57 results about "Local agent" patented technology

The invention discloses a cross-regional virtual power plant cooperative scheduling method and device, a medium and a product, and relates to the field of data processing. The method comprises the following steps: acquiring real-time characteristic data such as space-time positions, output / demand prediction and the like of distributed energy resources and loads, and determining dynamic weights of characteristic dimensions based on a global optimization target and data of a current scheduling period; generating a dynamic resource cluster division instruction containing a member list and a coordination constraint condition according to the dynamic weight and the real-time data, and sending an initial cross-regional coordination scheduling instruction containing a net exchange power target value and the like and a compensation price signal to each dynamic resource cluster local agent; after aggregation response boundary information returned by the local agent is received, an instruction and a signal are updated, a target collaborative scheduling instruction is obtained and finally sent to each dynamic resource cluster for execution, and effective control over cross-regional virtual power plant resources is achieved. According to the method, the problem that the adaptability of the cross-regional virtual power plant collaborative scheduling instruction and the actual resource capacity is insufficient can be relieved.

The invention relates to the field of power distribution control, and particularly discloses a self-healing control system and method based on multi-agent collaboration, and the system comprises a regional agent and a local agent which are disposed in a power distribution network. Wherein the local intelligent agent is configured to obtain real-time operation data of a monitoring point in the power distribution network, and perform rapid fault detection on the real-time operation data; the regional intelligent agent is configured to fuse the fault analysis information of the plurality of local intelligent agents to determine fault position information when the power distribution network has a fault, generate a self-healing control strategy according to the fault position information, and send the self-healing control strategy to the local intelligent agents for execution; through cooperative work of the distributed intelligent agents, the problems of low response speed, low positioning precision, weak autonomous capability and the like in traditional power distribution network fault management are solved. In addition, the system does not need to depend on centralized control of a master station, can still operate autonomously when communication is interrupted, and improves the efficiency and reliability of fault processing through cooperation of multiple agents.

The invention provides a KCP reliable transmission proxy method and system based on the Internet of Things, and relates to the technical field of Internet of Things communication, and the method comprises the steps: transmitting Internet of Things application protocol data to a local proxy through a client; packaging the received data networking application protocol data based on an application layer rapid and reliable protocol; forwarding the packaged request data packet to a remote agent of the target device based on a transport layer protocol; analyzing the request data packet to obtain data networking application protocol data and address data in a session message header; establishing connection with a target server according to server address information and a server port number in the address data; in response to the forwarded data networking application protocol data, the target server returns response data to the remote agent; the response data are packaged and transmitted back to the local agent to be analyzed, and the response data are forwarded to the client; and closing the connection between the client and the local agent based on the received response data. The invention aims to carry out efficient and reliable data networking data proxy.

In a threat management platform, a number of endpoints log events in an event data recorder. A local agent filters this data and feeds a filtered data stream to a central threat management facility. The central threat management facility can locally or globally tune filtering by local agents based on the current data stream, and can query local event data recorders for additional information where necessary or helpful in threat detection or forensic analysis. The central threat management facility also stores and deploys a number of security tools such as a web-based user interface supported by machine learning models to identify potential threats requiring human intervention and other models to provide human-readable context for evaluating potential threats.

The invention relates to a multi-microwave-source combined heating method for intelligent agent collaborative optimization under composite target consensus decision, and belongs to the technical field of microwave heating. The method comprises the following steps: constructing a simulation platform of a microwave heating environment; setting an optimization target and a constraint condition; initializing the intelligent agent; establishing a conflict detection mechanism, and designing a DDPG algorithm corresponding to execution actions of the global agent and the local agent according to the master target and the slave target; according to a conflict detection mechanism, the global agent and the local agent are divided into a non-conflict state and a conflict state in the process of executing the target task; by dynamically adjusting the actions of the global intelligent agent and the local intelligent agent, action sets of different intelligent agents are formed, intelligent agent network training is carried out, and the training effect is improved through an experience pool capable of dynamically reconstructing the intelligent agents. According to the invention, multi-target collaborative optimization and conflict resolution in the microwave heating process are realized, and the microwave heating performance is effectively improved.

