19 results about "Small-world network" patented technology

The invention discloses a goaf unmanned aerial vehicle inspection system based on monitoring and early warning, and the system comprises an observation data collection module which is used for collecting settlement and fracture main variables to form a time stamp observation set; the data fusion module is used for generating a unified monitoring data set by adopting a fractal small-world network coding consensus algorithm; the initial route planning module is used for constructing a route sequence based on a main variable evolution tensor and a risk map; the flight path execution module is used for collecting a flight path and environment data; the fault-tolerant control module triggers a fractional order sliding mode fault-tolerant and degradation mechanism based on the consistency error; and the closed-loop optimization module is used for dynamically updating the main variable tensor and the path cost function and outputting an optimized track and a structured early warning result. The system has high dynamic responsiveness and multi-source risk adaptability.

The invention relates to a brain inspired data service system and a brain inspired data service method, which are characterized in that a layered architecture is constructed, the layered architecture comprises a data node cluster deployed in a mixed manner, the data node cluster specifically comprises a core node, an acquisition node and a gateway node, and the core node, the acquisition node and the gateway node are respectively used for processing core service data, real-time edge data and cross-domain connection; the small-world connection layer realizes same-domain efficient communication through high-clustering local connection, and breaks a data island through dynamic long-range cross-domain connection; the intelligent control layer integrates a DMN control center, a collaborative state monitoring engine, a prediction analysis module and a reinforcement learning driven resource scheduler. The system initializes a topological structure through a small-world network manager, and when the communication frequency of a cross-domain node exceeds a threshold value, long-range connection is dynamically inserted to ensure that the hop count of a cross-domain path is stably not higher than a preset value. The DMN control center realizes dual-mode switching based on a system load threshold; in an idle period, data pre-cleaning, knowledge graph construction, predictive pre-fetching and resource pre-allocation are executed; and optimizing routing in combination with a graph traversal algorithm in a task period.

The invention discloses a social group simulation method and device based on multi-agent driving. The method comprises the following steps: performing user attribute sampling processing on a real e-commerce user data set, and constructing agent basic attributes based on user attributes in combination with a large language model to obtain agent basic attribute features; performing construction processing on the agent basic behaviors based on a preset memory mechanism in combination with the agent basic attribute features; according to the agent basic attribute features and the agent basic behavior features, performing construction processing on agent interaction behaviors in combination with a large language model; performing relation network construction processing based on a small-world network model on the plurality of agents; and performing simulation processing based on execution agent behaviors on the agent social group model to obtain a multi-user behavior simulation result. By constructing a multi-agent simulation system in an e-commerce scene and combining the multi-agent simulation system with a large language model, social group simulation in a complex e-commerce interaction scene is realized, and the accuracy of social group simulation in the e-commerce scene is improved.

The invention discloses a complex structure-oriented network node importance degree dynamic evaluation method, relates to the technical field of dynamic node identification analysis methods, and particularly aims to overcome the defect that the existing KPDN method cannot completely cover important node identification in a dynamic environment in the background technology. The improved network constraint coefficient INCC is introduced, the defect of the KPDN method in the aspect of complex network dynamic important node identification is made up, the network structural measurement can be well kept in the dynamic node deletion process, the structural importance of the node in the remaining network can be effectively judged, and the dynamic network dynamic important node identification method has the advantages of high reliability and high reliability. And particularly, the method has a better recognition effect on a small-world network with a smaller random connectivity probability.

The invention provides an FPGA (Field Programmable Gate Array) fixed fault diagnosis method based on an improved qualitative graph model, and solves the problems of blindness and high cost of manual screening of test points and large search space, long time consumption and low resolution of a traditional qualitative graph model. The method comprises the steps of fault modeling and fault injection. Selecting a preliminary test point; normal and fault simulation; optimizing a diagnostic standard and protecting a genetic algorithm of an elite gene site; and the two qualitative graph models complete fault diagnosis. According to the method, adaptive probability crossover and variation are adopted; elite gene sites are protected; a select process is added; the fault propagation directed graph is modeled in a layered mode and then divided into layers, edge weights are analyzed and defined according to reliability, and small-world network nodes are defined based on information entropy. The global search capability and the convergence speed are improved, the diagnosis efficiency and the resolution are improved, and the problems that the fault probability and the fault propagation time are difficult to obtain and the fault information is difficult to quantify are solved. The method is used for optimizing test points and performing FPGA system fault diagnosis.

