73 results about "Small-world network" patented technology

The invention provides a blockchain-based industrial Internet architecture, comprising a user terminal, an industrial cloud platform, an industrial intelligent terminal and a data collection device, wherein the user terminal performs data interaction with the industrial cloud platform to display parameter states of various systems and devices on an industrial site and to control an intelligent device connected to the industrial intelligent terminal; the industrial cloud platform constructs a plurality of distributed blockchain nodes by using the virtualization technology, the blockchain nodesare connected with a cloud server database, and the blockchain node verify each other by constructing a small world network to ensure the trustworthiness of the nodes; and the data collection device is configured to connect and collect the data of at least one industrial intelligent terminal, and transmit the collected data to the industrial cloud platform through the Internet, a virtual machine is created in the data collection device, and a virtualized data encryption program is operated in the virtual machine.

The present invention discloses a social network based message propagation model, which is a method, based on the special line of the crowd in the network and the average degree of the nodes in the social network, for carrying out modeling analysis on message propagation in the social network and proposing a node importance based rumor immune strategy. The dynamic equations of the <k> SLIR message propagation model are established in the small-world network; by virtue of the method disclosed by the present invention, influences of nodes in various states in the social network, on message propagation can be realistically taken into consideration; and according to the node importance based improved rumor immune strategy proposed by the present invention, propagation of rumors in social network can be better inhibited.

The present invention relates to a new network architecture and its data transmission method based on percolation. The disclosed network architecture is a server-free and router-free network structure, wherein efficient routing can be operated with percolations based on the six degrees of separation theory in small world network modeling. An information transmission will be divided into two phases: routing phase and data transmission phase. In the routing phase, probe packets will be transmitted and forwarded in the network thus path selections are performed based on small-world strategy. In the second phase, the information will be encoded and transmitted using the paths selected at the first phase. In such a way, an efficient routing and data transmission mechanism can be built which allow us to construct a low-cost, flexible and ubiquitous network.

A system (510) routes packets in a network (200) having multiple nodes. The system identifies a group (500) of the nodes (520–550) and determines routing distances to each of them. The system (510) then selects a set of the nodes from the group (500) based on the determined routing distances and updates a routing table based on the selection. The system (510) routes packets through the network (200) using the updated routing table.

The method of the present invention provides an automatic and optimised selection of the network topology for distributing scheduling of jobs on the computers of the modified network topology. The automatic and optimised selection of the network topology starts from the current topology and a desired number of additional connections. In this way the method of the present invention provides a higher convergence speed for the modified consensus algorithm in comparison, e.g., to a simple ring network. The method exploits the so called small-world networks. Small-world networks are more robust to perturbations than other network architectures. The preferred embodiment provides a workload scheduling system which is highly scalable to accommodate increasing workloads within a heterogeneous distributed computing environment. A modified average consensus algorithm is used to distribute network traffic and jobs amongst a plurality of computers.

The invention discloses a method and a system for optimally dispatching cascade reservoirs on the basis of quantum-behaved particle swarm algorithms. The method includes acquiring initialized population according to established objective functions for solving problems for optimally dispatching the cascade reservoirs and utilizing the initialized population as parent-generation particles; constructing small-world networks to obtain adjacent matrixes; updating the parent-generation particles according to the adjacent matrixes and generating child-generation particles; computing the fitness of the child-generation particles according to fitness functions; comparing the fitness of the parent-generation particles to the fitness of the child-generation particles by the aid of competition operators, selecting the child-generation particles with the good fitness and utilizing the selected child-generation particles as parent-generation particles for next iteration; judging whether current iteration numbers are larger than the maximum thresholds or not; carrying out computation if the current iteration numbers are larger than the maximum thresholds and outputting cascade reservoir optimal dispatching computation results. The method and the system have the advantages that the quantum-behaved particle swarm algorithms are improved by small-world network models, so that the population diversity can be kept by improved algorithms, the shortcoming of easiness in trapping in local optimization of basic quantum-behaved particle swarm algorithms can be overcome, and effects of optimally dispatching the cascade reservoirs can be improved.

