337 results about "Distributed intelligence" patented technology

A distributed intelligent authentication method based on dynamic multi-modal fusion relates to the field of network security, and adopts an alliance chain + DAG hybrid block chain architecture, combines a threshold signature to realize secret key fragment management, and switches among PBFT, Raft and probabilistic algorithms through a dynamic consensus mechanism to improve authentication efficiency. The multi-mode authentication module is based on a dynamic weight distribution algorithm, integrates biological characteristics, behavior analysis, equipment fingerprints and environmental factors, and combines an LSTM-GAN model and a quantum random number driven challenge-response mechanism to realize zero-trust verification under environmental perception. The session management module generates a session key by using a chaotic mapping algorithm. In the aspect of privacy protection, CKKS homomorphic encryption, zero-knowledge proof and attribute-based encryption are fused. According to the method, the block chain technology, the secure multi-party computing technology, the machine learning technology and the quantum cryptography technology are fused, and a high-performance, high-security and strong-privacy-protection distributed authentication solution is provided.

The invention discloses a seabed tracked robot path planning method and system based on reinforcement learning, and the method comprises the steps: obtaining environment data in real time through multiple sensors, extracting submarine topography three-dimensional features, obstacle distribution and ocean current dynamic parameters in combination with a neural network, and carrying out the combined feature extraction; establishing a seabed environment simulation model, and synthesizing various landform training data by using an adversarial generation technology; a hierarchical reinforcement learning framework is designed, a global layer collaboratively optimizes a long-distance path through a distributed agent, a local layer designs a high-frequency control strategy, and the global and local strategies realize multi-dimensional collaborative optimization through a dynamic weight adjustment mechanism; model parameters in a dynamic environment are updated in real time through an online optimization module, and a simulation strategy is quickly deployed to an entity robot through transfer learning. According to the invention, a neural network feature extraction and multi-level reinforcement learning collaborative autonomous decision-making system is constructed, and a high-reliability and low-energy-consumption autonomous operation solution is provided for a deep sea operation scene.

The invention relates to the technical field of traffic control, in particular to a traffic signal cooperative control method based on multi-agent reinforcement learning, and the method comprises the following steps: modeling each signal lamp intersection in a road network as an agent, and constructing a distributed multi-agent control environment; dividing the whole road network into a plurality of small subnets according to spatial correlation, and sharing and aggregating traffic information in the subnets through a neighborhood information sharing mechanism; utilizing a space-time diagram attention network to extract traffic state characteristics including a space-time dependency relationship in an intersection agent and a neighborhood thereof; and defining a traffic state, a traffic signal control strategy, a reward and punishment function, a network architecture and a target function required by training of the distributed intelligent agent, and carrying out joint training on the distributed intelligent agent until a training target is achieved. According to the invention, the real-time sensing capability of the signal control intelligent agent to the traffic flow dynamic state can be improved, and the collaborative decision-making capability among multiple intelligent agents is enhanced.

The invention provides a network attack detection method and system based on a distributed intelligent probe, and the method comprises the steps: receiving real-time flow data synchronously collected by the distributed intelligent probe at each node of a network, carrying out the inter-node interaction relation modeling processing of the real-time flow data, recognizing a flow communication mode between different network nodes, and carrying out the detection of the network attack. Generating a flow association map containing the node connection relationship and the communication frequency; carrying out abnormal communication path mining based on the flow association map, and extracting a node communication sequence with an abnormal mode by analyzing the deviation degree of a node connection relationship and the fluctuation characteristics of communication frequency; performing pattern matching processing on the node communication sequence and an attack behavior template in a preset attack feature library, calculating sequence matching similarity and generating an attack matching degree score set; and determining a network attack type and attack source node positioning information, and generating a network attack detection result. According to the invention, the practicability and effectiveness of network attack detection are improved.

The invention discloses a multi-dimensional computing power dynamic perception routing decision-making method and system based on SRv6 driving. The method comprises the following steps: firstly, screening an optimal target computing power node based on a service demand and a node real-time load; and then, based on the dynamic network topological graph fusing the service sensitivity, calculating an optimal network path reaching the computing power node by using an improved SPFA algorithm, and realizing a global collaborative decision of selecting the most suitable computing point and searching the most efficient connection path. Meanwhile, based on an SRv6 driving service chain dynamic generation and closed loop execution method, a target computing power node and necessary network functions are abstracted into a programmable SID, an SID sequence (service chain) is dynamically constructed according to a double-stage decision result and is packaged in an SRH head, second-level path issuing and state monitoring are achieved through a programmable controller, and a real-time monitoring result is obtained. According to the method, the problems of poor real-time performance, single dimension, weak cooperative capability and the like in the prior art are solved, and efficient, stable and intelligent development of a future-oriented distributed intelligent computing network system can be promoted.