The invention provides a multi-type micro-grid cross-layer collaborative scheduling method based on multi-agent reinforcement learning, and relates to the technical field of power system and micro-grid scheduling, and the method comprises the steps: firstly constructing a multi-type micro-grid layered collaborative architecture, a multi-agent reinforcement learning scheduling model, and a centralized coordination layer where global agents are deployed in a platform layer; and the local agents are deployed at each distributed micro-grid node of the platform layer, a cross-layer cooperative training mode is adopted to train the model, and finally, cooperative decision results of the global agents and the local agents are integrated to generate a global-local cooperative scheduling scheme. According to the method, global optimization and local flexibility can be considered, and the hierarchical multi-agent architecture adopts a multi-type micro-grid hierarchical collaborative architecture, so that the method can be suitable for multi-type micro-grid operation scheduling of a novel power system, and multi-type micro-grid collaborative scheduling considering global optimization and local flexibility is realized.

The invention provides a service debugging method and device, electronic equipment and a computer readable storage medium, and relates to the technical field of service debugging. Initiating a debugging request for a target service instance based on a local agent, and obtaining a short-term security credential by the local agent; wherein the short-term security credential comprises the target service instance identifier; establishing, by the local agent, an encrypted communication channel with a remote agent using the short-term security credential; the remote agent extracts the target service instance identifier from the short-term security credential and obtains a real-time network address of the target service instance; and sending, by the remote agent, the received debugging traffic to the real-time network address of the target service instance. Identity authentication and authorization are realized through a short-term security certificate, and security and credibility of a debugging request are ensured; the security risk in the debugging process is reduced, and the flexibility and efficiency of cross-network environment debugging are enhanced.

The invention discloses a one-way off-line security intelligent decision center system with local deployment as a main and external network service as an auxiliary and a control method, and belongs to the technical field of artificial intelligence security and edge intelligent decision. A three-level security domain physical and logic dual isolation architecture is adopted, extranet multi-model intelligent security enablement is performed on a local core decision domain through a physical one-way import channel, the local core decision domain completes reasoning calculation in combination with an enablement result in a non-extranet interaction state, a top-layer task scheme is generated, and a top-layer task scheme is generated. Through a multi-mode man-machine interaction mode, after human rigid authorization, issuing to an intranet equipment scheduling domain for execution, and feeding back a state in real time to form a full-link closed loop; the method realizes external network multi-model intelligent security enabling, local agent professional decision and rigid closed loop dominated by human beings in the whole process, aims to solve the problems of cloud dependence, data leakage, out-of-control AI, high cost, poor adaptability and the like, and is suitable for families, industries, confidential scenes, field non-support operation and other scenes.

The invention discloses a cloud and edge integrated industrial agent distributed task execution method and system. The method comprises the following steps: constructing a global agent at a cloud end, and deploying a local agent at an edge device; the global agent receives the production task and decomposes the production task into sub-tasks; sub-tasks are dynamically allocated based on the real-time state and capability of each edge device; the local agent executes the distributed subtasks and keeps state synchronization with the global agent; and establishing a cloud and edge collaborative learning mechanism. The system comprises a task analysis module, a resource sensing module, a dynamic allocation module, a collaborative execution module and an incremental learning module. According to the method, the problems of insufficient collaboration of cloud computing and edge computing and rigid task allocation in a traditional industrial system are solved, and intelligent collaboration of cloud edge resources and efficient execution of tasks are realized.

The invention discloses an implementation method of a security Agent system oriented to an operating system. The security Agent system comprises a local Agent and a security knowledge base. The local Agent has a multi-task learning help model; the implementation method comprises the following steps: S1, initializing a local Agent, and mounting a safety knowledge base; s2, loading a multi-task learning help model by the local Agent; s3, sensing and monitoring resources of the operating system by the local Agent, and acquiring resource data of the operating system; s4, the local Agent performs preliminary analysis on the security condition of the operating system based on the resource data, and judges whether a threat event exists or not; if the threat event exists, further analyzing to obtain an analysis result of the threat event; and S5, the local Agent helps the model to generate and adjust a security policy through multi-task learning based on an analysis result of the threat event and a load condition of an operating system.

This invention relates to the fields of artificial intelligence, multi-agent collaborative scheduling, and federated security management. Specifically, it is a federated capability management and unified scheduling system for multi-agent collaboration, comprising: a central scheduler receiving collaborative task requests across multiple local agent nodes and obtaining capability declaration information containing only the capability tags of the local agent nodes; constructing prompt word templates and generating an initial fine-grained task decomposition scheme; extracting cross-domain call links, statistically analyzing the frequency of cross-domain calls and data interaction dimensions, predicting the communication interruption probability of local agent nodes and comparing it with a security risk threshold; if the probability is lower than the threshold, a fine-grained scheme is adopted; if the probability is higher than or equal to the threshold, a minimum-privilege interaction coarse-grained scheduling scheme is generated and distributed; and local agent nodes executing sub-tasks and reporting their status to maintain core business continuity without exposing underlying private data.

