1965 results about "Configuration system" patented technology

The invention provides a PCIe resource dynamic configuration method and device, and relates to the technical field of computer hardware resource management. The method comprises the following steps of: establishing communication connection with the pluggable equipment through an expansion interface, and reading equipment identification information pre-stored in a characteristic value storage module of the pluggable equipment by utilizing an I2C (Inter-Integrated Circuit) interface; identifying mainboard PCIe resource allocation parameters and the equipment identification information; displaying configurable PCIe resource parameters on a system configuration interface, and providing configuration options which can be operated by a user; according to the device identification information and a configuration option selected by a user, a pre-stored PCIe configuration mapping table is inquired, a corresponding PCIe resource configuration instruction is generated, and the PCIe configuration mapping table comprises the device identification information and the corresponding relation between the configuration option and the PCIe resource configuration instruction; and configuring PCIe resource allocation of the mainboard according to the PCIe resource allocation instruction.

A predictive incident management system, consisting of: a sensor input module configured to receive heterogeneous telemetry data streams from mechanical, thermal, electrical and cyber sources; a temporal correlation control unit operationally coupled to the sensor input module, wherein the temporal correlation control unit is configured to normalize received data into a uniform time series envelope that includes identifiers, microsecond-precision timestamps, metric names, values, and context markers, and is further configured to compute sliding window-cross-sensor correlation matrices, event motifs, and lead-lag dependencies across multiple time granularities; a scalable rule processor that is communicatively linked to the control unit for temporal correlation, wherein the rule engine includes an in-memory runtime environment for processing complex events and a domain-specific declarative language, and is configured to apply rules that reference primitive sensor metrics, derived correlation features, and motive-based early warning vectors to classify, escalate, or resolve predicted incidents; A historical repository that is communicatively connected to both the temporal correlation control unit and the rule engine. The repository is configured to store tagged event histories, correlation motif dictionaries, rule versions, and rule origin metadata to ensure the verifiability and explainability of predictions; and An incident response interface is operationally connected to the rule engine. The incident response interface is configured to trigger automated workflows, including the generation of tickets for IT service management, chat ops notifications, the execution of orchestration playbooks, and direct machine control via industrial protocols. the system is configured to perform predictive analyses based on temporal correlations between sensors and to execute context-aware, rule-based incident management in real time.

The invention discloses a patent disclosure auxiliary writing method and system based on multi-agent collaboration. The method comprises the steps that a multi-agent system composed of a collaboration agent, an agent agent and an expert agent is constructed, a knowledge base and a decision module of each agent are configured, and a collaboration architecture of a task scheduler, an arbiter and a shared information space is established; the method comprises the following steps: receiving an original technical scheme of a user, and constructing a structured intermediate representation through multi-agent parallel semantic analysis and feature extraction; based on the representation, a writing task sequence is generated through dynamic task decomposition, intelligent agent division is coordinated through a contract network protocol, and content generation and verification are completed based on a multi-round debate mechanism; performing multi-dimensional consistency detection on the generated draft, and eliminating logic conflicts and expressing defects through iterative revision; and finally, an interest portrait is generated based on the user technical scheme and the optimized draft, interest retrieval is performed, a technical content report is output, and the efficiency and quality of disclosure book writing are improved.

The invention discloses an unmanned aerial vehicle real-time vegetation classification system and method based on a lightweight AI model, and belongs to the technical field of vegetation monitoring and analysis. On the basis of an existing processor of the unmanned aerial vehicle, vegetation real-time classification is achieved by optimizing the deep learning model, and the flight path is dynamically adjusted according to the classification result. The method comprises the steps of system configuration and initialization, lightweight AI model development and optimization, real-time data acquisition and processing, adaptive flight path adjustment and feedback optimization. The method has the following advantages: (1) real-time classification of vegetation and adaptive flight path adjustment are realized, and the acquisition efficiency is optimized; (2) the sampling density is adaptively adjusted for different value regions, and the precision and coverage efficiency of vegetation classification are improved; (3) the acquisition strategy is optimized in real time in combination with the battery state, and unnecessary flight time and energy consumption are reduced; and (4) the method is suitable for application fields such as agricultural monitoring, forestry management and ecological protection.

