12 results about "Physical computing" patented technology

The application provides a kind of industrial cloud platform computing resource dynamic scheduling method and system combined with deep learning, it is related to industrial cloud platform technical field, first, the real-time request queue parameter set and real-time container resource parameter set of containerized application instance set in industrial cloud platform are acquired, then correlation coupling analysis is carried out to generate service quality sensitivity descriptor set, then call deep learning request flow prediction network to identify request flow evolution mode, generate predicted request timing distribution set in future prediction time window, according to the node available resource parameter set of the two sets above Resource reservation dynamic programming is carried out to physical computing node cluster, generate dynamic binding relationship mapping set, finally, based on the dynamic binding relationship mapping set Migration scheduling instruction sequence is generated and issued to container orchestration scheduler, to realize the dynamic accurate scheduling of computing resource.

A nonlinear physical computing architecture comprising a continuous nonlinear domain in which excitation produces persistent reshaping of at least one spatially continuous parameter field. Said reshaping alters non-local coupling coefficients of governing nonlinear equations and modifies attractor basin topology. Subsequent excitation follows a measurably reduced state-space trajectory length relative to baseline measured following reset. Persistence magnitude may be continuously modulated, and multiple domains may be orchestrated in staggered reset configurations. The architecture excludes discrete memory cells and programmable weight matrices.

The application discloses an intelligent network anomaly monitoring method and device and a storage medium, relates to the field of communication, and is used for improving the efficiency of intelligent network anomaly monitoring, and comprises the following steps: acquiring time series data corresponding to key performance indicators of physical computing nodes and virtual computing instances; performing window slicing on the time series data in the time dimension, extracting multi-dimensional time series features from each data window to obtain a feature vector; determining a target prediction model from a plurality of prediction models according to the time characteristics of the abnormal mode corresponding to the time series data; analyzing the feature vector based on the target prediction model, determining a performance indicator prediction sequence of a target time window, and determining an abnormal probability score according to the deviation degree of the performance indicator prediction sequence and a preset normal baseline. The application is applied to the process of intelligent network anomaly monitoring.

The application belongs to the technical field of cloud computing and network space security simulation, and particularly relates to a traffic-aware resource scheduling method for large-scale network ranges, which comprises the following steps: constructing a hybrid virtualization system for large-scale network ranges; analyzing a network range description file uploaded by a user to generate an undirected attribute graph, and obtaining a predicted traffic correlation matrix L through graph convolution network inference; collecting resource load data of physical computing nodes and constructing a physical distance matrix, and combining the predicted traffic correlation matrix L to construct a multi-objective fitness function; based on the multi-objective fitness function, generating a global optimal resource mapping matrix through a genetic-whale hybrid evolutionary scheduling algorithm; generating a scheduling instruction according to the global optimal resource mapping matrix, and realizing collaborative instantiation scheduling of virtual machines and containers on specified physical computing nodes through a cloud platform interface; and the application effectively avoids the defects of traditional methods, such as complicated configuration and easy to fall into local extreme value.

The application discloses a software architecture evolution recommendation system based on a knowledge graph and an implementation method thereof, and the system comprises a bottom hardware resource support layer which is used for supporting the persistent storage of software asset data and the high-performance calculation of graph topology through physical computing resources and high-speed communication link support, and the bottom hardware resource support layer comprises a high-density computing cluster, a distributed storage array and a high-speed network switching matrix. All the technical features of the application jointly constitute an organic whole, and aim to solve the software architecture evolution problem under the driving of complex education business.

This invention discloses a method for predicting the phase equilibrium of gas hydrates based on the fusion of physical thermodynamic models and machine learning, comprising: S1: collecting mole fractions and temperature data of components in a gas mixture to generate an experimental dataset; S2: based on the experimental dataset, using a thermodynamic model to calculate the equilibrium pressure and determine the crystal structure type, generating a physical calculation result set; S3: based on the physical calculation result set, calculating the logarithmic difference between the experimental pressure and the predicted physical pressure, generating a logarithmic residual vector; S4: based on the physical calculation result set, constructing a feature matrix containing nonlinear and interactive features; S5: based on the logarithmic residual vector and the feature matrix, performing data partitioning and standardization to generate a training dataset; S6: based on the training dataset, training a gradient boosting model and an ensemble learning model and performing a weighted combination to generate a residual prediction model; S7: based on the residual prediction model and the predicted physical pressure, calculating the corrected equilibrium pressure.

This invention discloses an integrated collaborative modeling and bidirectional updating method and system for steel modular building structures, belonging to the field of physical computing technology. Addressing the problems of fragmented multi-disciplinary data, low optimization iteration efficiency, and reliance on manual reconstruction for output, this invention extracts geometric data from DXF drawings, fills in the code and integrates information from the architectural, structural, and energy consumption disciplines, and constructs an integrated digital foundation. Based on this foundation, it automatically calculates building material costs, structural performance indicators, and building heating and cooling loads; it constructs isomorphic and heteromorphic graph data, and trains the GCN structural proxy model and the HGCN energy consumption proxy model; with cost minimization and energy minimization as optimization objectives, it uses an improved MOEA / D algorithm incorporating a dynamic neighborhood strategy combined with the proxy model for optimization iteration; after selecting the optimal design variables, it updates the integrated digital foundation in reverse, realizing integrated collaborative modeling and bidirectional updating of steel modular building structures, and automatically mapping and outputting the IFC model.

The application discloses a cold chain sorting intelligent agent autonomous obstacle avoidance and operation scheduling method based on reinforcement learning, and belongs to the field of artificial intelligence and robot control, which comprises the following steps: fusing the environment thermal field distribution features identified through visual images and the intelligent agent running state containing real-time battery loss features to construct a physical-computing dual-domain state space; establishing a double-layer asynchronous architecture based on an energy-time terrain map, generating a work sequence from the upper layer, and introducing a virtual repulsive force field for obstacle avoidance in the lower layer; training a reinforcement learning strategy through a reward function containing energy entropy increase punishment; using digital twin reverse causal prediction to enhance the strategy, and migrating the parameter equation extracted through symbolic regression to the entity robot; and matching the intelligent agent and re-routing according to the energy margin when the task is preempted. The application adopts a deep coupling mechanism of physical dynamics and reinforcement learning, can realize precise scheduling and real-time obstacle avoidance with energy consumption perception, and improves the energy utilization efficiency and task response flexibility of the cold chain sorting system.

The present disclosure discloses a task scheduling system and method, and an electronic device and a storage medium, comprising: a management unit and at least one virtual machine; the management unit comprises a task perception module, a load perception module, a management module and a scheduling module; the task perception module is used for monitoring task information of a virtual machine task in each virtual machine; the load perception module is used for monitoring load information of each physical processor; the management module is used for updating the priority of a virtual processor in the case of determining that the task information and / or the load information meet a pre-warning condition, and generating a scheduling decision for the virtual processor according to the priority of the virtual processor after the update; the scheduling module is used for scheduling the virtual processor to the corresponding physical processor for execution; the accurate information monitoring and the dynamic adjustment of the priority optimize the allocation of physical computing resources, and the pre-warning response and the scheduling execution guarantee the stable operation of the virtual machine task, and improve the scheduling flexibility and the resource utilization efficiency of the system.