34 results about "Server log" patented technology

The invention relates to the technical field of electrical equipment testing, in particular to an intelligent detection and classification management method and system for IDC equipment, and the method 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 and the GPU occupancy rate, storing the I / O load and the network throughput rate, extracting server log entries, and storing the server log entries in an equipment exception log library; and obtaining IDC equipment operation state data. According to the invention, by synchronously monitoring the current, voltage, power consumption and temperature of the IDC equipment and collecting the operation data of the server, the new scheme optimizes the comprehensiveness of the data and the real-time performance of the monitoring, and the monitoring strategy provides finer risk assessment and classification management by dynamically analyzing the state fluctuation of the equipment, so that the reliability of the IDC equipment is improved. Therefore, the accuracy of fault prediction and the efficiency of maintenance are improved, the analysis of abnormal logs and operation data is comprehensively utilized, the prediction capability of potential problems is enhanced, and the system downtime rate and the maintenance expenditure are effectively reduced.

The invention discloses an intelligent early warning and decision making system and method based on large model driving. The intelligent early warning and decision making system comprises a server log data layer which is responsible for collecting various log data generated in the operation process of a server; the filtering layer is used for undertaking a task of quickly screening abnormal log information; the retrieval layer is used for matching similar or relevant information in a risk library for each abnormal log; the Agent analysis and decision-making layer is used for carrying out deep analysis on abnormal conditions by utilizing natural language processing and knowledge reasoning capability of an intelligent deep large model so as to identify attack types and formulate personalized defense schemes; the defense layer is responsible for calling a built-in defense tool library to execute a defense scheme; the execution layer is used for converting an instruction of the defense layer into a specific operation automatically executed through a script; the monitoring layer is used for monitoring results in and after scheme execution in real time; the inspection layer is used for automatically inspecting the production line after monitoring is normal; and the analysis layer is used for carrying out automatic chart analysis on attack or risk data. According to the invention, automatic processing from attack detection to system recovery is realized, and the response efficiency is improved.

The invention discloses a server BIOS log transmission method based on function decoupling, and the method comprises the steps: a processor obtains to-be-transmitted log data and corresponding log attributes, determines the log type of the log data based on the log attributes, calls a specified real-time clock, determines the generation timestamp of the log data, and transmits the log data to a server; sending the log data to a baseboard management controller to enable the log data to carry a generation timestamp, calling a log transmission path corresponding to the log type, and sending the log data to the baseboard management controller; the baseboard management controller sorts the high-level logs and the basic logs according to the sequence of the generated timestamps from morning to evening to obtain a global log queue; mapping the log data in the global log queue into a memory through memory mapping, and backing up the log data to a flash memory; and outputting the basic logs in the global log queue to a network console according to the sequence from front to back. According to the method, the conflict between high throughput and starting timeliness in the log transmission process can be effectively solved.

The invention provides a malicious external connection event detection method and device of a host. The method comprises the steps that multiple pieces of destination address information are collected in a server log of the host; the destination address information comprises a destination IP and / or a destination domain name; the destination IP is used for indicating IP addresses requested to be accessed by a plurality of servers of the host; the destination domain name is used for indicating websites requested to be accessed by a plurality of servers of the host; the server log is generated through a log generation system deployed in the host; determining malicious destination address information in the multiple pieces of destination address information; the malicious destination address information refers to destination address information successfully matched with a malicious address in the threat intelligence system; determining an alarm object according to the malicious destination address information; and outputting the malicious external connection event alarm information to the alarm object. According to the invention, the detection coverage can be improved, and the detection cost can be saved.

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 provides a multi-server log collection method, device and system, applied to the technical field of computers, to solve the problems of complex wiring and high cost in log collection in related technologies, and proposes the scheme, a first wireless transceiver module is arranged on each server, a second wireless transceiver module is arranged in the data transmission device of each cabinet, and the data transmission device is connected with a log management equipment, the method is applied to the data transmission device, and the method comprises the following steps: receiving the baseboard management controller system log sent by the first wireless transceiver module on each server in the current cabinet through the second wireless transceiver module of the current cabinet; determining a target data transmission device according to a current load condition; and sending the baseboard management controller system log to the log management equipment through the target data transmission device. The application can greatly reduce the number of wirings, thereby reducing the wiring complexity, reducing the wiring cost, and being easy to maintain.

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.

The application discloses a kind of server security access method, device, electronic equipment and readable storage medium, applied to server, server is connected with one or more client communication, method includes: according to server log file, the access log of each user corresponding to each client is determined;Each access log is analyzed using risk assessment rule, and the target risk category that corresponding user hits in risk assessment rule is determined;Risk assessment rule includes the combination of one or more of API connection failure assessment rule, server resource access frequency assessment rule, off-site login assessment rule, high concurrent access assessment rule;According to the corresponding relationship between risk category and restriction rule, the target restriction rule corresponding to target risk category is determined;The user that hits corresponding target risk category is limited using target restriction rule.This scheme can be more accurate user risk assessment to client user and realize more flexible risk user control.

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.

The present invention teaches a method for determining an accurate measure of unique download sessions in an environment where IP addresses are shared among multiple devices. According to a first preferred embodiment, the system of the present invention may analyze requested server log data to determine the number of unique client devices sharing the same IP addresses. The system may then dynamically adjust the length of lookback windows for each unique device based on observed download behavior and the number of user agents associated with each overlapping IP address.

