2132 results about "Coordinate control" patented technology

The invention discloses a park energy router multi-agent coordination control method based on a dynamic game model, and relates to the technical field of energy internet optimization control. The method comprises the following steps: according to real-time meteorological data and historical load fluctuation characteristics, identifying multi-energy supply and demand time sequence conflicts, constructing a multi-subject game framework, quantifying dynamic coupling strength of meteorological parameters and load fluctuation, and generating a dynamic constraint boundary; then, combining dynamic constraints and operation states, dynamically adjusting economic, reliability and low-carbon index weights, superposing a risk penalty term of a future supply and demand difference value, and constructing a coordination revenue function; when the photovoltaic output and the thermal load change rate exceed threshold values, an emergency game mode is triggered, Nash equilibrium is solved through a distributed optimization algorithm, and a coordination control instruction is output; and finally, collecting execution data, calculating a supply and demand deviation correction rate and an income balance degree, adjusting a coupling weight and a constraint boundary based on feedback, and realizing multi-subject benefit dynamic balance and stable and efficient operation of the park energy system.

The invention relates to the technical field of energy storage systems, in particular to a source network load storage coordination control method for multi-configuration network type energy storage coordination operation. According to the technical scheme, the source-network-load-storage coordinated control method for multi-construction-network-type energy storage coordinated operation comprises the following steps: generating a new energy output and load change trend through a dynamic prediction model based on historical power data, real-time load requirements and meteorological information; optimization is carried out to obtain a power generation plan containing a clean energy consumption target and economical efficiency constraints; decomposing the power generation plan into a photovoltaic power adjusting instruction, an energy storage charging and discharging instruction and a load control instruction according to the power grid safety priority, wherein the emergency control instruction is executed prior to the economic dispatching instruction; and controlling parallel operation of a plurality of pieces of networking type energy storage equipment, and realizing multi-machine power distribution and circulating current suppression through a virtual synchronous control technology. And through layered optimization scheduling and multi-machine cooperative control, the new energy consumption capability is remarkably improved, and the phenomenon of abandoning light and wind is reduced.

The invention discloses a micro-grid dynamic coordination control method and system for distributed energy, and relates to the technical field of micro-grid energy management. The method comprises the steps of 1, collecting multi-source micro-grid data in real time, performing edge calculation and data preprocessing operation, and performing early judgment of an island mode; 2, converging the structure and state of each micro-grid, dynamically constructing and updating a micro-grid topological graph, analyzing the structure change and health condition of the micro-grid topological graph, and evaluating the comprehensive risk of micro-grid nodes; 3, calculating the actual distributable power of each type of loads, and carrying out autonomous control and elastic mode switching; and step 4, on the basis of the actual distributable power of each type of loads, evaluating the collaborative energy scheduling capability of the micro-grid in real time, performing role identification and implementing an optimization strategy. The problem of island emergency scheduling response lag caused by sudden failure of a main network due to some extreme events along with continuous expansion of the access scale of distributed energy and a micro-grid is solved.

The invention discloses a deep sea underwater electro-hydraulic actuator (EHA) and a control system thereof. The system provides power through the power source module, balance of system oil pressure and pressure of external seawater is controlled through the pressure compensation module, oil circulation and oil pressure control are conducted through the integrated valve set module, a control instruction of the control module is executed through the execution mechanism module, and the control module conducts cooperative control over the whole system. According to the control system, the unbalanced pressure between the system and the deep sea high-water-pressure environment is compensated through the pressure compensator so as to reduce the influence of the deep sea high-water-pressure environment, meanwhile, the pressure compensator serves as an oil tank to supply oil, miniaturization of the system is achieved, and the reliability of the system during underwater work is improved; according to the deep-sea underwater electro-hydraulic actuator, the mounting positions and internal flow channels of all elements of the integrated valve bank are optimized, the pressure loss of fluid in the flow channels is reduced, and the energy utilization efficiency of the deep-sea underwater electro-hydraulic actuator is improved; the asymmetric hydraulic cylinder can operate in four working conditions of deep sea, and underwater high-precision movement can be completed.

