112 results about "Routing model" patented technology

Adaptive navigation techniques are disclosed that allow navigation systems to learn from a user's personal driving history. As a user drives, models are developed and maintained to learn or otherwise capture the driver's personal driving habits and preferences. Example models include road speed, hazard, favored route, and disfavored route models. Other attributes can be used as well, whether based on the user's personal driving data or driving data aggregated from a number of users. The models can be learned under explicit conditions (e.g., time of day / week, driver ID) and / or under implicit conditions (e.g., weather, drivers urgency, as inferred from sensor data). Thus, models for a plurality of attributes can be learned, as well as one or more models for each attribute under a plurality of conditions. Attributes can be weighted according to user preference. The attribute weights and / or models can be used in selecting a best route for user.

Adaptive navigation techniques are disclosed that allow navigation systems to learn from a user's personal driving history. As a user drives, models are developed and maintained to learn or otherwise capture the driver's personal driving habits and preferences. Example models include road speed, hazard, favored route, and disfavored route models. Other attributes can be used as well, whether based on the user's personal driving data or driving data aggregated from a number of users. The models can be learned under explicit conditions (e.g., time of day / week, driver ID) and / or under implicit conditions (e.g., weather, drivers urgency, as inferred from sensor data). Thus, models for a plurality of attributes can be learned, as well as one or more models for each attribute under a plurality of conditions. Attributes can be weighted according to user preference. The attribute weights and / or models can be used in selecting a best route for user.

The invention discloses a method and device for optimizing multi-constraint quality of service (QoS) routing selection. The method comprises the following steps: acquiring the topological structure and link parameters of an existing network in accordance with information of a prediction model; creating a corresponding multi-constraint QoS routing model in accordance with the determined topological structure and link parameters, and constructing penalty functions to transform multi-constraint conditions, as well as constructing fitness functions for evaluating paths; using a depth-first search method to acquire initial feasible paths and initializing particle swarms; calculating the fitness value of each particle, and finding out the optimal fitness value of the particle adjacent to each particle; using the generation algorithm and the genetic algorithm-particle swarm optimization (GA-PSO) to carry out iterative solution at the beginning of the initial feasible paths, and carrying out natural selection and variation operations; and finding out paths which meet conditions and are provided with the optimal fitness values, realizing optimal routing selection under the multi-constraint condition, and executing in accordance with the found routings.

A logistics distribution control method with soft time windows includes the following steps: (A1) a network model is built, cost resistances are assigned to roads to which network data are concentrated, and taking road nodes into consideration, toll weights are assigned to road traffic light intersections and toll stations; (A2) an optimized vehicle routing model with soft time windows (VRPTW) is built, a target function is established with lowest transportation costs, and the transportation costs are respectively composed of fixed distribution vehicle cost, transportation cost, vehicle waiting cost and delay cost; (A3) a fuzzy clustering analysis algorithm is designed, and a method based on the integration of quantitative analysis and qualitative analysis is adopted for clustering; (A4) a heuristic optimized vehicle routing algorithm is designed, the optimized vehicle routing algorithm is adopted for distribution target nodes in each class, and thereby a distribution result can be obtained. The logistics distribution control method with soft time windows adopts the distances of actual delivery road network routes between distribution nodes as a calculation basis and also takes the actual traffic capacities of roads, large network node number and transportation time needed by distribution nodes into consideration.

Adaptive navigation techniques are disclosed that allow navigation systems to learn from a user's personal driving history. As a user drives, models are developed and maintained to learn or otherwise capture the driver's personal driving habits and preferences. Example models include road speed, hazard, favored route, and disfavored route models. Other attributes can be used as well, whether based on the user's personal driving data or driving data aggregated from a number of users. The models can be learned under explicit conditions (e.g., time of day / week, driver ID) and / or under implicit conditions (e.g., weather, drivers urgency, as inferred from sensor data). Thus, models for a plurality of attributes can be learned, as well as one or more models for each attribute under a plurality of conditions. Attributes can be weighted according to user preference. The attribute weights and / or models can be used in selecting a best route for user.

