4943 results about "Operational system" patented technology

A federated distributed AI reasoning and action platform utilizing decentralized, partially observable hierarchical computing for neuro-symbolic reasoning. It features a federated Distributed Computational Graph (DCG) system integrating core components like pipeline orchestration, transformers, and marketplaces. The platform enables privacy-preserving dynamic resource allocation, intelligent task scheduling, and variable information sharing across diverse computing environments. By coordinating with an AI-based operating system and analyzing performance metrics, environmental conditions, and resource availability, the system optimizes efficiency across AI workloads and decision-making processes. This results in an adaptive, power-efficient, and scalable AI-enabled data processing system capable of handling complex tasks while maintaining peak performance under various operating conditions.

A federated distributed AI reasoning and action platform utilizing decentralized, partially observable hierarchical computing for neuro-symbolic reasoning. It features a federated Distributed Computational Graph (DCG) system integrating core components like pipeline orchestration, transformers, and marketplaces. The platform enables privacy-preserving dynamic resource allocation, intelligent task scheduling, and variable information sharing across diverse computing environments. By coordinating with an AI-based operating system and analyzing performance metrics, environmental conditions, and resource availability, the system optimizes efficiency across AI workloads and decision-making processes. This results in an adaptive, power-efficient, and scalable AI-enabled data processing system capable of handling complex tasks while maintaining peak performance under various operating conditions.

The invention provides an APT (Advanced Persistent Threat) attack detection method based on a large language model, which comprises the following steps of: S1, extracting original system call event data from a kernel audit log of an operating system, and preprocessing the data; s2, constructing a multi-model collaborative detection architecture based on a large language model, and realizing fine-grained classification of network entities according to the preprocessed data through prompt construction, model fine tuning and a confidence scoring mechanism; s3, constructing an adaptive graph search algorithm based on multi-modal feature correlation modeling, driving attack path topology reconstruction, and realizing maximum reduction of a malicious sub-graph topology structure; s4, carrying out combination with MITRE ATTamp; the CK tactical knowledge base constructs a cyclic enhancement analysis framework, a cyclic enhancement technology is adopted to drive a large language model to execute hierarchical association reasoning, a mapping relation from malicious subgraphs to attack tactics and tactical chains is derived step by step, and finally an attack report summary and a targeted defense strategy are generated. According to the invention, APT attack detection with high accuracy and high interpretability is realized.

The invention discloses a high-efficiency computer software system resource scheduling method, system and device and a medium, and the method comprises the steps: deploying a hardware counter in an operating system, and monitoring the CPU utilization rate, memory occupation, disk I / O, network bandwidth, the use condition of data aggregation system resources and the resource demand of each process or thread in real time; based on monitoring data, analyzing a current resource supply and demand state by adopting an advanced algorithm, and predicting a future resource demand trend; according to an analysis result, a resource allocation strategy is dynamically adjusted, it is ensured that a high-priority task obtains needed resources in time, and meanwhile fairness of a low-priority task is considered; data feedback after scheduling execution is collected, a scheduling algorithm is continuously optimized, and the scheduling efficiency is improved; the system, the equipment and the medium are used for realizing the high-efficiency computer software system resource scheduling method. The problems that an existing computer system is low in resource utilization rate, poor in key task stability and high in operation and maintenance cost are solved.

The invention discloses a mapping prediction method and system based on a hardware resource load and system operation relationship. The method comprises the following steps: collecting hardware index data in real time when an operating system operates; the method comprises the following steps: preprocessing hardware index data, performing association analysis on each index data by adopting an association rule mining algorithm, generating association rules, evaluating the association rules, screening out the association rules meeting conditions, and constructing a multivariable association model; based on a correlation analysis result, a load prediction model is constructed and trained, and the trained model is deployed in the system for load prediction; and dynamically adjusting a system hardware resource allocation strategy according to a prediction result, carrying out task migration and load balancing according to a predicted load condition, and automatically adjusting hardware resources through an automatic script or a scheduling tool. Through real-time monitoring and deep correlation analysis of indexes such as the CPU, the memory, the storage I / O and the network bandwidth, the resource utilization rate and the operation efficiency of the system are improved, and the stability and the reliability of the system are improved.

The invention discloses a solid state disk management system and a data processing method, and relates to the technical field of solid state disk management. Flexible task scheduling and resource management are provided through a lightweight operating system module, and a computing task program defined by a user is supported to be dynamically loaded; edge computing or machine learning operators are efficiently executed in combination with a programmable hardware processing unit and a DMA channel of the computing acceleration engine module, the data preloading module is utilized to predict and preload data to DDR based on LBA access history, access delay is reduced, an NVMe protocol is expanded by means of the task unloading interface module, host task issuing and result returning are achieved, and the data processing efficiency is improved. And hardware-level memory protection is ensured through the security isolation module, so that the data calculation processing capacity of the solid state disk is remarkably improved, localized calculation tasks such as edge calculation and machine learning are supported, mass data transmission is effectively reduced, and bus and network loads are relieved.

