40 results about "Smart computing" patented technology

This invention discloses a ship cloud computing load balancing method based on a hybrid intelligent algorithm, relating to the field of ship intelligent computing and cloud computing integration technology. The method includes the following steps: task classification modeling, edge-cloud resource status monitoring, multi-objective optimization decision-making, and dynamic load adjustment. This invention reduces the load standard deviation of ship edge-cloud nodes through a multi-objective optimization algorithm and a dynamic adjustment mechanism. It optimizes task processing efficiency, reducing the average latency of real-time tasks and shortening the total processing time of non-real-time tasks. It reduces system energy consumption by optimizing the energy consumption coefficient, thereby reducing the total energy consumption of edge nodes and cloud servers. It adapts to ship scenarios, improving the robustness and adaptability of scheduling strategies to address the heterogeneity of ship tasks and network dynamics.

The present invention belongs to the field of intelligent computing, and specifically relates to a multi-task collaborative attention TSK fuzzy system modeling method, which is used to provide an interpretable multi-task intelligent evaluation model for fermentation food safety assessment. The method includes two main parts: a new multi-task TSK fuzzy system model structure and a multi-task collaborative optimization process. In the intelligent evaluation model, the proposed multi-task collaborative processing unit is used to perform collaboration among multiple evaluation tasks. By using the multi-task feature selection layer and task attention structure respectively to extract the unique information of each task and the relevant information between tasks, the evaluation performance of each evaluation task can be better improved. In the collaborative optimization process, the present invention uses multi-task collaborative regularization to achieve more efficient mining and utilization of the unique information of each task.

This invention provides a method and apparatus for generating tokens using computing power acceleration on an intelligent computing cloud platform. It relates to the fields of intelligent computing centers, smart computing centers, computing infrastructure, and smart computing cloud technologies. The method for generating tokens using computing power acceleration on an intelligent computing cloud platform includes: Step S1, establishing a full-text index of a target data set; Step S2, responding to a received data query request, determining whether to enable document index acceleration; Step S3, if document index acceleration is enabled, extracting scalar filtering conditions and vector filtering conditions; Step S4, performing a full-text search based on the scalar filtering conditions to obtain a first data set; Step S5, performing a vector search on the vector index of the first data set based on the vector filtering conditions to obtain a matching second data set, and returning it to the user. This invention can improve the speed of vector database processing with scalar constraints on intelligent computing cloud platforms, reduce resource consumption, and increase token generation speed.

The application discloses a kind of modularization on-orbit edge intelligent computing platform, belong to spacecraft electronic information technical field.The platform uses distributed architecture, including at least one network and management module NMM and at least one general edge AI module UAM, NMM as the data exchange and control center of platform, including system management unit SMU, redundant network exchange unit and controlled wireless communication unit;UAM as the core processing unit of platform, responsible for executing data and computationally intensive tasks, using the separation type design of core board plus bottom plate, each UAM is connected the redundant network exchange unit by independent Ethernet link.The platform is through the distributed architecture with network exchange as center, in combination with hierarchical module level fault self-healing mechanism, multi-level redundant communication architecture facing application optimization and the flexibility of elastic computing power scheduling mechanism based on microservice, while guaranteeing high performance and flexible scalability, greatly improve the on-orbit survivability and task execution efficiency of platform.

This application relates to the field of food processor control technology, and discloses a control method and system for a food processor based on intelligent adjustment. The method or system includes: a data sensing unit that collects real-time data on the physical properties of ingredients and motor operating parameters; an ingredient characteristic identification unit that identifies the hardness, toughness, and processing stage of the ingredients; an adaptive control unit that intelligently calculates the optimal motor control command based on the identification results, motor status, and preset curves; a motor drive unit that executes the command; and a safety protection unit that monitors and triggers protection. By adopting the above technical solution, this application can avoid motor overload, food splashing, and uneven grinding, thereby improving processing efficiency, uniformity, and safety.