Provided is a robot posture optimization and follow-up control method and system for freeform surface repair. The system includes a worn surface reverse modeling module, a worn surface initial normal vector calculation module, an initial machining trajectory and normal vector extraction module, a normal vector rotation angle polynomial regression denoising module, a machining trajectory position and posture NURBS fitting module, a position and posture trajectory curvature adaptive segmentation module, a segmented trajectory position and posture synchronization acceleration and deceleration look-ahead module, and a robotic motion and laser process coordinated control module. The method and the system realize posture optimization and follow-up control in a cladding process of the robot, and ensures the processing quality of laser repair.

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.

A low earth orbit satellite (LEO)-based communication system and method. The system comprises a low-orbit satellite constellation, a ground station, a user terminal and a network management and control center. The network management and control center comprises a track and link prediction module, a link quality prediction and evaluation module, a predictive resource pre-allocation and dynamic adjustment module and a space-ground cooperative control module. According to the communication system, active resource optimization and dynamic adjustment before actual switching are realized through track and link prediction, link quality prediction, predictive resource pre-allocation and dynamic adjustment and air-ground cooperative control. According to the method, the switching time delay can be greatly reduced, the packet loss rate is remarkably reduced, and the system throughput and the access success rate are also remarkably improved. Through predictive management, the performance, reliability and user experience of LEO communication are comprehensively improved.

The invention discloses an intelligent air conditioner water chilling unit control method and system based on predictive optimization. Operation data and weather forecast data are collected to establish a dynamic response reference, building cold load sudden change opportunity is recognized, the mismatching relation between magnetic suspension frequency and wet bulb temperature is detected, and energy-saving opportunity recognition data is generated to determine a cooling starting judgment table; identifying equipment response delay characteristics by using a dynamic response reference, extracting time sequence advantage parameters to determine dislocation configuration among multiple pieces of equipment, and generating coordination control parameters; cloud-local transmission delay analysis is carried out according to the coordination control parameters, and a coordination control sequence is generated through buffer opportunity identification and delay compensation; mechanical refrigeration suppression data is generated through energy efficiency mode classification, free cooling potential mining is implemented to form a cold source optimization factor, and an emergency response strategy is generated; an emergency response strategy is used to identify a multi-device linkage trigger critical zone, a prediction correction feedback network is constructed, a global collaborative optimization instruction is generated, and the system operation efficiency and the control precision are improved.

The invention provides a big data intelligent decision analysis method and system. The method comprises the following steps: acquiring vibration data and mechanical node stress distribution data in power equipment transportation, and extracting high-frequency harmonic distortion characteristics aligned with a vibration data timestamp; performing cross-domain correlation analysis on stress data fluctuation and harmonic characteristic dynamic change, and identifying a symbiotic mode of mechanical stress abnormality and electrical harmonic distortion by using a machine learning model; based on logistics path planning and equipment constraints, a cooperative control instruction for adjusting the transportation path and the power supply loop parameters is generated through a multi-target cooperative optimization algorithm; and constructing a joint risk assessment matrix, and dynamically adjusting the cooperative relationship between the transportation path and the harmonic suppression parameter according to the quantitative analysis result and the cooperative control instruction to realize the closed-loop decision of the power equipment. According to the technical scheme provided by the invention, the power equipment transportation data processing efficiency is remarkably improved.

The invention relates to a humanoid robot double-arm body operation method based on reinforcement learning, belongs to the field of robot cooperative control, and is characterized in that a strategy of trajectory block prediction and time integration fusion is used for double-arm cooperative control, and continuity and stability of double-arm operation are improved; in reinforcement learning control, a three-dimensional pose track generation and correction module is introduced, condition generation and denoising correction of a three-dimensional space are carried out on a track block layer, and geometric consistency and naturalness of a generated track are guaranteed; the invention further provides a reinforcement learning optimization framework and a simulation-reality migration process, and through system integration of reward, value guidance and migration processes, strategy deployability and safety are guaranteed; compared with the prior art, the method has the advantages that the naturalness, the collaboration and the success rate of double-arm operation can be remarkably improved, the generalization ability is high, and the good simulation-to-reality migration ability is achieved.

