2032results about "Non-redundant fault processing" patented technology

The invention relates to the technical field of computer system structures and storage, in particular to a non-volatile memory quick recovery method based on metadata priority and on-demand loading. The method comprises the following steps: in response to a system fault signal detected by a voltage monitoring unit, freezing a processor context and traversing a page table structure to extract system configuration information; writing the system configuration information and business data codes in the volatile memory into a nonvolatile medium to generate a persistent state mirror image; analyzing the persistent state mirror image, extracting address conversion metadata, and reconstructing a mapping relation from a virtual address to a physical page frame in a volatile memory; and generating an address mapping table, wherein the physical page frame pointed by the address mapping table is set to be in an existing state but is not associated with the effective service data. According to the method, decoupling of the control flow and the data flow is realized by constructing a virtual ready state, and quick starting of the system and immediate response of key services are realized on the premise of not depending on the total capacity of a memory.

A hybrid, redundant, fail-safe architecture provides a unified fail-operational framework for autonomous agents operating across physical and virtual domains. The system employs a multi-modal data source suite, an adaptive hybrid data fusion module, and an intelligent decision-making module. A novel closed-loop interaction enables a health monitoring module that detects an incipient fault in a data source by monitoring ancillary performance metrics. Upon detection, the module generates a fault signature, including a quantitative prognostic estimate of a future failure time, and transmits it to an adaptive data fusion module. The fusion module proactively reconfigures its state estimation algorithm by decreasing reliance on the degrading data source in proportion to the prognostic estimate. This preemptive compensation ensures the system maintains a high-integrity environmental model and achieves true fail-operational continuity. The architecture is applicable to numerous embodiments providing a universal solution for proactive fault management and system resilience.

Techniques for applying a generative artificial intelligence (AI) model to identify and correct anomalies in remediation records are disclosed. A system trains and applies a generative AI model to displayed datasets to predict remediation record anomalies. If the system detects the generation of a remediation record in a dataset to reconcile the displayed datasets, the system generates a generative AI prompt that includes the remediation record. The generative AI model generates an output that identifies anomalies in the remediation record and the datasets being reconciled. The generative AI model further generates recommendations for remediating errors in the remediation record.

The invention discloses an interaction control method and system for an intelligent agent and a front-end and back-end system, and the method achieves the automatic control of the intelligent agent on the front-end and back-end system through the integration of a large language model. The method comprises the following steps: an intelligent agent performs natural language intention recognition through a large language model, automatically selects a proper tool, extracts parameters required by the tool, converts the intention into a target task, generates a structured control instruction, maintains a complete session history and supports multiple rounds of dialogue interaction; the back end receives a user request, calls an intelligent agent to generate a structured instruction, transmits the structured instruction to the front end, and dynamically adjusts a subsequent instruction according to the received front end feedback; and the front end receives the instruction transmitted by the rear end, automatically identifies an instruction format through a regular expression, performs corresponding operation after parameter verification and feeds back a result. According to the method, unified control of the agent on the front-end UI and the back-end business logic is realized, complex multi-round dialogue and multi-step operation are supported, and the method has a wide application prospect.

The invention provides a multi-dimensional operation parameter analysis fault detection method and system, and relates to the technical field of fault detection, and the method comprises the steps: building an inspection task system and a fault triggering system, and forming a task basic data set; generating an inspection task instance object; analyzing to obtain monitoring index data; effective fault report information is extracted, and a software system inspection result set is formed; operating parameters of the hardware system are collected, threshold detection and fluctuation analysis are executed, and a hardware system inspection result set is generated; and extracting multi-dimensional features, inputting a fault scoring function, calculating a comprehensive fault score of each device or system, and automatically generating an inspection report and a fault report sheet. According to the method and the device, the problems that fault evaluation depends on manpower and judgment standards are not uniform in the prior art can be solved, automatic risk grading and result generation based on comprehensive scores are realized, and thus the accuracy and the diagnosis efficiency of fault detection 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.

