2815 results about "Execution control" patented technology

The present invention relates to the technical field of circuit control, and relates to a direct-current high-voltage generator regulation and control method and system based on intelligent feedback. A fuzzy control logic operation is performed on an output current flux value and an input voltage fluctuation factor of a load experimental object, so as to obtain the disturbance amplitude of a voltage fluctuation state for a current flux change trend; if a step-up transformer can execute a PID control technology by adjusting a transformation ratio, PID real-time adjustment parameters are calculated, and a crossover mutation operation of individuals is performed on the basis of a fitness function, so as to generate a first boost regulation and control scheme; and if the step-up transformer cannot execute the PID control technology by adjusting the transformation ratio, a calibrated state feedback controller is constructed on the basis of a state variable of the current step-up transformer and an adjustment signal output amplitude, so as to generate a second boost regulation and control scheme. The present invention achieves intelligent feedback regulation and control by independently analyzing components of direct-current high-voltage generators, improving the stability and accuracy of outputting high-voltage direct current.

The invention provides a monitoring information identification method based on the Internet of Things and a server, and the method comprises the steps: firstly obtaining multi-modal perception data which are collected by a plurality of Internet of Things monitoring nodes in a target monitoring area in real time and comprise images, sounds and environment parameters, and then carrying out the cross-modal feature extraction and fusion processing of the multi-modal perception data; the method comprises the steps of generating a comprehensive spatio-temporal characteristic spectrum, determining abnormal event types and spatio-temporal distribution information in a region through dynamic mode matching based on a preset abnormal mode library, generating an adaptive regulation and control instruction set according to the abnormal event types and the spatio-temporal distribution information, and determining control parameters and execution priorities of different Internet of Things execution devices. And finally, distributing the instruction set to an associated Internet of Things execution device cluster, triggering the cluster to execute control parameters according to priorities, eliminating the influence of abnormal events, and realizing comprehensive and intelligent monitoring and exception handling of a target monitoring area.

The invention discloses an automatic driving system based on real-time holographic modeling and dynamic shielding compensation and a vehicle, and aims to solve the problem of complicated scene perception. The system comprises a real-time holographic environment modeling module for constructing a dynamic 3D environment model; the dynamic shielding compensation module outputs compensated environment data; the sensing data fusion module is used for integrating multi-source data; the high-precision positioning and mapping module is used for generating a centimeter-level environment map; the edge computing node is used for cooperatively processing fusion data; the AI self-adaptive decision making system generates a driving instruction, and the decision making efficiency is optimized and improved through a satellite auxiliary decision making unit; the V2Xcommunication module supports low-delay and high-security data interaction; the execution control unit is used for accurately controlling the vehicle; an FPGA + GPU parallel computing framework is adopted, and decision making and control are supported after modeling and compensation data are fused. According to the invention, optimal decision making and high-precision real-time control in a complex environment can be realized, and development of an automatic driving technology is promoted.

The invention, which relates to the warehouse management field, discloses an intelligent WMS system with an intelligent scheduling function, comprising a cargo sensing module, a data processing and analysis module, a scheduling decision module, a decision optimization module, and an execution control module. And the multi-agent scheduling module is used for defining each independent operation task in the warehouse as an agent, modeling the warehouse environment, and setting a reward function for each agent for guiding the agent to make an optimal decision. According to the intelligent WMS system with the intelligent scheduling function, the task completion time, the resource utilization rate, the cost saving and the service quality can be comprehensively considered through the set reward function, an intelligent agent can be guided to make an optimal decision, the accuracy and efficiency of future decision making can be improved through learning, and the intelligent WMS system has good application prospects. Therefore, the intelligent agent can gradually master the optimal scheduling strategy in different environments, and maximization of the overall warehouse operation efficiency is realized.

