1409 results about "Log data" patented technology

The invention relates to the technical field of database operation and maintenance, in particular to a database operation and maintenance decision-making method and device based on a large language model.The method comprises the steps that real-time operation indexes and historical log data of a database are collected, and multi-dimensional time sequence features are extracted; and analyzing the characteristics by using a large language model, judging whether a performance bottleneck or an abnormal risk exists or not, and determining the relevance between the performance bottleneck or the abnormal risk and a database performance problem. If the risk exists, acquiring a system load sudden change trend and a resource request rate, quantifying a system pressure degree, and inputting a performance optimization model to generate an adaptive tuning strategy; calculating statement complexity through an SQL audit log, and selecting an index adjustment strategy in combination with an index optimization model; evaluating the influence of the tuning strategy and the index strategy on the throughput, the response delay and the resource utilization rate, and determining a target high-performance strategy; according to the scheme, accurate optimization and efficient operation and maintenance of database performance are realized through intelligent analysis and dynamic adjustment and optimization.

The invention relates to the technical field of application state diagnosis, in particular to an application state dynamic diagnosis and automatic repair method and system.The method comprises the steps that multi-source heterogeneous logs are collected and standardized in real time, key fields are extracted, context information is injected, and structured log data are generated; inputting the structured log data into a dynamic anomaly detection model, constructing a dual-path detection mechanism based on LSTM time sequence analysis and a graph neural network, identifying an abnormal mode and positioning a fault root cause; according to the output of the anomaly detection model, a repair action is triggered in a grading manner through an intelligent repair strategy engine; in the repairing process, system state changes are stored and recorded through a pre-writing type redundancy log, automatic rollback during abnormity is achieved on the basis of check point information, and data consistency and system stability are guaranteed. The problem of service interruption or data inconsistency possibly caused by traditional automatic repair is avoided, and the reliability of automatic operation and maintenance is improved.

The invention provides a server cluster operation and maintenance method based on multi-source heterogeneous data fusion and a dynamic knowledge graph, and the method comprises the following steps: collecting a performance index, a log text and topological structure data of a server cluster, splicing the performance data and the log data based on a unified time window, and generating a multi-modal feature sequence; and analyzing the sequence by using an unsupervised deep learning model, constructing a dynamic health baseline, and generating a health degree portrait through the deviation with real-time data. When an exception is detected, mapping an exception event into a dynamic topological graph constructed based on a topological structure; analyzing a fault propagation probability between nodes by using a graph neural network algorithm, positioning a root cause node, and generating a disposal strategy to execute disposal operation; and collecting the processed recovery data as a feedback signal, and updating the deep learning model by using incremental learning. The method has the beneficial effects that the fault discovery accuracy is improved, the alarm storm is effectively inhibited, the root cause is directly positioned, and the model self-iteration adaptability is higher.

The invention provides a private intelligent code collaborative optimization method and system based on graph retrieval, and relates to the technical field of software engineering intelligent auxiliary tools, and the method comprises the steps: obtaining a source code file and operation log data of a private project; the method comprises the following steps: performing syntax tree analysis on a source code file through a Tree-siter analyzer to generate a corresponding abstract syntax tree; constructing a code knowledge graph by analyzing a function call relationship, an inheritance relationship and a module dependency relationship; monitoring the change of a project file in the code knowledge graph through a code change event processor, and updating the code knowledge graph in real time when the file is changed; establishing an association relationship between the running log data and a corresponding function node in the code knowledge graph; the method comprises the following steps: analyzing a natural language problem of a developer into a Cypher query statement through an intelligent Agent; obtaining a query result from the graph database; and formatting the query result to generate a final result.

Certain aspects provide techniques and apparatus for executing queries in a computing system using machine learning models. An example method generally includes receiving a plan to satisfy a request in the computing system and event log data associated with execution of the plan. The plan generally specifies a first plurality of function calls at a first level of granularity. Using a plan refinement machine learning model, a refined plan is generated when the event log data indicates that execution of the generated plan results in one or more execution errors and the one or more execution errors are solvable. Generally, the refined plan specifies a second plurality of function calls at a second level of granularity, the second level of granularity being finer than the first level of granularity.