State of the art systems used for industrial plant monitoring have the disadvantage that they fail to correctly assess reason for dip in performance of the plant and in turn trigger appropriate corrective measures. The disclosure herein generally relates to industrial plant monitoring, and, more particularly, to a system and method for development and deployment of self-organizing cyber-physical systems for manufacturing industries. The system monitors and collects data with respect to various parameters, from the industrial plant. If any performance dip is detected, the system determines corresponding cause, and also triggers one or more corrective actions to improve performance of the plant and different plant components to a desired performance level.

Techniques are provided for artificial intelligence (AI) based application error detection and resolution. Extensive amounts of time and resources are consumed by service providers when attempting to resolve application errors experienced by customers. Unfortunately, a service provider may spend tedious amounts of manual effort to evaluate and solve an error that is already known or already solved. The techniques provided herein reduce the amount of time and resources involved in detecting and resolving errors associated with applications. In particular, an error mapping is generated for a current troubleshooting case to resolve for an application. The error mapping is compared to error mappings of previously resolved troubleshooting cases. If a match is found, then a troubleshooting action associated with a previously resolved troubleshooting case is suggested or executed. Otherwise, a service ticket is created for solving the current troubleshooting cases.

The invention discloses an automatic fault repairing method for an operating system based on artificial intelligence, and relates to the technical field of automatic fault repairing, and the method comprises the steps: carrying out the feature analysis of system operation data through a pre-trained fault feature extraction model, generating a fault feature vector, inputting the fault feature vector into a fault classifier, and obtaining a fault feature vector; a current fault type is identified through a multi-classification algorithm, fault cause primary tracing is performed according to the fault type to obtain a fault generation factor, secondary tracing is performed on the fault generation factor to obtain a fault influence factor, positioning is performed based on the fault generation factor, and a corresponding repair strategy is matched from a knowledge base. The method comprises the following steps: acquiring historical system operation data with relevance on the basis of a fault influence factor, acquiring updated real-time system operation data after executing a repair operation to calculate a system optimization coefficient, judging a forward trend of a repair strategy according to a preset optimization threshold value, and updating the forward trend into a knowledge base to realize rapid and efficient automatic repair.

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 computers, and provides an automatic data acquisition and fault processing method based on a Kylin operating system, which comprises the following steps: a Kylin-Agent acquires full-dimensional observation data in real time from a monitoring component deployed in the Kylin operating system in a data direct acquisition mode; based on the full-dimension observation data, the Kylin-Agent carries out fault detection to identify an anomaly or a fault; the Kylin-Agent is combined with a Kylin exclusive knowledge base and the reasoning ability of a large language model to carry out root cause analysis on abnormity or faults so as to locate fault causes; based on a fault reason, the Kylin-Agent retrieves related solution knowledge from the Kylin exclusive knowledge base, and utilizes the related solution knowledge based on the specific type and severity of the fault; the Kylin-Agent calls a predefined self-defined tool set, carries out security verification on the specific operation instruction, and executes a fault repair scheme after the verification is passed; and the Kylin-Agent captures and feeds back an execution state and a result in real time, and generates a fault processing report based on a complete execution process.

Client support for applications to access services can be provided. In an example, a computing system can receive, from a client device, text input for an in-progress application to access a service. The client may be prevented from accessing the service prior to the in-progress application being approved. The computing system can detect an error associated with processing the in-progress application based on the text input and contextual information. the computing system can determine that the error is associated with the text input or with a technical issue associated with the in-progress application. The computing system can generate a recommendation associated with the error based on determining that the error is associated with the text input or the technical issue. The computing system may output the recommendation to the client device for use in resolving the error with processing the in-progress application.

