111 results about "Network agent" patented technology

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 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 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.

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.

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.

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.

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 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 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.

The present application relates to a kind of shield tunnel irregular load inversion method based on neural network agent model, belong to shield tunnel load inversion field.The method, discard the assumption of load distribution mode in traditional lining load inversion method, inversion result can reveal the irregular load distribution mode of on-site tunnel, provide technical support for the service safety of tunnel service;Inversion is based on the deformation of lining whole circumference obtained by three-dimensional laser scanning technology, significantly increase the amount of field data, with the increase of n, the dimension of deformation vector can be increased sufficiently, avoid the defect that traditional inversion method easily leads to the non-uniqueness of inversion result.

The application discloses a power distribution area collaborative planning method and system based on a neural network agent model, and the method comprises the following steps: constructing a double-layer optimization model of flexible interconnection and energy storage collaborative planning of a power distribution area; randomly generating a candidate site and capacity scheme satisfying the constraint conditions of the double-layer optimization model, and performing optimization calculation of a running model on each candidate site and capacity scheme to obtain a real total cost of the whole life cycle corresponding to each candidate scheme, thereby forming a training sample set; performing supervised training on a preset neural network agent model; generating and iteratively updating a site and capacity scheme, evaluating the predicted total cost of the site and capacity scheme by using the neural network agent model, optimizing the site and capacity scheme according to the predicted total cost, and outputting a final target site and capacity scheme and a corresponding collaborative running strategy until a convergence condition is satisfied. The method effectively solves the problems of disconnection of 'planning-construction-operation', low calculation efficiency and the like in traditional planning, and realizes collaborative improvement of safety and solution efficiency.

The present invention relates to a method for reconfiguring the topology of a drone cluster for emergency rescue missions, belonging to the field of drone communication technology. The method solves the problem of low communication efficiency after existing topology reconstruction. The method includes constructing and training a topology reconstruction network, which includes a global hybrid network and local hybrid networks and proxy networks corresponding to each drone. The proxy network receives the local state information of the corresponding drone at each moment and outputs the action function value of the action at each moment. The local hybrid network receives the action function value of the corresponding drone and outputs a local joint action value. The global hybrid network receives the local joint action values ​​of all drones and outputs a cluster joint action value. When a drone fails, the local state information and the previous action of each undamaged drone are obtained and transmitted to the corresponding trained proxy network. The corresponding action is selected according to the ε-greedy strategy to perform topology reconstruction. This achieves topology reconstruction with high communication efficiency.

The invention provides an agent model construction method, an automobile thermal management method and a related device, and the agent model construction method constructs a digital model of a to-be-simulated device, and carries out the calibration of the digital model through the pre-collected experimental data, and obtains a target digital model of the to-be-simulated device; acquiring simulation operation data of the to-be-simulated device by using the target digital model of the to-be-simulated device, and gathering the simulation operation data into a training data set; and training a pre-constructed neural network model by using the training data set to obtain a proxy model corresponding to the to-be-simulated device. By adopting the technical scheme of the invention, the neural network agent model of parts or systems related to automobile thermal management can be constructed, the simulation calculation load is reduced, and quick response is realized, so that the simulation efficiency of thermal management is improved, the development efficiency of a thermal management system is improved, and the efficiency of automobile thermal management is further improved.

The invention discloses a network connection method and device, terminal equipment and a storage medium, the method is applied to the terminal equipment, the terminal equipment is provided with a first operating system and a second operating system, and the first operating system is in communication connection with the second operating system. The method comprises the following steps: under the condition that a second operating system establishes wireless network connection with target network equipment through a wireless module, a first operating system sends a network proxy request to the second operating system; the second operating system establishes network proxy connection between the second operating system and the first operating system according to the network proxy request; and the first operating system is connected through the network agent and performs wireless data transmission with the target network equipment through the second operating system. According to the embodiment of the invention, different operating systems of the terminal equipment can be jointly connected with the network through one set of wireless module, the risk of network connection interruption can be reduced, and the stability and reliability of network connection of the terminal equipment are improved.

The invention relates to the technical field of communication, and provides a group communication-oriented fixed-mobile network intercommunication method, device and equipment. The method comprises the following steps: a fixed network node establishes a first session with an intercommunication user plane node through a network agent module independently serving different groups, and an IP address field used by the agent module allows cross-group overlapping; the control plane node completes first session establishment and updates a network connection identifier replacement strategy; when the mobile terminals in the same group access the fixed network, the target IP is detected to fall into the overlapped address field, and a second session taking the intercommunication user plane node as an anchor point is triggered to be established; and the intercommunication user plane node establishes data forwarding association of the second session and the first session according to the matching relationship between the group and the IP, so that the mobile terminal directly accesses the fixed network terminal through a path formed by the third session, the second session and the first session initiated by the mobile terminal. According to the invention, through a centralized proxy, address multiplexing and session association mechanism, fixed-mobile intercommunication in an operator network is realized, and the resource efficiency and deployment flexibility are improved.

This invention discloses a rapid stress field calculation method based on a graph network proxy model. First, a simulation model is created and a training dataset is constructed. Then, mesh information is exported. A graph neural network method based on message aggregation, propagation, and updating is employed, introducing parameters such as unit centroid and volume as node features, and area as edge features to complete the graph construction. Compared with traditional proxy model creation methods, this method fully considers mesh information and improves calculation accuracy. By introducing global features and utilizing external environmental parameters or partial measurement point stress values, rapid and real-time calculation of the structural stress field can be achieved, reducing sensor deployment requirements and hardware costs. This method not only accurately predicts the stress distribution of the structure but also improves calculation efficiency.

