159 results about "Network agent" patented technology

The invention provides an AI flood real-time simulation method for urban hydrological multi-physics field decoupling supervision, and the method comprises the steps: constructing an intelligent physical prediction model of urban flood, carrying out the layered decoupling of an urban hydrological hydrodynamic system, separating a one-dimensional drainage pipe network submodule from a two-dimensional surface flood module, designing a multi-scale physical constraint loss function for different links, and carrying out the real-time simulation of the AI flood. Microcosmic, mesoscopic and macroscopic multi-scale physical constraint fusion is realized, and residual constraints of the Saint-Venant equation are embedded in a neural network agent of a drainage system in a microcosmic level; in the mid-level, embedding the residual constraint of the shallow water equation in the surface flood routing system; in the macroscopic level, the water balance constraint of the whole system is realized; urban hydrology and hydrodynamic force are decoupled, and multi-scale physical field supervision constraint is achieved.

In one implementation, a device obtains code generated by a language model-based agent to perform an action of a particular type with respect to a computer network. The device determines one or more parameters to execute the code in a testing environment. The device performs a validation assessment of the code to assess whether it is able to perform actions of the particular type by executing it with the one or more parameters in the testing environment. The device makes, based on the validation assessment, the code available to the language model-based agent to perform a subsequent action of the particular type.

The invention relates to the technical field of structural design, and discloses a topology and size joint optimization design method and system for a hierarchical composite structure, and the method comprises the following steps: dividing a structural domain into a plurality of hierarchical sub-domains, defining the size constraint range of each sub-domain, and distributing a multi-phase material for each sub-domain; grid discretization processing is carried out on the structural domain, a topological design variable is given to each discrete unit, acoustic unit modeling is carried out on the sound field domain, sound pressure boundary conditions are defined, and coupling boundary conditions are applied to the interface of the sound field domain and the structural domain; solving an acoustic-structure coupling equation based on a finite element method, calculating an objective function and constraint conditions, analyzing the sensitivity of the objective function to topological variables by adopting an adjoint method, and iteratively updating the topological design variables through an optimization algorithm until a convergence criterion is met; and on the basis of a topological optimization result, constructing a neural network agent model of an input layer, a hidden layer and an output layer, and carrying out global optimization on sub-domain size parameters by adopting a genetic algorithm so as to minimize a target function.

The invention relates to an unmanned aerial vehicle cluster topology reconstruction method under an emergency rescue task, belongs to the technical field of unmanned aerial vehicle communication, and solves the problem of low communication efficiency after existing topology reconstruction. Comprising the steps that a topology reconstruction network is constructed and trained, and the topology reconstruction network comprises a global hybrid network and local hybrid networks and proxy networks corresponding to all unmanned aerial vehicles; the proxy network receives local state information of the corresponding unmanned aerial vehicle at each moment and outputs an action function value of an action at each moment, the local hybrid network receives the action function value of the corresponding unmanned aerial vehicle and outputs a local joint action value, and the global hybrid network receives the local joint action values of all the unmanned aerial vehicles and outputs a cluster joint action value; when the unmanned aerial vehicle breaks down, local state information of the undamaged unmanned aerial vehicle and actions of the undamaged unmanned aerial vehicle at the previous moment are obtained and transmitted into the corresponding trained proxy network, and the corresponding actions are selected according to an epsilon-greedy strategy for topology reconstruction. And topology reconstruction with high communication efficiency is realized.

The disclosure provides a method for authenticating a network agent deployed in a networking environment. The method generally includes receiving, by a network controller in the networking environment, a name of an external node where the network agent is running and a token associated with the external node; in response to receiving the name of the external node, obtaining, by the network controller, a secret associated with the token; parsing, by the network controller, the secret to determine an expected external node name corresponding to the token; comparing the expected external node name with the received external node name; and trusting the network agent when the expected external node name and the received external node name match.

