3236results about "Redundant operation error correction" patented technology

In one embodiment, a computer-implemented method executed using one or more processors of an incident management system comprises receiving a notification of an incident associated with a computer system, and in response to receiving the notification: extracting an error message from the notification; reading a set of computer program code changes that have been implemented in the computer system; matching the error message to the set of computer program code changes and outputting a set of one or more candidate code changes that may correspond to the error message; based on the set of candidate code changes, generating an automatic remediation for the incident; and using the one or more processors of the incident management system, executing the automatic remediation on the computer system.

A video gateway system at a worksite is coupled to multiple cameras on a network, and backs-up video streams generated by the cameras to a backend cloud backup video storage system and a frontend (cache) video storage system. The video gateway system generates an aggregated video asset from a plurality of streams of video from the multiple cameras, and generates metadata and a backup report associated with the video asset. The video asset, metadata, and backup report are stored on the backend cloud backup video storage system in an file system, and are also stored on the frontend (cache) video storage system such that for at least a period of time, the video asset and the associated the metadata and backup report are stored on both the cloud backup video storage system, facilitating quick access and retrieval to the stored video for retrieval for streaming, activity detection, and other uses.

The invention relates to the field of water quality monitoring analysis, in particular to a multi-modal water quality monitoring data intelligent processing method and system. The method comprises the following steps: obtaining a water quality monitoring parameter stream and a water quality monitoring image, and carrying out adaptive processing to obtain a filtering optimization parameter stream and an optimization monitoring image; performing water quality state evolution perception according to the adaptive filtering optimization parameter flow, and constructing a water quality state evolution graph; and performing biological index response analysis and water quality ecological influence evaluation based on the water quality state trend evolution graph to obtain a water quality evaluation report. And carrying out pollutant spatial distribution change analysis based on the optimized monitoring image, and executing final water quality monitoring evaluation according to a water quality evaluation report. The invention provides efficient and accurate water quality status analysis.

A content editor or a plugin thereto automatically generates authorship tokens that identify content authored by a human author or an artificial author. The authorship tokens are applied to the work while the work is being produced. Thus, subsequent review of the work can identify regions produced by a human author and other regions produced by an artificial intelligence.

The invention relates to the technical field of base frame operation and maintenance, can be applied to business scenes such as financial science and technology and medical health, and discloses a context object-driven patch processing method, device, equipment and medium, and the method comprises the steps: extracting an equipment attribute identifier, an operation state identifier and a routing path identifier from an operation environment of target equipment to generate a context object, creating a patch definition, verifying the patch definition in a security sandbox, submitting and approving the patch definition to form a patch version identifier, configuring an environment isolation condition in combination with an effective range, issuing a patch execution instruction carrying the patch version identifier, the environment isolation condition and a hook definition, triggering patch script application repair logic, and calling a cache cleaning tool to process a resource cache. And collecting logs and indexes to generate a monitoring record, and executing rollback or off-shelf operation based on the monitoring record to finish the patch task. Through combination of dynamic patch definition, instruction issuing and version management, safe distribution of front-end patches is realized, and the stability and maintainability of the system are improved.

The invention discloses a lithium battery full life cycle quality tracing system based on digital twinning, and relates to the technical field of lithium battery quality tracing, and the system comprises a physical entity layer which integrates multiple types of sensors and detection equipment to collect full-link data; the digital twinning modeling layer is used for constructing a multi-scale model to map a battery state; the data acquisition and transmission layer is used for realizing data real-time transmission preprocessing through an edge gateway and an encryption protocol; the data storage and management layer is used for establishing a data warehouse by using a distributed database and performing backup; the intelligent analysis and decision-making layer integrates an algorithm to realize anomaly detection, performance prediction and tracing; the visualization and interaction layer is used for providing a multi-terminal visualization and interaction function; and the block chain evidence storage layer constructs an alliance chain to ensure that the data cannot be tampered. According to the invention, full-life-cycle data connection and credible tracing of the lithium battery are realized, the quality problem positioning efficiency is improved, intelligent early warning and optimization capabilities are provided, the operation threshold is reduced, and industrial quality control and green development are facilitated.