Techniques for generating simulations to evaluate a performance of an object controller. The controller may be configured to control one or more functionalities of a simulated object (e.g., a “smart object”) in a simulation, such as make the smart object operate like a human in a given scenario. A simulation system may generate one or more simulations based on log data captured by a vehicle operating in a physical environment. The simulation system may cause the object controller to control at least one smart object in the simulation, the smart object being representative of an actual object detected in the physical environment. The simulation system may compare a smart object trajectory to an actual object trajectory of the object to determine a performance metric associated with the object controller. Based on a determination that the performance metric satisfies a threshold, the simulation system may validate the object controller.

The invention provides a cloud AI data leakage risk prediction and control method and system based on a flow map, and relates to the technical field of cloud computing data security. Based on the collected multi-source log data, constructing a time series data flow knowledge graph; inputting the knowledge graph into a space-time diagram risk prediction model, and obtaining a dynamic risk score of an entity and a predicted potential data leakage path by fusing a space-time diagram neural network and risk conduction simulation; according to the dynamic risk score and the potential data leakage path, gradient dynamic security management and control measures are generated and executed, and the measures comprise differentiated access control actions triggered according to the risk level; and generating a visual audit report of the data access link based on the risk prediction result and the security management and control process. Through a time sequence graph reflecting dynamic flow of data and utilizing STGNN to carry out risk conduction modeling and prediction, the active defense capability of cloud AI data security is improved, the accuracy of risk identification is effectively improved, and service interference is effectively reduced.

The invention relates to a new energy micro-grid multi-objective optimization control method and device, equipment and a medium. The method comprises the following steps: firstly, acquiring real-time output data of renewable energy sources of the micro-grid, operation parameters of energy storage equipment and load demand records, and performing time sequence analysis to obtain an energy output variation rule and a load demand curve; constructing a multi-objective function model containing economy-stability double objectives and a coupling correction term based on the law and the curve, generating an initial balance scheme through linear programming, and iteratively determining resource collaborative allocation parameters through a particle swarm optimization algorithm; the distribution parameters and the energy storage available capacity are input into the agent model, a real-time optimization decision instruction is generated, and an economic evaluation value is calculated in combination with running log data; and if the evaluation value does not reach the power grid stability threshold value, recalculating a balance scheme through a multi-objective function model, and generating a standardized equipment instruction set. According to the method, collaborative optimization of economy and stability of the micro-grid is realized, and stable operation of the micro-grid in different scenes is guaranteed.

The invention discloses a multi-tenant adaptive cooperative defense method and system in a hybrid cloud scene, and the method comprises the following steps: obtaining a remote credible proof of hardware, verifying the credibility of a tenant agent and the integrity of a code based on the remote credible proof, granting a mark, and detecting cloud information to generate a machine readable portrait containing key parameters; collecting local multi-mode log data coding embedding vectors of tenants, and after privacy processing, calculating that the abnormal confidence exceeds a threshold value by a local model to trigger current limiting or blocking; and constructing a hierarchical federated architecture containing local nodes of tenants, a regional cloud and a global control plane, encrypting aggregation parameters to generate a cross-tenant attack recognition global model and issuing the cross-tenant attack recognition global model to each tenant on the premise that log data is not out of a domain. According to the invention, integrity verification is carried out on the tenant side security agent through the trusted access mechanism, and the tenant portrait is constructed based on the trusted mark, so that the real and verifiable cooperative defense capability among a plurality of tenants is realized, and the cooperative process has dependency.

The invention discloses a data leakage early warning method, device and equipment based on buried point collection and a storage medium, and relates to the technical field of system exception monitoring, and the method comprises the steps: obtaining exception log information and historical data information sent by a buried point collection service node; performing aggregation counting on the abnormal log information according to preset time granularity to obtain abnormal quantity statistical data of each time granularity; performing transverse and longitudinal analysis on the abnormal quantity statistical data and the historical data information according to a preset transverse and longitudinal analysis strategy to obtain a comparison deviation value; and performing data leakage early warning according to the comparison deviation value. According to the method, time-sharing processing and multi-dimensional analysis are carried out on the abnormal log data of the system, so that real-time early warning and service subdivision monitoring of buried point data leakage are realized, and the reliability and fault positioning efficiency of the system are improved.