A system can include a memory device comprising a plurality of dynamic capacity devices and a processing device, operatively coupled with the memory device. The processing device is configured to perform operations including recording an error metric associated with a first tag, wherein the first tag is associated with a first memory section of the plurality of dynamic capacity devices, and wherein the first memory section is allocated to a first host system to store data; determining whether the error metric satisfies a threshold criterion of unrecoverable error; responsive to determining that the error metric satisfies the threshold criterion, excluding the first memory section from available memory sections of the plurality of dynamic capacity devices for future memory allocation; responsive to receiving a request for memory allocation in the memory device, determining whether a capacity size of the available memory sections of the plurality of dynamic capacity devices is not smaller than a capacity size specified in the request; and responsive to determining that the capacity size of the available memory sections of the plurality of dynamic capacity devices is not smaller than the capacity size specified in the request, identifying a second memory section of the plurality of dynamic capacity devices and associating a second tag with the second memory section.

A method for processing faulty memory module and apparatus, and an electronic device and a non-transitory readable storage medium are provided. The method for processing faulty memory module includes: acquiring a first power-down command, the first power-down command being used for instructing a baseboard management controller of a memory resource pool to perform power-down processing on a faulty memory module in the memory resource pool; and in response to the first power-down command, when a first memory module in the memory resource pool is in a fault state and is allowed to be powered down, sending a second power-down command to a target memory expander controller to which the first memory module belongs, the second power-down command being used for instructing the target memory expander controller to perform power-down processing on the first memory module.

A method of AI content evaluation includes receiving, from a generative artificial intelligence (AI) model, a set of AI-generated instructions that identifies steps for performing a task within an application, and selecting checkpoint interactions from an interaction index that define a plurality of interactions with a user interface. Each of the checkpoint interactions satisfies a similarity metric with a corresponding step in the set of AI-generated instructions. The method further includes determining, based on detected user interactions with the user interface, a subset of the checkpoint interactions completed by a user within an observation period, and evaluating a metric that to compute a quality score that quantifies user success with respect to performing the task associated with the AI-generated instructions. The metric depending at least in part on the subset of the checkpoint interactions completed by the user within the observation period. In response to determining that the quality score satisfies low-quality criteria, a remedial action is performed.

The invention discloses an operation and maintenance method and system based on an AI large model, and relates to the technical field of artificial intelligence operation and maintenance. The method comprises the following steps: acquiring monitoring data, analyzing the monitoring data, and decomposing the analyzed monitoring data into a plurality of field feature sets; determining a knowledge vector corresponding to each domain feature set; obtaining historical normal operation data of each knowledge vector, calculating a deviation value according to the historical normal operation data, and determining an abnormal symptom vector according to each deviation value; determining potential root cause vectors in the knowledge vectors according to the knowledge vectors and the abnormal symptom vectors; searching all paths, calculating a comprehensive weight value of each path, and taking the path with the highest comprehensive weight value as a root cause reasoning chain of the composite problem; and extracting a processing strategy corresponding to each knowledge vector, and performing optimization according to the logic sequence of the root cause inference chain and the processing strategy to generate a solution. By implementing the technical scheme provided by the invention, the system operation and maintenance efficiency can be improved.

A system and method for managing a cybersecurity policy exception on a software service in a computing environment is presented. The method includes detecting a software service in a computing environment, the service including a code object and a resource; generating a representation of the software service in a security database, the security database further including a representation of the computing environment; applying a policy on the representation of the software service, the policy including a conditional rule; detecting a policy exception in response to applying the policy resulting in a policy fail of the conditional rule; determining that the software service passes the policy in response to applying the policy exception resulting in a pass; and initiating a remediation action, in response to determining that applying the policy exception results in a policy fail.

Computer-implemented methods for recommendation prioritization for a container orchestration system. Aspects include receiving a set of recommendations for a cluster of a container orchestration system. Aspects also include selecting an optimal recommendation from the set of recommendations using a scored knowledge transform graph. Aspects further include generating a confidence score for the cluster based on the optimal recommendation. Aspects also include determining a category of a readiness assessment model for the cluster using the confidence score. Aspects further include modifying a computer resource of the cluster based on the category of the readiness assessment model.

