1429 results about "Goal node" patented technology

A number of techniques are described for routing methods that improve resistance to faults affecting groups of links subject to common risks. One of these techniques accounts for failure potentials in physical networks by considering shared risk link groups separately from performance and costs metrics in determining a primary routing path and a backup path. A shared risk link group (SRLG) is an attribute attached to a link to identify edges that have physical links in common and can therefore be simultaneously disrupted due to a single fault. Another technique considers node disjointness and provides a solution of two paths that are as node disjoint as possible and minimizes administrative costs. The techniques may further be combined in a priority order thereby providing a solution of at least two paths that are strictly SRLG disjoint, as node-disjoint as possible, and have minimum administrative costs. Due to the priority order of evaluation and typical network physical configurations of links, with the links associated common fault SRLGs, the priority ordering technique is very efficient in determining at least two paths for routing between a source and destination node.

A technique dynamically triggers an exchange of reachability information between a tail-end (remote) domain target node (e.g., a tail-end node) of a traffic engineering (TE) label switched path (LSP) and a local domain head-end node of the TE-LSP in a computer network. The inter-domain information retrieval technique is illustratively based on triggering a Border Gateway Protocol (BGP) session whereby at least a portion of the reachability, i.e., routing, information of the tail-end node is transmitted to the head-end node of the TE-LSP in accordance with BGP. Specifically, once a TE-LSP is established between the head-end node and the tail-end node, the head-end node triggers the tail-end node, e.g., through extensions to a request/response signaling exchange, to establish the BGP session. Establishment of the BGP session enables transmission of the routing information from the tail-end node to the head-end node. The head-end node uses the routing information to calculate routes, i.e., address prefixes and associated attributes, reachable from the tail-end node for insertion into its routing table.

A method of routing data from a source node to a destination node in a multi-hop network of nodes interconnected by links comprises: (a) determining that a link-flow vector satisfies one or more necessary scheduling conditions for achievability, wherein the link-flow vector represents a set of flows to be routed on one or more links from the source node to the destination node; (b) generating a scheduling multi-graph for the network, wherein the scheduling multi-graph comprises a graph having at least one pair of nodes with multiple edges therebetween; (c) deriving one or more sufficient scheduling conditions for achievability of the link-flow vector by edge-coloring the scheduling multi-graph; (d) solving a linear optimization problem over the one or more necessary scheduling conditions to obtain an upper bound on the achievability of the link-flow vector; (e) generating, based on the scheduling multi-graph, a solution comprising a set of routes and an associated schedule for achieving the link-flow vector, the solution being a lower bound on the achievability of the link-flow vector; and (f) implementing a routing method using the set of routes and the associated schedule to route the link-flow vector from the source node to the destination node. At least one node v of the network is adapted to receive transmissions from a specified plurality Ω(v) of other nodes, and at least one of the scheduling conditions depends on Ω(v).

The invention discloses a TOF and RSSI information fusion-based indoor wireless positioning method which is mainly used for solving a problem that in conventional indoor positioning technology lacks effective error control and is low in data utilization rate. An implementation solution comprises the following steps: step 1, according to communication time information between a target node and an anchor node, a symmetric bidirectional bilateral distance measuring algorithm is used to calculate distance between the two, and a fault threshold and an error threshold are set to filter the distance;step 2, according to RSSI value information between the target node and the anchor node, an MK model is used for converting the RSSI value information into distance between nodes after a Gaussian model is used for screening operation; step 3, the two types of distance is subjected to weighted fusing operation, and final distance is obtained; step 4, estimated solutions for the target node are obtained via circulation maximum likelihood estimation; step 5, the obtained estimated solutions are subjected to residual error weighted fusing operation, and coordinates of the target node are obtained. Via the method, insufficiency of conventional technologies that are large in positioning calculating errors and low in positioning result reliability, and data utilization rates and positioning andtracking precision are improved.

The invention relates to the field of spatio-temporal data mining or intelligent traffic, in particular to a traffic flow prediction method and device based on deep learning. The traffic flow prediction method comprises the following steps: receiving a flow prediction request sent by a user terminal; then, responding to the flow prediction request, and obtaining traffic flow information of a target road network corresponding to the target node identifier; acquiring spatiotemporal feature information based on the traffic flow information; and inputting the space-time feature information into aspace-time diagram convolution model to perform model prediction operation, and obtaining a flow prediction result, so that the influence of the spatial-temporal characteristics of the traffic flow ofthe whole road network on the traffic flow of the target node can be more comprehensively considered from the perspective of spatial-temporal correlation. The traffic flow prediction method and device based on deep learning can improve the accuracy of predicting the traffic flow of the road network.