The invention discloses an operating system health assessment method and system based on multi-dimensional index dynamic weighting, and the method comprises the steps: collecting the state of each hardware module, calculating the index, carrying out the weighted summation to obtain the score of the hardware module, adjusting the weight parameter of each hardware module through combining with the real-time load characteristic or business priority, and carrying out the real-time evaluation of the health of each hardware module. Performing weighted summation on the scores of the hardware modules to obtain a hardware health score; software state information in the operating system is collected, scores of all software modules are calculated, the software modules comprise key components, key services and key processes, and the scores of all the software modules are weighted and summed to obtain software health scores; and performing weighted summation on the hardware health score and the software health score to obtain a health score of the operating system so as to determine the health state of the operating system. The invention aims to solve the systematic defect in the existing health monitoring technology system, improve the comprehensive evaluation capability of the health evaluation of the operating system, and dynamically adapt to the change rule of the actual operating environment.

The invention discloses a specific detection value identification method and device and a computer program product, and the method comprises the steps: S1, collecting the power data of a to-be-detected node of a power system, and transmitting the power data to an edge calculation layer in real time through a distributed operation system; s2, preprocessing the power data at an edge calculation layer to generate standardized time sequence data; s3, extracting time domain statistical features and frequency domain transformation features of the standardized time series data, and fusing high-dimensional features output by a pre-training deep learning model to construct a multi-dimensional feature vector; s4, performing pattern recognition on the multi-dimensional feature vector based on a pre-trained classification or clustering model, and outputting an anomaly detection result; and S5, feeding back an abnormal detection result to the power equipment control unit in real time through the distributed operating system, and triggering an alarm or executing parameter adjustment. According to the invention, efficient distribution of detection results and multi-node parallel processing can be realized, and the system performance is further optimized.

The invention provides an infrastructure security analysis method and system based on a large model, and the method comprises the following steps: collecting multi-source heterogeneous data of an infrastructure, including physical equipment sensor data, network flow data, operating system logs and external threat intelligence; converting the multi-source data into standardized feature vectors of a unified time axis through a space-time alignment module; and inputting the standardized feature vector into the fine-tuned large language model for semantic understanding, and generating an equipment behavior semantic description and threat intention analysis result. According to the method provided by the invention, a large language model and reinforcement learning collaborative architecture is provided, and the large language model is improved, so that end-to-end mapping from multi-source data to threat semantics is realized, and the attack detection rate is improved.

The invention discloses an AI-based localized environment application offline deployment method and device and a medium. The method comprises the steps that a localized environment is pre-detected based on probe service, a system layer configuration list is generated, heterogeneous features are extracted through a dual-channel deep learning model based on the system layer configuration list, and adaptive priority labels of the heterogeneous features are generated; the method comprises the following steps: analyzing an application program through static analysis and dynamic scanning technologies, constructing a dependency list, and constructing a version compatibility atlas based on metadata of a domestic operating system warehouse; generating a minimum-cost replacement path by using a graph attention network, performing hash check on each dependency packet, and generating an incremental update packet through a differential packaging algorithm; the resource occupation condition of the system is monitored in real time through a reinforcement learning model so as to dynamically optimize a deployment sequence and a resource allocation strategy, a deployment log is analyzed based on a natural language processing model, an error type is recognized, a repair operation is triggered, and application offline deployment is achieved.

The invention discloses a task scheduling method and device, a medium and a product, and relates to the technical field of artificial intelligence. According to the scheme, the task is received, the corresponding task information is determined, the agent modeling is carried out on the multiple existing agents to determine the capability portraits of the agents, various basic information of the to-be-executed task and the task execution capability of the agents in the system are clarified, and therefore the basis of multi-agent cooperation is formed; in consideration of resource competition and coordination problems in agent task allocation, when task scheduling is executed, tasks are sequenced to generate a task sequence, and the tasks are sequentially allocated to corresponding agents according to the task sequence, capability portraits and an operating system scheduling algorithm. The task allocation strategy can be dynamically adjusted according to the characteristics and task importance of the agents, the resource utilization rate is maximized, the task execution performance is optimized, and therefore the efficient and stable task scheduling requirement of the multi-agent system in the complex environment is met.