The invention relates to an injection mold temperature control method and device and computer equipment. The method comprises the following steps: acquiring temperature data of a plurality of temperature measuring points of the injection mold to obtain a temperature data matrix; establishing a heating and cooling input matrix and a temperature response matrix, and solving to obtain a heat transfer path coefficient matrix; taking the temperature data matrix as state input, constructing a depth deterministic strategy gradient control network comprising a gating circulation unit and a full connection layer, and performing linear mapping to obtain a hybrid action space; inputting the heat transfer path coefficient matrix and the mixed action space into a training environment, performing iterative training on a depth deterministic strategy gradient control network based on an experience playback mechanism, and updating network parameters by calculating a reward function to obtain a temperature control model; and deploying the temperature control model to a target control system, and calculating a real-time control parameter set. According to the invention, coordinated control of the heating stage and the stabilizing stage is realized, the injection molding period is shortened, and the energy consumption is reduced.

The invention discloses an intelligent control type expansion coating preparation reaction kettle device which comprises a reaction kettle body, the interior of the reaction kettle body is divided into a plurality of independent stirring cabins through partition plates, each stirring cabin is internally provided with an independent stirring assembly, a feeding assembly and an observation assembly, and each stirring assembly comprises a stirring shaft, stirring blades and a driver; the stirring blade is provided with a semiconductor chilling plate and a heating element, and the feeding assembly comprises a plurality of feeding channels connected with the stirring cabin, is provided with a flow meter, an electromagnetic valve and a dynamic adjusting module, and is used for independently controlling the feeding amount and the feeding speed of each cabin; the control system comprises a multi-channel data acquisition module, a learning module and a coordination control module, the multi-channel data acquisition module is provided with a sensor network for monitoring the temperature, the pressure and the material state of each stirring cabin in real time for each stirring cabin, and the control system performs coordination control on the stirring cabins according to preset parameters or autonomous learning results. And the stirring speed, temperature and feeding strategy of each cabin are dynamically adjusted.

The invention relates to the technical field of production line control, and discloses an intelligent control method and system for production line mold machining. According to the method, a pressure distribution state is determined by obtaining initial cutting parameters of a cutter and surface pressure data of a mold, mold node stress distribution is calculated by utilizing a three-dimensional finite element model, and a stress concentration position is identified. And predicting a mold deformation trend through a linear regression model according to the thermal expansion coefficient and the environment temperature data. Meanwhile, the initial cutting parameters are optimized by combining a cutting parameter optimization objective function, and the objective function comprehensively considers factors of tool wear, surface quality and residual stress. And on the basis of a die deformation prediction result, a dynamic compensation algorithm is adopted to adjust supporting structure parameters and optimize cutting parameters, and finally optimal cutting parameters and a target tool path are generated. According to the method, precise coordination control over pressure distribution and mold deformation under the complex working condition is achieved, the residual deformation risk is reduced, and the mold machining precision is improved.

The present invention relates to a multi-region collaborative control method and device for a high-precision pressure sensor array. The method: based on a first collaborative sampling parameter, data is collected from multiple sensor units in a pressure sensor array to obtain a pre-processed pressure data set and a sensor unit spatial position matrix; a spatial weighted pressure covariance matrix is ​​constructed; eigenvalue decomposition and information entropy calculation are performed, and the number of target pressure points is determined according to the minimum description length criterion and the information gain ratio; a pressure feature subspace and a local perception domain are constructed; the pre-processed pressure data set is projected onto the pressure feature subspace to generate a sensor unit state data set; a multi-layer attention encoder is input for processing, and the first collaborative sampling parameter of the pressure sensor array is adjusted to obtain a second collaborative sampling parameter. The implementation of the present invention improves the system's response sensitivity to local pressure changes, thereby realizing multi-region collaborative control of pressure sensing.