The invention belongs to the technical field of a spatial database and in particular relates to a method for querying a road network k aggregation nearest neighboring node (k-ANN) based on a Voronoi graph. The method comprises the following steps of: resolving a first nearest neighboring node (1-NN) of each query point through an R tree index; constructing a priority queue to store the 1-NN of all query points, wherein the weight is the current aggregation distance from each query point to a target node; extending a certain query point according to a certain sequence on the basis of a road network Voronoi graph, updating the aggregation distance of the next NN of the query point, inserting or updating and performing other kinds of operation on the priority queue until the target node at the head of the queue is extended by all the query points, then the target node is a first aggregation nearest neighboring node (1-ANN); and after the 1-ANN is deleted from the queue, acquiring a second aggregation nearest neighboring node (2-ANN) which accords with the conditions for the second time, and acquiring the k-ANN according to the characteristics. The method has excellent performance in the aspect of response time and page views of user query.

The invention relates to a method for quickly calibrating multi-level forwarding load balance of a multi-level forwarding network and a multi-level forwarding network system based on the method. Except the forwarding node of the last level, each forwarding node of each level monitors the load state of each forwarding node of the next level, determines the forwarding priority according to the load weight of the forwarding node of the next level, and preferentially forwards the data which needs to be forwarded to the forwarding node with the lightest load of the next level; if high-priority forwarding fails, the secondary-priority forwarding nodes are sequentially selected to forward the data; the forwarding nodes sending the data form a forwarding source node; and the nodes receiving the data form a forwarding destination node. Through the method and system provided by the invention, the load balance among the forwarding nodes as well as the detection and processing on the link and network system conditions are realized so that the resource utilization of the entire network system is optimized; and the core algorithm of the method can also be used for performing load balance at a server end.

A method for migration of containers in a container orchestration platform, such as Kubernetes or Docker swarm, between compute nodes of a seamless computing platform present in different computing domains is provided. The state of the compute nodes of the seamless computing platform is continuously monitored. On determining a trigger, a container to be moved from a current compute node to a target compute node of a number of compute nodes is identified. A container information of the container to be moved is generated which includes a container context and the current state of the container. The container information is used by the target node to retrieve the current state of the container to be moved which enables restarting the container on the target compute node.

The invention discloses a vulnerability detection method combining deep reinforcement learning and a program path instrumentation technology. The method includes: obtaining a path corresponding to input from a control flow diagram of a to-be-tested program in an instrumentation mode; calculating and obtaining a reward value according to the path and a target node in the control flow graph, using the reward value to train a neural network of deep reinforcement learning so as to select a variation action; and performing variation on the input of the to-be-tested program according to the variation action to obtain an updated input and an updated path thereof, calculating an updated reward value, training the neural network again and performing input variation processing, and performing circulating until the to-be-tested program is collapsed to obtain a corresponding input vulnerability. According to the method, the accuracy is higher, the input corresponding to the path where the vulnerability is located can be obtained more efficiently, and compared with a traditional fuzzy test, the method is higher in detection speed and has a certain code coverage amount.

The invention relates to the technical field of information, and discloses a method for controlling routing actions based on a multi-agent reinforcement learning routing strategy. The method comprisesthe following steps: training a reinforcement learning model, updating decision values of routing nodes by the reinforcement learning model by using a Q learning algorithm, and updating strategy parameters by using a strategy gradient algorithm in combination with the updated decision values; determining strategy parameters by using the reinforcement learning model according to the target node towhich the routing nodes forward the data packets and the network load in the communication network where the routing nodes is located; and determining an outgoing link of the routing nodes accordingto the strategy parameter. According to the method, routing strategies can be adjusted in time for dynamically changing network connection modes, network loads and routing nodes; the appropriate shortest path is selected according to the target node of the data packet, and finally the average delivery time of the data packet is greatly shortened.

The present invention discloses a multicast route method of a mobile self-organizing network system. The method of the present invention is put forward against the prior art that the present multicast route method can not satisfy the self-organizing network with rapidly moving nodes, and the method comprises the steps as follows: a node regularly sends a HELLO message to perform detection and update of a neighbor node; a source node performs a route orientation prediction by using geological position of the node; the source node obtains positional information of a multicast group object node,then adds object node ID and the positional information into a data head object node domain, and sends a DATA message; and other nodes determine how to forward the data according to the head information. The present invention puts forward a data transmission guiding mechanism based on positional information prediction, and applies a delay tolerance technology to the network, therefore in an environment where the nodes move rapidly and topology changes quickly, the method of the present invention can raise use efficiency and data delivery rate of network data transmission with the preconditionthat the requirement of low time delay for the network is satisfied.

The invention provides a network node state control method and device and a gateway, relating to the technical field of communications, including executing by a gateway, wherein the gateway is connected with a plurality of network nodes; and if the message sent by the network node is received, judging whether the message contains the wake-up identifier of the network node, if so, determining thatthe network node is a target node, controlling an associated node of the target node to be in a wake-up state, and controlling other network nodes except the target node and the associated node in thenetwork node to be in a dormant state. According to the invention, the energy consumption can be effectively reduced, and the user experience is better improved while the energy is saved.