The invention discloses a server actual hard disk physical slot position and hard disk information mapping method and device, and relates to the technical field of hard disk testing, and the method comprises the steps: marking a unique number through a hard disk recognition tool and a visual recognition case slot position under an operating system, highlighting a hard disk in combination with a lighting mechanism, and carrying out the recognition of the unique number through the slot position association after lighting. According to the method and the device, the actual hard disk SN code is established, the mapping relation between the actual hard disk SN code and the visual physical slot position is established, and the unique mapping between the actual hard disk position and the hard disk information is established through the mapping relation between the SN and the actual physical slot position, so that the problems of low efficiency of a hard disk testing technology, easiness in mistaken formatting, mistaken unloading and mistaken plugging after disk letter disorder, and high reliability are solved. According to the technical scheme, the technical problems that a storage path cannot be found in time, and the accuracy and efficiency of hard disk management are seriously affected are solved, and the technical effects that decoupling of the storage path and physical hardware is ensured, the influence of system restart or hardware change on services is reduced, the fault hard disk can be directly positioned through slot position identification, and the fault recovery time is shortened are achieved.

Example systems and methods monitor a cloud compute environment. An example method includes: opening, by an agent deployed in a cloud environment, a communication channel between the agent and a kernel of an operating system of a node within the cloud environment; determining, by the agent and via the communication channel, an event associated with a namespace of the operating system; determining, by the agent and based on the event, a status of a container associated with the node; and providing, by the agent to a data platform, a message indicative of the status of the container.

The invention discloses an intelligent fusion and standardization processing system and method for multi-source heterogeneous operation and maintenance data, and belongs to the technical field of operation and maintenance data. The system comprises a data acquisition module, a data preprocessing module, a data fusion module and an information extraction module; the data acquisition module is used for collecting original multi-source heterogeneous operation and maintenance data from different data sources, and the data sources comprise facilities, operating systems, basic components, middleware, databases and / or information systems; the data preprocessing module is used for cleaning, denoising and standardizing the acquired data; the data fusion module is used for selecting a low-level data fusion model, a middle-level data fusion model or a high-level data fusion model according to the fusion scene and fusing the preprocessed data; and the information extraction module is used for carrying out feature extraction on the fused data. According to the method, deep fusion and standardized processing of the multi-source heterogeneous operation and maintenance data are realized, and a unified and high-quality data basis is provided for data analysis and decision making.

The invention provides a PCIe equipment fault monitoring and dynamic processing method based on intelligent prediction, and the method comprises the steps: obtaining operation parameters from a BMC (baseboard management controller), an operation system and a PCIe equipment register, carrying out the processing and time sequence feature extraction, and inputting a lightweight long short-term memory network LSTM model deployed in the BMC for fault probability prediction. And combining the prediction result and the operation parameters to calculate an equipment health score, and triggering an alarm, bandwidth degradation or hardware isolation operation according to the score. And the hardware isolation module sends a control instruction to the complex programmable logic device CPLD through the IC bus to realize non-interruption power-off isolation of the target equipment. And uploading real-time data to a cloud server through encryption communication, executing fine adjustment of the model, and returning an update weight to realize continuous optimization and version control of the model. The method can be widely applied to computing equipment such as a server, and the availability and the fault-tolerant capability of the system are improved.

The invention relates to the technical field of distributed storage verification, and discloses a storage information verification system based on a consensus mechanism. The system comprises a consensus network communication module, a storage feature analysis module, a verification decision core module, a time sequence prediction module, a decision compensation module and a verification execution engine module. The consensus network communication module is connected with a distributed storage node and receives a storage verification request; the storage feature analysis module extracts multi-dimensional feature vectors of the target data to form a feature descriptor set; the verification decision core module is combined with an embedded consensus verification space and dynamic confidence coefficient constraint condition operation to output an initial verification decision scheme; the time sequence prediction module divides a time window and generates a prediction feature descriptor set; the decision compensation module carries out deviation compensation according to the result to obtain an optimization scheme; and the verification execution engine module sends an instruction to trigger the distributed nodes to execute verification operation. A dynamic verification system constructed by the system adapts to dynamic characteristics and heterogeneous environments of distributed storage.

The invention relates to the technical field of industrial operation, and discloses a multi-interface industrial control device based on an OpenHarmony industrial operating system and a control method thereof, and the method comprises the steps: scanning an interface resource list of an industrial control computer after the OpenHarmony industrial operating system is started; industrial equipment connected to each interface is automatically found according to the interface resource list, and an equipment connection table is obtained; performing protocol conversion based on the device connection table to obtain a device communication data stream, and extracting a feature data set from the device communication data stream; and calculating an interface coordination scheme based on the feature data set, issuing a control instruction to each industrial device, and collecting execution feedback to obtain unified management and control data. Unified control and remote cooperation of the industrial control equipment are realized through the OpenHarmony distributed soft bus, the conflict problem during multi-protocol concurrent access is effectively solved, and local intelligent decision and cloud resource scheduling of the industrial operation system are realized.