The invention discloses an unmanned aerial vehicle cluster collaborative target hunting method based on brain-like calculation, and belongs to the technical field of autonomous navigation robot and multi-robot coordination control, and the method comprises the steps: obtaining a multi-view image under a camera view, and obtaining the local pose and covariance of each unmanned aerial vehicle; obtaining a global consistent relative pose of the unmanned aerial vehicle cluster according to the local pose and the covariance of each unmanned aerial vehicle; performing state estimation on the target by adopting an unscented Kalman filtering algorithm to obtain target state information under a unified coordinate system; using a Bezier curve generation algorithm to obtain a target motion trail in a section of historical state; the method comprises the following steps: expanding an unmanned aerial vehicle cluster through a centroid extension method to obtain a surrounding queue of target surrounding, solving the minimum surrounding cost by adopting a Gaussian Newton method, and generating a surrounding position of each unmanned aerial vehicle; the motion trajectory of the unmanned aerial vehicle is generated by adopting mixed A * search, and a final surrounding trajectory sequence is generated, so that the accuracy of motion state estimation is remarkably improved.

The invention discloses an intelligent coordination control method and system for power plant equipment based on multi-source heterogeneous data, and belongs to the technical field of intelligent manufacturing and industrial automation, and the method comprises the steps: deploying a multi-mode sensor network in the power plant equipment, collecting the multi-source heterogeneous data in real time, and carrying out the real-time data preprocessing through an edge calculation node; carrying out collaborative modeling on the preprocessed data by adopting a hybrid analysis framework, predicting an equipment state trend, identifying a fault propagation path, positioning a root cause and optimizing a maintenance decision scheme; the equipment failure probability is evaluated through a fault diagnosis result, a grading early warning mechanism is triggered, and a rule base is updated and optimized in combination with a dynamic knowledge base; a three-dimensional model is constructed by using a digital twinning technology to carry out virtual simulation and remote control, and maintenance guidance is carried out through an augmented reality auxiliary technology. According to the method, efficient real-time monitoring and fault prediction are achieved, the fault diagnosis time and the operation and maintenance cost are remarkably reduced by combining the fault tree model and the digital twinning technology, and the equipment operation safety and reliability are improved.

The invention relates to a boiler combustion optimization control system of a thermal power plant, relates to the technical field of thermal power generation control, and aims to solve the problems of inaccurate multi-source data perception, slow control response and insufficient multi-target collaborative optimization capability under fire coal quality fluctuation and load change. The system comprises a data perception and fusion module, a working condition self-adaptive identification module, a multi-target dynamic optimization decision module and a distributed execution control module, and through multi-source data acquisition and feature extraction, depth time sequence mode identification, multi-target optimization model solving and distributed coordination control, the multi-target dynamic optimization decision module and the distributed execution control module are subjected to multi-source data fusion. Dynamic balance of boiler heat efficiency improvement and nitrogen oxide emission reduction is achieved, and adaptability and control precision of the system under complex working conditions are enhanced.

The invention discloses a processing control method and system for a carbon fiber composite transmission shaft, and relates to the technical field of intelligent transmission shaft manufacturing, and the method comprises the steps: dynamically adjusting the control current of a magneto-rheological damper through a PID controller based on a process control parameter set, and recording tension fluctuation and temperature gradient data; inputting tension fluctuation and temperature gradient data into an ANSYS coupling simulation engine, establishing a three-dimensional temperature field model, calculating a resin flow compensation coefficient, generating a temperature-pressure cooperative control curve through an NSGA-III multi-objective optimization algorithm based on the resin flow compensation coefficient, and calculating the resin flow compensation coefficient and the temperature-pressure cooperative control curve according to the resin flow compensation coefficient and the temperature-pressure cooperative control curve. And integrating through a time sequence control engine to generate a solidification control instruction. According to the invention, a multi-source data fusion algorithm is combined with the industrial Internet of Things edge computing gateway, so that accurate monitoring and synchronous processing of the process control parameter set are realized, and the dynamic behavior of the carbon fiber composite material in the processing process is comprehensively reflected.