The invention discloses a building quality inspection management system, and the system comprises a distributed intelligent sensor network which comprises a multi-type sensor array disposed at a key node of a building structure, a mobile detection terminal, and an unmanned plane inspection unit; the multi-modal data fusion module is used for performing space-time alignment and feature extraction on the sensor data, the image data and the BIM model data; the digital twinborn visual platform comprises a quality evaluation engine, a defect traceability module and a three-dimensional visual interface; the defect detection rate in a dark light environment is obviously improved, full-coverage detection of floors can be realized in a short time, field sensor data and a BIM model are synchronized in real time through a 5G network, and when a quality problem is found, the system analyzes 12 types of influence factors such as materials, processes and environments through a Bayesian network, so that the quality problem is solved. The system can trace back to specific construction teams, equipment numbers and even meteorological data, and can reduce the requirements of personnel through intelligent detection, thereby reducing the working cost.

The invention provides a distributed intelligent cooperative energy-saving control method and device for an industrial air conditioner, electronic equipment and a computer readable storage medium, and is applied to the field of data processing application. The method comprises the following steps: acquiring industrial air conditioner operation related data including environment parameters and equipment state data; an intelligent cooperative control mechanism is constructed based on multi-device cooperation and hierarchical adjustment requirements, and the intelligent cooperative control mechanism comprises a multi-device linkage strategy for dynamically distributing refrigeration resources according to regional cooling and heating loads and a hierarchical adjustment algorithm of a chilled water system and a cooling system, and a control framework for achieving global optimization is formed; processing the environment parameters and the equipment state data to generate a dynamic control strategy; generating target energy-saving control information based on the data analysis result and the dynamic control strategy; and all operation data and control effects generated based on the target energy-saving control information are integrated, a high-energy-consumption link optimization scheme is generated, energy consumption data credible tracing is achieved, and the distributed air conditioning system is aggregated to participate in power grid frequency modulation.

The invention relates to the technical field of distributed agent collaboration and graph database query optimization, in particular to a graph query processing method and system based on multi-agent collaboration, and the method comprises the following steps: reasoning a coordination agent to analyze the semantics and intention of user query by using a large language model; a structured multi-agent execution plan is output, and an entity search agent or a relation analysis agent needing to be called is explicitly called; the agent coordinator receives the multi-agent execution plan and selects agents according to task types and task allocation; obtaining a distributed intermediate result set; starting a result aggregation mechanism, and performing typed processing, priority ranking and deduplication merging on the distributed intermediate result set to obtain an arrangement result; and outputting an answer for answering the initial question of the user. The system comprises an agent execution plan output module, a multi-task execution module and an arrangement result reasoning module. According to the method, efficient, extensible and fault-tolerant graph query processing is realized.

The invention discloses a protocol analysis method and device based on multi-agent cooperation and a medium, and belongs to the technical field of industrial internet. The method comprises the following steps: constructing and associating a distributed agent cluster and a global protocol knowledge base; preprocessing the original Internet of Things protocol based on the analysis agent to generate a protocol analysis task packet; processing the protocol analysis task packet based on the scheduling agent so as to distribute the protocol analysis task packet to the target analysis agent; performing feature extraction and protocol analysis on the analysis task packet through an analysis agent, and outputting structured analysis data; if the unknown protocol format is recognized, feature extraction is carried out on the unknown protocol format and the unknown protocol format is sent to the management agent; and processing the unknown feature vector through the management agent to determine a new protocol analysis rule of an unknown protocol format, and synchronizing the new protocol analysis rule to the global protocol knowledge base. According to the method, the technical effect of efficient dynamic analysis adaptation of the industrial Internet of Things protocol is achieved.

The invention relates to the technical field of underground pipe gallery distributed intelligent control, and discloses an underground pipe gallery intelligent illumination and energy consumption optimization system, which comprises a dynamic credibility evaluation module for analyzing multi-modal environment sensing data in real time and generating a dynamic credibility score; the edge-cloud collaborative optimization module is used for realizing distributed collaborative learning through local model fine tuning and global knowledge distillation; and the event-driven resource allocation module dynamically optimizes a lighting strategy and a communication priority based on credibility scores and emergency information, and through multi-modal data cross validation and adaptive weight adjustment, the robustness of anomaly detection is significantly improved, and misjudgment caused by sensor failure or environmental interference is effectively suppressed; the dynamic planning algorithm is used for synchronously optimizing energy consumption and equipment service life, efficient balance of safety and energy efficiency is achieved, the system is particularly suitable for complex underground environments and can automatically adapt to sudden anomalies, and the intelligent level and the energy utilization efficiency of pipe gallery illumination are remarkably improved.