Techniques are described for enabling concurrent and non-blocking replication object deletion during cross-region replications. In some embodiments, in a target file system, a target replication pipeline as part of a cross-region replication, and a deletion pipeline operate in parallel. The deletion pipeline deletes processed objects reaching the last pipeline stage of the target replication pipeline after each checkpoint in the target replication pipeline. In some embodiments, after a non-recoverable failure during the cross-region replication, the cross-region replication can be restarted from the beginning (i.e., fresh restart) without waiting for its unused objects in the Object Store to be deleted by utilizing a generation number associated with each object to delete the unused objects in a background process while allowing deleting processed objects as normal for the freshly restarted cross-region replication.

The invention discloses a digital archive online management system based on big data. The digital archive online management system comprises a digital acquisition layer which is used for carrying out semantic perception and structured extraction on heterogeneous archives; and the intelligent classification layer is used for mapping the collected archive entities, attributes and relationships to a dynamically expanded domain knowledge graph based on a knowledge graph construction technology and a graph neural network, realizing association mining and dynamic classification of cross-modal archives through node embedding and link prediction, adapting to evolution requirements of archive themes in combination with a weak supervised learning mechanism, and realizing dynamic classification of the cross-modal archives. A classification system with causal interpretation is formed; the storage retrieval layer is used for encoding the generation time, the space trajectory and the event causal chain of the archive into space-time causal metadata; the archive utilization layer is used for actively pushing associated archives and generating a personalized analysis report by analyzing user behavior preferences and scene requirements; and the backup layer is used for dynamically sensing the threat type and calling an evolutionary algorithm to adjust the backup frequency, the storage position and the recovery path.

The invention relates to the technical field of data disaster recovery, and provides a disaster recovery drill and disaster switching method and system, and the method comprises the steps: automatically recognizing multi-source heterogeneous database assets through a rule engine and a machine learning algorithm, and constructing a disaster recovery topological graph; a disaster recovery strategy is generated and optimized by adopting an intelligent algorithm based on the disaster recovery topological graph, and the disaster recovery strategy is dynamically adjusted in combination with real-time service requirements; fusing the historical disaster recovery data and the real-time monitoring data to reconstruct a disaster recovery strategy; executing data are collected in real time in the drilling process, and a disaster recovery strategy is optimized through a closed-loop feedback mechanism; monitoring a whole process state through a visual interface and displaying key indexes; based on the multi-dimensional evaluation model, quantitatively analyzing the drilling result, and determining a target disaster recovery strategy; and a security verification mechanism is adopted to authorize the key operation, a disaster recovery scene scheme is matched according to the fault type, and an automatic execution unit is controlled to complete disaster switching based on the target disaster recovery strategy. According to the invention, the disaster recovery switching accuracy in a complex scene is improved.

The invention relates to the technical field of data backup, in particular to a data backup strategy automatic generation method and system. The method comprises the following steps: acquiring and quantifying strategy generation input information; evaluating strategy-related attributes of the data assets, and estimating resource input costs meeting different preset protection levels according to strategy generation input information; based on the strategy generation input information, the strategy related attributes and the resource input cost, constructing and updating a dynamic model reflecting the strategy tradeoff relation in real time; performing strategy decision based on the dynamic model, wherein the strategy decision selects a backup strategy based on compliance laws and regulations in combination with real-time resource conditions; and deploying a backup strategy, continuously monitoring an execution effect of the backup strategy, and taking actual operation data of the execution effect as feedback for adjusting parameters of the dynamic model. The limitation of an existing data backup strategy automatic generation method in complex conditions is solved, and the ability of actual recovery result feedback optimization strategies is improved.

A content editor or a plugin thereto automatically generates authorship tokens that identify content authored by a human author or an artificial author. The authorship tokens are applied to the work while the work is being produced. Thus, subsequent review of the work can identify regions produced by a human author and other regions produced by an artificial intelligence.

The invention provides a dynamic tool integration method and system for an intention-driven AI dialogue system, and the method comprises the steps: S5, extracting resource occupation information from a transaction log according to a state persistence record, carrying out the resource release and temporary context cleaning of each heterogeneous component, and obtaining a released resource list; s6, searching a breakpoint state snapshot and a transaction log from the distributed database according to the released resource list, and reconstructing a tool chain execution flow historical state to obtain a fault context analysis result; and S8, according to the context recovery state and the semantic feature vector, updating a tool chain execution stream, and reallocating computing resources through a heterogeneous component coordination mechanism to obtain a task continuity confirmation result. According to the method, the recovery efficiency and the resource utilization rate of the tool chain in an abnormal scene are remarkably improved, and the task continuity and the system robustness in a high-concurrency environment are guaranteed.