The invention relates to a clustering and multi-hop communication method of network nodes of a wireless sensor, in particular to a clustering and multi-hop communication method of a wireless sensor network based on a small-world model. The algorithm utilizes the small-world model to cluster the sensor network, which improves the clustering coefficient at the network side and enables that the sensor network has favorable performances of small-world network and large clustering coefficient; nodes in each cluster and average hop numbers are restricted so that the load of each cluster can be balanced; and the node with largest residual energy can take turns a cluster head so that the energy of all nodes can be oppositely balanced. The invention can balance the energy consumption of the networknodes in the whole situation, avoids the situations that the network is partitioned into a plurality of complementary connections as a part of nodes are premature failure, prolongs the life cycle ofthe whole network and improves the robustness of the network simultaneously.

The invention discloses a hotspot information finding method and system. The method includes the steps of obtaining a to-be-processed text, conducting word dividing and part-of-speech tagging on the to-be-processed text, conducting parsing on the text subjected to word dividing to obtain a dependence syntactic tree of each sentence in the to-be-processed text, removing stop words in the dependence syntactic tree of each sentence in the to-be-processed text to obtain to-be-analyzed dependence syntactic tree, establishing a small world network through the to-be-analyzed dependence syntactic tree, conducting hotspot analysis according to the to-be-analyzed dependence syntactic tree and the small world network, and obtaining hotspot information in the to-be-processed text according to the hotspot analysis result. By means of the method and system, the hotspot information in the to-be-processed text can be efficiently and accurately found.

The invention relates to an FPGA-based graph data processing method and system. The method is used for carrying out graph traversal on a graph with small world network characteristics. The method comprises the following steps: obtaining a sample; carrying out graph traversal by using the first processor and a second processor in communication connection with the first processor, the first processor is a CPU, The second processor is an FPGA, The first processor is used for transmitting graph data needing to be traversed to the second processor. the first processor obtains result data of the graph traversal for result output after the second processor completes the graph traversal of the graph data according to a layer sequence traversal mode; the second processor comprises a low peak processing module and a high peak processing module; wherein the low-peak processing module and the high-peak processing module respectively utilize on-chip logic resources of different areas of the secondprocessor, the high-peak processing module has higher parallelism relative to the low-peak processing module, the low-peak processing module is used for carrying out graph traversal in a starting stage and / or an ending stage, and the high-peak processing module is used for carrying out graph traversal in an intermediate stage.

According to the transaction routing algorithm under the blockchain with the minimum cost disclosed by the invention, and the algorithm comprises the steps: taking the minimum transaction cost as a benchmark, and dynamically constructing a node routing table in a simulated small world network, so that the transaction is propagated along the path with the minimum transaction cost; in the transaction process, enabling an initial node to send the encryption information and a transaction message from the target node according to the constructed route, enabling an intermediate node to receive the transaction from the previous node and forward the transaction to the next node, wherein the transaction is regarded as an unverified transaction, and the balance of the account is not changed; after the verification key sent by the transaction target node successfully decrypts the encrypted information, taking transaction as a verification transaction, changing the account balance, and completingthe transaction. According to the invention, the total path cost paid to the intermediate node by the user can be minimized.

The invention discloses a multi-region dynamic economy scheduling method and system. The method comprises that a target function of a multi-region economic scheduling problem is established; an initial population is generated by initialization, the fitness of the initial population is calculated, and the initial population serves as a parent population; an NW small world network model is used to obtain an adjacent matrix; the parent population is updated according to the adjacent matrix to obtain a filial population, and the fitness of particles in the filial population is calculated by utilizing a fitness function; the particle fitness in neighborhoods divided by corresponding adjacent matrixes in parent and filial populations is compared by utilizing a competition operator, and particles of high fitness are reserves and serve as a parent population in next iteration; and when a preset maximal iteration frequency is reached, a result of the multi-region economic scheduling problem is output. The NW small world network improved differential crisscross algorithm is used to overcome the defect of population diversity loss in the optimization searching process of basic differential evolution algorithm and crisscross algorithm.

The invention provides an unmanned system-oriented starling-group-type intelligent group dynamic network topology construction method. Network topology construction is carried out to realize agent group cooperation, and initialization of an agent group and corresponding parameters is included; a network status transition mechanism is set, corresponding network state operation is judged and executed for a current frame according to physical locations of the agent group and obstacles, a labeled small-world network is constructed if no obstacle exists, a 6-neighbor network is constructed if the group is about to approach an obstacle influence area, and network topology splitting is carried out if the group is located in the obstacle influence area; and next moment speed and location updatingof each agent in the agent group is carried out until the agent group reaches a destination or a maximum iteration number is reached. Agent group system synchronization capability of the method can behigher than that of traditional regular networks, random networks and the like, and the method has scalability and stability, and can have a very broad application prospect in the aspect of unmannedsystems.