The present application discloses a data transmission method based on a high-speed network, a system, and a computing device cluster. The method is applied to a service grid system based on a high-speed network, the system comprises a gateway connected via the high-speed network, a shared memory pool, and computing nodes where service containers and network proxies are deployed, and the service container is connected to the network proxy. The method comprises: after acquiring first data, a gateway determines, according to container information and a routing rule, an address for sending the first data or a first transmission mode of the first data and a first service container for receiving data, wherein the transmission mode may be any one of a plurality of types of rapid channels supported by the gateway and comprising the use of RDMA technology; and a first network proxy connected to the first service container directly acquires the first data. In the process, data exchange is directly implemented between the gateway and the network proxy by means of an intelligent routing rule, the RDMA technology, etc., so that the network transmission speed can be increased, and computing resources are saved.

The invention relates to the field of process control, in particular to a multi-target particle swarm optimization scheduling system for flood control and waterlogging removal in a plain river network polder area. The method comprises the specific implementation steps that a coupling hydrodynamic model module is constructed, the water flow exchange amount between a one-dimensional hydrodynamic model and a two-dimensional hydrodynamic model is calculated, multiple times of simulation are carried out to generate a training data set, and a neural network agent model is trained and generated; generating an initial population based on topological structure information of the plain river network and a heuristic rule by adopting a particle swarm optimization algorithm; according to external work condition information, dynamically adjusting feasible region constraints of an optimization algorithm, inputting candidate scheduling schemes conforming to the constraints into the neural network agent model, and outputting a predicted scheduling state; based on the predicted scheduling state and the weight coefficient, the fitness value of each candidate scheduling scheme is calculated, the optimal scheduling scheme is output through iterative optimization, and the decision-making efficiency, robustness and safety of the scheduling system in the face of complex working conditions and emergencies are improved.

The invention discloses a highway super-large-scale road network management and control response method based on heterogeneous multi-agent reinforcement learning, and the method comprises the steps: dividing a super-large-scale road network based on a graph theory and a fuzzy C-means clustering algorithm to form different sub-road network agents, and constructing a multi-agent reinforcement learning method to search for an optimal strategy for each sub-road network. Traffic event parameters are abstracted, macroscopic, mesoscopic and microscopic traffic state input of each intelligently formulated sub-road network and a lane control strategy set of the sub-road network are determined, and a reward function form is comprehensively determined through average speed, accident punishment and vehicle emission. Furthermore, on the basis of an entropy function of a maximized joint strategy and meeting security constraints, a heterogeneous agent soft actor-commentator algorithm is constructed to construct a commentator and actor network, and action constraints are met during full exploration; and finally, asynchronously updating the network parameters of the actor, and carrying out soft updating on the reviewer network so as to iteratively obtain an optimal sub-road network lane strategy.

The invention relates to the technical field of digital twinning, physical modeling and multi-source data fusion, and provides a digital twinning dynamic construction method based on multi-source data fusion and physical simulation. The method comprises the following steps: acquiring a multi-source heterogeneous data stream from a preset sensor array, a numerical simulation result and a historical database, identifying a key feature mode of a dominant physical process in the multi-source heterogeneous data stream, acquiring a key feature mode time-varying physical field evolution rule corresponding to the key feature mode by using a time sliding window and a forgetting mechanism, extracting a low-dimensional sparse characteristic parameter set reflecting dynamic behaviors from a high-dimensional observation space, and constructing a reduced-order proxy model by adopting Gaussian process regression, a neural network proxy model or an intrinsic orthogonal decomposition combined interpolation technology; and receiving a corresponding real-time observation data stream to establish a full-closed-loop feedback link from model prediction, high-fidelity solution verification to observation data correction in combination with the reduced-order proxy model so as to complete the construction of the digital twin.