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 bank of a memory device is divided into column planes. Each column plane is associated with column selects. A portion of a column plane associated with one column select used to store metadata associated with data of the remaining column selects. Both the metadata and the data are provided to an error correction code circuit as a combined code word that provides error correction for both the data and the metadata.

A system and method for implementing a Persistent Cognitive Machine (PCMs) that extends beyond the traditional prompt-response paradigm of artificial intelligence are disclosed. A PCM maintains persistent cognitive processes regardless of external interaction, stores and organizes thoughts in a thought cache, retrieves relevant thoughts based on current stimuli, generates new thoughts through reasoning processes, and curates stored thoughts during periods of reduced external interaction. The PCM includes language and reasoning model components, a thought cache, an executive component, and an embedding system. The PCM remains continuously active, remembers previous experiences, learns from these experiences, creates new thought experiences independently, and initiates interactions without waiting for external prompts. The PCM enters sleep-like states during which it curates its thought cache, generalizes experiences, and performs other memory management functions. Applications may include but are not limited to synthetic cognitive colleagues, strategic war gaming platforms, and personal cognitive assistants.

Ransomware detection accuracy is improved by analyzing patterns of changes in filename extension counts, relative to each other, that occur in a file system over time. The disclosed approach is malware-agnostic and does not rely on recognizing malware extensions or on real-time monitoring of the target file system. Instead, during each successive backup job of the target file system, the disclosed technology counts different types of filename extensions and compares the counts to each other and to corresponding counts taken in earlier backup jobs. Preferably, the anomaly detection analysis uses machine learning to discern a behavior pattern of the file system, which indicates how filename extensions are distributed and how much they change between backup jobs over time.

The invention discloses a rapid exchange chip resetting method and system, and belongs to the technical field of chips. The method comprises the steps of abnormal trigger type identification, reset granularity dynamic determination, operation state freezing, hierarchical reset execution, reset validity verification and redundant reset system reconstruction, rapid abnormal positioning is achieved through hardware monitoring and feature matching, a dynamic reset strategy generation mechanism is combined, the reset granularity is accurately controlled according to the abnormal severity degree, and the system reliability is improved. Excessive reset or insufficient reset is avoided, the system resource utilization rate and the fault processing efficiency are improved, a state freezing technology is operated to maintain the state of a key register, progressive pulse injection and priority control of hierarchical reset execution are matched, it is ensured that a key module is preferentially recovered, the energy consumption is reduced while the reset effect is ensured, and the system performance is improved. Reset effectiveness multi-dimensional verification is combined with redundancy reset and a system reconstruction mechanism, a standby repair scheme is dynamically triggered for different fault types, and the reliability, fault-tolerant capability and maintainability of the system are improved.

The invention discloses a cultural heritage file multi-dimensional storage system, which relates to the field of cultural heritage digital protection and intelligent storage and comprises a data acquisition and processing module, a double-library management module, a multi-dimensional index retrieval engine, a security and convergence module and a backup and disaster recovery module. According to the method, structured texts and unstructured files are uniformly accessed through various interfaces, a special tool chain is adopted for format standardization, audios and videos are transcoded into an H.264 / AAC format through FFmpeg, images are subjected to resolution adjustment and compression through ImageMagick, plain texts are extracted from texts through ApacheTika and are converted into UTF-8 codes, standardized data are uniformly converted into an intermediate format, and the format of the intermediate format is converted into the intermediate format. According to the technical scheme, the method comprises the following steps of: standardizing input data by adopting a three-layer duplicate removal strategy, facilitating other modules to receive data, performing refined redundancy processing on the standardized input data by adopting a three-layer duplicate removal strategy, remarkably reducing storage redundancy, partitioning a cultural heritage data main library according to three layers of ''project type-medium type-age'', and partitioning an inheritor information library according to two levels of ''province-inheritor number prefix''.