The invention relates to an improved TLBO-based parameter optimization method for a PI controller of a flexible high-voltage DC power distribution system, and belongs to the technical field of power system control. The improved TLBO-based parameter optimization method is implemented according to the steps of (1) building a VSC-HVDC system simulation model; (2) improving basic TLBO; and (3) allowing the improved TLBO to be used for parameter optimization of the PI controller of the VSC-HVDC system, building an optimization model and obtaining an optimization result. On the basis of the basic TLBO, a plurality of classes are introduced to expand the optimal searching range, a small world network is built among teaches or students in different classes, accurate searching is achieved by deep interaction learning, and local optimal algorithm is effectively prevented; controlled quantity of a VSC-HVDC system is brought to the optimization model, and the optimal parameter of the PI controlleris solved by the improved TLBO; the embodiment shows that the searching range and the searching accuracy of the optimal parameter of the PI controller in the VSC-HVDC system can be reasonably balanced by the improved TLBO, and local optimal algorithm is effectively prevented.

The method of the present invention provides an automatic and optimised selection of the network topology for distributing scheduling of jobs on the computers of the modified network topology. The automatic and optimised selection of the network topology starts from the current topology and a desired number of additional connections. In this way the method of the present invention provides a higher convergence speed for the modified consensus algorithm in comparison e.g. to a simple ring network. The method exploits the so called small-world networks. Small-world networks are more robust to perturbations than other network architectures. The preferred embodiment provides a workload scheduling system which is highly scalable to accommodate increasing workloads within a heterogeneous distributed computing environment. A modified average consensus algorithm is used to distribute network traffic and jobs amongst a plurality of computers.

The invention discloses a myoelectricity classification method based on an improved small-world echo state network. Firstly, a small-world network is used for improving a reserve pool structure of theESN, then an edge adding probability is used for improving the small-world network, and the network is called as an improved small-world echo state network, so that the adaptability of a reserve poolis improved, and the generalization ability and stability of the ESN are improved. Then, the output weight of the network can be obtained by training the network, and the output weight is used as a corresponding feature. Electromyographic signals of six actions of falling down, walking, sitting, squatting, going upstairs and going downstairs are collected, corresponding features are extracted through ISWLESN, and then feature dimensions are reduced through PCV. And finally, the performance of the network features is represented by using the scatter diagram, the class separability index and the DBI. Results show that ISWLESN has good clustering performance, and has high precision when used for support vector machine classification.

The invention discloses a comprehensive assessment method for vulnerability of high-voltage transmission lines in a freezing disaster, and belongs to the technical field of power grid safety analysis. The assessment method comprises: for a small world network based high-voltage transmission line vulnerability analysis result, a Monte-Carlo weather sampling based high-voltage transmission line freezing disaster vulnerability analysis result, and an expert scoring based high-voltage transmission line freezing disaster vulnerability analysis result, conducting united-decision by using a DS evidence fusion theory, and taking a final fusion result as a comprehensive index for assessing the vulnerability of the high-voltage transmission lines in the freezing disaster; and then, according to the value of the index, removing lines with relatively high vulnerability, and calculating a lost load percentage of a power system, thereby finally determining the vulnerability of the high-voltage transmission lines in the freezing disaster. The assessment method has the advantages that the method is a fused decision of a plurality of theories or methods, the accuracy of assessment of a power grid system on the vulnerability of the high-voltage transmission lines in the freezing disaster is improved, and an effective basis is provided for high-voltage transmission line inspection.

A chaotic secret communication system based on a digital chaotic coding algorithm comprises (1) a channel used for enabling an information sending end A to be communicated with an information receiving end B and (2) digital chaotic networks connected with the channel and used for generating chaotic numerical codes. A function g1 is generated through codes of chaotic coding according to the selected chaotic numerical codes with certain lengths from the numerical codes generated in the digital chaotic networks and converted to a chaotic code k1(t), wherein an equation that k1(t)=g1(X, p) is satisfied. The digital chaotic networks are characterized in that (1) the digital chaotic networks are identical in topologic and dynamics structures, driven by common signals and in a chaotic synchronic state, the digital chaotic networks comprise a complex chaotic network which is of a random topologic structure, a regular network, a random network, a small-world network, a freedom scaling network and a module network; (2) the information sending end A is used for complex chaotic networks which generate the chaotic numerical codes and a kinetic equation is decrypted by a differential kinetic equation.