The application provides a kind of intelligent computing resource scheduling terminal of vehicle cloud cooperation, is deployed in edge cloud node, including full source data sensing and interactive interface, cognitive front-end processing, strategy reasoning decision, computing resource arrangement instruction distribution and performance monitoring and reward calculation module, can realize with scheduling task description generation and receive candidate computing node's node state report and maintain the resource index table of node, node matching feature extraction, target node decision, instruction issuing and execution feedback whole process operation.The application faces intelligent transportation, autonomous driving and other cross-domain collaborative tasks, and for the decision-making uncertainty area formed by the lack of priori discriminability of the execution side selection executed by the vehicle end / cloud end, through the priori matching evaluation of cognitive front-end and strategy reasoning cooperation, the decision-making computing burden of high-dimensional multi-source state can be reduced, and the risk of improper execution side selection can be reduced, so as to improve the computing real-time performance, success rate and system operation safety of vehicle cloud cooperation task.

This invention provides an intelligent computational method for predicting effector proteins in the secretory system of Gram-negative bacteria, comprising: S1: collection and preprocessing of protein sequence datasets; S2: model construction; S3: model training; and S4: model prediction. This invention has advantages such as high prediction accuracy and good anti-interference performance.

This invention provides a method and apparatus for measuring computing power on an intelligent computing cloud platform based on GPU computing power consumption and inference token values. It relates to the fields of intelligent computing centers, smart computing centers, computing infrastructure, and smart computing cloud technologies, and includes the following steps: Step S1, collecting task data, GPU runtime data, and model and engine parameters during inference on the intelligent computing cloud platform; Step S2, extracting first feature information, second feature information, and third feature information from the task data, GPU runtime data, and model and engine parameters; Step S3, performing token fitting computing power calculation based on the first feature information, GPU model, and third feature information to obtain a first computing power consumption value; Step S4, performing GPU computing power consumption benchmark calculation based on the first, second, and third feature information to obtain a second computing power consumption value; Step S5, determining a reliable computing power consumption value based on the first and second computing power consumption values. This invention can significantly improve the accuracy of computing power measurement on intelligent computing center cloud platforms.

This invention belongs to the field of intelligent computing and offshore wind power monitoring technology, specifically involving a collaborative monitoring method for corrosion prevention in offshore wind farms that integrates swarm intelligence. The method includes: acquiring real-time salt spray deposition rate and real-time relative humidity; generating local initial screening; obtaining an environmental stress index; obtaining initial pheromones; calculating a comprehensive pheromone for assessing the swarm's collaborative filtration status; calculating the collaborative focusing degree for assessing high-risk targets across the network and guiding inspection resources; obtaining depth probing measurements characterizing substantial damage to the metal substrate; calculating a degradation risk score for extrapolating the probability of potential structural brittle fracture; and generating corrosion prevention scheduling instructions to guide monitoring status. This invention solves the technical problems in existing technologies, such as the lack of collaborative linkage among offshore wind farm nodes, the difficulty in extracting initial corrosion characteristics due to environmental noise, and the inability of inspection resources to dynamically focus on truly high-risk areas.

1. Name of the product in this design: Graphical User Interface for a Full-Scene Adaptive Intelligent Computing System for Electronic Devices. 2. Purpose of this design: An electronic device. 3. The key design feature of this product is its graphical user interface. 4. The image or photograph that best illustrates the design's key features: the front view. 5. Purpose of the graphical user interface: Used for PC programs to automatically match different claims scenarios and calculation rules, and intelligently calculate the compensation amount. It supports multi-dimensional analysis of complex cases (such as liability ratio and deductible accumulation) to achieve unified and accurate claims settlement across scenarios. 6. Human-computer interaction method of graphical user interface: The main view is the display page of the intelligent claims system that is adapted to all scenarios. Clicking the blank space in the middle of the main view can upload paper insurance policies and automatically recognize them to enter the interface change status diagram.

