, GPU occupancy rate, storage I / O load and network throughput rate, extracting server log entries and storing the server log entries into an equipment exception log library, and obtaining IDC equipment running state data.In the application, the current, voltage, power consumption and temperature of the IDC equipment are synchronously monitored, and server running data is collected, a new scheme optimizes the comprehensiveness of data and the real-time performance of monitoring, the monitoring strategy provides more fine risk assessment and classification management by dynamically analyzing the state fluctuation of the equipment, the accuracy of fault prediction and the efficiency of maintenance are improved, the prediction capability for potential problems is enhanced by comprehensively utilizing the analysis of abnormal logs and running data, and the system downtime rate and maintenance cost are effectively reduced.

The application discloses a method and system for port scanning detection by using server logs, comprising a feature extraction step and a port scanning detection step; first, access logs on a server cluster are collected, then feature extraction is carried out to construct aggregated records describing access IP behaviors, and finally, whether port scanning occurs is judged by using rules or a clustering method according to the number of positive samples, and meanwhile, the detection result is fed back to an administrator and a positive sample database, so that the accuracy of the method is further improved. The application fully considers the actual needs of operation and maintenance safety audit managers, and has strong implementability and applicability.

Embodiments of the present application provide a method and apparatus for generating a server log, a non-transitory readable storage medium and an electronic device. The method includes: in response to starting to update target firmware in a server from a first version to a second version by a target program in an operating system of the server, acquiring first version information of the target firmware, and sending update start information to a baseboard management controller (BMC) of the server; in response to the target firmware being completely updated from the first version to the second version by the target program, acquiring second version information of the target firmware, and sending update end information to the BMC. Through the present application, it solves the problem of BMC being unable to record version information of firmware in the log when updating the firmware in the server by the target program in the operating system of the server.

This system provides a notification system that allows the user of a terminal device to collectively access print log information from multiple image forming machines that perform printing on print data sent from a terminal device to a print server. [Solution] In a notification system 100 according to one aspect of the present invention, the print server 2 outputs log information including the print status of print data to the log collection server 4, and the log collection server 4 stores the output log information. The notification server 5 acquires a plurality of stored log information based on predetermined conditions, converts the acquired log information into notification information, and transmits the converted notification information to the terminal device 1. The terminal device 1 notifies the notification information.

The invention discloses a BMC (Baseboard Management Controller) regular log acquisition and intelligent error analysis and positioning method, which integrates advanced technologies such as regular acquisition, content difference analysis, intelligent error identification and positioning and the like, and realizes efficient management and accurate error diagnosis of server logs. By combining the experience summary of previous project troubleshooting and positioning, the system troubleshooting efficiency is improved, the need of manual participation is reduced, the system has important practical application value, the intelligent level of server operation and maintenance and the fault response speed are improved, the operation and maintenance cost and the human resource investment are reduced, and the method is suitable for popularization and application. And powerful support is provided for efficient operation of the data center.

The application relates to the field of log compression, and particularly discloses a server log data compression method, a server log data compression system, a terminal and a storage medium, which collect log files; read text content in the log files, split the text content in the log files by taking spaces as identifiers to obtain multiple segments; cyclically compare the segments, and record segments with the same content as an element; replace each element in the text content by using a marker to obtain a new log file; wherein the marker comprises a two-bit length marker and a three-bit length marker; one element corresponds to one marker; and the new log file is compressed. The application divides segments by taking spaces as identifiers to find repeated elements, performs replacement, and then performs compression, so that the number of characters in the log file is reduced, and the compression effect is improved.

This disclosure provides a problem diagnosis method, apparatus, system, device, medium, and product based on artificial intelligence, relating to the field of artificial intelligence technology, particularly to the fields of large models, intelligent agents, and cloud computing. The AI-based problem diagnosis method includes: generating a diagnosis request, the diagnosis request containing a target problem; employing a first intelligent agent to obtain target client logs corresponding to the target problem, obtaining target server logs corresponding to the target problem, and merging the target client logs and target server logs to obtain a target log; employing a second intelligent agent to diagnose the target problem based on the target log to obtain an initial diagnosis result and confidence level; and responding to a confidence level greater than or equal to a preset threshold, using the initial diagnosis result as the target diagnosis result for the target problem. This disclosure can improve the efficiency and accuracy of problem diagnosis.

The invention discloses a network security threat detection method based on artificial intelligence, and relates to the technical field of information security, behavior events in network equipment and server logs are extracted and uniformly aligned to construct a time sequence, and then a graph structure with structure and time semantics is formed; therefore, the system has the capability of identifying the attack intention from the perspective of the global behavior chain. According to the mode, the problem that discrete and cross-time-period attack behaviors are difficult to identify in a traditional method is effectively solved, and potential attack association among a plurality of seemingly independent behaviors can be identified. Event nodes are embedded into a vector space, and context learning is carried out in combination with a memory enhanced neural network, so that the system can automatically construct an evolution relationship among events in an attack path and capture behavior logic and a propagation path of an attacker. According to the mechanism, the technical defect that the history cannot be reviewed in the attack behavior modeling of the existing method is overcome, so that the capability of reducing latent attacks and intermittent threats is improved.