Examples described herein relate to a network agent, when operational, to: receive a packet, determine transmit rate-related information for a sender network device based at least on operational and telemetry information accumulated in the received packet, and transmit the transmit rate-related information to the sender network device. In some examples, the network agent includes a network device coupled to a server, a server, or a network device. In some examples, the operational and telemetry information comprises: telemetry information generated by at least one network device in a path from the sender network device to the network agent.

The invention discloses a post-earthquake railway structure state evaluation method and system based on multi-source data fusion. The method comprises the steps that a finite element model is constructed according to the structure of a regional railway network, a simulation data set is generated, a physical information neural network model is trained, and a proxy model library is formed; acquiring multi-source observation data, and performing inversion according to the Bayesian theory, the structural damage parameters of the physical information neural network agent model and the multi-source observation data to obtain complete posterior probability distribution of the damage parameters; according to the method, statistical characteristics of damage parameters are extracted from posterior probability distribution, probability grading is carried out on the damage degree of the structure, driving constraint suggestions are generated according to the combination of damage probability grading and specifications, and Bayesian inversion time is reduced from several days to several hours through a physical information neural network agent model, so that the evaluation efficiency is improved; the output of the Bayesian method is probability distribution, the uncertainty range of the evaluation result is clearly displayed, and the uncertainty is quantified.

The invention belongs to the technical field of computational mechanics and artificial intelligence crossing in civil engineering, and discloses a foundation pit retaining wall deformation prediction method based on a physical information neural network, and the method comprises the following steps: 1, building a retaining wall physical information neural network agent model based on multi-source data fusion; 2, building a displacement-force inversion model of the retaining wall; and 3, carrying out iterative coupling solution on the support system. According to the method, mixed training is carried out by fusing physical rules and field measured data, so that the physical information neural network agent model follows a basic mechanics principle and can also fit specific engineering practice, the prediction result is more reliable, and compared with traditional finite element analysis, the efficiency is improved by several orders of magnitude; therefore, parameter optimization and real-time safety evaluation based on a large amount of calculation become possible.

An effective strategy provides an intuitive starting point for an enterprise network agent to resolve issues before the issues increase the probability of an outage. Being able to predict whether and when a current anomalous state will transform into an outage is valuable to an enterprise network agent tasked with network administration, including monitoring the network; configuring the network; recommending software or hardware licenses, updates, or additions; obtaining software or hardware licenses or devices; generating reports and alerts; and launching countermeasures in association with the enterprise network.

The invention provides a deep learning method and system based on a station-city fusion space energy consumption model, and the method comprises the steps: analyzing a core energy consumption system of a core space region in a station-city fusion three-dimensional network space through employing a space region analysis method, and constructing a station-city fusion three-dimensional network energy consumption model of the core energy consumption system; constructing a carbon emission mechanism model through a station-city fusion three-dimensional network energy consumption model, defining a loss function of the carbon emission mechanism model according to a deep learning neural network structure of an initial input variable and an output variable in the carbon emission mechanism model, and performing iterative training on the loss function by adopting an iterative optimization function to obtain a carbon emission mechanism model; and outputting a deep learning neural network agent model until the loss value is lower than an allowable value, and finally evaluating the green operation performance of the station-city fusion three-dimensional network space by adopting the deep learning neural network agent model according to specification requirements to obtain an evaluation result. Compared with the prior art, the method is higher in evaluation efficiency and higher in reliability.

The invention provides a pipe network model parameter inversion method and device based on an FMU standard and an optimization algorithm, and relates to the technical field of fluid simulation and parameter calibration. A pipe network physical model is established, and a to-be-inverted parameter is defined as an adjustable parameter and compiled and exported as an FMU format file; an FMU file is loaded in an optimization environment, a parameter sample set is generated through uniform random sampling, and a neural network agent model is trained to establish a mapping relation from parameters to system response; and a weighted error loss function is designed, deviation of measured data and simulation output is taken as an optimization target, optimal parameters of the neural network agent model are solved by adopting an optimization algorithm, and the pipe network system is operated according to the optimal parameters. According to the method, the combination of the Modelica modeling advantage and the Python optimization algorithm is realized through the FMU standard; optimization iteration is accelerated through a neural network agent model, and the parameter calibration efficiency is greatly improved; and high-precision automatic inversion of the key parameters of the pipe network is realized.