This invention provides a method and apparatus for dynamically quantifying hybrid heterogeneous computing power loss in intelligent computing cloud platforms, relating to the fields of intelligent computing centers, smart computing centers, computing infrastructure, and smart computing cloud technologies. The method includes: Step S1, running standard test tasks on each homogeneous computing power cluster to obtain load parameters and the standard computing power of each chip type; Step S2, determining the chip adaptation loss coefficient for each chip type in the hybrid heterogeneous computing power cluster of the intelligent computing cloud platform; Step S3, obtaining the load parameters and real-time total computing power of the hybrid heterogeneous computing power cluster running real-time test tasks; Step S4, determining the load fluctuation coefficient based on the load parameters of the standard test tasks and real-time test tasks; Step S5, determining the dynamic computing power loss ratio of the hybrid heterogeneous computing power cluster based on the load fluctuation coefficient, the real-time total computing power, and the standard computing power and chip adaptation loss coefficient for each chip type. This invention can significantly improve the accuracy of quantifying hybrid heterogeneous computing power loss in intelligent computing center cloud platforms.

A Smart Computer Device with Integrated Multimedia Security and Watermark Verification

The application discloses a kind of based on hybrid order memristor hetero neural network simulation circuit, by hybrid order memristor synapse computing module, neuron circuit and cross crosstalk coupling adjustment module composition.Memristor synapse computing module receives system state and membrane potential voltage signal, parallelly executes the dynamic operation of nonlinear memductance weight, neuron circuit and nonlinear crosstalk coupling module carry out membrane potential difference, function activation and synapse current crosstalk summation and continuous integral operation based on Kirchhoff's law, obtain the output result of characterizing neural network multi-mode brain-like discharge.The circuit efficiently realizes the bottom physical mapping of neural network from microcosmic heterosynaptic current crosstalk to macroscopic dynamics evolution.Compared with traditional isomorphic neural network circuit, the application greatly improves the dynamics dimension and biological authenticity of neural network.The application provides a kind of high quasi-state, high parallel hardware solution for brain-like intelligent computing, has originality and important application value in the field of neuromorphic hardware.

This invention relates to an intelligent computing power scheduling method based on dynamic knowledge graphs, belonging to the field of computer resource scheduling technology. The method includes: constructing and updating in real-time a dynamic knowledge graph that integrates computing power resources, tasks, network, and environmental information; inputting the graph into a decision model based on graph neural networks and deep reinforcement learning, generating a scheduling strategy through graph representation learning and multi-objective optimization; executing the strategy and monitoring feedback to form a closed-loop optimization. This invention utilizes a dynamic knowledge graph as a unified digital twin of computing power, overcoming the shortcomings of traditional scheduling methods such as single resource modeling, lack of relational understanding, and delayed response. It achieves global perception, intelligent matching, and dynamic optimization of heterogeneous computing power resources, significantly improving resource utilization, scheduling efficiency, and decision interpretability.

This invention relates to a method for monitoring ancient building fires based on multimodal fusion perception and edge intelligent computing, comprising: multimodal data acquisition; constructing a dual-path fusion neural network model; inputting the multimodal data to be monitored into the trained model and outputting fire detection results; temporal behavior analysis and verification; triggering a graded alarm when the detection confidence exceeds a corresponding threshold, and simultaneously uploading alarm data via wireless communication. This invention features high multimodal fusion perception accuracy, with visible light, thermal imaging, and lidar co-sensing. The 1.5μm lidar fills the gap in smoke particle detection and three-dimensional spatial reconstruction capabilities. Multi-physical quantity cross-validation improves the confidence level in complex scenarios to over 0.92, achieving an early fire detection rate greater than 95% and an open flame detection rate greater than 99%. It also boasts high fire identification accuracy: reducing the false alarm rate from 15% in single-frame detection to less than 2% during 24-hour continuous operation, with an average response time of less than 3 seconds.

1. Name of the product in this design: Intelligent Computing Cluster Demonstration Platform. 2. Purpose of this design: A platform for demonstrating and experiencing the functions of an intelligent computing cluster. 3. The key design features of this product are its appearance and shape. 4. The image or photograph that best illustrates the design's key points: 3D view 1.

This invention relates to the field of logistics management technology, and more particularly to an intelligent calculation and dynamic optimization method for logistics park operation processes, comprising: calculating the changes in the overall equipment status, task progress, and vehicle queuing density respectively, and merging the changes into an operation status index; for each pending task, reading the corresponding task completion ratio and task queuing density, calculating the node process pressure index and task priority index, until the calculation of all pending tasks is completed, and constructing a task priority queue; for each task in the task priority queue, calculating the matching score between the task and the equipment, generating a task execution allocation table, and simultaneously updating the load status of the equipment through an update function to generate equipment status update values; reading the task execution allocation table, calculating the task adjustment index, re-evaluating the matching between tasks and equipment, and generating an optimized task execution allocation table and equipment status update values.