Various embodiments of a system and method related to a topology and routing model for a peer-to-peer network are disclosed. A plurality of nodes may be coupled to each other to form a peer-to-peer network. A routing table may be created on each node in the peer-to-peer network. Each node may be operable to route messages to other nodes in the peer-to-peer network using information stored in the routing table. Messages may be propagated among nodes in the peer-to-peer network in a decentralized manner. For example, the peer-to-peer network may not utilize centralized servers of any kind. Each node in the peer-to-peer network may perform substantially the same routing functionality.

The invention provides a method for planning the routes of multi-unmanned aerial vehicles based on a particle swarm optimization algorithm. The method comprises the following steps: establishing a three-dimensional map for planning space of the routes of the multi-unmanned aerial vehicles at first; then constructing multi-unmanned aerial vehicle route planning models under the three-dimensional map, wherein the models mainly comprise a barrier model, a route model, an unmanned aerial vehicle state model, a constraint model and a multi-unmanned aerial vehicle route planning mathematic model; and solving the problem of multi-unmanned aerial vehicle route planning under the three-dimensional map by using the particle swarm optimization algorithm. The method provided by the invention improves multi-unmanned aerial vehicle route planning capacity in a complex environment and provides technical support for air traffic management platforms for unmanned aerial vehicles, autonomous flight systems for multi-unmanned aerial vehicles, etc.

Adaptive navigation techniques are disclosed that allow navigation systems to learn from a user's personal driving history. As a user drives, models are developed and maintained to learn or otherwise capture the driver's personal driving habits and preferences. Example models include road speed, hazard, favored route, and disfavored route models. Other attributes can be used as well, whether based on the user's personal driving data or driving data aggregated from a number of users. The models can be learned under explicit conditions (e.g., time of day / week, driver ID) and / or under implicit conditions (e.g., weather, drivers urgency, as inferred from sensor data). Thus, models for a plurality of attributes can be learned, as well as one or more models for each attribute under a plurality of conditions. Attributes can be weighted according to user preference. The attribute weights and / or models can be used in selecting a best route for user.

The invention discloses a route selection method and system based on road abnormal area evaluation. The method comprises that a traffic route model data storage module is initialized; physical sensors in vehicles acquire and transmit vehicle traveling data to a nearest traffic route information acquisition module which filters and integrates the data into hot spot (HSP) information data of a current road section; every once in a while, all traffic route information acquisition modules upload HSP information data of all road sections to an HSP processor; a processing module calculates impedance generated by each current road section of each HSP, collects and generates the impedance into traffic road model data and updates the data to the traffic route model data storage modules; optimal route selection modules calculate optimal routes after vehicles update and download the traffic road model data. By means of the route selection method and system, the route selection efficiency can be improved, traffic transportation pressure can be well relieved, traffic road resources are well distributed, and the traffic transportation capacity is improved.

There is provided an apparatus configured to perform operations comprising: acquiring motion data sets from the plurality of mobile devices; estimating route models of the travelled routes on the basis of the motion data sets; generating EMF fingerprints for the travelled routes by associating the acquired EMF data sets with corresponding points of the route models; and determining the locations of the routes with respect to each other on the basis of at least one of the following: the EMF fingerprints and the route models.

The invention discloses a method for designing a spatial information network routing strategy under an SDN architecture. The method comprises the following steps: establishing a spatial information network architecture based on a software defined network SDN; establishing a network routing model based on SDN space information; and establishing an improved genetic algorithm to solve a routing model. According to the method, the thought of SDN numerical control separation is utilized, logic is highly concentrated on a control plane, the global network state can be obtained in real time, a routing strategy is calculated, LEO and MEO do not need to calculate routing by themselves, the load of a satellite is reduced, and power consumption is reduced. Aiming at the characteristics of complex communication environment, strong node dynamics, limited on-board processing capability, various service types and the like of the spatial information network, an optimal spatial information network routing scheme is formulated under the SDN architecture, so that the transmission time delay of data forwarding of each service type in the spatial information network is reduced, and the packet loss rateis reduced.

A method for routing calls suited to use in a call center includes receiving a call from a customer, extracting features from an utterance of the call, and, based on the extracted features, predicting a class and a complexity of a dialogue to be conducted between the customer and an agent. With a routing model, a routing strategy is generated for steering the call to one of a plurality of types of agent (such as to a human or a virtual agent), based on the predicted class and complexity of the dialogue and a cost assigned to the type of agent. A first of the plurality of types of agent is assigned a higher cost than a second of the types of agent. The routing strategy is output.