An illustrative storage management system is configured to provide persistent data storage to workloads managed by a container orchestration platform deployed in a cluster of nodes. The storage management system is configured to receive a request comprising identifiers indicative of a plurality of layers associated with a virtual machine to be run in the cluster, generate, based on the request, a virtual machine disk image comprising the plurality of layers, the virtual machine disk image configured to be used to run the virtual machine in the cluster, and provide the virtual machine disk image in response to the request. In some implementations, the virtual machine disk image is used by a virtual machine handler associated with the container orchestration platform to run the virtual machine in a virtual machine type pod that is managed by the container orchestration platform.

A system for managing future costs associated with a product includes a future requirement system programmed to estimate an amount of resources required for manufacturing, distributing, and selling the product at a future point in time. The system includes an adverse contingency system configured to identify adverse contingencies and calculate changes in costs associated with obtaining the amount of resources at the future point in time. The system includes a smart contract system programmed to autonomously configure and execute a smart futures contract based on the amount of resources required and on the changes in costs to manage the future costs associated with the product.

The invention discloses an intelligent valve actuator remote monitoring and fault early warning system based on the Internet of Things, which relates to the field of valve actuator monitoring and comprises a data acquisition module, a data transmission module, a data storage and management module, an analysis and fault diagnosis module, an early warning module, a remote control module, a user interaction module, a safety protection module, a log recording and auditing module, an environment perception module and an intelligent prediction module. A plurality of sensors collect data, a hybrid communication architecture and quantum encryption transmission are used, distributed storage and cloud computing are combined, deep learning and reinforcement learning are fused to diagnose faults, early warning is performed based on a dynamic threshold model, remote control is performed by using digital twinning and a block chain, an immersive interface and voice interaction are supported, and system configuration is adaptively adjusted. A multi-level security protection system is adopted, and logs are analyzed and recorded by means of a block chain and big data. The valve actuator is monitored in real time in an omnibearing mode, data are accurately collected, transmission is stable and safe, faults are accurately diagnosed, early warning is conducted in time, remote control is supported, interaction is convenient and safe, and industrial operation is 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.

The invention provides a code agent construction system, a construction method thereof and a code generation method, and relates to the technical field of computers, in particular to the technical field of artificial intelligence such as large models. According to the specific implementation scheme, the code agent construction system comprises an intelligent cooperation engine, a layered template library, a semantic understanding and layered retrieval system, a T-RAG generation and optimization system and a closed loop verification system; wherein the layered template library comprises a project-level template, a function-level template and an API-level template; the semantic understanding and hierarchical retrieval system is configured as follows: analyzing user requirements through a natural language understanding module to generate an intention vector, and sequentially executing project-level, function-level and API-level three-level progressive template retrieval; and the T-RAG generation and optimization system is configured to inject the hierarchical retrieval result into the cue word of the large language model to generate deployable codes containing front-end and rear-end logics.

A system and method for automated qualitative analysis of computerized conversational agent (CCA) conversational data. In an embodiment, the system comprises a system configuration manager for establishing CCA characteristics and factors to be included in analyses, a data ingestion engine for ingesting conversations from a CCA, a conversation indicator extractor for identifying and classifying key aspects of conversations, a fallback analyzer for identifying the type and frequency of CCA failures, a topic modeling engine for identifying trends in conversations, and a CCA score generator for generating score assessments for qualitative aspects of the CCA's performance.

The present disclosure relates to cybersecurity architectures and systems for assessing and quantifying security threats and risks associated with machine-readable codes, such as quick response codes, barcodes, data matrix codes, and other types of codes. A security application comprises a multi-context threat assessment system configured to analyze a broad spectrum of risk assessment attributes across multiple contexts. These contexts relate to the machine-readable code itself, target network resources identified by the code, entities affiliated with the code, end-users interacting with the code, and enterprise systems policies. The system can evaluate various risk assessment attributes for each of these contexts to more accurately quantify potential security risks associated with the machine-readable codes. The security application further includes an API for extending its threat assessment capabilities to various digital ecosystems and an AI-powered learning network comprising language models and computer vision systems to enhance threat detection and risk quantification capabilities.