The invention discloses a multi-source business process self-adaptive operation and maintenance scheduling method, which relates to the technical field of business process operation and maintenance scheduling, and comprises the following steps of: under the condition that a source system state coverage behavior exists, extracting a jump rule, a behavior interruption fragment and a manual intervention identifier from a behavior acquisition vector, generating a coverage deviation feature sequence, real task execution track information under the condition that the source system state coverage behavior exists is restored based on the sequence; and forming a scheduling credibility judgment factor group by the state change mode, the behavior continuity and the intervention position in the real execution track information of the task, and forming a parameter set which can be used for quantifying the track credibility. According to the method, the problem of misscheduling caused by source system state coverage in the multi-source business process is solved, and the real restoration of the task trajectory and the accurate evaluation of the scheduling credibility are realized, so that the accuracy of the scheduling decision and the stability of system operation and maintenance are improved.

Machine vision-based technical support is provided herein. An example method includes receiving an image of an electronic device, the image including an output of the electronic device indicative of an error code associated with an operation of the electronic device, executing a first trained model to recognize the error code from the output included in the image, executing a second trained model to output an error diagnostic based on the error code recognized using the first trained model, in response to verification of the error diagnostic output by the second trained model, executing a third trained model to output at least one solution to remedy an error of the electronic device corresponding to the error code recognized using the first trained model, and retraining the third trained model based on whether the at least one solution was effective in remedying the error.

The invention discloses a cloud service fault prediction and restoration method and system based on dynamic load characteristics, and relates to the technical field of cloud computing and intelligent operation and maintaining.The cloud service fault prediction and restoration method based on the dynamic load characteristics mainly comprises the steps that multi-source monitoring data in a cloud platform is preprocessed; extracting a dynamic load feature vector from the preprocessed multi-dimensional monitoring data; predicting the dynamic load feature vector by using a prediction model fused with multiple machine learning algorithms to obtain a prediction result, identifying a potential fault type and evaluating a risk level in combination with a current system state, automatically matching an optimal repair strategy and executing intelligent repair operation; and optimizing the prediction model and the repair strategy according to the repair effect. By implementing the cloud service fault prediction and repair method and system based on the dynamic load characteristics provided by the invention, the reliability and operation and maintenance efficiency of the cloud platform can be improved.

A method includes obtaining defect data and context data in association with a substrate, and providing the defect data and the context data to a first trained machine learning model as input. The method further includes obtaining output from the first trained machine learning model based on the defect data and the context data. The output is indicative of a predicted root cause in association with the defect data. The method further includes performing a corrective action in view of the output.

Embodiments of the present disclosure provide a method and apparatus for reporting asset information, a non-transitory computer readable storage medium, and an electronic device. The method for reporting asset information includes: reading, in a case where a target device supports memory expansion, Serial Presence Detect (SPD) information of a designated memory bank mounted to the target device from a configuration space register of a Memory Expander Controller (MXC) of the target device by means of a Basic Input Output System (BIOS), wherein the target device is a device that supports a Computer Express Link (CXL) protocol, which is an open interconnection standard; and reporting, in a case where target SPD information has been read, the target SPD information to a Central Processing Unit (CPU) of the target device as system asset information by means of the BIOS.

Techniques are described for using a cloud-based actions service to provide IT and security-related applications with a centralized interface for requesting the performance of a wide range of actions involving third party services and devices. Any application with the ability to send API requests to the actions service can thus request the invocation of actions supported by the service without the need for independent implementations of such actions. Furthermore, the actions service provides a source for a continuously evolving set of actions with only minimal changes needed to applications desiring to use new and updated actions.

Systems and methods for an Artificial Intelligence (AI) agent adapted to support end users includes performing monitoring of one or more users via a cloud-based system and logging device metrics based thereon, wherein the device metrics are associated with one or more devices of the one or more users; providing an Artificial Intelligence (AI) agent adapted to troubleshoot issues related to the one or more devices; and responsive to the AI agent being invoked by a user of the one or more users, providing one or more remediation recommendations for one or more issues based on the device metrics.