The invention provides a distributed scheduling method for railway traction stations and peripheral micro-energy grids, and belongs to the technical field of energy supply of micro-energy grid groups containing the railway traction stations, and the method comprises the steps: building a micro-energy grid sharing mutual aid grid structure containing the railway traction stations, obtaining a power model of each railway traction station, and optimally configuring energy storage equipment, obtaining operation constraints of the energy storage equipment; constructing an energy sharing benefit function by taking each micro-energy network agent as a game main body and taking individual benefit maximization as a target, setting constraint conditions, and establishing an energy sharing non-cooperative game model; and solving the energy sharing non-cooperative game model by adopting an alternating direction multiplier algorithm to obtain a distributed energy transaction behavior result and a price result. According to the method, distributed solution is carried out on the micro-energy network energy sharing non-cooperative game model based on the alternating direction multiplier method, original centralized optimization is converted into coupling optimization among a plurality of micro-energy network agents through interactive variables, the efficiency of the whole iteration process is guaranteed, and the privacy of each energy network is protected.

Systems, methods, and instrumentalities are disclosed herein for implementing an augmented reality (XR) service broker. A first network node may receive a session request for a data session and a MOQ service from a WTRU. The first network node may determine a MOQ agent deployment, including a local agent and a network agent. Based on a network agent deployment, the first network node may determine a second network node for providing the MOQ service and a network MOQ agent. The first node may transmit a proxy setup message for requesting the network MOQ proxy and a session setup message for requesting a local MOQ proxy from the WTRU.

The invention discloses a data asset visualization and collaborative governance method and system based on an AI agent, and the method comprises the steps: carrying out the mapping of attribute parameters, such as the type, scale, value and the like, of data assets, building a feature space, completing the nonlinear mapping from high dimension to low dimension through a t-SNE model, and achieving the visualization feature presentation of the data assets. Initializing a deep Q network agent, setting a reward function by taking visual features as a state space, governance operation as an action space and parameter change, selecting actions by the agent according to the reward function to execute collaborative governance, and storing empirical data into a buffer region to train the network; and training is repeated until network convergence, and a visual and cooperative treatment strategy is generated. The system is composed of a data asset feature mapping unit, a t-SNE conversion unit, an agent initialization unit and the like, all the units are mutually connected and cooperatively operate, data asset visualization and intellectualization and automation of cooperative governance are achieved, and the data asset management efficiency and the value mining capacity are effectively improved.

The invention relates to a shield tunnel irregular load inversion method based on a neural network agent model, and belongs to the field of shield tunnel load inversion. According to the method, assumption of a load distribution mode in a traditional lining load inversion method is abandoned, an inversion result can reveal an irregular load distribution mode of a field tunnel, and technical guarantee is provided for safe service of the tunnel; according to the method, the lining full-circumference deformation obtained based on the three-dimensional laser scanning technology is inverted, the field data volume is remarkably increased, the dimension of the deformation vector can be fully increased along with the increase of n, and the defect that an inversion result is not unique easily caused by a traditional inversion method is overcome.

Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for generating an execution plan for a request that involves accessing and synthesizing data assets from siloed data repositories. In one aspect, a method comprises receiving a first request, generating a request execution plan comprising (i) a sequence of second requests and (ii) a respective indicator of a relationship between a corresponding response for each second request and an overall response to the first request by processing the first request using a request planner artificial intelligence (AI) neural network agent, sequentially, assigning and providing each second request in the sequence of second requests to a particular retriever AI neural network agent in accordance with metadata corresponding with a particular data repository, obtaining the corresponding response to the second request, and generating the overall response to the first request using a synthesizer AI neural network agent.