The invention relates to a source-containing power distribution network multi-target collaborative voltage optimization control method based on an FACMAC algorithm, and belongs to the technical field of photovoltaic inversion control. According to the technical scheme, a power distribution network physical system is composed of a plurality of feeder lines, a transformer, a line and a plurality of grid-connected photovoltaic inverters, and each inverter can measure operation information such as local voltage and current in real time; the data acquisition and communication system is used for acquiring node operation data and realizing low-delay communication; the multi-agent reinforcement learning control system is composed of a plurality of distributed agents and factorization centralized Critic modules, and whole-network voltage optimization decision can be carried out in training and execution stages. And the execution unit adjusts the reactive power output of the inverter in real time according to the control instruction. According to the method, the whole-network cooperative regulation and control capability is improved, the training efficiency bottleneck in a high-dimensional scene is relieved, the expression capability on a complex nonlinear coupling relationship is enhanced, and efficient, stable and extensible power distribution network voltage optimization control is realized.

The invention relates to a data lake-based industry templated modeling and dynamic access control method, which comprises the following steps of: acquiring multi-source heterogeneous data of a data center in real time, and performing data verification, key field extraction and metadata extraction through an analyzer; according to the data classification and grading key points, grading protection is carried out on the currently obtained data matching industry grading and classification template, and the protection grade is dynamically adjusted according to the data dynamic access control rule, the sensitivity and the service importance; performing threat detection and risk prediction on the abnormal behavior data by using a machine learning algorithm; constructing a distributed intelligent data lake storage architecture, and storing the currently acquired data into a data lake in a layered manner according to a classification result; and displaying the security situation and the early warning information through a visual interface. The invention also relates to a corresponding system. By adopting the industry templated modeling and dynamic access control method and system based on the data lake, the limitation of traditional data lake modeling is effectively solved.

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.

According to the lightweight helicopter fault diagnosis method and device based on cloud edge cooperation and the medium, a dynamic cooperative distributed intelligent diagnosis system is constructed between the cloud end and the edge end, so that efficient lightweight reasoning of the edge end and centralized optimization training of the cloud end are realized; therefore, an aviation health management platform with self-learning, self-adaption and online updating capabilities is constructed. The whole system adopts a cloud-edge-end three-level architecture, wherein the end side is responsible for data acquisition and preprocessing; the edge side undertakes real-time diagnosis and lightweight model reasoning; and the cloud is responsible for global training, model scheduling and strategy optimization. Cyclic interaction of model parameters, task instructions and diagnosis results is achieved between the cloud and the edge through a secure communication link, and a closed-loop intelligent updating mechanism is formed. According to the method, the real-time performance, the computing power efficiency, the model generalization ability and the system adaptability of a fault diagnosis system are remarkably improved.

The invention relates to the technical field of on-load voltage regulation, in particular to a transformer on-load voltage regulation control method. According to the technical scheme, the transformer on-load voltage regulation control method comprises the following steps that S1, a transformer cluster running in parallel is decomposed into distributed intelligent nodes, and each node executes local voltage prediction and constraint condition management; s2, adopting a distributed model prediction control framework, and cooperatively solving a voltage regulation optimization target through an edge calculation unit; s3, a harmonic disturbance signal with a preset amplitude is injected into the transformer, equivalent impedance is dynamically identified based on response data, and virtual impedance compensation is triggered; and S4, establishing a hierarchical cooperative control architecture, and realizing clock synchronization, dynamic weight distribution and multi-objective optimization decision among nodes. Through a cooperative mechanism of distributed predictive control and dynamic impedance compensation, the response speed and the regulation precision of the on-load voltage regulation system to the power grid voltage fluctuation are remarkably improved, and the voltage out-of-limit problem under the complex working conditions of new energy grid connection, load sudden change and the like is effectively solved.

