4 results about "Small-world network" patented technology

The application relates to a brain-inspired data service system and method, characterized by constructing a hierarchical architecture: a mixed deployment data node cluster, specifically composed of core nodes, collection nodes and gateway nodes, respectively processing core business data, real-time edge data and cross-domain connection; a small-world connection layer realizes efficient communication in the same domain through high-cluster local connection and breaks data islands through dynamic long-range cross-domain connection; an intelligent control layer integrates a DMN control hub, cooperates with a state monitoring engine, a prediction analysis module and a resource scheduler driven by reinforcement learning. The system initializes the topological structure through a small-world network manager, dynamically inserts a long-range connection when the cross-domain node communication frequency exceeds a threshold value, and ensures that the cross-domain path hop count is stable and does not exceed a preset value. The DMN control hub realizes double-mode switching based on the system load threshold value: in the idle period, data pre-cleaning, knowledge graph construction and predictive prefetching and resource pre-allocation are performed; in the task period, a routing optimization algorithm is combined with graph traversal.

The application discloses a seasonal frozen region railway roadbed settlement prediction method based on an improved echo state network. First, a multi-sensor monitoring platform is built, railway roadbed settlement, air humidity and multi-depth soil humidity time series data are collected, and correlation analysis and feature screening are performed. Then, the VMD (Variational Mode Decomposition) and sample entropy reconstruction method are used to adaptively denoise and enhance the multi-scale features of the screened input feature sequence. Then, an improved echo state network (IESN) model is constructed. The model introduces a small-world network topology to generate a reserve pool connection weight matrix, and adds a configurable delay mechanism to enhance the ability to capture complex time series dynamic characteristics. Finally, an improved ivy algorithm (IIVYA) is proposed, which is a fusion of multi-elite reverse learning, Cauchy mutation and stable climbing strategy, which is used for global and efficient optimization of the hyperparameters of the IESN model to obtain the final prediction model.

The application discloses a video propagation efficiency evaluation method and system based on audience agents, and belongs to the field of information propagation. The method limits the evaluation range through theme and platform double constraints, synchronously collects high propagation volume videos and low propagation volume videos and associated comments and fan data, constructs a multi-dimensional user portrait containing general dimensions and theme personalized dimensions, identifies similar user groups through unsupervised clustering after semantic vectorization conversion, generates audience agents with proportionally matched real user distribution based on group cognitive characteristics, and constructs an agent relationship network by adopting a small-world network model. Then, the original video is converted into a structured script to drive the agent network to carry out multi-round propagation simulation, and finally, comprehensive evaluation results containing quantitative indicators and descriptive indicators are output. The application solves the problems of lack of audience differentiation modeling, non-recovery of social network structure and single evaluation indicator, realizes pre-position prediction of propagation efficiency, and improves the authenticity and interpretability of the evaluation results.

The invention discloses a student companion teaching research system based on computer simulation, and relates to modeling, simulation experiments and data analysis of student companion teaching behaviors. The system is composed of a student companion teaching behavior strategy simulation module, a social simulation module and a data collection and analysis module, can simulate various student interactive behaviors, and performs experimental analysis on the influence of emotion, experience, neighbors and other factors on the behaviors. A core experiment framework of the system is formed by five behavior selection strategies (based on emotions, win-win people and input people exchange, the highest accumulated reward, most of historical behaviors of opponents and most of neighbors) and three network structures (full connection, grids and small world networks), the complex dynamic state of student companion teaching can be efficiently simulated, and factors influencing the interactive behaviors of the students are revealed. The system adopts an agent modeling technology, and each student is regarded as an independent agent and can autonomously select behaviors and update emotional states in interaction. Experimental results show that a behavior selection strategy and a network structure have significant influence on student interaction behaviors, for example, an emotion-based strategy can better encourage companion teaching in a full-connection network, and existence of a fixed behavior agent also affects a behavior mode of a whole group. The invention provides an efficient and flexible research platform for exploring the relationship between the student companion teaching behaviors and various factors, and provides a reference basis for the school to optimize the companion teaching environment.