Certain aspects provide techniques and apparatus for executing queries in a computing system using machine learning models. An example method generally includes receiving a plan to satisfy a request in the computing system and event log data associated with execution of the plan. The plan generally specifies a first plurality of actions to be performed by the computing system at a first level of granularity. Using a plan refinement machine learning model, a refined plan is generated when the event log data indicates that execution of the generated plan results in one or more execution errors and the one or more execution errors are solvable. Generally, the refined plan specifies a second plurality of actions to be performed by the computing system at a second level of granularity, the second level of granularity being finer than the first level of granularity.

An automated method is provided for comparing a source database and a target database that are intended to be kept in synchronization with each other. An application processes transactions that are posted to the source database, and a synchronization process processes transactions that are posted to the target database. Change log data is chunked from a change log associated with the source database to create first chunks of change log data. Change log data is also chunked from a change log associated with the target database to create second chunks of change log data. A comparison engine compares representations of the first chunks of change log data with representations of the second chunks of change log data, wherein the second chunks of change log data correspond to the same respective first chunks of change log data that were made to the source database and kept in synchronization with the target database via the synchronization process. The source database and the target database are presumed to be in synchronization with each other via the synchronization process when the comparison indicates that the representations of the first chunks of change log data match the representations of the second chunks of change log data. The source database and the target database are presumed not to be in synchronization with each other when the comparison indicates that the representations of the first chunks of change log data do not match the representations of the second chunks of change log data. An action is performed when the comparison indicates that the representations of the first chunks of change log data do not match the representations of the second chunks of change log data. The action may be a corrective action that resynchronizes the target database with the source database, or that suspends or stops the application from processing transactions.

The invention relates to the technical field of knowledge graph construction, and discloses a knowledge graph-based agent behavior description method, which comprises the following steps of: firstly, analyzing an unstructured agent operation log data stream through a processor, and mapping the unstructured agent operation log data stream into a discretized module access sequence by utilizing a topological structure of a code knowledge graph so as to remove text redundancy; performing multi-dimensional time sequence analysis on the sequence, and calculating resident distribution data representing operation continuity, frequency domain fluctuation data representing switching rhythm and multi-scale coverage extension data representing a traversal range; and then aggregating the data into a fixed-length multi-dimensional index vector, constructing an index node associated with a session entity in a graph database, and writing the vector as a binary structured attribute into a storage field. According to the method, massive unstructured logs are converted into compact graph structured indexes, and quick positioning and direct retrieval of a complex agent operation mode are supported while the storage space is remarkably saved.

The invention provides a log data reading method and device, equipment, a medium and a program product, and relates to the field of big data. Obtaining a mapping relationship between a plurality of timeline identifiers in a database and corresponding start log serial numbers; the timeline identifier is an identifier generated when the main database and the standby database are switched, whether the log data cross different timeline identifiers or not is judged according to the mapping relation and the starting log serial number and the ending log serial number of the log data, and under the condition that the log data cross different timeline identifiers, the log data cross different timeline identifiers. Reconstructing a log reading process according to a start log serial number of a new timeline identifier, wherein the new timeline identifier is different from the plurality of timeline identifiers; according to the method, automatic reconstruction of the log reading process after main and standby switching is achieved, the problems of low efficiency and poor real-time performance caused by manual operation are avoided, and continuity and consistency of data synchronization of the source end and the target end are guaranteed.

The invention provides a defect repairing method and device based on a large model, electronic equipment and a medium, and relates to the field of defect repairing, and the method comprises the following steps: obtaining original log data generated in real time; analyzing the original log data through a preset model, and determining defect logs and user operation parameters; determining a target code file corresponding to the defect log; repairing the target code file through a preset model, and generating a repairing scheme to determine a repaired code file; and submitting the repair code file to a code warehouse to cover the target code file, determining an updated code warehouse, and determining a production system instance according to the updated code warehouse. According to the method, the original log of the deployed system is obtained in real time, the defect log and the associated user operation parameters are automatically analyzed and determined, the target code file is positioned, the repair scheme adjustment code is generated, then the repair code is submitted to the warehouse for updating, the production system instance is determined, and automation of the whole process of defect repair is achieved.