The invention provides a real-time shortest path search algorithm in an interactive layout tool, belonging to the technical field of the integrated circuit layout design automation, which in particular relates to an interactive layout automatic routing algorithm which can search the shortest path between designated points or units on a published picture in real time. The invention adopts a non-grid routing model with level block management, is not required to pre-extract the layout, reduces the expense for model conversion at the same time of improving the routing flexibility, and improves the path search speed by using buffered data, thereby meeting the requirement in the real-time routing in a layout editor.

An offline teaching apparatus (10) for generating, in an offline mode, a robot operation relating to tracking and working relative to a workpiece traveling along a carrier route. The apparatus includes a model-image generating section (12) for generating images of a carrier-route model (CM), a workpiece model (WM) and a robot model (RM); an indicator generating section (14) for generating a base-point indicator (BI), and upstream-end and downstream-end indicators (UI,DI) defining a spatial range (S) for performing the robot operation; a display section for displaying, on a screen (22), the images of the carrier-route model, the workpiece model and the robot model, together with the base-point indicator, the upstream-end indicator and the downstream-end indicator; a carrying-operation simulating section (18) for causing the workpiece model to simulate a workpiece traveling motion along the carrier-route model; and a robot-operation simulating section (20) for causing the robot model to simulate the robot operation, during a period from an instant the workpiece model passes by the upstream-end indicator until an instant the workpiece model arrives at the downstream-end indicator.

The invention provides a multi-path routing method based on reinforcement learning and transfer learning, which is used for solving the technical problem of poor equivalent path load balance in a network environment with less flow data in the prior art, and comprises the following implementation steps of: constructing a real network Z and an experimental network G consistent with a Z topological structure; establishing a two-dimensional array H; constructing a multi-path routing model based on reinforcement learning; initializing a flow matrix DM and an equivalent path flow proportion matrix PM; performing iterative training on the multi-path routing model based on reinforcement learning in the experimental network G; migrating global neural network weight parameters in a routing decisionmodel obtained by training in an experimental network G to a real network Z based on a transfer learning method; and carrying out adaptive training on the global neural network initialized in the realnetwork Z to obtain a multi-path routing result conforming to the real network environment characteristics. The method can be used for data center networks and other scenes.

An apparatus for routing data flows through a data communication network includes: a network interface, hardware processor, non-transitory memory, route selection module, analysis module and learning module. The network interface receives requests for routing a flow of data packets within the data communication network. Route selection module routes data flows through the network, based on a routing model applying routing rules to a model of network structure and network parameters. The analysis module determines when routes fail to comply with a respective required level of service (LOS) specifying at least one required performance measure for the flow. Flow routes and their respective failure or success in adhering to the LOS are stored in the routing log. The learning module updates the routing model by processing the routing log, optionally using ML analytics. Optionally, when the model is updated the route selection module reroutes one or more failed flows based on the updated model.

The invention discloses a shift-arrangement system, which comprises an overflow priority matrix determination device which is used to generate a skill-group overflow priority matrix, an overflow priority model artificial device which is connected with the overflow priority matrix determination device and is used to optimize the skill-group overflow priority matrix generated by the overflow priority matrix determination device, a manpower requirement determination device, a traffic analysis and prediction device, and a client representative shift-arrangement device. In addition, the invention also discloses a shift-arrangement method. Through the invention, the equation-solved efficiency can be increased under the circumstances of skill shift-arrangement and route model size increasing, at the same time, the shift-arrangement under the circumstance of combining a plurality of techniques and a route model can be achieved.

There is provided an apparatus configured to perform operations comprising: acquiring motion data sets from the plurality of mobile devices; estimating route models of the traveled routes on the basis of the motion data sets; generating EMF fingerprints for the traveled routes by associating the acquired EMF data sets with corresponding points of the route models; and determining the locations of the routes with respect to each other on the basis of at least one of the following: the EMF fingerprints and the route models.