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

The present disclosure relates to systems, non-transitory computer-readable media, and methods for selecting machine-learning models and hardware environments for executing a task. In particular, in one or more embodiments, the disclosed systems select a designated machine-learning model for executing a task based on workload features of the task and task routing metrics for a plurality of machine-learning models. In addition, in one or more embodiments, the disclosed systems select a designated hardware environment for executing the task based on workload features for the task and task routing metrics for a plurality of hardware environments. In some embodiments, the disclosed systems select a fallback machine-learning model and a fallback hardware environment for executing the task if the designated machine-learning model or designated hardware environment are unavailable. Moreover, in one or more embodiments, the disclosed systems can pause and initiate tasks based on bandwidth availability.

The invention belongs to the technical field of artificial intelligence, and particularly relates to an old people falling risk dynamic assessment and real-time early warning system and method based on multi-modal deep learning. The system comprises a multi-modal data acquisition module used for synchronously acquiring behavior data, physiological data and environmental data; the data preprocessing module is used for preprocessing the video data and the sensor data; the multi-modal feature fusion module is used for realizing cross-modal feature interaction of multi-modal data by adopting a hierarchical network architecture and fusing generated comprehensive features; the dynamic risk assessment module is used for realizing long-time-sequence risk prediction based on a prediction model and outputting a risk probability; the real-time early warning module performs real-time early warning based on the predicted risk probability by constructing a multi-level response mechanism; and the model adaptive updating module is used for constructing a closed-loop optimization mechanism and realizing adaptive updating of the model by adopting a transfer learning fine tuning model. The problem of misjudgment caused by one-sided data in the prior art is solved.

Some embodiments provide a method for a first data compute node (DCN) operating in a public datacenter. The method receives an encryption rule from a centralized network controller. The method determines that the network encryption rule requires encryption of packets between second and third DCNs operating in the public datacenter. The method requests a first key from a secure key storage. Upon receipt of the first key, the method uses the first key and additional parameters to generate second and third keys. The method distributes the second key to the second DCN and the third key to the third DCN in the public datacenter.

The invention discloses a cloud hard disk data backup and recovery method and device, electronic equipment and a storage medium, and relates to the technical field of cloud computing. A cloud hard disk is segmented into a plurality of segmentation blocks and a multi-thread parallel processing mode is adopted, a backup and recovery task of a large-capacity cloud hard disk is decomposed into a plurality of sub-tasks to be processed at the same time, and the data backup and recovery efficiency is improved. The operation time is greatly shortened, the number of threads in the thread pool is determined according to the number of the segmentation blocks and the current available computing resources, the utilization rate of the computing resources can be improved, cloud hard disk segmentation strategy parameters can be flexibly configured according to service requirements and cloud hard disk characteristics, data protection requirements in different scenes are met, and the user experience is improved. Consistency verification is carried out on the transmitted data, so that the integrity and consistency of the data in the backup and recovery process are ensured, the data loss risk is reduced, and the purposes of improving the backup and recovery efficiency of the cloud hard disk and enhancing the system reliability and the computing resource utilization rate are achieved.

Methods, systems, and devices for data management are described. A method may include obtaining a new snapshot of a relational database of a source computing environment, comparing the new snapshot of the relational database to a prior snapshot of the relational database, updating, in response to the comparison indicating that data of a row of a relational database table changed between the prior snapshot and the new snapshot, a field of a second versioning control column of a corresponding row of a backup database table of the set of backup database tables to indicate a version of the new snapshot, and adding, in response to the comparison indicating that the data of the row of the relational database table changed, an additional row to the backup database table, where a field of a first versioning control column of the additional row includes an indication of the version of the new snapshot.

A check point remote backup method based on large model parallel strategy perception comprises the following steps: S1, constructing a Checkpoint backup scheme: S11, constructing different cross-node parameter backup mapping relationships according to different parallel modes; s12, constructing a model parameter fragment distribution scheme according to the parameter load capacity of each card and the redundant copy number; s13, grouping the training nodes according to the number of backup copies set by a user, and generating a plurality of full mutual backup groups with equal sizes; the training nodes in the full mutual backup group are mutually backed up; s2, managing memory allocation in a Checkpoint backup process on the basis of a memory multiplexing mechanism; scheduling of CPU resources in the Checkpoint data transmission process is managed according to the asynchronous transmission process; s3, realizing punctual writing of the Checkpoint through a round and time threshold combined triggering method; and S4, controlling the startup and scheduling of the Checkpoint backup. According to the method, when the node breaks down, the system can quickly recover the model state from other nodes according to a set backup strategy, and the continuity of a training task and the fault-tolerant capability of the system are ensured.