The invention discloses a high-weather-resistance composite modified road hot-melt coating and a low-temperature preparation process thereof, and relates to the technical field of road engineering materials. Comprising 35-50 parts of a composite resin matrix, 15-20 parts of a gradient interface reinforcing agent, 18-25 parts of a nano composite filler, 8-12 parts of a low-temperature toughening network agent and 5-8 parts of a dynamic self-repairing agent. The material still keeps high elongation at break at-30 DEG C through the composite resin matrix, interface modified filler is prepared through the gradient interface reinforcing agent and the nano composite filler, interface bonding strength is enhanced, filler agglomeration is prevented, coating master batch is combined with the dynamic self-repairing agent, and the dynamic self-repairing coating has the advantages that the dynamic self-repairing coating has excellent mechanical properties, and the service life of the dynamic self-repairing coating is prolonged. The dynamic self-repairing capability is realized, and the service life is prolonged.

This disclosure provides systems, methods and apparatus, including computer programs encoded on computer storage media, for centralized management cloud connecting multiple enterprise networks. A network agent may be deployed within a private cellular network and act as an interface between the node(s) of the private cellular network and a cloud network controller. The network agent may obtain a local-based request to initiate a cloud-based procedure associated with network parameter(s), the network parameter(s) being associated with cloud network credential(s) that correspond to the private cellular network. The network agent may output a cloud-based request to the cloud network controller to initiate the cloud-based procedure. The cloud-based request may indicate at least a portion of the network parameter(s) and omit at least a portion of local credential(s) of the private cellular network. The cloud-based request may hide the cloud network credentials of the private cellular network from the cloud network controller.

The invention discloses a battery mechanism model parameter identification method and system based on a differential evolution algorithm, and the method comprises the steps: constructing an agent model training data set; training the neural network agent model to obtain a trained agent model; screening individuals to obtain an initial population; taking the trained proxy model as a target function of a differential evolution algorithm; inputting the initial population into a differential evolution algorithm to obtain an optimal population; inputting each group of data of the optimal population into an experiment system and a simulation system for evaluation to obtain a real voltage error corresponding to each group of data in the optimal population; whether the real voltage error is smaller than a set threshold value or not is judged, and if yes, the to-be-identified parameter corresponding to the minimum value of the real voltage error is directly output; and if not, setting the set proportion part in the evaluated optimal population as a dynamic data set, supplementing the dynamic data set into the proxy model training data set, and returning to the step of training the neural network proxy model.

In one implementation, a device receives a prompt for input to a large language model of a troubleshooting agent for a computer network. The device generates, using the troubleshooting agent, an intermediate answer by inputting the prompt to the large language model. The device identifies a hyperlink to a view associated with the intermediate answer and provided by an online application associated with administration of the computer network. The device provides the hyperlink in conjunction with the intermediate answer for display.

The invention provides a suspension bridge cable clamp digital twin system based on wireless intelligent washers, and belongs to the crossing field of bridge engineering and intelligent monitoring technologies. According to the system, a wireless intelligent gasket is used as a core sensing unit, finite element analysis, a neural network agent model and a three-dimensional visualization technology are combined, and real-time mapping of a cable clamp physical entity and a virtual model is constructed; the system obtains data such as wireless intelligent gasket pressure, cable clamp-main cable friction force, cable clamp displacement and cable clamp structure node stress and strain through finite element analysis under different working conditions, and a parameter mapping proxy model is formed by training a mapping relation between the gasket pressure and other parameters through a neural network. And relevant models and data are developed and integrated by means of an Unreal Engine engine, and visualized real-time rendering is carried out, so that bolt fastening control in a construction stage and / or structural health monitoring in an operation and maintenance stage are / is realized. According to the system, digital management and control of the full life cycle of the cable clamp can be realized, and the monitoring precision and the engineering economy are remarkably improved.