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 server log file backup method and device, equipment, a medium and a product, and relates to the technical field of cloud computing, and the method comprises the steps: obtaining a target log file package uploaded by at least one target log server, and determining a target log type of a target log file contained in the target log file package; according to the target log type and candidate log types associated with the candidate storage containers, determining a target storage container from the candidate storage containers, and storing the target log file as a to-be-backed-up log file in the target storage container; when it is detected that the current moment meets the log file backup moment, file backup operation is executed on the to-be-backed-up log files stored in the candidate storage containers respectively. According to the method, the effects of centralized storage and backup of the to-be-backed-up log files are achieved, and compared with an existing mode of sequentially accessing each log server to conduct log file backup, the backup efficiency of the log files is improved, and the workload of log file backup is reduced.

A method and apparatus for estimating bitrate, buffering events and / or other QoE metrics of video reception, based on CDN logs, are provided. The method and apparatus may rely on an AI model, including a neural network. The neural network may receive a training data set comprised of a plurality of CDN server logs. The CDN server logs may be associated with known QoE metrics collected from a plurality of user devices. Once the neural network is trained, a CDN server log, without associated QoE metrics, may be received as input. With the trained neural network and the CDN server log, buffering events and average bitrate QoE metrics may be estimated for one or more user devices without explicitly receiving QoE metrics from the user device.

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.

To provide a technique for effectively performing processing required for machine learning or the like by using an unused device in an ETC test control system.SOLUTION: A test management interface device connected to a log collection device that stores log data of a lane server and an ETC test control device includes: a log collection device communication unit that acquires log files from the log collection device; a central processing unit that acquires the log data from the log files, converts the log data into format data that is a format used by the ETC test control device and performs failure prediction of the lane server by using the format data; and a memory unit that stores target data including the format data and prediction results of the failure prediction.SELECTED DRAWING: Figure 1

A method or system for generating recommendations based on detected correlations of static and dynamic data. For example, the system generates feature inputs for the machine learning model that is based on labeled static data as well as dynamic data indicating current usage. Notably, the dynamic data is not retrieved from a data store of known training data, but instead is streamed in real time from active databases accessible to the network. Furthermore, the system provides a formatting mechanism that translates user selected requirements and parameters from a human-readable format indicating particular attributes of a database to optimize (e.g., database performance, security, compliance, capacity planning, etc.) into data that may be included in a machine learning feature input that specifies which correlations to use (e.g., server operational statistics data, server log data, monitoring metrics, etc.).

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.

Methods and systems for predicting time of occurrence of future server failures using server logs and a stream of numeric time-series data occurred with a particular time window. Method performed by processor includes accessing plurality of server logs and stream of numeric time-series data, applying density and sequential machine learning model over plurality of server logs for obtaining first and second outputs, respectively, applying a stochastic recurrent neural network model over the stream of time-series data to obtain third output. The method includes aggregating first, second, and third outputs using an ensemble model, predicting likelihood of at least one future server anomaly based on the aggregating step, and determining time of occurrence of the at least one future server anomaly by capturing server behavior characteristics using time-series network model. The server behavior characteristics include time-series patterns of the stream of numeric time-series data.

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.

This application provides a model testing method, apparatus, device, and storage medium, which can be applied in the computer field. The model testing method includes: inputting a benchmark dataset for the current time period into a test model and a model under test to obtain multiple first diagnostic results for the test model regarding any first test parameter of multiple first server logs and multiple second diagnostic results for the model under test regarding any first test parameter; obtaining benchmark test results based on the multiple first diagnostic results and multiple second diagnostic results; inputting a baseline test dataset for the current time period into the model under test to obtain multiple third diagnostic results for any second test parameter of multiple second server logs; obtaining baseline test results based on multiple baseline values ​​for any second test parameter in the baseline test sample and multiple third diagnostic results; and determining that the model under test has an anomaly if the benchmark test results and the baseline test results do not meet preset conditions.

The invention discloses an error log analysis solution method and device based on large model driving and a medium, and the method comprises the steps: deploying a corresponding local large model based on the current server performance; scanning the to-be-analyzed server log through a preset log monitoring parameter to obtain a corresponding error log; according to the error log and a preset cue word template, generating a corresponding cue word, taking the cue word as input, and outputting a corresponding solution through a local large model; and creating a specified file based on the file path of the error log, and storing the solution through the specified file. The error logs are automatically scanned and extracted through the preset log monitoring parameters, and the solution is generated by using the large model, so that the tedious process of manually searching and analyzing the logs by operation and maintenance personnel is reduced, and the human resources and the time cost are saved.

To enhance the ability to analyze network DNS server logs, this invention innovatively proposes constructing a knowledge graph for the domain name resolution system, integrating multiple technologies. First, an automated crawler model combining aiohttp and dig technologies was designed based on principles of domain name resolution, authoritative name servers, alias resolution, and autonomous systems, and a corresponding domain knowledge base was constructed. Second, based on this knowledge base, a prototype knowledge graph for the domain name resolution system was designed and the knowledge graph was completed, with nearly 5 million nodes. Finally, this knowledge graph was used to assist in the construction of a web log abnormal behavior recognition model. Experimental and practical application feedback shows that this knowledge graph plays a crucial role in the detection and analysis of abnormal behavior in network server logs, improving the recognition rate of the web log abnormal behavior model.

The application provides a kind of server log automatic saving method, device, equipment, storage medium and product, comprising: running first shell script, initializing automatic saving program, starting log automatic saving process, and obtaining process occupation resource and configuration information of log automatic saving process;Collect server log data through target serial port;Server log data is recorded as text file, and the text file is temporarily saved under target folder;Archive the text file and save it to the memory;Release the process occupation resource of log automatic saving process.The scheme of the application can realize the automatic saving of server log data, facilitate the timely positioning of server problems, and improve the reliability of the system.

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.