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 invention provides a complex network construction method and electronic equipment, and relates to the field of complex networks. Constructing at least two small-world networks, and combining the small-world networks into an integral network; selecting scale-free nodes and connection nodes from the small world nodes, and creating connection edges between the scale-free nodes and the connection nodes to obtain an initial complex network; the probability that the small-world nodes are selected as the connection nodes is positively correlated with the first degrees of the small-world nodes, so that the node degrees of the initial complex network follow power law distribution. And dynamically evolving the network based on the characteristic parameters of the initial complex network to obtain a target complex network. The target complex network has the characteristic of high clustering coefficient, and the node degrees of the target complex network follow power law distribution, so that the characteristics of close connection among individuals, rapid information propagation and huge influence of a small number of individuals can be reflected, and a complex interaction rule among the individuals can be accurately reflected. Moreover, the target complex network can simulate the dynamic change of the individual relationship through dynamic evolution, and the effectiveness is improved.

The invention discloses an industrial chain characterization method based on a complex network theory and considering an energy network, and the method comprises the steps: abstracting an enterprise covering core product transaction into a network node, and constructing an initial directed network of a coupled material flow-energy flow; network edges are weighted based on material flow and energy flow analysis, and a weighting system comprehensively considering transportation parameters and waste heat and waste energy utilization is established; establishing a four-dimensional scoring function fusing product compatibility, energy complementarity, geographical proximity and industry chain relevance, and implementing a differential supervised edge adding mechanism; constructing a small world characteristic verification index system, and judging topological characteristics of the reconstructed network; and an iterative tuning mechanism is established, and small-world characteristic representation is realized through a clustering enhancement or path shortening strategy. According to the method, the industrial chain network is constructed by unifying the material flow and the energy flow, cross-level cooperation and same-level linkage opportunities are accurately identified, intelligent reconstruction of a small world network is realized, and the method is of great significance to energy conservation, consumption reduction and layout optimization of the industrial chain.

The invention discloses a college student depression monitoring information system integrated service platform, and belongs to the technical field of platform service management. The technical problem that an existing platform system is poor in robustness and stability is solved. Transmission delay fluctuation is sensed in advance through chaos time sequence prediction, coupling strength is dynamically adjusted in combination with load entropy, the high clustering characteristic of small-world network topology is utilized, the data transmission hop count can be reduced, the data loss rate can be effectively reduced through distributed redundancy storage, and the stability of the system is enhanced. The real-time correction of the coupling strength can realize the online evolution of the network topology; the topological entropy of a global structure and the betweenness centrality of local key nodes are combined, so that global and local cooperative scheduling can be realized; fractal interpolation is used for predicting resource requirements, the error rate can be effectively reduced, the activation threshold value is dynamically adjusted through the Hurst index, and the accuracy of the standby resource activation time can be effectively improved.

The invention relates to the technical field of cloud manufacturing services, in particular to a common evolution analysis method for cloud manufacturing low-carbon cooperation and related products. According to the method, suppliers and demander groups are connected through a small world network, the suppliers select learning neighbors based on the companion effect, supplier group strategies are updated through a Fermi rule, and interaction between the suppliers is achieved. And the demanders update the demander group strategy through the Aspiration-drive rule, and update the willing level based on the conformity effect to realize interaction between the demanders. Therefore, in the double-layer network of the supplier group and the demander group, the common evolution process of low-carbon cooperation of the supplier and the demander group is analyzed, and strategy evolution rules of the supplier group and the demander group are designed based on the companion effect and the conformity effect. Reference is provided for the policy formulation of low-carbon cooperative excitation of the cloud platform to the supply and demand parties, and low-carbon transformation of enterprises is promoted.

The invention discloses an optimal power generation control method based on deep reinforcement learning and a small world network. And in combination with a deep reinforcement learning algorithm and a droop control strategy, intelligent power adjustment among the DGs is realized, and the power generation economy is effectively improved. The system quickly responds to power fluctuation through a primary control layer, power distribution and adjustment among the DGs are ensured, and the real-time response capability of the micro-grid is further improved. In the secondary control layer, a reinforcement learning method based on a PPO algorithm is adopted, self-adaptive learning is performed according to real-time data, and the microgrid can continuously maintain the stability of voltage and frequency under the condition of load change or renewable energy fluctuation by dynamically adjusting a power compensation signal and eliminating steady-state deviation. Besides, by combining the small-world network theory, the communication topological structure between the DGs in the micro-grid is optimized, the information transmission efficiency is enhanced, it is ensured that the system can quickly respond to external disturbance, and the overall robustness and the self-adaptive capacity of the micro-grid are improved.

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.