The invention provides a building energy-saving control system based on centralized control of the Internet of Things, and relates to the technical field of data processing, and the system comprises a data collection module which is used for collecting personnel activity data, environment parameter data and equipment state data in a building in real time; the data preprocessing module is used for carrying out cleaning, denoising and time sequence alignment processing; the area use mode recognition module is used for analyzing the use conditions of equipment in different areas in the building according to a multi-layer classification rule; the energy-saving control strategy generation module is used for analyzing equipment in the building and generating a region-level energy-saving control instruction set; the execution control module is used for controlling an air conditioning system, a lighting system and an elevator system in the building in real time; the feedback regulation module is used for monitoring real-time feedback data of the equipment operation parameter set and feeding back to the energy-saving control strategy generation module in combination with environmental parameter data change; according to the invention, autonomy and accuracy of building energy-saving control are improved.

The invention discloses a scene-based emotional interactive accompanying doll system and method, and relates to the technical field of artificial intelligence, and the system comprises a data collection module, a feature analysis module, a portrait construction module, an interactive decision module, an execution control module and a database module. The data acquisition module is used for acquiring interaction data and scene data; the feature analysis module generates an emotion feature vector and a scene feature vector by using a multi-modal feature network, and determines a user emotion state and a scene type; the portrait construction module is used for constructing a user portrait; the interaction decision module comprises an emotion evolution unit, a scene interaction unit and a physiological regulation unit and is used for generating an execution instruction sequence; the execution control module is used for controlling the doll to complete emotion interaction behaviors. Through the scene perception and sentiment analysis technology, accurate recognition and personalized interaction response of the doll to the user sentiment state are achieved, and intelligent sentiment accompanying service is provided.

A data-driven dynamic energy management system for the adaptive coordination of renewable and conventional energy sources, consisting of: a processing unit configured to perform real-time calculations to optimize the generation, storage, and distribution of electrical energy by continuously analyzing operational data, forecasting future energy demand, and generating control instructions to match available generation resources with forecasted consumption demand; a storage unit connected to the processing unit, configured to store records of historical energy production and consumption, environmental data, operating thresholds and learned model parameters, and to provide said data as input for the forecasting and optimization routines performed by the processing unit; a multitude of IoT-based monitoring units, each comprising at least one sensor configured to measure instantaneous parameters of generation, storage level, consumption rate and environmental conditions, with each monitoring unit being configured to periodically transmit measurement packets to the processing unit via a secure communication network; a forecasting unit implemented in the processing unit, configured to process historical and real-time data to create forecast curves for demand and generation using statistical and probabilistic forecasting techniques, and to dynamically update the weights of the forecasting model in response to observed deviations between forecasted and actual output; an optimization control unit implemented in the processing unit and configured to evaluate the outputs of the forecasting unit together with current operational data to determine a set of optimized control variables representing the target generation contribution of each energy source, and to pass these targets to a lower-level controller for execution; a controller that is communicatively connected to the processing unit and the multiple energy generation sources and is configured to regulate the operation of each source by adjusting the activation state, output level and operating priority based on the control signals received from the processing unit; an energy storage management unit comprising at least one battery array and a power conditioning circuit, configured to receive control instructions from the processing unit, store excess generated energy, release stored energy when forecasted demand exceeds available generation, and report charging and discharging characteristics in real time to the processing unit for continuous recalibration; an alarm and notification control unit connected to the processing unit, configured to continuously compare storage levels and generation reserves with stored operating thresholds, trigger predefined responses when critical or abnormal conditions are detected, and transmit acoustic, visual, and digital remote alerts to designated operators; a user interface terminal connected to the processing unit, configured to display real-time generation statistics, demand forecasts, energy storage status, and system alerts, and to accept operator-defined parameter inputs that are transmitted to the processing unit for recalibration of forecast or optimization parameters; and a secure server interface configured to synchronize operational logs, learning data, and performance indicators with a remote monitoring or analysis server for centralized monitoring, long-term data analysis, and distributed decision support.

The invention relates to the technical field of temperature control, in particular to an intelligent temperature control system for the whole cable production process. Comprising a cable parameter acquisition module, a production process modeling module, a temperature control decision module, an execution control module and a monitoring early warning module. According to the system, cable production parameters are collected, and a graph neural network and a symbolic regression algorithm are used for modeling production temperature distribution and a temperature-quality relation. And based on the model, an optimal temperature control strategy is generated by adopting reinforcement learning, and precise temperature control is realized through adaptive fuzzy control. Meanwhile, the system monitors temperature parameters in real time, early warning signals are sent out when abnormal detection, over-limit detection or equipment failure are detected in combination with sparse coding and variation inference, and the production stability and the product quality are ensured. According to the invention, through intelligent modeling, decision making, control and monitoring means, the temperature control precision and efficiency of the whole cable production process are comprehensively improved.