Methods, systems, and devices for enhanced error handling in memory systems are described. A memory system may enter a read error handling procedure to recover data from a memory cell. For example, the memory system may perform multiple read operations at respective first read levels to identify a voltage valley associated with the data in the memory cell. Based on identifying the voltage valley, the memory system may read the data from the memory cell according to a second read level, where the second read level may be based on one of the first read levels, a first learning rate parameter, and a first momentum parameter. The memory system may determine whether to exit the error handling procedure or continue with the error handling procedure based on whether the data was successfully decoded in response to the read operation at the second read level.

Systems and methods are provided for automated incident investigation. Anomaly detection is used to identify anomalies in incident data (e.g., alerts, changes, metrics, logs, and / or system health), and the identified anomalies are converted into facts (or textual prompt inputs for a large language model (“LLM”)). A troubleshooting or diagnostic system is run on the anomalies to provide additional facts to identify a root cause of an incident. The facts from the diagnostics, the facts from the anomaly detections are entered into a consolidated explainer that generates a summary of what happened, what is a likely cause, and what to do next to resolve the issue. In examples, anomaly enrichment data including a time correlation result, a weighted list of abnormal transaction patterns, a list of abnormal trace patterns, a list of exception patterns, a difference pattern, and / or region data are input as further facts to enhance the incident investigation process.

The invention relates to an RS-485 bus anomaly detection and self-recovery method and device, electronic equipment and a storage medium. The method comprises the following steps: setting a transceiving state machine comprising an overtime timer in an MCU (Microprogrammed Control Unit) in the uninterruptible power supply, and controlling the transceiving state of the transceiving state machine and / or the level of a DE pin of RS-485 based on the monitoring time of the overtime timer; a timing protection circuit is arranged between the MCU and the DE pin, and the level state of the DE pin is adjusted based on the first duration for maintaining the high level of the DE pin and the timing protection circuit; setting a periodic monitoring mechanism in the MCU, and monitoring communication between the uninterruptible power supply and the RS-485 based on the periodic monitoring mechanism to obtain a monitoring result; based on the monitoring result, determining a corresponding grading recovery strategy; the hierarchical recovery strategy is used for recovering communication between the uninterruptible power supply and the RS-485. Therefore, the reliability and the stability of the communication link between the UPS and the external monitoring equipment are improved, and the maintenance cost of the equipment is reduced.

Systems and methods for generating an enhanced error message are provided. An example method includes: receiving one or more raw error messages. The one or more raw error messages include one or more stack traces. The method further includes matching at least one raw error message of the one or more raw error messages to one or more error rules from a plurality of error rules. The one or more error rules include regular expression patterns. The method further includes parsing the at least one raw error message, based on the one or more matched error rules from the plurality of error rules; and generating one or more enhanced error messages, based on the at least one parsed raw error messages. The one or more enhanced error messages include one or more natural language sentences. The method further includes embedding the one or more enhanced error messages into a website.

The computer implemented methods, systems, and program instructions detect anomalies in a core network of a telecommunications network. The method comprises: receiving data representative of streams of time series data of a plurality of Key Performance Indicators (KPIs) of the performance of nodes of the core network; comparing the received time series data for each of the KPIs to predicted time series values for each KPI generated by one or more time series analysis algorithms trained with historical data for each KPI to predict the KPI over time; determining any KPIs having deviations between the received time series data and the predicted time series data during a specific time period, wherein each deviation is an anomaly; grouping the streams of time series data for each KPI determined to be deviated to generate anomaly data; using an artificially intelligent clustering algorithm to generate a plurality of clusters, wherein each cluster comprises a subset of the KPIs determined to be deviated that have been assigned to said cluster by the artificially intelligent clustering algorithm; wherein each of the clusters is identified as having an associated root cause.