The invention relates to the field of data processing, in particular to a multi-parameter collaborative optimization method and system based on a data center cooling system, and the method comprises the steps: obtaining a cooling subsystem of a computing equipment integrated cabinet, and carrying out the short-time temperature analysis of the computing equipment integrated cabinet based on infrared monitoring data and temperature sensing data, if it is confirmed that the limiting temperature of the component is not higher than the corresponding preset temperature threshold value according to the current temperature distribution, and it is confirmed that the continuous heating temperature is lower than the preset temperature change threshold value based on the temperature change trend, the temperature change trend and the current temperature distribution are obtained; if yes, returning to the step of executing multi-modal data acquisition on the computing equipment integration cabinet and the cooling subsystem, and otherwise, synthesizing the predicted temperature data, obtaining a cooling subsystem configuration strategy and setting the cooling subsystem. According to the invention, the cooling monitoring and adjustment of the computing equipment of the data center are realized by integrating the data collected by the temperature sensor and the thermal imaging probe which are reasonably arranged.

The invention discloses a data center infrastructure personnel intelligent configuration system, and belongs to the technical field of data center operation and maintenance management. Comprising an acquisition module, a man-hour prediction module, a configuration module and a feedback module, the man-hour prediction module is used for calculating the health coefficient of the equipment according to the equipment list and performing man-hour prediction; the configuration module is used for configuring the number of personnel according to the prediction result; the feedback module is used for outputting an executable personnel configuration scheme through a preset scheduling system; according to the method, a multi-dimensional configuration scheme meeting the SLA requirement is output through a real-time optimization algorithm of precise man-hour modeling and fusion of dynamic factors, real-time generation of the scheme is achieved, and traditional manual measurement and calculation which consumes several days are replaced.

The invention relates to the technical field of new energy automobile electric control, and discloses a torque compatible intelligent matching system and method for a new energy automobile, and the system comprises a parameter pre-storage module, an EOL configuration module, a dynamic selection module, a data collection module, a torque calculation module, and an execution output module. A driving system configuration identifier is written in through a vehicle production line EOL terminal to realize hardware adaptation selection; an eagle optimization algorithm is innovatively integrated and improved to construct a historical driving database, an output proportion table is periodically adjusted to improve the driving smoothness, and meanwhile a supplier batch compensation mechanism is established to solve the hardware tolerance problem; fault grading processing is achieved through parameter table dual redundancy check, and torque distribution efficiency is continuously optimized in combination with a full-life-cycle closed-loop self-learning module. According to the scheme, the development cost adaptive to different power systems is reduced, and the core pain points of poor multi-source hardware compatibility, weak working condition adaptability and extensive energy efficiency management of the new energy automobile are solved.

The invention discloses a vehicle-mounted fault diagnosis system, method and device and a storage medium, and relates to the technical field of rail transit, and the method comprises the steps: configuring system basic parameters through a data preloading module, so as to control the system to start and operate according to the system basic parameters; the log analysis module receives the running log of the train, analyzes the log, obtains abnormal events of a train subsystem and a vehicle-mounted controller, and obtains basic fault information based on the abnormal events; the alarm module performs verification and fault hardware positioning on the basic fault information, obtains a fault positioning result and generates alarm information; the database management module queries a downtime log or / and an event log according to a fault positioning result, and constructs an event curve to determine a fault reason; the parameter tracking module tracks the event curve and judges whether the fault has cross-system influence or not; accurate and reliable fault correlation analysis in a complex scene of multi-system interaction of the train is realized, so that the fault diagnosis and operation and maintenance efficiency is improved.

The invention discloses a multi-model dynamic image analysis system, and relates to the technical field of artificial intelligence. The system adopts a heterogeneous computing architecture and comprises a preprocessing model, a multi-modal analysis model, an anisotropic correction model, a large language model, a task knowledge base embedding model and a KV cache management system. An incongruous task reconstruction mechanism based on hardware isomerism is constructed, a large language reasoning model is nested for analysis, and efficient reuse of computing resources is achieved by using a cross-model KV cache optimization technology. The system is provided with multiple graphics cards and a double-channel NUMA, and a multi-mode large language model carries out unsupervised or weak supervised training on a target detection model in the operation of the system. According to the system, through a dynamic load scheduling strategy, a mechanism for reasoning by using a processor and a memory enhanced display card in a full-load mode is innovatively designed, and three-section real-time streaming enhanced output is achieved. According to the system, through a dynamic load scheduling strategy, a mechanism for reasoning by using a processor and a memory enhanced graphics card in a full-load mode is innovatively designed.

The present disclosure relates generally to robotic systems, and more specifically to systems and methods for specifying a robot task configuration by means of annotating a visual workspace representation in coordination with a waypoint optimization process. This system enables a much broader use of robotic automation by saving time and reducing technical complexity. This system can be used to configure any visually enabled robotic task, such as tasks in warehouse management, manufacturing, delivery, inspection, logistics, etc.