The invention relates to the technical field of ship unloader linkage, in particular to a port ship unloader full-automatic multi-machine linkage system and a method thereof.The port ship unloader full-automatic multi-machine linkage system comprises a distributed intelligent scheduling module, the distributed intelligent scheduling module is in coupling connection with a full-link data middle station, and the full-link data middle station is in coupling connection with a self-adaptive edge computer unit; the self-adaptive edge computer unit is in coupling connection with a three-dimensional perception safety monitoring network, the three-dimensional perception safety monitoring network is in coupling connection with a man-machine collaborative interaction platform, and the man-machine collaborative interaction platform is in coupling connection with a production management module. The port ship unloader full-automatic multi-machine linkage system and the method thereof solve the problems that in the prior art, a ship unloader full-automatic multi-machine linkage system is mostly controlled in a centralized mode in the actual using process, depends on a fixed algorithm and is difficult to dynamically deal with complex operation scenes (such as ship position deviation and uneven cargo distribution), and the working efficiency is high. And equipment conflict or idle waiting is caused.

One or more embodiments of the invention provide a distributed agent system, a request processing method, equipment and a storage medium. The distributed agent system comprises an agent scheduler, an event queue, a database and a plurality of agent actuators. And the agent scheduler creates a running instance according to the processing request, stores the running instance in an execution state into a database, and sends a starting event to an event queue. The multiple agent actuators monitor the event queue at the same time, and the same event is acquired by only one agent actuator. After obtaining the initial event or the intermediate event, the agent executor executes a corresponding predefined execution process, generates the intermediate event or an end event carrying an execution result, and returns the intermediate event or the end event to the event queue; after the ending event is obtained, the state of the running instance in the database is updated to be ending, and an execution result is stored. And if the agent scheduler monitors that the operation instance is in an end state from the database, obtaining an execution result from the database and outputting the execution result.

The invention provides an edge server dynamic activation method and system based on deep reinforcement learning, and the method comprises the following steps: S1, building system models, including building a network model, an energy consumption model, a communication and service delay model and a state switching cost model, the network model including RES and MES; s2, optimizing a network model by calculating the sum of the cost of the energy consumption model, the communication and service delay model and the state switching cost model; s3, constructing a Markov decision process model, and improving RES stability; s4, predicting a traffic load; s5, calculating a baseline value; s6, training the network model through a centralized intelligent scheduling algorithm CDDS to obtain a trained strategy network model; s7, dividing the trained strategy network model through a federated distributed intelligent scheduling algorithm FDDS, and training the model based on federated learning to obtain a DDPG model; and S8, the DDPG model is deployed to each RES.

The invention discloses an automatic UI self-healing test method and system based on multi-agent AI, and belongs to the technical field of automatic UI tests.The automatic UI self-healing test method comprises the steps that an original test script is semantized through a test reasoning interpretation class, and an expected intention is output; when the test fails, synchronously collecting multi-source data and extracting multi-modal features, constructing a fault propagation graph according to an expected intention and the multi-modal features, and performing fault propagation analysis by using a graph attention network to obtain a fault diagnosis result; according to a fault diagnosis result, a deep Q network, a greedy strategy and a Byzantine fault-tolerant algorithm are adopted to negotiate and decide a repair strategy through a distributed agent cooperation mechanism, and feedback is obtained; optimizing and dynamically adjusting the repair strategy and the test reasoning interpretation class through a meta-learning algorithm based on feedback; a test intention is deeply understood by constructing a test reasoning interpretation class, and fault diagnosis and automatic repair are cooperatively completed by adopting a distributed intelligent agent, so that the semantic understanding capability, the diagnosis accuracy and the repair success rate are improved.

The invention discloses a power supply and distribution control system based on a virtual power plant, and relates to the technical field of power distribution systems. The system comprises five core modules: a digital twin simulation control closed loop module constructs a complete closed loop of data acquisition, simulation calculation, control execution and model iteration, and realizes real-time synchronization of a physical system and a digital mirror image and accurate issuing of a control instruction; the multi-agent source-load-storage optimization module improves the source-load-storage collaborative scheduling efficiency through distributed agent collection and reinforcement learning; the multi-scene energy storage adaptive control module generates a scene-based energy storage strategy based on meteorological, load and electricity price multi-dimensional perception and model prediction control; and the multi-type energy storage ratio linkage module realizes multi-type energy storage cooperative operation based on an energy storage characteristic database and scenarized weight optimization. All the modules work cooperatively, the system response speed, the control precision and the power supply stability are effectively improved, and the system is suitable for industrial parks and other complex power supply and distribution scenes.