The invention relates to the field of industrial automation control, and provides a remote equipment control method, an edge controller and a building automatic control system. The method is applied to a remote control agent module of an edge controller, and comprises the following steps: if it is determined that remote equipment controlled by the edge controller comprises a to-be-configured physical point location, locally creating a local agent point location corresponding to the to-be-configured physical point location; related configuration information of the to-be-configured physical point location is written into the local agent point location to establish a mapping relation between the local agent point location and the to-be-configured physical point location; and performing remote monitoring on the to-be-configured physical point location of the remote equipment based on the mapping relation. According to the invention, the number of point locations which can be supported to be monitored by the edge controller can be obviously increased, network and bus communication load balancing of the building automatic control system can be realized, centralized control of massive physical point locations on the edge side can be realized, and reasonable and effective utilization of edge side resources can be realized.

Embodiments of the present application relate to the technical field of artificial intelligence, and provide a communication method, apparatus, and system. The method comprises: when it is determined that a first subnet has been established, an agent instance corresponding to a first terminal determining a first user plane network element for the first terminal and a second terminal to establish the first subnet; then sending a first request message to an agent instance corresponding to the second terminal, the first request message being used for deciding to create a local agent instance corresponding to the second terminal on a first artificial intelligence proxy function network element; and finally sending a first rule to the first user plane network element to instruct the first user plane network element to send, to the first artificial intelligence proxy function network element, data requiring multimodal conversion in the first subnet. On the basis of the first rule, the local agent instance corresponding to the second terminal enables the first terminal and the second terminal communicating with each other without relying on a same protocol to implement conversion between different protocols and different modalities between terminals by means of the local agent instance corresponding to the second terminal, thereby implementing cross-ecological interconnection of the terminals.

A wireless communication device is disclosed that includes a non-transitory memory, a processor, and a local agent. The local agent transmits context information to an AI engine and receives and stores sign language data sets selected based on the context information from the AI engine in the non-transitory memory. The local agent receives content in need of language assistance. In response to a determination to process the content locally, the local agent converts the content into first sign language gestures based on the data sets. In response to a determination to process the content remotely, the local agent sends at least some of the content to the AI engine, receives keys from the AI engine, and determines second sign language gestures based on the keys and the data sets. The local agent sends the first or second sign language gestures to a display of the wireless communication device for presentation.

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 network local connectivity demand prediction method (100), comprises:a step (105) of long-term local connectivity demand prediction, by a machine learning model operated by a local agent,in a duration equivalent to the duration of the long-term prediction, more than one short-term iteration of:a step (110) of aggregating values representative of, at least:a connectivity demand of the network comprising at least a physical network device,a purpose of use of the network comprising at least a physical network device,a local long-term connectivity demand prediction for the physical network device,a step (115) of training a machine learning device to produce a machine learning model to provide a long-term local connectivity demand prediction, anda step (120) of deploying the machine learning model, anda step (125) of updating the long-term local connectivity demand prediction.

The embodiment of the invention provides a communication method, device and system, and relates to the technical field of artificial intelligence. The method comprises the following steps: after an agent instance corresponding to a first terminal determines to establish a first sub-network, determining a first user plane network element used for establishing the first sub-network by the first terminal and a second terminal; and sending a first request message to the agent instance corresponding to the second terminal, wherein the first request message is used for deciding to create a local agent instance corresponding to the second terminal on the first artificial intelligence proxy function network element. And finally, a first rule is sent to the first user plane network element, and the first user plane network element is indicated to send data needing multi-mode conversion in the first subnet to the first artificial intelligence proxy function network element. According to the embodiment of the invention, the local agent instance corresponding to the second terminal can realize the conversion of different protocols and different modes between the terminals through the local agent instance corresponding to the second terminal on the basis of the first rule under the condition that the first terminal and the second terminal do not depend on the same protocol for communication, so that the cross-ecological interconnection of the terminals is realized.

The application discloses a kind of dynamic load balancing management methods, equipment and medium based on AI intelligent agent, method includes: calculating node correlation coefficient, constructs GCN-GRU hybrid load prediction model, predicts the space-time feature matrix of future node, obtains the predicted node load data;Calculate the normalization weight of each load data, load comprehensive value and load balancing degree;Local agent, coordination agent are deployed, and local agent state space vector is constructed;The mapping relationship of local policy network is constructed using local agent state space vector, and the action space vector and migration amount of node execution load migration action are generated, global value is calculated, and the parameters of local policy network are optimized based on global value, and the optimal action space vector and corresponding migration amount are generated to carry out load balancing migration.Computer equipment includes processor and memory;Computer readable storage medium stores at least one instruction, at least one program, code set or instruction set.