The invention provides a data verification method, electronic equipment and a computer program product. The method comprises the steps of obtaining a to-be-verified target bill from an upstream first database; monitoring log change information of successful library falling of the target bill in a downstream business database, and obtaining original log data corresponding to the target bill from the business database; performing format conversion processing on the original log data to obtain target log data, and storing the target log data in a second database; and performing consistency comparison verification on the target log data obtained from the second database and the target bill.

The invention relates to the field of environmental analysis, and provides a gas hydrate three-phase coexistence region modeling method and system. The method comprises the following steps: acquiring three-dimensional seismic data and original logging data, and preprocessing the original logging data to obtain corrected logging data; respectively performing logging interpretation and structural interpretation according to the corrected logging data and the three-dimensional seismic data to obtain interpretation results; converting a structure interpretation result in the interpretation results into a three-dimensional geological structure model, and performing grid division on the research area by adopting a corner grid system to obtain a structure grid model; and performing attribute modeling on the constructed grid model based on variation function analysis, and predicting porosity and saturation in a three-dimensional space through a Kriging interpolation algorithm to obtain a three-phase coexistence region model. According to the method, the influence of complex geological conditions on the accuracy of the model is reduced, and the precision of the three-phase coexistence region model is remarkably improved.

The invention relates to the technical field of computers, in particular to a network security operation method, device and equipment and a storage medium, and is used for providing a network security operation scheme which can give consideration to efficiency and precision and is adaptive to a current dynamically evolved network threat environment. The method comprises the steps of obtaining to-be-processed operation data, wherein the operation data is obtained by preprocessing network flow data and log data; processing the operation data by using a lightweight model to generate initial alarm data; based on the initial alarm data, carrying out alarm authenticity discrimination and threat level determination through a heavy model, and outputting confirmed threat event information; the threat event information indicates alarm data and threat levels corresponding to threat events confirmed by the heavy model; based on the threat event information, risk disposal is executed, affected asset information is obtained, a traceability evidence obtaining tool is matched, and a standardized evidence obtaining report is generated; and based on the standardized evidence obtaining report, executing vulnerability repair.

An example operation may include one or more of implementing a trained artificial intelligence (AI) model using a neural network training capability with at least one of call log data of calls determined to have an activity risk, activity risk data, activity context, and model feedback data, capturing audio from an ongoing telephone call, converting the audio from the ongoing telephone call into text, obtaining context of an activity from a computing device, executing the trained AI model to generate an activity risk profile based on the text and the context of the ongoing telephone call, and executing an action during the ongoing telephone call based on the activity risk profile.

The invention provides a vehicle-mounted test abnormal log automatic lading method based on rule matching, and the method comprises the steps: firstly collecting log data from multiple data sources of a vehicle-mounted test, converting the log data into structured log entries through preprocessing, and forming a log event pool; comparing the structured log entries with the standardized rule base in real time, and generating abnormal candidate events if matching succeeds; performing context correlation analysis on the abnormal candidate events through a log time sequence and multi-source cross information, eliminating noise and false alarms, and merging correlation events to obtain a confirmed abnormal event set; extracting abnormal event key information, automatically filling a defect list template, generating a defect list containing a log attachment, and submitting the defect list to a defect management platform; and finally, recording the whole process information of the lading bill, and establishing traceability association of the defect bill, the original log and the matching rule to realize the version management of the lading bill. The processing efficiency of the test log can be improved.

The invention discloses a semantic unification and anomaly traceability analysis method and system for heterogeneous program logs, and the method comprises the steps: firstly collecting original logs in various formats, and packaging the original logs into standard objects in a unified manner; thirdly, identifying fields through a rule and a semantic model, uniformly mapping and labeling semantic tags, eliminating semantic differences, and constructing log data with uniform semantics; automatically constructing a cross-system call chain based on the request identifier and the like; secondly, extracting log features and comparing the log features with a normal behavior model to carry out anomaly detection, and generating an abnormal event; combining time, calling and dependency to construct an anomaly propagation path, and reversely backtracking and positioning an anomaly source; abnormal priority scores are calculated according to the propagation range, the influence degree and the like, and sorting and visual display are carried out; through unified semantic modeling and automatic association analysis of heterogeneous logs, accurate tracing and intelligent positioning of cross-service link anomalies are realized, the operation and maintenance efficiency is effectively improved, and the dependence on artificial experience is reduced.