The invention discloses a virtual-real combination-based power generation side industrial control system network security target construction method. The method comprises the following steps of: constructing a virtual-real combination target environment consisting of a physical equipment layer and a virtual model layer; the heterogeneous industrial control protocol between the physical equipment layer and the virtual model layer is analyzed, protocol semantic information is extracted, and a bidirectional dynamic mapping rule of a physical equipment state and a virtual model state is generated based on the protocol semantic information; based on a state change event of the physical equipment layer, according to a bidirectional dynamic mapping rule, synchronizing changed equipment state data to the virtual model layer in real time, and simulating protocol behavior logic corresponding to the equipment state data in the virtual model layer according to a security test requirement; and based on the attack instruction or the abnormal state signal generated by the virtual model layer, according to the protocol specification format of the target physical equipment, converting the instruction or the signal into an executable control command, and driving the physical equipment layer to execute an operation corresponding to the control command.

The noise online monitoring system comprises a multi-source sensing module, a positioning analysis module, a baseline generation module, a state association module and an execution control module. The multi-source sensing module synchronously collects target area parameter signals and generates noise identification signals through coherence analysis; the positioning analysis module receives the noise identification signal and generates a positioning signal containing a noise source position and a motion trail; the baseline generation module calls historical noise spectrum data of a corresponding scene according to a noise source position in the positioning signal to generate an adaptive noise baseline threshold signal; the state association module carries out space-time alignment on the baseline threshold signal and the equipment working condition data; the execution control module receives the positioning signal and the correlation degree signal. The noise on-line monitoring system can solve the problems that a traditional noise monitoring system is inaccurate in positioning, poor in base line adaptability and disjunction between noise and the equipment state.

The invention relates to a full-automatic injection molding optimization method, device and equipment based on digital twinning, and belongs to the technical field of intelligent manufacturing equipment. Inputting the sensor data into a digital twin model to construct equipment dynamic response simulation, and synchronously comparing the actual mode locking force with the offset of a preset pressure threshold; generating compensation data of mold locking pressure according to the offset, and dynamically correcting an acceleration curve of a servo motor in a glue injection stage on the basis of the displacement of a screw rod; in hot runner control, a thermal interference compensation coefficient is calculated according to a temperature difference value of adjacent temperature zones, and an output power gradient signal of each heating ring is adjusted; the corrected parameter information is transmitted to an equipment PLC through an industrial bus to execute control, and a compensation coefficient threshold value is iteratively updated according to a product weight fluctuation value in the next forming period; closed-loop data interaction of an equipment control system is achieved, and the injection molding process is optimized.

The invention relates to the technical field of supply chain management, and discloses an intelligent supply chain optimization management and control method. The method comprises the following steps: acquiring full-link real-time operation data of a supply chain through a distributed data acquisition module; extracting topological feature data by using a graph neural network; inputting the multi-target collaborative optimization model, and generating supply chain strategy optimization parameters by adopting a mixed integer linear programming framework and a dynamic constraint relaxation mechanism; constructing an adaptive resource scheduling model, realizing node dynamic scheduling by using an improved particle swarm algorithm, and outputting optimal configuration data; and establishing a layered supply chain management and control model which comprises a strategic planning layer, a dynamic coordination layer and an execution control layer, and realizing full-link intelligent optimization management and control. The invention also relates to risk event processing, strategic planning layer resource network planning and the like. The system can comprehensively collect data, optimize resource configuration, improve response speed, reduce cost, effectively control risks and improve the overall competitiveness of a supply chain.