The application discloses a kind of cross-regional virtual power plant collaborative scheduling method, equipment, medium and product, it is related to data processing field.The method includes collecting the real-time characteristic data such as time-space position, output / demand prediction of each distributed energy resource and load, determines each characteristic dimension dynamic weight based on the global optimization target and data of current scheduling period;According to dynamic weight and real-time data, generate dynamic resource cluster division instruction containing member list and collaborative constraint condition, and send initial cross-regional collaborative scheduling instruction containing net exchange power target value and the like and compensation price signal to each dynamic resource cluster local agent;After receiving the aggregated response boundary information returned by local agent, update instruction and signal, obtain target collaborative scheduling instruction, finally sent to each dynamic resource cluster execution, realize the effective control of cross-regional virtual power plant resource.The application can alleviate the problem that the adaptability of cross-regional virtual power plant collaborative scheduling instruction and actual resource capacity is not enough.

A system for networking and computing is provided herein which can include one or more processors and a memory storing instructions that cause the system to receive a request for a SFC in a network having a plurality of data centers arranged in clusters, wherein the SFC includes one or more VNFs located in the data centers in which packets traverse for various services, provide details of the request to one or more local agents each covering a cluster, and is configured to determine provisioning aspects of the SFC in the corresponding cluster, and receive the provisioning aspects for the SFC from the one or more local agents.

Disclosed in the present invention are a Co-DMPC-based cooperative control method for an autonomous vehicle chassis multi-agent system, a controller, and a storage medium. In the present invention, a distributed state space equation having state coupling and control input coupling features is established, reflecting the mutual coupling between multiple agents, and taking into comprehensive account the influence of states and control inputs of neighbor agents, thus improving the accuracy of prediction of a future state sequence by a local agent; and the meanings of predicted trajectories, assumed trajectories and optimal trajectories of a state and a control input and conversion modes thereof are designed, thus providing a communication basis for information interaction between the agents. In order to coordinate global performance indices of a vehicle, a local agent optimization problem that accounts for cost coupling is established, and the influence of a cooperative relationship on the control effect is quantitatively analyzed by means of adaptive weight coefficients. Moreover, in the present invention, a method is used that involves performing multiple instances of iterative solving per unit sampling time, and an iterative error is used to balance the solution accuracy and solution efficiency of a controller.

The application provides a kind of multi-region integrated energy system optimization scheduling method and system, the method is each regional integrated energy system by corresponding local agent management, local agent is based on the idea of reinforcement learning to preset energy optimization scheduling model training obtains local optimization model, and according to the neighbor agent's encryption parameter data packet and the double random matrix based on the topology relationship between integrated energy systems is updated to local optimization model, and after the local optimization model of each local agent is updated, the overall convergence of all local optimization models is evaluated and the collaborative training between local agents is stopped when the preset optimization target is reached, and the local optimization model of each local agent is executed according to the corresponding regional integrated energy system optimization scheduling.The application not only can effectively avoid the risk of data privacy leakage of each regional integrated energy system, but also can improve the real-time performance and reliability of multi-region integrated energy system optimization scheduling.

The invention discloses a multi-agent system and a deep reinforcement learning load balancing method thereof, which reduce the complexity of agent selection action and can more easily achieve the optimal performance on the whole. According to the method, the request distribution is divided into two stages, the central agent selects the data center in the first stage, the local agent selects the node in the second stage, and therefore the problem that the multi-agent environment is unstable is avoided. In addition, request distribution of each agent is modeled into a Markov decision process, and definition of a state space, an action space, a reward function and state transition is described in detail. In the aspect of a network structure, a decision network is adopted, and compared with a single-flow Q network, the decision network has the advantage of being faster and better in learning. In the aspect of exploration and utilization, an improved load balancing-oriented action exploration strategy is used. Finally, a pseudo code and a learning process chart of the algorithm are given, and the training process of the algorithm is explained.

The invention discloses a network slicing management method based on a large language model and a related device, and relates to the technical field of network slicing, and the method comprises the steps: calling a global agent to receive a network slicing demand in a natural language format, and obtaining a demand intention; calling a global agent to interact with the large language model based on the demand intention to obtain a slicing request; calling a global agent to obtain a resource state in a current network; calling a large language model to determine a global resource allocation strategy based on the slicing request and a resource state in a current network, obtaining a resource allocation strategy of each sub-region, and obtaining a region slicing request of each sub-region; calling a local agent to interact with the large language model based on the region slicing request of each sub-region to obtain an operation instruction of network slicing operation performed by each sub-region; and executing the operation instruction to perform network slicing operation on each sub-region. The purpose of improving the management efficiency of the network slices is achieved based on the large language model.