The application discloses a security hierarchical isolation system and method and electronic equipment, and relates to the technical field of artificial intelligence security, which comprises the following steps: when a system operation request is received, a system agent module determines the operation authority of an intelligent agent and / or the file layer corresponding to the operation authority, and then executes corresponding actions according to the operation authority and / or executes modification or writing actions in the file layer; when a network calling request is received, a network agent module identifies a request object, the level of the request object and the real credentials of a target external system, generates first pseudo credentials corresponding to the real credentials and an available range according to the level, and sends the first pseudo credentials to the request object, so that when a calling request initiated by the request object through the first pseudo credentials is received, the target external system is accessed by proxy according to the real credentials queried by the first pseudo credentials, thereby solving the problems of uncontrollable intelligent agent behavior and easy leakage of credentials, and realizing the consideration of security and controllability based on security isolation strategies under different situations.

The invention discloses a multi-modal data driven complex product installation and adjustment feature and key event cognition method and system. The method comprises the following steps: firstly, synchronously collecting and performing time sequence alignment on vision, point cloud, force sense and motion data; extracting geometric invariant features decoupled from the visual angle through a dynamic graph convolutional neural network, and generating mechanical residual time sequence features based on a physical information neural network agent model; then, improving the heterogeneous features into structured scene semantic representation by using a multi-modal fusion network and an installation and adjustment domain knowledge graph; and finally, through a vertical domain large language model integrated with a temperature scaling probability calibration mechanism, real-time identification and quantitative confidence evaluation of installation and adjustment events such as stable slippage and abnormal clamping stagnation are realized. According to the method, the robustness of state perception, the physical interpretability of event identification and the credibility of decision making in the installation and adjustment process are effectively improved.

The invention relates to a network element service recovery method and device, computer equipment, a readable storage medium and a program product. The method comprises the following steps: simulating a current network element network state in a virtual isolation environment, deploying a network element network agent, simulating a service fault through the network element network agent, obtaining a candidate switching network element, obtaining an evaluation parameter of the candidate switching network element under the condition that the current network element network state does not meet an emergency risk condition, performing comprehensive calculation on the evaluation parameters through an entropy weight method to obtain a comprehensive evaluation value of the candidate switching network element, inputting the comprehensive evaluation value, the current network element network state, the network topology and the service grading label into a pre-trained AI model to obtain a grading switching strategy, and simulating the grading switching strategy through a network element network agent to obtain a network element network state. And under the condition that the simulation result meets the network state condition, executing a hierarchical switching strategy, and recovering the service of the current damaged network element. By adopting the method, the service load capacity can be improved.

The invention relates to a route planning method and device based on network intelligent agents, a medium and equipment, and relates to the technical field of computer networks, and the method comprises the steps that a state management layer constructs a distributed network topological graph according to the network intelligent agents and the connection relation between the network intelligent agents; wherein the network agent comprises at least one of a local agent, a regional agent and a global agent; the MCP coordination layer determines a network state vector according to the distributed network topological graph; the routing decision layer determines candidate path delay, candidate path congestion cost, candidate path packet loss rate and candidate link utilization rate of the candidate routing path according to the network state vector; and the routing decision layer determines a target routing path from the candidate routing paths according to the candidate path delay, the candidate path congestion cost, the candidate path packet loss rate and the candidate link utilization rate. The path planning efficiency is improved.

The invention discloses a hull motion digital twinning modeling method and system based on a time sequence circulation framework physical information neural network, and the method comprises the steps: building a neural network agent model which employs a framework combining a long-short-term memory network with a physical information neural network; comprising a time sequence feature extraction branch and a spatial feature coding branch, physical constraints are introduced, displacement of large-scale nodes does not need to serve as samples, and only a small amount of samples are needed to guide a model to learn a mechanical law; through the time sequence feature extraction branch, the high-precision and high-resolution dynamic deformation cloud picture of the whole structure can be generated only by relying on the input of a very few sensors. The invention provides a set of complete and landing technical scheme for realizing a low-cost and high-efficiency hull motion digital twin system.

Devices, systems, and techniques for mapping tokens generated by neural networks to application programming interfaces (APIs). In at least one embodiment, a processor includes one or more circuits that cause one or more tables to map one or more tokens generated by one or more neural networks to one or more tokens that are to be used by one or more APIs.

The application discloses a hull motion digital twin modeling method and system based on a timing cycle framework physical information neural network, wherein a neural network agent model is built, the neural network agent model adopts a framework of a long short-term memory network combined with a physical information neural network, includes a time sequence feature extraction branch and a space feature coding branch, by introducing physical constraints, without taking large-scale node displacement as samples, only a small amount of sampling is needed to guide the model to learn the mechanical law, and through the time sequence feature extraction branch, high-precision and high-resolution dynamic deformation cloud maps of the whole structure can be generated only by relying on the input of a small number of sensors. The application provides a complete and applicable technical scheme for realizing a low-cost and high-efficiency hull motion digital twin system.

A system performs operations of a neural network agent and a circuit simulator for simulating circuit sizing. The system receives input indicative of a specification and design parameters of a simulated circuit. The system iteratively searches a design space until a circuit size is found that satisfies the specification and the design parameters. In each iteration, the neural network agent computes measurement estimates for random samples generated in a trust region, which is a portion of the design space. Based on the measurement estimates, the system identifies a candidate size corresponding to an optimized value indicator. The circuit simulator receives the candidate size and generates a simulation measurement value. The system computes updates to weights of the neural network agent and the trust region based at least in part on the simulation measurement value for a next iteration.