The invention discloses a method for realizing text feature extraction based on an improved small-world network model. According to the method, a semantic relevancy function is determined according to a Chinese word segmentation preprocessing process and determined vocabulary position weights and word class weights in combination with a (HowNet) two-vocabulary relevancy algorithm and a vocabulary-to-text importance method, wherein the function is subjected to normalization processing, and calculation conditions of values are more standard; and two parameters, namely a density parameter and a weight parameter are set for a lexical semantic network model graph, the two parameters are effectively fused, and an appropriate threshold value is set to extract text feature vocabularies. The method has higher accuracy and overcomes the defect that a traditional method is only suitable for extracting text features of one category; the method has higher application value, contribution degrees of different vocabularies to text thought can be precisely calculated, data processing is more standard, the result error rate is lowered, the constructed lexical semantic network model graph better conforms to the actual condition, and meanwhile a good theoretical basis is provided for subsequent text clustering.

The invention provides a wireless digital image transmission system based on Android in the field of electronic information science and technology, which comprises a construction and configuration unit of a server, a communication unit of a server and a client, a client data processing unit and a display unit of a client image file. Under the Small World Network, The server can implement the function of sharing image files with multiple clients, That is, the client can download the image file of the server synchronously for display, and upload the local image file to the server for sharing with other clients, and the Android device with various screen resolutions has no adaptation problem, which has certain practical value for meetings and teaching in small and medium-sized occasions.

The invention discloses a fractional order PD controller design method based on an N-W small world network model. The method comprises steps that firstly, the stability characteristics and the balancepoint information of the N-W small world network model are analyzed; secondly, a fractional order PD controller is applied to the N-W small world network model, and a balance point is solved; thirdly, the controlled N-W small world network model is linearized at the balance point, and characteristic equations of the linearized controlled N-W small world network model are obtained; and lastly, delay parameters are selected as bifurcation parameters, and stability analysis and bifurcation analysis of the characteristic equations of the linearized N-W small world network model are carried out, proper fractional PD controller parameters are selected, and the controlled N-W small world network model is stabilized at the balance point. The method is advantaged in that relatively strong applicability is realized, generation of bifurcation can be effectively controlled by a fractional-order PD controller, so critical bifurcation is made ahead or lag.

The invention discloses a method for determining an FMRI dynamic brain function time window. The method mainly comprises the following steps of obtaining functional magnetic resonance imaging data byusing magnetic resonance imaging and preprocessing the data, obtaining a single tested function connection network according to the sizes of 50TR, 100TR, 150TR and 195TR sliding time windows in the experiment, processing the obtained windows according to the sparsity of 0.05-0.5 (the step length is 0.05), and finally acquiring ten sparse matrixes for each window, solving small-world parameters forthe sparse matrix obtained by each window, solving a mean value and a variance, calculating a total mean value and a mean value of the variance, and comparing the attribute strength and robustness ofthe small-world network with different sliding time window sizes, and according to the attributes of the small-world network under different sliding time windows, using a similar binary search methodfor narrowing the range, and searching for an optimal sliding window. According to the method, a relatively reliable sliding time window in dynamic brain analysis is obtained by taking small-world parameter strength and attribute stability as standards, and support is provided for related research on brain function connection networks.

The embodiment of the present invention discloses a multi-regional economic scheduling method based on the improved quantum particle swarm algorithm. The improvement of the quantum particle swarm algorithm by using the NW small world network can improve the shortcoming that the basic quantum particle swarm is easy to fall into a local optimum in the optimization process. The method in the embodiment of the present invention includes: S1: Establishing the objective function of the multi-regional economic scheduling problem; S2: Optimizing the objective function using the NW small-world network improved quantum particle swarm optimization algorithm, specifically including: S2-1: Population initialization; S2 ‑2: Build a small-world network and get the adjacency matrix; S2‑3: Update individuals and update the population; S2‑4: Calculate the fitness based on the updated population; S2‑5: Use the competition operator to adapt the parent particles Compared with the fitness of offspring particles, the one with better fitness is reserved as the parent of the next iteration; S2‑6: If the number of iterations calculated reaches the preset maximum number of iterations, calculate and output the multi-regional economic dispatch calculation If not, go to step S2‑2.