The invention relates to the field of three-dimensional modeling design, and discloses a railway subgrade engineering design method based on a BIM technology, which meets the requirements of three-dimensional rapid modeling and engineering drawing of railway subgrade retaining engineering, realizes rapid three-dimensional display of the railway subgrade engineering, and is convenient to modify andconvey construction key points. The method comprises the following steps: a, reading route information elements, importing the route information elements into three-dimensional modeling software, andgenerating a railway route model; b, associating the route design parameters with a railway route model; c, associating design data of railroad bridges, culverts, tunnels and stations with routes; d,importing roadbed cross section data into the three-dimensional modeling software; e, generating a roadbed model according to the railway line model and the roadbed cross section data; and f, selecting an engineering creation command, inputting corresponding engineering design parameters, and creating a corresponding engineering model on the basis of the roadbed model.

The invention relates to a wireless sensor network distributed routing method on the basis of a Lagrange-Newton method, comprising the steps of: firstly switching life cycle maximization routing model of a wireless sensor network into problem with equality constraint convex optimization, and then adopting a sequential unconstrained minimization method to solve; and particularly, in the core step of the sequential unconstrained minimization method on the basis of the Lagrange-Newton method, designing a matrix splitting technology for solving dual Newton increment by distributed iteration, and utilizing a distributed step-length calculation method to guarantee that the flow of any wireless chain is controlled in the allowed maximum flow range, thus achieving the purpose of using the Lagrange-Newton method to solve the wireless sensor network distributed routing problem. In the wireless sensor network distributed routing method, the model is reasonable, the energy confinement is considered, the convergence rate is fast, and the wireless sensor network distributed routing method can be directly applicable to the problem of optimum route for the life cycle in the wireless sensor network.

A device even at the time of initial planning of plant design easily and rapidly generates three-dimensional arrangement adjustment CAD data which describes a route for arrangement of cable housing components. An area generation section 410 generates a prescribed rectangular parallelepiped area from the minimum and maximum coordinates. A cable information acquisition section 420 acquires information relating to cables such as cable specifications, starting point information, and end point information. A route generation section 430 generates a cable housing component route between a starting point area and an end point area through via areas. A route consolidation section 440 consolidate into a single route any route having the same cable specification, starting point information, via information, and end point information. A three-dimensional model conversion section 480 adds a prescribed width and height to the consolidated route to generate a three-dimensional cable housing component route model.

Methods and systems for scheduling tasks for a two-route process for appointment booking include: receiving a request to book a new appointment for a service; outputting a first booking response for the request, wherein the first booking response is determined using a multi-route model, wherein the multi-route model is configured to determine booking responses in multiple different ways; and verifying the first booking response based on a second booking response determined using the multi-route model.

The invention relates to a RapidIO route configuration device and a RapidIO route configuration method based on a VPX system. The device comprises an upper computer and a VPX system, wherein a node in a RapidIO network of the VPX system serves as a RapidIO main node; the upper computer is used for building a corresponding RapidIO route model according to the network topological structure of the VPX system, and also used for traversing the whole RapidIO route model, computing an optimal path between any two RapidIO nodes in the model according to path weights, forming a route configuration table and transmitting the route configuration table to the RapidIO main node in the VPX system; and the RapidIO main node is used for performing route configuration on the whole RapidIO network of the VPX system according to the route configuration table.According to the device and the method provided by the invention, the upper computer is responsible for computing the route configuration table of the RapidIO network, and then configuration is performed by the RapidIO main node in the VPX system; compared with a traditional single-point configuration method, the method provided by the invention is significantly improved in configuration speed and configuration accuracy via cooperative configuration of the upper computer and the RapidIO main node.

Systems and processes are disclosed for routing callers to agents in a contact center, along with an intelligent routing system. An exemplary process includes combining multiple output variables of a pattern matching algorithm (for matching callers and agents) into a single metric for use in the routing system. The pattern matching algorithm may include a neural network architecture, where the exemplary method combines output variables from multiple neural networks. The method may include determining a Z-score of the variable outputs and determining a linear combination of the determined Z-scores for a desired output. Callers may be routed to agents via the pattern matching algorithm to maximize the output value or score of the linear combination. Another process includes routing a caller, if agents are available, to an agent based on a pattern matching algorithm (which may include performance based matching, pattern matching based on agent and caller data, computer models for predicting outcomes of agent-caller pairs, and so on), and if no agents are available for the incoming caller, the method includes holding the caller in a shadow queue, e.g., a set of callers. When an agent becomes available the method includes scanning all of the callers in the shadow queue and matching the agent to the best matching caller within shadow queue.