The embodiment of the invention discloses a double-truck-crane cooperative hoisting monitoring method and device, electronic equipment and a storage medium. The method comprises the steps that component parameters and operation environment parameters of hoisting operation are collected, a reference data model is constructed, and an action expected value and a safety control threshold value are set; state acquisition devices are arranged on the two truck cranes to acquire multi-source data in real time and transmit the multi-source data to a central control system so as to realize synchronous sensing of the two trucks; processing multi-source data based on a data fusion algorithm, and constructing a dynamic attitude model and a collaborative operation state model; a state prediction correction value in the model is compared with the reference data model in real time, deviation is judged, and safety early warning is triggered; and generating a feedback control instruction, dynamically adjusting the hoisting operation, correcting the coordination deviation, and realizing the coordination control of the hoisting posture. In this way, through multi-source data fusion and real-time feedback control, the safety and coordination of cooperative lifting operation of the double truck cranes are remarkably improved.

The invention belongs to the technical field of optical storage cooperative control, and discloses a new energy grid-connected optimization control method based on an energy storage system, which comprises the following steps: collecting a power grid impedance fluctuation amplitude, a harmonic distortion rate and environment temperature data, obtaining an initial SOC value, correcting the SOC value, calculating an adjustable power margin and a response delay parameter of a photovoltaic unit, and calculating the optimal power grid impedance fluctuation amplitude and the optimal power grid impedance fluctuation amplitude. Generating an efficiency responsibility table; constructing a response cluster, generating a frequency modulation task package, and further generating a three-dimensional efficiency mapping table; the inertia demand is dynamically calculated in combination with the power grid frequency change rate, a responsibility transfer strategy is automatically activated in a high-altitude and high-temperature scene, and an efficiency instruction set is generated; analyzing response deviation by simulating typical disturbance of a high-altitude weak power grid scene, positioning a deviation source in combination with historical data, and dynamically adjusting an efficiency weight correction factor; and updating a responsibility allocation rule of the efficiency responsibility table, disrupting the low-efficiency cluster, and recombining the high-adaptability unit.

The invention discloses an intelligent control system and method for a multi-unit parallel water-cooling refrigerator, and belongs to the technical field of refrigeration equipment. The control system comprises a data acquisition module, a load prediction module, an energy efficiency optimization module and a cooperative control module. The data acquisition module is used for acquiring the running state and environmental parameters of the refrigerator in real time; the load prediction module is used for predicting the refrigerator thermal load based on the operation state and the environmental parameters; the energy efficiency optimization module is used for optimizing operation parameters of each unit according to the predicted thermal load of the refrigerator; and the cooperative control module is used for cooperatively controlling the compressor frequency and the cooling water flow of each unit according to the optimized operation parameters. According to the intelligent control system and method for the multi-unit parallel water-cooling refrigerator, precise temperature control and energy efficiency optimization of the multi-unit parallel water-cooling refrigerator can be achieved, load and water flow distribution is uniform, temperature fluctuation is small, system energy consumption is low, and the temperature control and energy saving requirements of a high-quality cold chain can be met.

The invention discloses a coordinated control method and system for comprehensive energy multi-agent coordinated group control and autonomous decision making. The method comprises the following steps: establishing a relaxation strategy of an agent corresponding to a distributed power supply; the main coordinator iteratively solves to determine a global coordination signal according to the global operation data of the integrated energy system and the operation data of each agent by taking the lowest total cost, the highest system energy efficiency, the minimum carbon emission and the minimum global penalty coefficient meeting the global relaxation constraint as a relaxation global optimization target; the minimum operation cost of each intelligent agent, the minimum response deviation to a global coordination signal and the minimum deviation between an actual state and a reference state are taken as control targets; establishing an electricity price autonomous decision-making model of each agent and a response power boundary autonomous decision-making model for a global coordination signal; and based on the control target of each agent, the electricity price autonomous decision-making model and the response power boundary autonomous decision-making model, predicting to obtain a control target value of each agent, and realizing balance between a global optimization target and local autonomy.