The invention discloses a content semantic mining method for a non-structured big data stream. The method comprises the steps of S1: extracting a text link, a label attribute and a semantic tendency keyword in the big data stream, and correspondingly defining text nodes, label nodes and content nodes; S2: constructing a text node set containing the text nodes and a label node set containing the label nodes, calculating and outputting weight values from the text nodes to the label nodes and weight values from any label node to other all label nodes; S3: according to the text node set, the label node set, the weight values from the text nodes to the label nodes and the weight values from any label node to other all label nodes, performing semantic classification on the content nodes and constructing different content node classification sets; and S4: according to the text node set and the content node classification sets, performing weighted small-world network clustering calculation on the text nodes to obtain a text node cluster set.

The invention discloses an image retrieval method, a device, a computing device and a medium based on a small world network. The method includes creating an image base; extracting the local features of each image in the image library; learning the local features of each image in the image library to obtain a learning dictionary; according to the learning dictionary, the eigenvector of each image in the image library is generated, and the small world network of the eigenvector is constructed; extracting local features of the target image; matching the local features of the target image with thefeatures of the image in the small world network; according to the matching result, the images that satisfy the similarity threshold condition in the image library are selected and / or the images in the image library are sorted from high to low according to the similarity measure. A learn dictionary is obtained by machine learn, and a small-world network with image characteristics is constructed according to that learn dictionary, and the small-world network is used as an image retrieval model, thereby greatly improving the processing efficiency and accuracy of image retrieval and increasing the service throughput.

The invention relates to a power system, in particular to a hidden failure prediction and treatment method for an overhead transmission line. By means of the method based on a small-world network layer and Bayesian network mixed recommendation network, the overhead transmission line is taken as the object to establish a small-world network layer and Bayesian network mixed recommendation network based mathematical model for evaluating the operation state of the overhead transmission line, hidden failures of the overhead transmission line are predicted effectively and accurately by means of high clustering performance of small-world networks and a bidirectional reasoning technology of the Bayesian network, a failure treatment method can be found out timely when a hidden failure exists, and health warning of the overhead transmission line is guaranteed reliably.

The invention discloses a complex network construction method based on adjacent matrixes. A complex network is constructed based on a Kronecker multiplication operation and a Kronecker addition operation generating network adjacent matrixes. The method comprises the main steps of determining the generated network, calculating the generated network adjacent matrixes, calculating a generated network degree distribution polynomial, calculating the adjacent matrixes of the complex network, calculating the degree distribution polynomial of the complex network and the like. The complex network obtained through the complex network construction method is different from a classical stochastic network, a small-world network, a scale-free network, a self-similarity network and the other networks. By the adoption of the degree distribution polynomial expression method, according to the Kronecker addition operation, the operation of coefficient addition and frequency multiplication of normal polynomial multiplication is adopted; according to the Kronecker addition operation, the operation of coefficient addition and frequency multiplication of similar polynomial multiplication is adopted, and the complex network degree distribution is worked out strictly in theory. Particularly, the self-similarity network can be obtained based on the Kronecker multiplication operation generating the network adjacent matrixes.

The invention discloses a post-stroke rehabilitation evaluation deep learning model construction method based on brain muscle network graph theory characteristics, and relates to the crossing field of neurophysiology and machine learning. According to the method, a pathological topological structure after stroke is represented through a brain muscle closed-loop function network, and on the basis, a deep learning model is further established based on graph theory characteristics to evaluate the recovery degree of a stroke patient and predict the recovery process; the method mainly considers consistency characteristics of hooked small-world network characteristics and a neural network in evaluation and prediction of dyskinesia, and how to realize multi-objective learning and joint optimization and the like. According to the method, a novel post-stroke hospitalization recovery period motor function evaluation and return visit period rehabilitation effect prediction method is constructed by utilizing electroencephalogram and myoelectricity bimodal neural electrophysiological information, and the clinical rehabilitation evaluation efficiency is expected to be improved, so that the method has an important application value.

An FPGA-based graph data processing method is provided for executing graph traversals on a graph having characteristics of a small-world network by using a first processor being a CPU and a second processor that is a FPGA and is in communicative connection with the first processor, wherein the first processor sends graph data to be traversed to the second processor, and obtains result data of the graph traversals from the second processor for result output after the second processor has completed the graph traversals of the graph data by executing level traversals, and the second processor comprises a sparsity processing module and a density processing module, the sparsity processing module operates in a beginning stage and / or an ending stage of the graph traversals, and the density processing module with a higher degree of parallelism than the sparsity processing module operates in the intermediate stage of the graph traversals.