The invention discloses a smelting waste gas recycling optimization system based on fuzzy control, and relates to the technical field of automatic control. The problems of low control precision and response lag caused by the fact that an existing static membership function cannot adapt to multi-scale working condition changes in real time are solved. Comprising a sensing acquisition module, a membership reconstruction module, a feature decoupling module, a coupling compensation module, a rule optimization module and an execution control module. Standardized data are acquired through sensing acquisition and filtering, an adaptive membership function is constructed by adopting incremental fuzzy clustering, and closed-loop self-correction of a multi-modal control instruction is realized by combining wavelet packet decoupling, feed-forward compensation and double-depth Q network reinforcement learning online optimization; the waste gas desulfurization efficiency, energy consumption optimization and treatment system stability of waste gas treatment can be effectively improved.

The invention relates to the field of power system supply and demand interaction, and discloses an intelligent greening irrigation system and a water-saving control method, the system comprises a multi-source data acquisition module, a space-time tensor modeling module, a dynamic decision generation module, an edge calculation acceleration module, a distributed execution control module and a drip irrigation module; the method comprises the following steps: collecting multispectral, meteorological and soil moisture data of a plant canopy in real time to generate an original data set; a fusion data set is obtained through space-time alignment and normalization preprocessing; the water demand is predicted based on an LSTM-Transform model, and an irrigation safety interval is screened; the irrigation uniformity and water pressure distribution are simulated and evaluated through multi-physics field coupling; a double-delay DDPG algorithm is used for optimizing valve opening parameters; the model and the execution error are corrected through Bayesian modeling, and an NDVI feedback optimization strategy is combined. According to the invention, a double-delay depth deterministic strategy gradient algorithm is introduced for dynamic optimization of an irrigation strategy, so that the capability of quickly adapting to environmental changes is achieved.

The invention provides a multi-agent control system based on a large language model. The system comprises an analogue simulation subsystem and a coordination control subsystem. The analogue simulation subsystem comprises a data acquisition module, an initial reward function generation module, an error correction module, a dense reward function generation module and a strategy network updating module, wherein the data acquisition module is used for acquiring a multi-agent reinforcement learning training code; the initial reward function generation module is used for generating an initial reward function code; the error correction module is used for generating executable reward function codes; the dense reward function generation module is used for generating dense reward function codes; and the strategy network updating module is used for obtaining the strategy network with the maximized reward. The coordination control subsystem comprises a data collection module and a strategy distribution module; wherein the data collection module is used for receiving observation data; and the strategy distribution module is used for guiding multiple agents to execute a control task by adopting a strategy network generation action for maximizing rewards based on the observation data.

The invention discloses a control method and device for a mounting and dismounting integrated robot of a sound barrier, and relates to the field of robot control. According to the method, a digital twinborn model and a dynamic environment model are constructed according to original sensor data, and a current environment risk value is determined; a mounting or dismounting sequence is generated based on a sound barrier topology rule, and a motion path and a mechanical arm motion track are generated according to the digital twin model, the dynamic environment model and the mounting or dismounting sequence; rehearsing the motion path and the motion track of the mechanical arm in the digital twin model, and when a rehearsing result meets a preset requirement, generating an initial instruction based on the motion path and the motion track of the mechanical arm; the current clamping force is determined through an impedance control algorithm, the current hoisting parameter is determined according to the load weight, the initial instruction is adjusted according to the current clamping force, the current hoisting parameter and the current environment risk value to obtain a control instruction, and the control instruction is executed. By implementing the technical scheme provided by the invention, the mounting and dismounting efficiency is improved, and the operation safety is enhanced.

The invention discloses a multi-robot task conflict resolution and dynamic scheduling system and method, and belongs to the technical field of robot control. The system comprises a perception detection layer which is used for acquiring operation information of a plurality of robots and a space-time semantic map of a to-be-executed task executed by the robots, and generating conflict information under the condition that at least two target robots are detected to conflict; the decision scheduling layer is used for determining task execution priorities of the to-be-executed tasks according to the conflict information and priority factors and value functions of the to-be-executed tasks corresponding to the target robots so as to rearrange the to-be-executed tasks corresponding to the target robots and generate a task sequence; and the execution control layer is used for generating a dynamic scheduling instruction according to the task sequence, the space-time semantic map and the operation information of the target robot and issuing the dynamic scheduling instruction to the corresponding target robot. The system can flexibly cope with a dynamic scheduling scene of multiple robots in real time.