The invention relates to the technical field of building environment intelligent control, provides an air conditioner distributed regulation and control system based on a neural network and a method thereof, and is suitable for energy efficiency optimization and comfort guarantee of multi-area buildings. The system comprises a coordination control module, an optimization decomposition module, a region control module, a model evolution module and a strategy self-learning module, and region-level scheduling and parallel solving of a control task are realized by constructing a multi-objective optimization model and performing decomposition based on an alternating direction multiplier method; constructing a control model by using a hypergraph neural network, and supporting online updating, migration deployment and adaptive adjustment; strategy self-learning is realized through a strategy evaluation and aggregation mechanism driven by control errors, and a control closed loop is formed. The system improves the precision, flexibility and energy efficiency performance of air conditioner regulation and control, and is suitable for building scenes such as intelligent buildings and green parks.

The invention discloses a flexible robot motion control system and method based on a visual language action model, and the system comprises visual sensors which are disposed at a plurality of joints of a flexible robot, and are used for observing the multi-joint sensing information of the flexible robot; the remote voice input unit is installed on the flexible robot body and used for inputting a voice instruction of remote operation to the flexible robot; the motion control module based on the VLA framework is mounted in the flexible robot and used for controlling the flexible robot to act based on a visual language action model according to the multi-joint sensing information and the voice instruction; through the design of the intelligent control system, the complexity of the system is remarkably reduced, the control precision and flexibility are optimized, the coordination control problem of complex body segment units is solved, high-performance autonomous motion control is achieved, the system cost is reduced, the adaptability of the robot in diversified environments is enhanced, and the robot can be widely deployed in complex application scenes.

The invention discloses a chassis coordination control method and system based on tire side deviation state evaluation, and relates to the technical field of four-wheel steering four-wheel drive vehicle dynamics control. The method comprises the steps that an expected front wheel rotation angle is determined based on an expected steering wheel rotation angle input by a driver, and a front wheel rotation angle is determined; further determining a feed-forward front wheel steering angle, a feed-forward rear wheel steering angle and an additional yaw moment based on feed-forward control and feedback control, determining a first tire slip angle and a second tire slip angle based on a road adhesion coefficient, and further determining a wheel torque control weight, a feedback front wheel steering angle control weight and a feedback rear wheel steering angle control weight; and further determining a driving yaw moment, a feedback front wheel steering angle and a feedback rear wheel steering angle, determining a total longitudinal force based on an expected longitudinal acceleration input by a driver, and performing four-wheel torque distribution based on the total longitudinal force and the driving yaw moment to obtain a four-wheel driving torque. The driving stability of the vehicle under the limiting working condition can be improved.

The invention relates to an aircraft-trailer system coordination control method fused with reinforcement learning, and the method comprises the steps: constructing an aircraft-trailer system mathematical model, and determining the coupling relation between control variables and state variables, the control variables including trailer movement control variables and system steering control variables; aiming at trailer movement control variables, designing a model-based self-adaptive controller; aiming at the steering control variable of the system, designing an attitude compensation controller based on reinforcement learning; the self-adaptive controller and the attitude compensation controller are fused, corresponding control signals are output to the aircraft and the trailer, and motion control over the aircraft and the trailer is completed. Compared with the prior art, the self-adaptive control law based on the virtual variable is designed through the decoupling kinematics model; and aiming at system parameters and external uncertainty, designing a compensation method based on reinforcement learning. A series of simulation is carried out under different conditions, and results show that the method can ensure coordinated movement of the aircraft and the trailer under different conditions.