The invention discloses a river flood forecasting method based on a discrete generalized Nash routing model. By introducing the detention storage curve, simplifying a generalized Nash routing model, and establishing a discrete generalized Nash routing model, a river flood forecasting method is provided based on this model. The method expresses the outflow process of a river lower section as a linear combination of the inflow at the beginning and the end of a period and the outflow of the current time and a plurality of previous time moments, and obtains a weight coefficient according to the detention storage curve. The river flood forecasting method based on a discrete generalized Nash routing model, because integrating some historical effective information, can improve forecasting accuracy, is simple and visual, and can be easily popularized and applied in engineering practice.

The invention provides a multiple spatial scale watershed runoff and sediment yielding prediction method and device. The method comprises the steps that according to DEM topographic data, remote sensing images and a river network of a watershed to be predicted, the watershed to be predicted is divided into m three-level ditches; according to the runoff yielding at the fracture surface outlet of each three-level ditch, the confluence amount of a drain net of each two-level ditch is calculated by using a nahi instantaneous unit line; furthermore, a Muskingum multi-reach continuous flow routing model is used for calculating the runoff yielding at the fracture surface outlet of the watershed to be predicted; according to the sediment yielding of the gully slope geomorphologic units, ravine slope geomorphic units and groove geomorphic units in the three-level ditches, the sediment yielding of the three-level ditches is determined; and furthermore, the sediment delivery ratio is used for determining the sediment yielding at the fracture surface outlet of the watershed to be predicted. Through model coupling calculation, the multiple spatial scale watershed runoff and sediment yielding prediction method can simulate and predict multi-spatial scale watershed, and the accuracy and promotion practicability of soil erosion sediment yielding simulation and prediction are improved.

A method for processing with a document processing system is provided. The method includes generating a sheet assembly by executing a production route model of at least one function leading to a plurality of actions, the sheet assembly including a set of transitions that result from invocation of the plurality of actions, extracting from the sheet assembly a document imposition recipe including a set of imposition descriptions generated by computing transformations and parameters for each transition performed on the sheet assembly, and rendering the imposition descriptions of the document imposition recipe on behalf of at least one source file in order to deliver impositions in a source format describing at least placement of content of pages of the source file and action marks for subsequent displaying of the impositions on a user interface of the document processing system and / or for subsequent processing on the at least one printer and / or an the at least one finisher. A document processing system is configured to execute the method.

The invention discloses a wiring modeling optimization method and device based on a BP neural network, and the method comprises the following steps: obtaining the lamination information of a board card, and carrying out the wiring modeling through the parameter information of the board card; utilizing a BP neural network to obtain optimization parameters, meeting impedance and loss indexes, of theto-be-modeled wiring; and calling the optimization intermediate file to establish a plurality of wiring optimization models. According to the method, the BP neural network is added to realize self-adaptability of routing impedance and loss optimization, so that the precision of a routing model and the efficiency of modeling optimization are improved. By utilizing the intermediate file generated by routing modeling and combining modeling software, a plurality of routing models can be established at one time, and batch processing of wiring optimization is realized, so that the efficiency is improved.

The invention discloses a route selection method for an OTN (optical transport network), and the method comprises the steps: S100, determining the conditions of an optical network element transfer path in the OTN; S200, determining a source node and a target node; S300, searching each feasible path from the source node to the target node according to a node route model; S400, calculating the costof each feasible path, and selecting the optimal path; S500, writing the optimal path into the route selection table. According to the invention, the visited nodes and the feasible optical paths are sequentially numbered, an all visited nodes and feasible optical paths between the source node and the target node are connected sequentially, thereby forming a feasible path. Moreover, through the calculation of the cost of each feasible path, the method determines the optimal path and a path selection sequence table, so a data package can be transferred through the optimal path during the transferring. When the optimal path has a fault, the data package can be transferred according to the sequence of the path selection sequence table, thereby reducing the communication cost of the OTN, and improves the communication quality.