An infection control tool for a building HVAC system includes processing circuitry configured to receive one or more inputs defining a HVAC system configuration for each of a baseline scenario and one or more other scenarios. The processing circuitry is also configured to estimate infection risk and at least one of energy consumption or operating cost for each of the one or more other scenarios relative to the baseline scenario. The processing circuitry is also configured to operate a display to provide the infection risk and at least one of the energy consumption or operating cost to a user.

The invention discloses a network communication link fault detection and positioning method, device and equipment and a medium, and relates to the technical field of computers, and the method comprises the steps: starting a virtual USB service of a substrate management controller to establish a network communication link between the substrate management controller and a remote server; obtaining link configuration information of the network communication link; performing fault detection on the network communication link based on the link configuration information to obtain a link fault detection result; wherein the link fault detection result comprises a connectivity verification result, a function test result and a performance evaluation result; and performing fault positioning by using the link fault detection result to obtain a fault positioning result. The efficiency of network communication link fault detection and positioning is improved, and the original system configuration is not affected.

An information technology system for a distributed manufacturing network includes an additive manufacturing platform configured to manage workflows for a set of distributed manufacturing network entities associated with the distributed manufacturing network. The information technology system includes a set of digital twins generated by the additive manufacturing platform. The information technology system includes an artificial intelligence system configured to be executed by a data processing system in communication with the additive manufacturing platform. The artificial intelligence system is trained to generate process parameters for the workflows managed by the additive manufacturing platform using data collected from the set of distributed manufacturing network entities. The information technology system includes a control system configured to adjust the process parameters during an additive manufacturing process performed by at least one of the set of distributed manufacturing network entities.

The invention discloses a manufacturing industry ERP dynamic resource optimization configuration system based on reinforcement learning, and relates to the technical field of manufacturing industry ERP resource configuration, and the system comprises an ERP state data perception module which is in butt joint with a manufacturing industry ERP system, collects multi-source heterogeneous data in real time, fuses the multi-source heterogeneous data in real time by using a Kalman filtering technology, detects an abnormal value, and sends the abnormal value to the ERP state data perception module; an improved Robust Scaling method is adopted for standardization processing, and initial data of a unified structure are output and transmitted to the feature extraction and integration processing representation module; multi-source heterogeneous data are collected in real time through the ERP state data sensing module, fusion is carried out through the Kalman filtering technology, dynamic changes in the production environment can be captured in time, strategies can be continuously adjusted according to the real-time dynamic change data through the reinforcement learning method, and the real-time dynamic change data can be obtained. And outputting an optimal resource allocation action adapting to a dynamic environment, so as to greatly improve the adaptability of the system to a complex production environment.

The invention provides a collaborative data migration scheduling method based on topology awareness and deep reinforcement learning, and the method comprises the steps: initializing system configuration, constructing a weighted graph comprising a source server, a working machine, a target storage and network equipment, carrying out the embedded processing of a graph structure through a graph neural network, and generating a node and a path representation vector; in combination with source data, a working machine and a target storage state, constructing a system state vector, inputting the system state vector into a deep reinforcement learning agent for decision making, and outputting a composite action including data block selection, working machine selection, path indexing and migration rate suggestion; the network control module converts path information into flow rules, the flow rules are issued and executed through the software defined network controller, the task management module continuously monitors the migration state and processes failed data blocks, and the integrity and non-repeatability of migration tasks are ensured. According to the method, the defects of an existing data migration system in the aspects of task allocation, path selection, resource control and dynamic adaptability can be overcome.

In some aspects, a cloud-based computer system includes: a quantum computing system comprising a quantum processing unit; a container management and execution system configured to receive a container and execute a program within the container; and a communication channel between the container management and execution system and the quantum computing system for providing program instructions to the quantum computing system. The container management and execution system and the quantum computer system may be co-located in a data center or located in different data centers. The latency of the communication channel may be selected to optimize cost for a required computer performance.

A storage system is configured with pools of processor cores. Each pool corresponds uniquely to one of the supported service levels of the storage system. In a dynamic time series forecast adjustment mode, processor cores within each pool run at a clock speed that is statically defined for the service level corresponding to the respective pool and core affiliations with pools are dynamically adjusted based on modelled data access latency. During a scheduled event, an event guided core matrix profile overrides the time series forecast adjusted core matrix profile. The event guided core matrix profile includes core clock speeds and pool affiliations, thereby enabling rapid reconfiguration.