The invention discloses an intelligent assistant multi-terminal screen sharing linkage control method based on a block chain and a network optimization engine, and the method comprises the following steps: S1, heterogeneous equipment interconnection adaptation: a heterogeneous equipment compatible engine completes cross-architecture instruction conversion through a WASM drive sandbox, and achieves the zero-configuration interconnection of Windows, Android, iOS and Linux multi-system equipment based on a UDI protocol stack; s2, network transmission optimization: a small-world network optimization engine sequentially executes link evaluation, path optimization, topology construction and bandwidth allocation, and improves the network transmission efficiency in combination with multiple models and algorithms; s3, decentration consensus is achieved, specifically, an acceleration consensus mechanism elects a verification group through a VRF algorithm, an improved two-stage PBFT architecture is adopted, and linkage instruction decentration verification and synchronization are completed in combination with SGX hardware acceleration; and S4, security communication guarantee: the quantum security communication layer generates a session key based on a hybrid key exchange protocol, and realizes data encryption transmission and illegal access interception through on-chain identity authentication and an event-driven smart contract.

The invention provides a method and device for determining the birth position of a numerous intelligence person in a meta-universe scene and a storage medium, and the method comprises the steps: obtaining the model information of the meta-universe scene, and generating a first feature vector of the meta-universe scene based on the model information of the meta-universe scene; obtaining user information corresponding to the numerous wisdom in the meta universe scene, and generating a second feature vector of the numerous wisdom based on the user information; constructing a target small-world network based on the first feature vector and the second feature vector; and determining the birth position of the numerous intelligence person based on the target small-world network and the meta-universe scene model information. According to the method and the device, the birth position of the user entering the meta universe scene is determined through the target small world network, so that the birth position is located at the region position in which the user is interested and beside other users with relatively high social association degree, scene exploration and interactive communication of the user are facilitated, and user experience and usability of the meta universe scene are improved.

The invention relates to the field of power grid design, in particular to a high-robustness power grid design method based on parameter adjustable heterogeneous modeling. The scheme comprises the following steps: constructing a network topology based on a small-world network; constructing a heterogeneous collaborative model; optimizing the permeability of the asynchronous machine, and reconstructing and calculating an admittance matrix; whether the permeability of the asynchronous machine needs to be adjusted is judged, if yes, the step of optimizing the permeability of the asynchronous machine is executed, and if not, node betweenness centrality analysis is conducted, and then the layout strategy of the asynchronous machine is adjusted; judging whether the asynchronous machine layout strategy needs to be adjusted or not, and if yes, returning to the synchronous machine layout strategy adjustment step; if not, adjusting the coupling strength of the synchronous machine, and adjusting the inertial parameter and the damping coefficient of the asynchronous machine; judging whether the coupling strength needs to be adjusted or not, if yes, returning to the step of adjusting the coupling strength of the synchronous machine, and if not, outputting the permeability of the current asynchronous machine, the node sequence number of the asynchronous machine, the coupling strength of the synchronous machine, the inertial parameter of the asynchronous machine and the damping coefficient. The method is suitable for power grid design.

The invention discloses a data protection method based on a genetic algorithm. The method comprises the steps of predefining, uniform sampling set initialization, global non-dominated sorting, crossover operation design, mutation operation design, iteration strategy and data protection. The invention belongs to the field of data protection, and particularly relates to a data protection method based on a genetic algorithm, and the method comprises the steps: introducing a uniform sampling set initialization strategy, and carrying out the unbiased coverage in a whole feasible space; a small-world network-based self-adaptive pairing mechanism is introduced to perform pairing selection, so that low search efficiency caused by later excessive random is avoided; the data protection effect is improved; a dynamic retention probability and disturbance factor design interlace operation is introduced, an excellent data protection strategy is adaptively retained, and smooth transition of exploration-utilization tradeoff is provided in different iteration stages; and polynomial variation and local perturbation design variation operation are introduced to avoid local optimum, so that the data protection strategy is finer and more accurate, and the data protection security is improved.

This invention discloses a data protection method based on a genetic algorithm. The method includes predefinition, uniform sampling set initialization, global non-dominated sorting, crossover operation design, mutation operation design, iterative strategy, and data protection. This invention belongs to the field of data protection, specifically referring to a data protection method based on a genetic algorithm. This scheme introduces a uniform sampling set initialization strategy to perform unbiased coverage of the entire feasible space; it introduces an adaptive pairing mechanism based on a small-world network for mate selection, avoiding the inefficiency caused by excessive randomness in later stages, thereby improving the data protection effect; it introduces a dynamic retention probability and perturbation factor to design crossover operations, adaptively retaining excellent data protection strategies and providing a smooth transition of exploration-utilization tradeoffs at different iteration stages; it introduces polynomial mutation and local perturbation design for mutation operations to avoid local optima, thus making the data protection strategy more refined and accurate, and improving data protection security.