The present invention discloses a hierarchical formation control method for a linear multi-agent system, comprising the following steps: establishing a state-space equation for the multi-agent system to construct a disturbance system; decomposing the multi-agent formation instructions into a global anchor point instruction and a local deviation instruction; defining a multi-agent error tracking strategy; establishing a communication network between the multi-agent system and the global anchor point system using a directed graph; utilizing neighboring information in the communication network to establish a distributed observer to enable each agent to estimate the global anchor point system; utilizing the estimated information from the distributed observer to establish a dynamic gain compensator to obtain an asymptotic solution to a regulator equation associated with the global anchor point system; solving the regulator equation associated with the disturbance system and the local deviation system; and combining the solutions to the regulator equation to establish a collaborative output regulation controller. The present invention reduces the amount of global formation instruction data transmitted, reduces the computational load of local agents, and improves system operational efficiency and stability.

The invention provides an engine system based on integrated privacy federated computing capability, which comprises the following steps that: a federated computing coordinator receives a joint computing task, splits the joint computing task into a plurality of sub-queries, and issues the sub-queries to a deployed local agent process; the local agent process executes the sub-query to obtain a local intermediate result, performs secrecy processing on the local intermediate result based on the privacy computing protocol to obtain a secrecy intermediate result, and sends the secrecy intermediate result to the federal computing coordinator; the federal computing coordinator adopts a federal aggregation algorithm to aggregate the secret intermediate result to obtain a global computing result, and sends the global computing result to a version control system; the block chain module obtains aggregation operation information, generates an operation abstract based on the aggregation operation information and writes the operation abstract into a block chain account book; and the version control system generates new version data according to a global calculation result, and records a data version evolution path based on a content addressing storage structure and a copy-on-write mechanism. According to the method and the device, privacy protection cooperative calculation of distributed spatio-temporal data can be realized, and centralized processing risks are avoided.

In a threat management platform, a number of endpoints log events in an event data recorder. A local agent filters this data and feeds a filtered data stream to a central threat management facility. The central threat management facility can locally or globally tune filtering by local agents based on the current data stream, and can query local event data recorders for additional information where necessary or helpful in threat detection or forensic analysis. The central threat management facility also stores and deploys a number of security tools such as a web-based user interface supported by machine learning models to identify potential threats requiring human intervention and other models to provide human-readable context for evaluating potential threats.

A computer-implemented method comprising: receiving an electronic message at an endpoint device associated with an organization; analyzing the electronic message by a local agent executed on the endpoint device; classifying the electronic message into one of a plurality of trust levels by the local agent; generating, by the local agent, a real-time alert or action at the endpoint device based on the classification; transmitting, by the local agent, metadata associated with the electronic message and its classification to a centralized machine learning engine; and updating, by the centralized machine learning engine, one or more machine learning models based on aggregated metadata received from a plurality of endpoint devices, wherein the one or more updated machine learning models are used to improve subsequent classification of electronic messages at the endpoint devices.

The invention discloses a smart power grid adaptive control system and method, and relates to the technical field of smart power grid operation control, and the system comprises a physical sensing layer, an edge calculation layer, a cloud super-simulation digital twin platform and a communication network. According to the method, through a central coordination agent in a digital twinborn platform, a multi-agent reinforcement learning algorithm is adopted to carry out cooperative training on a local agent. According to the method, a super-simulation digital twinborn model integrating geometry, physics, behaviors and rules in multiple dimensions is constructed, and through a training normal form of deep fusion of a physical information neural network and a generative adversarial network, the model has both a physical rule following capability and an uncertainty deduction capability; and safe, rapid and adaptive optimization of power grid control in a large-scale and uncertain environment is realized.

Some embodiments provide a local controller on a set of host computers that reduce the volume of data that is communicated between the server set and the set of host computers. The local controller executing on a particular host computer, in some embodiments, receives a portion of the namespace including only the policies (e.g., opcode) that are relevant to API-authorization processing for the applications executing on the particular host computer provided by a local agent executing on the computer to authorize the API requests based on policies and parameters. The local controller analyzes the received policies (e.g., policy opcodes) and identifies the parameters (e.g. operands), or parameter types, needed for API-authorization processing (e.g., evaluating the policy opcode upon receiving a particular API request) by the local agent. In some embodiments, the local controller performs this analysis for each updated set of policies (e.g., policy opcodes).