The invention relates to the technical field of multi-agent collaboration, in particular to a multi-agent collaboration method and system based on shared memory data exchange, and the method comprises the steps: creating and initializing a shared data area which is mapped and accessed by a plurality of agent processes in a physical memory; constructing an annular buffer structure on the shared data area, and realizing a lock-free read-write pointer propulsion mechanism based on atomic operation and a memory barrier so as to support a plurality of intelligent agents to perform data read-write concurrently; asynchronous notification of data updating is carried out between agent processes through a lightweight event notification mechanism; and each agent performs autonomous decision making and task scheduling based on the shared data so as to realize distributed collaboration. A data block version control and verification mechanism is introduced into the annular buffer structure, and when it is detected that the agents are abnormal, resources of the abnormal agents are automatically recycled, and a data consistency recovery process is triggered. Multiple times of data copying between a kernel mode and a user mode are eliminated, and communication delay is reduced.

The invention relates to a system starting method and device based on a UKEY, equipment and a storage medium, and the method comprises the steps: verifying the operation environment of the UKEY when it is detected that the UKEY is triggered to be powered on and started; if the running environment of the UKEY passes the verification, performing multiple rounds of verification on a firmware program in the UKEY; and if the firmware program passes multiple rounds of verification, controlling the UKEY to enter an operation mode so as to enable a user to enter an operation system. Through the method, after the running environment of the UKEY passes the verification, the firmware program in the UKEY is subjected to multiple rounds of verification, an unauthorized user is prevented from entering a system through the UKEY by using a tampered, forged or implanted malicious program, if the firmware program passes the multiple rounds of verification, the UKEY enters the running mode, the user can enter an operating system or use an application program, and the user experience is improved. The safe starting of the operating system or the application program is realized, and the running safety of the operating system or the application program is improved.

The invention discloses a data tracing method and device based on a runtime snapshot. Comprising the steps of generating a runtime memory snapshot file based on report traceability information; analyzing the runtime memory snapshot file, extracting all keys in the first mapping relation to construct a header character set, and querying N metadata identifiers corresponding to N header characters according to the first mapping relation; accessing a report data result storage area based on the N metadata identifiers to obtain a data value list, and rendering to generate a visual report snapshot; for each metadata identifier, extracting the metadata identifier of each metadata which directly depends on and indirectly depends on the metadata identifier from the task detail information associated with the metadata identifier, and obtaining a single metadata tree based on the hierarchical dependency relationship among the metadata; rendering and generating N metadata tree snapshots based on the N metadata trees; and displaying the metadata circulation process corresponding to the abnormal data value by skipping to display the metadata tree snapshot of the metadata identifier corresponding to the report data column where the abnormal data value is located.

The invention discloses an intelligent financial lading bill system based on AIAgent and Browserless technology, which belongs to the technical field of financial lading bill management and comprises a data acquisition module, an AI intelligent auditing module, an accounting processing module, a user interaction module and a system management module. According to the method, the Browserless technology is utilized to realize automatic data capture and integration, so that the workload of manual data entry and possible errors are reduced, and the data acquisition time is greatly shortened; the AI intelligent auditing module applies a rule engine and a machine learning model, so that data can be comprehensively and accurately audited, potential risks and abnormities are found, and the accuracy and efficiency of auditing are improved; manual intervention is reduced through an automatic accounting processing flow, and voucher generation and account updating are quicker and more accurate.

The invention discloses a risk control strategy-based decision engine and method, and relates to the technical field of financial risk control, and the decision engine comprises a strategy warehouse module, a mapping service module, an execution engine module, a data management module, a version rollback module, a security scale module, and a monitoring alarm module. According to the invention, function decoupling is realized through collaborative design of the seven modules, and the modules interact through a unified interface, support strategy dynamic loading, gray release, distributed parallel execution, version governance and security compliance capabilities, and ensure high cohesion and low coupling of the system; through unique strategy version management, intelligent mapping and gray release, plug-in parallel execution, multi-version consistency view, block chain audit rollback, security compliance management and control, intelligent monitoring and other technical innovations, a complex, dynamic and high-concurrency risk control business scene is effectively supported; the security, the flexibility, the expandability and the auditing performance of the decision engine are remarkably improved, and the technical advancement and the industry creativity are high.