The invention provides a high-efficiency ship automatic control system, and the system comprises a comprehensive control platform which carries out the unified integration of subsystems, sets a target function, and carries out the data sharing, collaborative optimization and global control among the subsystems. The intelligent sensing network module is used for collecting historical data and real-time data; the self-learning control module is used for constructing a control strategy generation model; the ship digital model module is used for constructing a digital model for simulating ship navigation so as to test different ship control strategies; the decision execution module is used for generating and executing a control instruction; and the safety monitoring module detects abnormal behaviors of the ship and gives an alarm. According to the invention, a plurality of subsystem modules can be integrated in a unified manner, data sharing, collaborative optimization and global control are realized, an optimal control strategy can be provided, the cost and risk of actual navigation are reduced, and the control precision, response speed and operation efficiency of a ship are improved.

The invention relates to the technical field of robot control, and particularly discloses a robot indoor moving path planning method and system. An environment sensing module collects environment data through a multi-mode sensor array and constructs a three-dimensional semantic map; the dynamic decision-making module generates a candidate path set based on the three-dimensional semantic map data output by the environment perception module, and transmits a decision-making result to the path optimization module through a priority arbitration mechanism; the path optimization module performs multi-objective optimization according to candidate paths of the robot kinematics constraint and dynamic decision module; the execution control module is used for converting an optimized path into a bottom layer driving instruction through kinematics calculation and feeding back an execution state to the dynamic decision-making module in real time to form closed-loop control. A dynamic environment modeling mechanism is adopted, through variable resolution perception and semantic map construction, a layered decision-making framework and multi-target collaborative optimization are adopted, and the dynamic environment modeling mechanism is optimized; and the adaptive control system is selected to significantly improve the environmental adaptability and reliability of the system.

The invention discloses an intelligent control system and method for gas-liquid photoelectric slip ring injection molding machining, and relates to the technical field of injection molding process control. The data acquisition module is used for acquiring multi-dimensional production data by using a sensor array and an advanced optical technology; the data analysis and processing module fuses and optimizes data through deep learning; the decision-making module combines reinforcement learning and a knowledge graph to generate a strategy; the execution control module adopts an advanced control algorithm to realize accurate control; the monitoring and early warning module guarantees production by using technologies such as digital twinning; the man-machine interaction module applies a leading-edge interaction technology; the communication module adopts a high-speed communication architecture; all the modules cooperate to achieve intellectualization and precision of injection molding machining. The gas-liquid photoelectric slip ring injection molding processing level is greatly improved, the product percent of pass is improved, the production efficiency is improved, the unit product energy consumption is reduced, meanwhile, intelligent interaction and remote management are achieved, and the production benefit and quality are improved.

The invention provides a transformer area source load storage intelligent adjustment method based on multi-scene cooperation, and belongs to the technical field of intelligent power grid adjustment, and the method comprises the steps: a data collection module which collects the related data of a power supply, a load and a power grid, and generates corresponding feature parameters; the data analysis module is used for determining comprehensive adjustment parameters based on the characteristic parameters, obtaining a plurality of scenes and generating corresponding scene labels; the data prediction module is used for generating a prediction load curve and further determining a charging quantity parameter in a load trough period; the strategy execution module is used for executing a control strategy corresponding to the scene label based on the predicted load curve, the charging quantity parameter in the load trough period and the scene label; and the strategy updating module is used for comparing the real-time load curve and the predicted load curve after the strategy is executed, determining an adjustment deviation ratio, correcting the predicted load curve based on the adjustment deviation ratio, and then executing the control strategy again. And the energy utilization rate and the self-adaptive capability of the system are obviously improved.