The application discloses a kind of childbirth process energy consumption monitoring and individualized energy demand management system, including multimodal perception layer, transmission layer, computing layer and application decision layer, multimodal perception layer includes multimodal data acquisition module and non-invasive metabolism monitoring module, and the physiological sign parameter of pregnant woman is collected using wearable sensor array in multimodal data acquisition module, and non-invasive metabolism monitoring module carries out collection to tissue oxygenation data, body fluid distribution and metabolism data and oxygen consumption and carbon dioxide production data, computing layer establishes individual basal metabolic rate model by intelligent computing module, and calculates total energy consumption of labor, and then balanced state is calculated by dynamic energy demand prediction model, and application decision layer generates individualized nutrition supplement scheme according to the data of computing layer, triggers multi-level early warning and pushes clinical decision suggestion, and the application realizes real-time accurate monitoring of childbirth process energy consumption, individualized energy demand prediction and dynamic nutrition supplement.

This invention relates to the technical field of steel structure damage detection, and in particular to a steel structure damage identification system and equipment, including a multimodal sensing acquisition module, an edge intelligent computing module, a wireless communication transmission module, a cloud monitoring and management platform, and a graded early warning module. The multimodal sensing acquisition module employs an electro-ceramic ultrasonic sensor, a micro-strain sensor, a vibration acceleration sensor, an acoustic emission sensor, and a temperature and humidity compensation sensor. During damage identification, these sensors are simultaneously attached to the test points on the steel structure components to acquire ultrasonic guided wave signals, surface micro-strain signals, three-dimensional vibration acceleration signals, damage acoustic emission signals, and environmental temperature and humidity signals. This improves the accuracy and comprehensiveness of steel structure damage identification, provides graded responses according to damage severity, offers all-weather, blind-spot-free early warning, enables autonomous movement on various steel structure surfaces of different shapes, optimizes the limitations of fixed-point monitoring, and adapts to the comprehensive monitoring needs of complex steel structures.

This invention provides a multi-dish cooking control method, device, electronic device, and computer storage medium, relating to the field of smart home technology. The method includes: acquiring ingredient information for each ingredient to be cooked; determining the required cooking time for each ingredient in a preset database based on the ingredient information; acquiring a cooking mode; determining the start and end times of cooking for each ingredient based on the cooking mode and the required cooking time, and executing the cooking process; by acquiring the ingredient information for each ingredient, determining the required cooking time for each ingredient based on the preset database, and combining this with the user-selected cooking mode, intelligently calculating the start and end times of cooking for each ingredient and issuing reminders, this method improves cooking efficiency while significantly reducing user complexity, ensuring food quality and user experience.

This invention relates to the field of new energy hybrid power technology, specifically disclosing a remote monitoring and control platform for fuel cell hybrid power systems based on cloud-based web page interaction. Its key technical features include: a vehicle-side data acquisition terminal, an embedded communication module, and cloud-based communication protocol processing, data processing, remote control command, and web page interaction interfaces. Standardized synchronization of vehicle-cloud data is achieved through a shared field mapping dictionary; real-time parameters are centrally distributed using a shared status table; the cloud module performs online calculations and updates of energy management and safety strategies; and visual monitoring and command input are provided through the web page interface. Finally, closed-loop control is formed after command fusion and arbitration. This platform integrates vehicle-side data acquisition, cloud-based intelligent computing, and remote closed-loop control, significantly improving system operational safety, energy management efficiency, and remote controllability.