The invention discloses a large-model context control system and method based on hierarchical memory and an association graph, and the method comprises the steps: constructing a tree graph mixed memory structure and a logic hierarchical memory model for a context control unit between a user and an LLM, and carrying out the self-adaptive processing of a super-long context through a self-adaptive processing engine, a single-theme persistent dialogue control mechanism, and the like. According to the method, the problem that an LLM context window is limited (including introduction of a mechanism similar to an Agent) is solved, just like an operating system helps developers / users to solve the problems that hardware standards hidden behind are not uniform, and complex resource control is caused, a reliable LLM memory (context) control service with multiple details hidden is provided for upper developers, and the method has the advantages of being high in practicability and easy to popularize and use. The final objective of the method is to approximately relieve the limitation of an LLM context window through a set of self-consistent system mechanism, so that a developer does not need to pay too much attention to the details of a bottom layer, and more energy can be focused on the implementation of an application function.

Systems and methods for capabilities and interfaces in a firmware framework. In some embodiments, an Information Handling System (IHS) may include: a controller, where the controller comprises firmware that, upon execution by a processing core, causes the processing core to instantiate an orchestrator; and a plurality of devices coupled to the controller, where each device comprises firmware that, upon execution by a corresponding processing core, causes the corresponding processing core to instantiate a node, and where each node is configured to communicate, to the orchestrator, one or more exposed capabilities and one or more exposed interfaces usable to access the one or more exposed capabilities without any involvement by any Operating System (OS) of the IHS.

The invention discloses a compiling construction method and device of a client program, a storage medium and electronic equipment, is applied to a server, and relates to the technical field of computers. Comprising the following steps: generating a dynamic link library corresponding to each kernel version according to a kernel information function of which a return value is modified into a kernel version number corresponding to each kernel version of each operating system; according to the dynamic link library and the target drive file, creating a compiling construction mirror image corresponding to each kernel version, and further constructing a compiling construction container consistent with the environment of an operating system of each kernel version in a server according to each compiling construction mirror image, and compiling and constructing the client program of each kernel version in each compiling and constructing container. Therefore, the problem that resources are occupied due to the fact that different operating systems are canned on different servers and kernel clients of corresponding versions are compiled and constructed on the operating systems of the different servers in the prior art can be solved.

The invention discloses an intelligent resource scheduling method and system based on task characteristics and system state perception, and relates to an operating system kernel optimization technology. According to the method, resource demand types, importance labels and time sensitivity of tasks are captured in real time through an eBPF technology, system bottlenecks are recognized by combining / proc and sysfs file system data, a dynamic scheduling strategy is generated by adopting an improved Q-Learning model, and resource allocation is executed through a schemed * function and a cgroup v2 interface. The problems that a traditional scheduler is poor in self-adaptive capacity and resources are mismatched are solved, the actually-measured resource utilization rate is increased to 85%-92%, and the key task delay standard reaching rate gt is achieved; the method is suitable for edge computing, cloud servers and other high-concurrency scenes.

The invention provides an automatic vulnerability identification and repair method for an intelligent universal domestic operating system, and belongs to the field of operating system security technology and artificial intelligence. EBPF (extended Burkley data packet filter), TEE (trusted execution environment) and bidirectional GNN-lite (simplified graph neural network) technologies are utilized to automatically identify and repair vulnerabilities of the operating system; an efficient, AI intelligent and safe vulnerability real-time identification and dynamic repair system is constructed, a closed loop of real-time monitoring, credible analysis, automatic patching and effect evaluation is established, and kernel defects are efficiently repaired on the premise of not interrupting services.

The invention belongs to the technical field of computer software, and particularly relates to a heterogeneous database unified deployment management method based on a cluster management platform, which comprises the following steps: predefining metadata of various database services; the method comprises the following steps: pre-constructing a multi-version program package warehouse, and establishing a structured warehouse directory system; obtaining a chip architecture and an operating system type of the target node, and generating environment fingerprint information; dynamically selecting a database installation package matched with the current environment and a dependency item set of the database installation package by mapping a warehouse directory system; through a unified database adaptation layer, a standardized operation interface is called, and installation, configuration and start-stop operation of the database are executed; a visual deployment and arrangement function based on component roles is provided by expanding a Web service interface of the management platform; and according to a deployment arrangement result and the environment fingerprint information, calling a deployment script to complete automatic deployment and configuration of the database cluster. According to the invention, unified adaptation, deployment and full-life-cycle management of various domestic databases are realized.