The invention belongs to the technical field of wireless communication, and discloses an Internet of Vehicles optimization method and system based on edge computing and time division multiple access, and the method comprises the steps: carrying out the joint optimization of service deployment and replacement, RSU deployment, access control, and task unloading rates in VTUs and RSUs, and achieving the optimization of the Internet of Vehicles. And efficient, low-delay and stable task processing service is provided for vehicle-mounted communication in urban traffic, expressways or disaster areas. According to the method, a double-time-scale optimization strategy is adopted, the decision of service deployment and replacement is carried out on a coarse-grained time scale (frame), and other optimization problems are processed in a fine-grained time scale (time slot). The invention provides an innovative hierarchical DRL algorithm, and in the algorithm, a high-level agent adopts a deep Q network (DQN) proxy and is responsible for optimizing a service deployment and replacement strategy in a frame; and the bottom layer intelligent agent adopts a depth deterministic policy gradient (DDPG) for proxy, and specially solves related optimization problems of task unloading rate, access control and the like in the time slot.

The invention relates to the technical field of enterprise data security exchange, and particularly discloses a data space system and a connector assembly and a data circulation method thereof. The system comprises: a data space platform providing enterprise registration, purchase and registration of a connector assembly, issuing and transaction functions of a data product, and issuing a signed electronic transaction contract; the at least two connector assemblies are deployed in the corresponding enterprise intranet and have public network access addresses, and the zero-trust gateway controls the access permission granularity of a network port of the deployment equipment based on a dynamic strategy; the network agent module receives a data request of a user, performs asymmetric encryption processing on request data, and initiates a validity verification request to the connector service module through the Socket communication interface; the connector service module performs validity verification on the validity verification request based on the transaction evidence and the electronic contract information and returns a result; the method has the advantages of solving the data island phenomenon, improving the safety protection capability and reducing the deployment operation and maintenance complexity.

The invention provides a network agent quality detection method, and relates to the technical field of network agent quality detection. According to the method, a dynamic evaluation system based on real environment sample comparison is established, the affinity of the proxy IP and the target website can be accurately quantified, key indexes such as the anti-crawling trigger rate and the DOM matching degree can be included, and a scientific basis is provided for selection and use of the proxy IP; according to dynamic weight distribution of different task types (such as data crawling and account registration), the method can intelligently recommend adaptive proxy IPs, such as automatic distribution of residence IPs and high-frequency collection of preferable machine room IPs in a strong anti-crawling scene, thereby improving the success rate and efficiency of data crawling.

The embodiment of the invention provides a multi-node network proxy system and method, electronic equipment and a storage medium, and relates to the technical field of network communication. The system comprises a plurality of entry hosts used for being connected with any proxy node; the agent nodes comprise a plurality of agent nodes of a public network and a plurality of agent nodes of at least one internal network, and are used for storing state information of adjacent nodes and generating a connection information table, so that an entrance host connected with the agent nodes performs line planning according to the connection information table. A plurality of entrance hosts are adopted to access an internal network and a network at the same time, automatic route planning is achieved according to a network topological graph, flexibility is enhanced, and the problem that an existing method cannot automatically plan a route is solved.

The invention relates to the technical field of resource cache management, and discloses a page cache-based page loading method and device, electronic equipment and system.The method comprises the steps that after it is detected that a network agent tool meets a preset operation condition, a network request is intercepted through a capture event monitor of the network agent tool, and the network request is loaded to the electronic equipment; if the network request does not meet the special processing condition, judging whether the network request contains a target resource request, and if so, executing a resource response operation according to a resource loading strategy corresponding to the target resource request contained in the network request to obtain a first resource; otherwise, loading a second resource required by the network request through the network based on a preset network loading instruction; and displaying the target resource obtained by loading on the current page. Therefore, by implementing the method and the device, different resource loading and caching strategies can be adopted for different types of page resources, adaptive page caching is realized, the loading efficiency and stability of the page resources are improved, and the page browsing experience is improved.