The invention discloses an intelligent grabbing recognition and execution control method and system of a mechanical arm, and relates to the technical field of artificial intelligence. The intelligent grabbing recognition and execution control method and system for the mechanical arm comprises the following steps that S1, grabbing data are collected by integrating various sensors, and abnormity elimination, missing complementation, coordinate registration and normalization processing are completed on the grabbing data; s2, analyzing a structural feature and a time sequence relationship of the captured data, extracting a visual feature vector, a physical contact state vector and a dynamic behavior feature vector, and deducing a stability expression in a clamping process according to the visual feature vector, the physical contact state vector and the dynamic behavior feature vector; s3, confidence analysis is carried out on the grabbing candidate actions based on the mechanical relation and sensing data distribution, and action screening and standard action coding are completed; and S4, a track and a control instruction are generated, the expected clamping force is analyzed in real time in combination with clamping jaw movement and contact state changes, and feedback is collected and executed to update strategy data. The problem that in the prior art, a flexible transparent object is difficult to accurately recognize and stably grab is solved.

The invention provides a hydraulic generator inner bore inspection robot based on a dynamic behavior tree and an intelligent decision and operation method thereof, and the robot comprises an upper mechanical arm end executor, a six-axis mechanical arm, an intermediate control system, a mobile platform, a multi-physical quantity fusion detection system, and a central control platform. The method is specially designed for the complex dynamic shielding environment of the inner bore of the hydro-generator. According to the method, by constructing a three-level behavior tree architecture composed of a task decision-making layer, a scene adaptation layer and an execution control layer, inspection requirements are decomposed into environment perception, task execution and resource constraint events, and the events are dynamically mapped to preset behavior nodes. Weight factors are adjusted in real time based on a multi-dimensional evaluation system, and functions of autonomous obstacle avoidance, narrow space operation and anomaly detection are realized in combination with hardware characteristics of the robot. The problems that a traditional inspection method is poor in adaptability and low in efficiency in a dynamic shielding environment are solved, and the intelligent level and safety of hydraulic generator inner bore inspection are remarkably improved.

The invention provides an industrial control-oriented cloud-side collaboration system and an implementation method thereof, and relates to the technical field of cloud-side collaboration, and the system comprises an equipment layer which comprises an industrial equipment access gateway and is responsible for collecting equipment operation data and executing a control instruction; the edge layer comprises a data preprocessing unit used for processing the real-time data uploaded by the equipment layer; the lightweight inference engine is used for loading a lightweight model issued by the cloud layer and generating a control decision based on the real-time data; the deterministic scheduling engine is used for hierarchically controlling tasks according to task time sensitivity and distributing execution positions; the cloud layer comprises an industrial knowledge model center used for constructing a scenarized knowledge model based on the equipment operation data; the model lightweight unit is used for converting the scenarized knowledge model into a lightweight model; and the decision distribution unit is used for issuing the lightweight model to the edge layer. The technical problems of knowledge expression, model adaptation and task allocation in an industrial control scene can be solved.

The invention discloses a charging pile dynamic load balancing scheduling system based on reinforcement learning, and the system comprises a data collection module which comprises 5G communication units installed at the charging piles, and is used for obtaining the power of the charging piles, vehicle battery parameters and power grid load data in real time; the reinforcement learning decision module maps the data in the data acquisition module into a state vector through a feature extraction network, and generates a power distribution strategy by adopting a PPO + DQN hybrid algorithm; the communication protocol module realizes state synchronization and control instruction transmission between the charging piles based on an OCPP2.0 standard; the execution control module converts the control instruction into a PWM control signal, and adjusts the output power of each charging pile in real time; through the reinforcement learning decision module, the system can analyze the charging pile power, the vehicle battery parameters and the power grid load data in real time, generate an accurate power distribution strategy, ensure accurate matching of the charging pile power and the electric vehicle demand power, and thus significantly improve the charging efficiency.

The invention discloses a power distribution terminal control system and method based on multi-data analysis, and relates to the technical field of smart power grids. The system integrates a multi-data acquisition module, a parameter management module, a fault analysis decision module, an execution control module and a communication interaction module; the multi-data acquisition module collects electrical quantity and switch position signals, and the parameter management module provides basic data support; the fault analysis decision-making module analyzes FTU types and fault types in sequence to generate differentiation strategies, switching operation is executed through the execution control module, and information sharing and collaboration are achieved through the communication interaction module; according to the system, through dynamic FTU type identification, a precise fault processing mechanism and multi-source data deep fusion, the problems of data island, single fault processing strategy and insufficient multi-source data fusion in a traditional power distribution automation system are solved, the power supply reliability is improved, the manual configuration workload is reduced, and the equipment damage risk is reduced.