The invention discloses a data use control method and system based on a smart contract, and relates to the technical field of data use control, and the system comprises an identity authentication module, a data classification and grading module, a data sandbox module and an evidence storage traceability module. Performing identity authentication on a user request of a data user based on an authentication protocol; performing classification rating on the accessed data; the data user and the data provider negotiate a contract signing detailed rule; the data user and the data provider sign a block chain smart contract; deploying the intelligent contract to a data sandbox, performing authority evaluation and distribution on the user, and performing behavior monitoring on the user; when the data user violates the signed smart contract, terminating the data access permission of the user, and calling the data sandbox to execute operation according to the contract signing content; logs are automatically generated, data access and use circulation are traced in the whole process, and it is ensured that data can be traced, audited and managed from authorization to use. According to the invention, the security and credibility of data circulation are guaranteed in a data transmission use control mode with decentration and multi-party cooperative participation.

The invention relates to a multi-source data fusion type passenger station intelligent management and control method and system based on AI drive, and belongs to the technical field of infrastructure intelligent management, and the intelligent management and control method comprises the steps: obtaining video stream data through an intelligent camera, obtaining environment monitoring data through an Internet of Things sensor, and obtaining ticket checking gate log data through a business system interface. And performing edge calculation processing on the video stream data to generate structured metadata. And according to the space grid ID alignment data, aggregating and generating a fusion feature matrix according to a time window. And calling a passenger flow prediction model to process the fused feature matrix, and outputting a passenger flow prediction value and a confidence score. The predicted value is compared with a dynamic threshold, and a standardized decision event is generated when the threshold is exceeded. And a linkage task package is generated through rule engine matching, the linkage task package is distributed to each device through message middleware to execute cooperative operation, and model parameters and threshold library data are updated according to a device response message. The management and control requirements of large-scale passenger flow in the passenger station can be effectively met.

The invention discloses an automatic operation and maintenance system based on fusion of an AI agent and a configuration management database, and the system comprises a data collection module which is used for automatically collecting configuration information of IT infrastructures through a multi-mode collection technology; the configuration management database is used for converting the configuration information into configuration items and dependencies to construct a knowledge graph; the AI agent comprises a log analysis unit used for carrying out structured processing on the unstructured log data; the anomaly detection unit is used for carrying out anomaly detection on the structured log data and time sequence index data by adopting an unsupervised machine learning algorithm; the root cause positioning unit is used for performing topological relation reasoning on the detected abnormal events based on a knowledge graph, and determining a fault type and a root cause configuration item causing the fault; and the automatic executor is used for calling an automatic operation and maintenance tool to execute repair operation according to the fault type and the root cause configuration item. The operation and maintenance efficiency can be improved, and the fault repair time can be shortened.

The invention provides an on-off control method and device for a gateway port and a medium. The on-off control method comprises the steps of obtaining historical access log data and threat intelligence data of a target port in a gateway; based on the historical access log data and the threat intelligence data, performing time sequence analysis by using a pre-trained long-short-term memory network model to obtain a predicted security access time period of the target port in a future preset time period; generating a temporary port exposure preset rule of the target port according to the predicted security access time period; determining a traffic feature vector of the real-time traffic data packet of the target port; using a preset deep reinforcement learning agent to determine an on-off control instruction for the target port based on the traffic feature vector and a temporary port exposure preset rule; and executing the on-off control instruction to modify an access control list state of the gateway firewall to the target port. According to the method and the device, dynamic and refined gateway port on-off control can be realized, so that the service flexibility and security are considered.