The invention discloses a Docker migration method and system for a cloud data center. Load information values of all physical nodes in a cluster are collected and quantized; when the condition that the load of a certain node reaches a set threshold value or a container is suspended due to damage is monitored, feedback information is generated, a to-be-migrated container is selected on the overloador no-load node by using a hotspot container selection algorithm, affinity matching is performed according to a resource demand vector of the to-be-migrated container and the residual resource quantity of other nodes, and a target node set is selected. Then, in the target node set, an optimal target node is selected according to a probability selection algorithm, and the to-be-migrated containeris migrated to a target node to recover execution. According to the method, the container migration method is designed by taking the minimum migration amount, the shortest migration time and the lowest energy consumption as principles, so that the loss of the cluster caused by the fault of a certain computing node can be effectively reduced, the reliability and the utilization rate of the existingcluster are improved, the load balance is realized, and higher availability is provided for the cluster.

The invention provides a graph embedding method and device, and a storage medium, and the method comprises the steps: reading graph structure data and node feature values in a target graph, and building a graph structure model; regarding each node in the graph structure model as a target node, and sampling a first-order neighbor node of each target node according to the non-uniform neighbor node sampling function to obtain a first-order neighborhood of each target node; constructing second-order neighborhoods of the target nodes according to the first-order neighborhoods of the target nodes, aggregating the second-order neighborhoods to the first-order neighborhoods corresponding to the target nodes, and inputting the aggregated features of the second-order neighborhoods into the fully-connected neural network to obtain new features of the first-order neighborhoods of the target nodes; and aggregating the new features to the corresponding target nodes, and inputting the aggregated newfeatures of the first-order neighborhood into the fully-connected neural network to obtain output features of the target nodes. The neighborhood can be flexibly and effectively constructed for each node in the graph, and feature aggregation can be rapidly carried out, so that the graph embedding effect based on the graph neural network is improved.

The embodiment of the invention provides a cloud gateway configuration method, and the method comprises the steps: monitoring gateway configuration data stored in a tree-shaped structure in a cooperative service cluster, with the tree-shaped structure comprising a plurality of nodes in one-to-one correspondence with a plurality of APIs; judging whether the plurality of nodes comprise at least onetarget node or not, with the at least one target node comprising a data change node and/or a newly added node; if the plurality of nodes comprise the at least one target node, updating the gateway configuration of the API corresponding to each target node in the at least one target node to obtain the latest network configuration of each API; and configuring a gateway processing logic model according to the latest network configuration of each API. According to the embodiment of the invention, open configuration of the cloud gateway can be realized, and the fusion degree of accessed systems ofall parties can be improved.

The invention provides a knowledge graph relation reasoning method based on a graph neural network, and the method comprises the steps: mainly dividing a relation reasoning model in knowledge graph reasoning into a scoring function and a prediction result, wherein a candidate result set composed of the target node and the relationship can be obtained according to different modes according to the result of the relationship reasoning. In the process of reasoning the correct relationship in the candidate result set, the method mainly combines the structure information in the knowledge graph with the semantic and attention mechanism of the knowledge graph to realize the reasoning prediction of the unknown relationship in the knowledge graph. In the process of obtaining semantic information of the knowledge graph, structural information around target head and tail entities is extracted by using an isomorphic information theory, attention information collection is performed on the knowledge graph around a target relationship, and a candidate result with the highest score is output as a final result through effective fusion of an attention mechanism.

The invention discloses an SCATS system road traffic flow prediction method based on a spatial domain graph convolutional neural network. First, using lanes as nodes, obtaining nodes connected with each other according to the traffic adjacency matrix; sorting the correlations of the connected nodes from high to low, then finding a neighborhood node of each target node, constructing a high-order neighborhood traffic state matrix by using the target nodes and the traffic flow state data of the neighborhood nodes of the target nodes as the input of the CNN, and finally obtaining a traffic flow state prediction result of the predicted lane. According to the method, the correlation between the traffic flow state time and the space is fully mined, the accuracy is improved, and the anti-interference performance of a random result is enhanced.

The invention discloses an application testing method and device and belongs to the field of software tests. The application testing method includes the steps that an initial current node is determined in a node list; whether the current node has an unexecuted task or not is judged; if the unexecuted task exists, the task is executed, and a node corresponding to a skipped interface is set to be the current node; if the unexecuted task does not exist, the node list is searched for a target node with the unexecuted task, the current node is skipped to the target node, and the target node is set to be the current node; if the target node can not be found, a traversal test is ended. The method and device have the advantages that the problems that a pseudorandom event stream does not have to accord with an actual operation stream of a current APP, multiple main interfaces of the APP are frequently and repeatedly tested through related testing methods, and certain hidden interfaces are possibly omitted are solved, the traversal test is conducted according to the actual operation stream of the APP as much as possible, and the testing efficiency of the APP is improved.

Disclosed is a self-adaptive routing method oriented to a mobile sensor network. According to the method, multiple paths are created and maintained through mutual connection among a source node, an intermediate node and a destination node so as to achieve transmission of data from the source node to the destination node. When a mobile sensor network node selects paths, firstly node probabilistic information of a forward list is read, the nodes are selected and utilized, and the optimal path between the source node and the destination node is solved through strategies of threshold limiting and the like. The self-adaptive routing method oriented to the mobile sensor network furthest reduces power consumption as possible in the shortest time delay, the service life of the mobile sensor network is prolonged, and the whole network has better reliability and practicability.