This application discloses a server log reporting control method, apparatus, storage medium, and electronic device. The server log reporting control method includes: deploying a target memory in the server; running an operating system and a basic input / output system in the server; the basic input / output system executing files in the target memory; and applying the method to the operating system, including: adding multiple control code segments to the system source code of the basic input / output system while the server is running server services, obtaining multiple reference system codes; compiling the multiple reference system codes to obtain multiple reference control files of target file types; selecting a target control file from the multiple reference control files that matches the current resource load state of the server; and writing the target control file into the target memory. This solves the problem of lack of flexibility in adjusting control options for server log reporting functions in related technologies.

Dynamic logging adjustment can be performed during a debug session to facilitate issue resolution. A user of a software platform can initiate a debug session with a specified logging profile. The logging profile includes respective log levels for backend components of the software platform which only apply to server requests associated with the debug session. During the debug session, the user attempts to reproduce the issue which triggers corresponding server requests. The server determines whether a given server request is part of the debug session, and if so, dynamically adjusts the log level for actions associated with the server request. A debug session identifier for the debug session is inserted in log entries associated with the server request. After the debug session has concluded, server logs can be filtered by the debug session identifier to obtain a targeted set of log entries pertaining to the issue.

This invention discloses a server log intelligent analysis and fault early warning system and processing method, belonging to the field of computer system operation and maintenance and intelligent monitoring technology. It includes a multi-source log acquisition module, a log data preprocessing module, an intelligent log analysis module, a fault classification and early warning decision module, a fault closed-loop processing module, and a cloud-edge collaborative storage and retrieval module. The multi-source log acquisition module integrates server full-process operation log data from four categories: system, application, security, and hardware. Combined with a dual-model collaborative architecture of "LSTM neural network time-series analysis + association rule mining," it accurately locates the root cause of faults using a strong correlation standard of ≥0.8. Simultaneously, it constructs a dynamic early warning threshold based on nearly 6 months of historical server data trained using the XGBoost algorithm. The threshold range is adjusted in real time according to the fault occurrence rate and abnormal log fluctuation rate, and a graded differentiated processing scheme is pushed out.

The application provides a method, device, terminal and medium for optimizing a fault diagnosis log collection process, and belongs to the technical field of server log collection.The method steps are as follows: S1.BMC pre-maintains a mapping file of a mapping relationship between device asset information and a state register; S2.BMC records downtime after detecting a set error register, informs an ACD tool to collect state register information, and records a collection completion time; S3.BMC informs a BAFI tool to analyze fault device information and error type information, records an analysis completion time after the analysis is completed, and records and informs a user to collect fault diagnosis logs when the fault device information is located.The application realizes accurate acquisition of the time used in each stage in a fault diagnosis process, a simple and fast fault analysis process, and can prompt after the analysis is completed and realize separate collection of fault diagnosis logs.

A system and method for analyzing the quality of data in a server or network component a log file. The system imports a quality log analyzer (QLA) plugin into an integrated developer environment (IDE). The developer application code is compiled within the IDE. The QLA plugin invokes a log analyzer suggestion module (LASM) during the developer application code runtime. During the application code runtime, the LASM, reads the developer application code to parse server log strings associated with the developer application code. The LASM analyzes the developer application code server log strings for similarities, and calculates the similarity between two or more application code server log strings to determine if they are identical, similar, or unrelated. The system uses these comparisons along with an optimized server log rules engine to determine which application code server log strings can be optimized to reduce use of the server log memory.

The invention discloses a reinforcement learning-based log data quality evaluation and quantification model method, which comprises the following steps of: performing desensitization, deduplication, timestamp alignment and structured analysis on server logs to form a log sequence; outputting a quality score based on an analysis result, an entity extraction result and quality indexes such as missing and abnormal statistical calculation integrity, consistency and timeliness; setting a rapid scoring device and a reference scoring device, identifying distribution drift according to the scoring deviation of the rapid scoring device and the reference scoring device, and updating a scoring model; and constructing a state space and an action space, learning a governance action selection strategy by adopting Q-learning, re-scoring the governed log, and updating strategy parameters according to quality improvement and resource consumption. According to the method, the log data quality can be continuously evaluated, the quality problem can be found and positioned, and the balance between quality improvement and resource consumption is realized.

The application discloses a kind of deep learning driven server log anomaly detection methods, it is related to server operation and maintenance technical field, the method includes log data preprocessing, isolated forest dynamic sample selection, online contrast learning semantic self-adaption, deep anomaly detection model training and anomaly detection and output five steps, first to original unstructured log is parsed and standardized processing, again through isolated forest algorithm screening and resampling sample, generate dynamic balanced training dataset;Log template vector is updated using online contrast learning continuously, so that it adapts to system dynamic change, and both form closed loop optimization through collaborative mechanism;Finally, based on deep sequence model, learn normal log sequence mode, carry out anomaly detection to new log sequence and output result, the present application does not need frequent manual retraining, reduce operation and maintenance cost, improve long-term robustness and accuracy of anomaly detection simultaneously, suitable for intelligent operation and maintenance scene of various server clusters.

An image-processing device includes: a network interface; a user interface; and a controller. The image-processing device has a server logging function to store a log on a server connected to the image-processing device via the network interface. The controller is configured to perform: a log transmission process; and a warning process. The log transmission process is performed when a first condition for storing a log is met while the server logging function is enabled. The log transmission process including: generating; and transmitting. The generating included in the log transmission process generates a log. The transmitting included in the log transmission process transmits, to the server, the log generated by the generating included in the log transmission process via the network interface. The warning process includes issuing. The issuing included in the warning process issues a warning related to the server logging function via the user interface.