The invention relates to the field of energy-saving computing, and discloses a low-power-consumption control method and system of a controller, which are used for realizing prediction-sleep-wake-up-response full-link hardware in the controller. Time sequence state data of a controlled object is collected in real time, and a predictive sleep window value is dynamically generated by utilizing a predictive cooperative computing unit, so that intelligent scheduling of sleep and awakening is realized, and the power consumption of a controller is effectively reduced. Meanwhile, in combination with the technologies of threshold comparison, DMA data transmission optimization and the like, the data processing efficiency is improved, and invalid data transmission is reduced. And on the awakening mechanism, an asynchronous awakening circuit and an interrupt request mechanism are adopted to ensure that power supply is quickly recovered and control response is executed when an effective instruction is received, so that the real-time performance of the system is guaranteed. According to the invention, a prediction algorithm, hardware acceleration and an intelligent scheduling technology are fused, the performance of the controller is ensured, the power consumption is obviously reduced, and the endurance time of equipment is prolonged.

The invention relates to the technical field of energy load prediction distribution optimization, in particular to an energy terminal-oriented dynamic load prediction distribution optimization method and system. The method comprises the following steps: generating a load prediction value by using a long short-term memory network based on acquired terminal load data; constructing a load priority matrix and a load distribution conflict rule set, and constructing a multi-factor integration constraint model in combination with the optimization parameters of the load predicted value; dynamically correcting the model based on the load prediction value; an integer programming problem is solved, and a distribution instruction of each terminal is obtained; and performing execution control based on the distribution instruction. The distributed acquisition unit supports multiple communication modes, acquires and normalizes various types of terminal data, loads a virtual value compensation model for checking abnormal nodes, guarantees data integrity and reliability, improves the processing capability of the system for multi-source heterogeneous data, and enhances compatibility and robustness.

The invention relates to the technical field of equalization control of a battery management system, and discloses a lithium battery hierarchical equalization control method based on a hierarchical model predictive control algorithm, comprising the following steps: constructing a hierarchical control architecture which comprises a state monitoring layer, an equalization decision layer and an execution control layer, each layer realizes cooperative control through closed-loop data interaction; the state monitoring layer collects multi-dimensional state parameters of the lithium battery system, pre-processes the collected data and then transmits the data to the equalization decision-making layer, the equalization decision-making layer constructs a multi-target optimization model based on a hierarchical MPC algorithm, and the model takes SOC consistency, equalization energy consumption minimization and cycle life maximization of the lithium battery system as optimization targets. The abnormal state of the sensor or the balancing module can be identified in time through a fault diagnosis mechanism, and when a fault occurs, a standby model is automatically switched or a balancing task is shared through an adjacent module, so that the system is ensured not to generate abrupt reduction of balancing performance due to the fault of a single component.

The invention provides a spacecraft full-cycle monitoring and operation and maintenance decision-making system based on digital twinning, and belongs to the field of spacecraft full-life-cycle intelligent operation and maintenance. Structural data and environmental data are monitored in real time through a multi-source sensor network deployed on a spacecraft structure; fusing physical entity monitoring data and damage evolution rule virtual data in a twin database, constructing a structure damage resistance and mechanical property evaluation model, and executing geometric modeling, physical attribute analysis, behavior response simulation and damage evolution rule modeling; based on a simulation result of the virtual model, life prediction and reusability evaluation are carried out, and an operation and maintenance decision instruction is generated; and feeding back the operation and maintenance decision instruction to the physical entity to execute control, and updating state data in the twin database to perform iterative optimization. According to the method, full-scene coverage from design demonstration to scrap recovery is supported, the problem of stage splitting in the traditional technology is solved, and a closed-loop optimization mechanism is formed through real-time interaction between the physical entity and the virtual model.