The invention provides a distributed intelligent multi-machine cooperative operation system and method, a deployment device and equipment, belongs to the technical field of industrial automation and intelligent operation system crossing, and aims to solve the problems of low reliability, rigid static task distribution, poor compatibility of heterogeneous equipment and insufficient fault-tolerant capability caused by a centralized architecture in the prior art. The operating system comprises a communication module, a task scheduling module, a heterogeneous equipment adaptation module, a fault-tolerant and self-healing module and a human-computer interaction module. The operating method comprises the following steps: S1, establishing an equipment communication network; s2, equipment state visualization and task instruction issuing; s3, dynamically allocating tasks and scheduling the tasks; and S4, carrying out equipment fault monitoring and self-healing recovery strategies.

The invention discloses a ship route optimization and safety management method based on real-time environment information, and relates to the technical field of route planning. The ship route optimization method provided by the invention can predict the service life of key parts of an unmanned ship, find potential faults in advance, suggest maintenance measures through an early warning mechanism, and improve the safety of the unmanned ship. Through deep reinforcement learning and a distributed intelligent decision-making system, each unmanned ship can make an intelligent decision based on shared environment information and the state of the unmanned ship, cooperative avoidance of obstacles among fleets is realized, and meanwhile, the route of the unmanned ship is dynamically adjusted by using real-time weather forecast and environment change information, so that the unmanned ship is more intelligent. The method is advantaged in that the method is advantaged in that the method can better adapt to emergencies and environment changes, adaptability and flexibility of ship navigation, real-time environment perception and data sharing are improved, dynamic factors are considered in a planning stage through a distributed intelligent decision-making system, fleet collaborative decision-making is realized, and the method adapts to real-time changes of the marine environment.

The invention provides a deep reinforcement learning-based method and a deep reinforcement learning-based system for integrating communication, sensing and calculation in unmanned aerial vehicle edge calculation, and the method comprises the steps: S1, constructing a system model, including constructing a network model which comprises a central unmanned aerial vehicle and a plurality of auxiliary unmanned aerial vehicles; s2, constructing a service process model; s3, constructing a sensing process model; s4, constructing a communication process model; s5, constructing a calculation process model; s6, constructing an energy consumption model; s7, constructing a joint optimization problem based on the network model, the service process model, the sensing process model, the communication process model, the calculation process model and the energy consumption model; s8, based on the joint optimization problem, constructing a Markov decision process conversion model; and S9, according to the Markov decision process conversion model, constructing a network architecture based on an A3C algorithm, and carrying out distributed DRL agent design and training, so that the affiliated unmanned aerial vehicle and the central unmanned aerial vehicle directly generate a resource allocation strategy based on a local state.

The invention relates to an inverter control system, and the system comprises distributed intelligent nodes which are used for collecting sensor data of a target part, obtaining a fault signal, and simply controlling an inverter according to the sensor data; the intelligent control module is used for generating an optimal power control instruction to control the inverter and communicating with external equipment; the intelligent feedback regulation module is used for fusing the sensor data and obtaining an optimal control strategy to control the inverter; the intelligent energy interconnection and scheduling module is used for collecting information of distributed energy, obtaining a global optimal energy scheduling strategy according to the information of the distributed energy, and controlling the inverter according to the global optimal energy scheduling strategy; and the intelligent energy efficiency evaluation and optimization module is used for monitoring the inverter, obtaining and evaluating the energy efficiency level of the inverter, and optimizing the control parameters and the operation mode of the inverter according to the evaluation result. According to the invention, the performance and efficiency of the inverter can be improved.

The invention relates to the technical field of data management, in particular to an intelligent flow scheduling method for a high-performance oil way of a metal plate pipe network. The specific implementation process comprises the following steps: establishing a distributed intelligent executor and a resource optimization model based on a sheet metal pipe network comprising a schedulable unit and production management information comprising a historical performance baseline and a production plan; the distributed intelligent executor obtains and evaluates the operation data of the schedulable unit and generates real-time state data; combining the real-time state data and the historical performance baseline to analyze the drift trend and generate performance degradation data; based on the production plan and the performance degradation data, quantifying the comprehensive operation cost as a target function to calculate and generate a resource allocation strategy and an operation instruction; and the distributed intelligent executor combines the operation instruction, the real-time state data and the performance degradation data to execute dynamic compensation management to generate a scheduling instruction. According to the method, resource allocation is optimized by evaluating the real-time and long-term states of the equipment, so that risk avoidance and cost dynamic optimization are realized.