The invention discloses a service log abnormity early warning method and system based on an AI intelligent agent, and relates to the field of communication services. Comprising the steps of 1, carrying out log data acquisition and preprocessing, 2, constructing an AI agent model, 3, carrying out intelligent feature extraction: extracting semantic features of logs by utilizing a multi-modal log analysis model, analyzing time sequence features and statistical features of the logs, constructing multi-dimensional feature vector representation, and carrying out intelligent feature extraction; 4, performing abnormal mode identification: identifying an abnormal log mode based on a multi-mode log analysis model, discovering a potential abnormal type, and performing abnormal severity evaluation in combination with historical data, and 5, providing an intelligent early warning decision: making an early warning strategy according to the abnormal type and severity, generating a detailed abnormal analysis report, and performing early warning on the abnormal log mode. And step 6, carrying out model optimization: collecting user feedback and processing results, continuously optimizing model parameters and early warning thresholds, and updating the knowledge graph and the abnormal mode library.

The application provides a CPU platform fault log recording method and device, comprising: receiving the running log data stream output by the CPU debugging serial port in real time; collecting the sampling value of the key power supply voltage and monitoring the hardware signal line to obtain the restart event signal and the shutdown event signal; packaging into a structured log recording unit; and writing the compressed log data block back to the flash memory; in response to the external reading instruction, reading the log data block from the specified log area of the flash memory and transmitting to the host end diagnostic tool; reorganizing and displaying in time sequence for fault analysis. The application can construct a log recording channel completely independent of the main CPU and operating system without increasing any additional hardware cost, ensuring that even in the most extreme system failure scenario, the complete context information at the fault moment can be reliably saved, thereby greatly improving the diagnosis efficiency and accuracy of the occasional fault.

Techniques are discussed herein for generating and evaluating driving simulations based on synthetic scenarios. Simulated objects may be controlled based on parameters determining the attributes and behaviors of the objects, and scenarios may be synthetically modified by changing the parameters for a simulated object. For a driving scenario with a synthetic simulated object, a simulation system may analyze driving log data to determine a probability or prevalence associated with the driving scenario. In various examples, the simulation system may determine marginal density estimates for individual attributes of the simulated object, as well as a cumulative distribution function modeling the dependence between the attributes. The joint probability distribution determined for the synthetic simulated object can be used for evaluating the efficacy of the simulation and the performance of the simulated vehicle controllers.

The invention discloses a thermal power plant multi-source heterogeneous data quality evaluation method and system, and relates to the technical field of data evaluation, and the method comprises the steps: carrying out the comparison based on an alignment path length, a preset path length threshold value and a deviation threshold value, obtaining a consistent data flow and a conflict data flow, the method comprises the following steps: respectively calculating an accuracy index, an integrity index and a timeliness index for consistent data flow and equipment log data, obtaining a three-dimensional quality index matrix, weighting the three-dimensional quality index matrix through an entropy weight method, and calculating a comprehensive quality score of the multi-source heterogeneous data in combination with a real-time weight value of the multi-source heterogeneous data, and comparing the comprehensive quality score of the multi-source heterogeneous data with a preset score threshold to obtain a quality evaluation result, and directly generating a conflict alarm signal for a conflict data flow. According to the evaluation method based on the data indexes, not only is the sensitivity to data logic contradictions enhanced, but also context awareness and weight optimization in the evaluation process are realized.

A power consumption prediction and management method and system based on a solid state disk, a medium and a product relate to the field of solid state disks. The method comprises the following steps: predicting the workload intensity of a host in a first preset time window based on an I / O (Input / Output) instruction stream to obtain an expected host workload curve, and determining an expected host idle time period; calculating an expected host power consumption value in the expected host idle time period; extracting internal state parameters of the solid state disk according to the historical operation log data; establishing a background task triggering probability model, and calculating the triggering probability of starting the high-power-consumption background task by the solid state disk in a first preset time window; performing power consumption conflict judgment according to the expected host power consumption value and the triggering probability, and generating a comprehensive predicted power consumption value; and based on the comprehensive predicted power consumption value, determining an optimal target power consumption state, and instructing the solid state disk to be switched to the optimal target power consumption state. By implementing the technical scheme provided by the invention, the stability of state switching of the solid state disk is improved.