This invention provides a method and apparatus for achieving self-evolution of intelligent agents through computing power on an intelligent computing cloud platform, relating to the technical field of intelligent computing cloud platforms and computing power infrastructure. The method includes: Step S1: The intelligent agent acquires target behavior data of the user using the intelligent agent; Step S2: The intelligent agent analyzes the target behavior data using a large language model to obtain behavioral statistical features; Step S3: The intelligent agent identifies user preference features based on the behavioral statistical features using the large language model; Step S4: The intelligent agent determines the quality assessment result of the intelligent agent based on the behavioral statistical features using the large language model; Step S5: The intelligent agent determines the evolution scheme of the intelligent agent based on the quality assessment result and user preference features using the large language model, and optimizes the intelligent agent based on the evolution scheme. This invention can effectively save the development cost of the intelligent agent, and the optimized intelligent agent better meets the actual user needs.

This invention provides an intelligent computing cloud platform that provides computing power, based on a multi-agent intelligent operation and maintenance method and apparatus. It relates to fields such as intelligent computing centers, smart computing centers, computing power infrastructure, and smart computing cloud technology. The method includes: the intelligent computing cloud platform constructing interactive agents such as a perception agent, a diagnostic agent, a planning agent, a scheduling agent, an evaluation agent, a graph database, and a decision engine; the perception agent acquires computing power operation data, searches the graph database for graph matching, and calculates information entropy when no matching graph is found; when the information entropy exceeds a threshold, the diagnostic agent performs root cause analysis based on the computing power operation data to determine the cause of the anomaly; the planning agent generates an intelligent operation and maintenance process based on the cause of the anomaly; the scheduling agent calls operation and maintenance tools to perform operation and maintenance on the abnormal computing power according to the intelligent operation and maintenance process; and the evaluation agent evaluates the operation and maintenance results to execute subsequent operation and maintenance processing operations. This invention significantly improves the autonomy and comprehensiveness of operation and maintenance technology.

This invention relates to the field of intelligent computing technology, and discloses a node management method, apparatus, and electronic device for an intelligent computing cluster. The method includes: loading an operating system image file onto the target intelligent computing node in response to a startup request from the target intelligent computing node; receiving a connection request sent by the target intelligent computing node during the loading of the operating system image file, the connection request including the out-of-band management address of the target intelligent computing node; obtaining network configuration information associated with the target intelligent computing node based on the out-of-band management address; generating a network configuration file for the target intelligent computing node based on the network configuration information; and distributing the network configuration file to the target intelligent computing node so that the target intelligent computing node performs network configuration according to the network configuration file. This invention can improve the network configuration consistency problem and effectively improve the deployment and configuration efficiency of intelligent computing clusters.

The application provides a method and device for limiting traffic in a computing power distributed routing mode of an FWaaS of an intelligent computing cloud platform, and relates to the technical fields of intelligent computing cloud platforms and computing power infrastructure. The method comprises the following steps: in the case of triggering the FWaaS function, identifying the current routing mode; in the case of the current routing mode being the computing power distributed routing mode, judging whether the current to-be-configured device is a network node; if the current to-be-configured device is a network node, configuring a SNAT namespace for the network node, configuring an FWaaS rule for an SG interface in the SNAT namespace, if the current to-be-configured device is a computing node, configuring a router namespace for the computing node, and adjusting the FWaaS rule, and configuring the adjusted FWaaS rule for an RFP interface in the router namespace.

The application discloses a somatotype robot domain control system and method adopting heterogeneous computing hierarchical design, and relates to the technical field of robots.The system comprises a heterogeneous hardware platform taking RZ / T2H chips as carriers, a scheduling system, and a physical-computing fusion control algorithm framework.The scheduling system and the physical-computing fusion control algorithm framework are both arranged on the heterogeneous hardware platform.The heterogeneous hardware platform integrates intelligent computing wings, real-time control wings, special-purpose acceleration units, and deterministic data pipelines.The application realizes deep decoupling and efficient cooperation of intelligent decision-making and real-time control, guarantees full-link timing determinacy and task priority, and processes hardware of the special-purpose acceleration units, thereby greatly improving the sensing and execution efficiency, eliminating data jitter and synchronization errors, and realizing deep cooperation of the physical-computing fusion control algorithm framework in the interaction between computation and physics, thereby significantly improving the control performance.The parallel initialization, cooperative diagnosis and safety monitoring mechanism improve the system reliability and safety, and are suitable for multiple types of somatotype robot scenes.