Embodiments of the present invention provide a system for performing multi-platform software code merging and migration using hybrid neural networks. The system is configured for identifying source files in a software code development environment, wherein each of the source files comprise software code associated with entity applications that is developed by one or more users in one or more software programming languages, translating the software code in each of the source files that is developed in the one or more software programming languages into a translated code that is in a first software programming language, merging the translated code associated with the software code in each of the source files into a unified code, wherein the unified code is in the first software programming language, automatically performing build and packaging of the unified code for deployment, and automatically deploying the packaged unified code into a software code production environment.

A configurable power supply bay system. The configurable power supply bay system includes a power supply bay and a power supply bay system configuration component, the power supply bay configuration component being configured to be mounted to the power supply bay, the power supply configuration component configuring the power supply bay to mount a plurality of types of power supply units within the power supply bay, each of the plurality of types of power supply units having respective external characteristics.

The invention provides a thermoelectric collaborative energy supply method and system for an Antarctic inland environment, and relates to the technical field of energy supply systems.The thermoelectric collaborative energy supply method specifically comprises the steps that annual environmental data of a preset scientific investigation area are collected, and at least one typical week working condition is extracted through a clustering algorithm; constructing a multi-objective optimization model containing decision variables, an optimization objective function and constraint conditions; performing iterative solution on the multi-objective optimization model by adopting a multi-objective optimization algorithm, and in each iteration, simulating a candidate system configuration scheme by utilizing a preset dynamic thermophysical model, a battery dynamic state updating model and a multi-source energy supply control strategy under the typical cycle working condition to obtain a multi-objective optimization model; the corresponding objective function value is evaluated; and obtaining an optimal configuration scheme of the energy system according to an iterative solution result.

The invention discloses a starting display method of electronic equipment, equipment, a medium and a product, and relates to the technical field of starting display, and the starting display method comprises the following steps: in the starting process of the electronic equipment, in response to execution of a power-on self-test stage, displaying a power-on self-test stage interface on a display component of the electronic equipment, the power-on self-test stage interface comprises at least two first display areas, and the at least two first display areas display at least one of the following information: first system configuration information and starting execution information. According to the method, the key hardware configuration and the real-time starting state are visually presented in the starting process without additionally entering a BIOS interface and executing a complex instruction, so that the problem of low operation efficiency caused by tedious operation of obtaining starting related information of the electronic equipment in a starting display method of the electronic equipment in the related technology can be solved, and the starting efficiency of the electronic equipment is improved. And the operation efficiency of obtaining the starting related information is obviously improved.

The invention discloses a lip language-to-voice conversion method and device, a computer storage medium and a terminal, and aims to solve the problems that a lip language-to-voice conversion technology cannot be deployed on terminal equipment and voice quality cannot meet application requirements. The method is combined with a neural vocoder which reduces calculation complexity and resource requirements, system configuration requirements are reduced while system parameters are reduced, a design basis is provided for deploying a lip language-to-speech method on terminal equipment, and context-related visual feature sequences and user audio embedding vectors are fused, so that the user audio-to-speech conversion efficiency is improved, and the user audio-to-speech conversion efficiency is improved. A Mel spectrum acoustic feature sequence used for being converted into a voice waveform is obtained, and a user audio vector fused with the Mel spectrum acoustic feature sequence is obtained, so that the output voice waveform is more consistent with the real voice of a user; according to the embodiment of the invention, technical support is provided for deploying and applying the lip language-to-speech method meeting the speech quality requirement on the terminal equipment.

The invention provides a server configuration system and a server configuration method, which can be applied to the technical field of servers. The server configuration system comprises a main control module arranged on a main circuit board and used for acquiring configuration attribute information of a plurality of network connection devices and a plurality of first processors and sending the configuration attribute information and a generated power-on and power-off instruction to a serial expansion module; the serial expansion module is arranged on the serial expansion switching board and comprises a first controller and a plurality of switching units, and the first controller is used for controlling the plurality of switching units, the plurality of network connection devices and the plurality of first processors to perform power-on and power-off operation according to the power-on and power-off instruction and issuing configuration attribute information to the plurality of switching units; and the switching unit is used for carrying out bandwidth configuration on the plurality of network connection devices and the plurality of first processors according to the configuration attribute information.