The invention discloses a multi-target cooperative control method and device for a direct drive motor, and relates to the related technical field of motor control, and the method comprises the steps: obtaining a motor control scheme corresponding to a predetermined control scene; establishing a motor control expectation vector; obtaining a control expectation deviation evaluation result; if the control expectation deviation evaluation result does not meet the deviation evaluation multilateral constraint, generating a control optimization first guide factor; generating a first motor control strategy set; and performing multi-target joint optimization on the first motor control strategy set to generate a motor control optimization strategy, and controlling the direct drive motor in combination with a predetermined control scene. The technical problems that in the prior art, multiple targets of a direct-drive motor are difficult to control in a coordinated mode, complex working conditions lack an effective deviation evaluation and optimization mechanism, and the adaptive capacity is insufficient, so that the control accuracy of the direct-drive motor is insufficient, and the operation reliability is poor are solved. The technical effects of realizing multi-target cooperative control of the direct-drive motor and improving the control precision and the operation reliability are achieved.

The invention discloses a target tracking method fusing visual perception and motion control. The method comprises the following steps: improving a YOLOv8 model and pre-training the improved YOLOv8 model; s2, preprocessing an image acquired by a camera, inputting the preprocessed image into the YOLOv8 model trained in the step S1, and outputting a target bounding box and a target center pixel coordinate; target center pixel coordinates are transmitted to a development board of a pre-burning algorithm program through a serial port, the development board is designed according to a stepping motor mathematical model, sine acceleration and deceleration serve as the basis, PID control, sliding mode control and fuzzy control algorithms are fused, and code burning is generated after in-loop simulation optimization; and the development board drives a stepping motor to adjust the posture of the holder according to the target center pixel coordinate control instruction, so that the target stops moving after being kept in the view center. The problems that a traditional YOLOv8 model is large in target recognition error under the complex background, PID control is prone to oscillation under the nonlinear working condition, buffeting exists in sliding mode control, and response lags when fuzzy control is independently used are solved.

The invention discloses a power coupling-considered capacity ratio estimation method for a network-constructing / network-following multi-machine hybrid system, and the method comprises the steps: building a GFL-GFM hybrid system model, combining VSG power coupling small signal modeling and a dynamic relative gain matrix (DRGA), and quantifying the coupling strength of active / reactive power to output voltage; the frequency domain impedance characteristic of the GFM converter is simplified, and an ideal voltage source series equivalent reactance equivalent model is constructed; deriving an analytical relationship between a generalized short-circuit ratio (gSCR) and a capacity ratio based on the model, and generating a ratio formula in combination with engineering boundary sub-scenes to form a ratio range suitable for multiple scenes; for coupling time-varying characteristics, a closed-loop mechanism of'mixed mode-coupling sensing-correction calculation-execution feedback 'is constructed, and the ratio is dynamically adjusted. According to the method, coupling quantization and multi-machine coordination control are deeply fused, the problem that the external characteristic model precision and the multi-working-condition adaptability are difficult to consider under complex environment coupling is effectively solved, and the engineering applicability of the hybrid fan system is enhanced.

The invention discloses a wind and light storage coordination control method and system for a virtual synchronous machine, and the method comprises the steps: building a wind power plant-energy storage power station double-layer response mechanism at a first stage, and employing a multi-time scale coordination control strategy for a second-stage energy storage system; and in combination with the virtual synchronous control characteristics of the optical storage power station, establishing a multi-scene optimization model of a wind and light storage multi-source collaborative double-layer coupling mechanism, and realizing dynamic optimization of output of each unit among inertia support, frequency modulation capacity and energy storage health state. According to the method, wind power plant rotor kinetic energy release combined overspeed load shedding control, a fuzzy control means based on an energy storage charge state and a VSG control means based on a virtual inertia and damping coefficient self-adaption photovoltaic system are considered, then wind and light energy storage frequency modulation coordination is optimized, and the system inertia safety is effectively improved on various working conditions and different time scales.