The invention discloses a distributed intelligent power distribution method and system for a power network, and belongs to the technical field of power distribution control, and the method comprises the steps: collecting multi-dimensional electrical parameters of a power generation node in real time, and carrying out the dynamic power monitoring at a feeder terminal of a transformer substation through an edge calculation node; executing line loss calculation and load prediction in the power distribution unit, generating a demand feedback instruction to a feeder terminal node, and triggering an adaptive power distribution strategy; and on the basis of a digital twin system of a regional dispatching center, credibility verification is carried out on operation decisions of all edge nodes, and global optimization compensation control is executed. The method has the remarkable advantages in the aspects of optimizing power distribution, reducing line loss, improving system stability and safety and achieving efficient energy utilization, and intelligent management of a power network becomes possible.

The invention discloses a vehicle-mounted distributed intelligent cooperative reasoning method. The method comprises the following steps: acquiring natural language input of a user in a vehicle; analyzing the natural language input through the vehicle-mounted large model to obtain a user intention; generating a plurality of reasoning tasks according to the user intention, and calling a plurality of reasoning models based on the plurality of reasoning tasks; generating a plurality of preliminary reasoning results based on the plurality of reasoning tasks through the plurality of reasoning models; the plurality of reasoning tasks are in one-to-one correspondence with the plurality of reasoning models; and generating a target reasoning result based on the plurality of preliminary reasoning results by using the vehicle-mounted large model. According to the method, the results after independent reasoning of each module are integrated to the vehicle-mounted large model in a centralized manner, and deep fusion and final decision making are performed on multi-system data by means of the vehicle-mounted large model, so that a cross-module collaborative reasoning mechanism is constructed, the user operation complexity is remarkably reduced, and the system response efficiency is effectively improved.

The invention provides a demand side response power consumption management method based on a power gap architecture, and relates to the technical field of information, the power gap architecture especially relates to a distributed intelligent communication framework oriented to a novel power system, and multi-source power consumption and environment data are collected through power gap edge computing nodes of a transformer substation; through cleaning, feature extraction and clustering, classification results of three types of power consumption subjects of industry, business and residents are obtained, and the nodes carry computers and auxiliary equipment repair adaptation modules to guarantee operation; a classification result is encrypted and transmitted to a trusted execution environment, a power consumption behavior model is constructed by fusing multiple factors, a dynamic electricity price strategy and a response rule are generated and pushed to a terminal, and a regional response strategy is optimized by combining a computer and an auxiliary equipment repair intelligent operation and maintenance mechanism; a final management instruction is generated through load balancing judgment, optimized data are obtained after execution, and the model and the strategy are dynamically updated. Accurate regulation and control of power utilization are realized, the power grid load is balanced, and the power utilization efficiency is improved.

The invention relates to the technical field of artificial intelligence service development and intelligent operation and maintenance, and discloses a system for developing and designing an AI service, and the system comprises a multi-dimensional target configuration module which defines a business target and a KPI, and generates an initial configuration parameter; the life cycle layered modeling module is used for receiving parameters and constructing a digital twinborn model in a layered (macroscopic, mesoscopic and microscopic) manner; the global prediction optimization module is used for executing multi-objective optimization based on digital twinning and generating a global control strategy; the distributed intelligent execution module is used for executing a global strategy in a microcosmic layer by an intelligent agent and performing local autonomous optimization; the intelligent resource management module is used for dynamically distributing system resources according to strategies and task requirements; and the monitoring module collects execution and resource data and is used for updating the digital twinborn model and adjusting a global strategy. According to the method, AI service accurate modeling, global intelligent optimization, efficient cooperative execution and continuous closed-loop self-adaption are realized.

The invention discloses a power transmission line distributed fault positioning method and system, and belongs to the technical field of line faults. The method comprises the steps that monitoring nodes are generated through distributed intelligent sensing equipment, line sections are dynamically divided, and the dynamic coverage relation between the monitoring nodes and the sections is established; constructing a power transmission line digital twin platform, mapping sections and nodes, and generating a multi-source operation parameter feature cluster; and based on an error accumulation boundary function, calculating a parameter correlation degree, dynamically quantifying a correlation coefficient, and screening a strong correlation section as a fault priority positioning object. The system comprises an intelligent sensing equipment cluster module, a digital twin mapping module, a data processing module and a fault positioning module, and realizes real-time monitoring of the running state of the power transmission line and efficient positioning of faults. Through dynamic coverage relation updating, multi-source data fusion and quantitative analysis, the accuracy and efficiency of fault positioning are improved, and the method is suitable for distributed fault diagnosis of a complex power transmission network.