The invention relates to the technical field of intelligent facility management, and discloses an intelligent facility management control system based on the 5G Internet of Things, which comprises a facility data acquisition module, a configuration module, a real-time monitoring module, an instruction matching module and an execution module. The facility data acquisition module acquires data through a 5G terminal, and generates an operation characteristic value and a dynamic adjustment threshold parameter; the facility configuration module generates personalized parameters and dynamic adaptation strategies according to the characteristic values; the real-time monitoring module obtains and screens real-time state data; the instruction matching module compares the data to generate a correlation degree, and selects a target instruction; the instruction execution module drives facility operation. The system is further provided with a threshold value self-optimization module, and threshold value parameters are optimized through feedback data. According to the system, real-time monitoring, dynamic parameter adjustment, precise instruction matching and self-optimization are realized, and the intelligence, precision and self-adaptive capability of facility management are improved.

Systems and methods for runtime selection and distribution of modules in a firmware framework. In some embodiments, an Information Handling System (IHS) may include a controller, where the controller comprises firmware that, upon execution by a processing core, causes the processing core to instantiate an orchestrator; and a plurality of devices coupled to the controller, where each device comprises firmware that, upon execution by a corresponding processing core, causes the corresponding processing core to instantiate a node as part of a firmware framework, and where the orchestrator is configured to distribute a firmware module to a given node in response to a determination that the given node requires the firmware module to provide a capability, without any involvement by any Operating System (OS) of the IHS.

The invention discloses a server starting control method and electronic equipment, relates to the technical field of computers, is applied to a universal boot loader, and comprises the following steps: obtaining a signature verification public key through an input interface; verifying and decrypting the signed mirror image header of the kernel mirror image by using the signature verification public key so as to recover the original mirror image header of the kernel mirror image; extracting a reference metric value of the kernel mirror image from the head of the original mirror image; calculating a to-be-verified metric value based on the kernel mirror image of which the head of the original mirror image is recovered, and comparing whether the to-be-verified metric value is consistent with the reference metric value or not; and if yes, loading the operating system. On the basis of not modifying the design of the server mainboard, active, complete and efficient trusted measurement of the BMC starting process is realized.

The invention provides a computing power center resource centralized self-adaptive control scheduling system and method, and the method comprises the steps: S1, a user submits a computing power training task through an API, and sends the task to a DAG task composer, and forms a task list to be scheduled; s2, the intelligent monitoring analysis module is used for continuously collecting node data and calculating real-time CS and HS scores of each node; s3, analyzing the dependency relationship type data by a task composer, and calling a dynamic programming algorithm in an adaptive scheduler to make a decision; s4, the adaptive scheduler performs dynamic task distribution according to the task list of the DAG task orchestrator and the intelligent monitoring analysis module, and S5, the distributed runtime and environment management module is used for uploading the prepared Docker mirror image to the distributed storage; and S6, through the unified communication adaptation layer, the mirror image is pulled from the distributed storage on the target node and the container is started, the user does not need to concern the difference of the operating system in the whole process, and efficient and intelligent adaptive scheduling is realized.

The invention provides a power centralized control center data verification and backup method and device, and the method comprises the steps: obtaining hardware resource information corresponding to an operating system, and constructing a thread resource pool; the method comprises the following steps: connecting data sources of an electric power centralized control center, classifying and rating the data sources, and carrying out real-time demand analysis by adopting a parameterized mixed time algorithm to obtain a data task queue with a priority mark; monitoring the load state of an operating system and the length of a task queue, performing resource evaluation by applying an online distributed queue length estimation algorithm, dynamically creating a working thread and allocating resources; based on a thread resource allocation scheme, executing parallel data verification by adopting a sub-exponential algorithm on an independent set; and evaluating a backup demand according to the data verification result set, determining a backup strategy, and executing parallel data backup. According to the method, the characteristics of a domestic operating system are fully utilized, efficient verification and backup of the data of the electric power centralized control center are achieved, and the system resource utilization rate and the data safety are improved.

Systems herein describe a secured transaction system for accessing a camera stream from a front-facing camera unit of a wearable computing device, identifying a visual marker in the camera feed associated with a payment application, in response to identifying the visual marker, transmitting a request to an operating system of the wearable computing device to isolate access of the camera stream to the payment application and in response to transmitting the request, initiating a transaction using the payment application.

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.