1915 results about "Control models" patented technology

The invention discloses an air valve on-line self-adaptive adjusting method and system fusing digital twinning, and belongs to the field of air valve intelligent adjusting.The adjusting method comprises the following specific steps that firstly, edge devices collect and preprocess multi-source data of a corresponding air valve system, and based on the preprocessed multi-source data, the multi-source data of the corresponding air valve system are obtained; constructing a virtual simulation environment corresponding to the current air valve system; iI, in a virtual simulation environment, pre-training an air valve control model, identifying a causal relationship between each sensing parameter and an air valve adjustment result, and dynamically optimizing each control variable; according to the method, model prediction precision and simulation credibility are remarkably improved, dependence on a large amount of new data is reduced, cross-system rapid adaptive adjustment is realized, misjudgment and interference of confounding factors can be avoided during strategy optimization, interpretability and credibility of model decision are enhanced, data privacy is protected, convergence of a global strategy is accelerated, and the method is suitable for large-scale popularization and application. The overall intelligent level of the system is improved.

The invention discloses a tunnel ventilation control system and method based on artificial intelligence, and relates to the technical field of ventilation control, and the method comprises the steps: in a tunnel design stage, building an unsteady-state computational fluid dynamics model based on tunnel three-dimensional linear parameters, calculating turbulence structures under different traffic conditions through a large eddy simulation method, and calculating an unsteady-state computational fluid dynamics model; determining an optimal space configuration scheme of the fan group according to the distribution of the velocity field and the pressure field; in the tunnel construction stage, multiple types of sensor arrays are arranged along the vault and the side wall of a tunnel in a layered mode; in the tunnel operation stage, real-time vehicle tracks and speed distribution information of a traffic monitoring system are obtained; establishing a ventilation demand dynamic prediction model based on space-time correlation analysis, constructing a fan cooperative control model considering airflow organization optimization, and solving an optimal operation strategy by adopting a multi-target adaptive weight distribution algorithm; when a fire characteristic signal is monitored, the multiple sets of fans are coordinated to form a relay type smoke exhaust airflow organization.

The invention relates to the technical field of robot control, and discloses a master-slave cooperation-based control method, device, equipment and medium, and the method comprises the steps: collecting a spatial pose, a contact force and a dual-view image, and constructing a multi-modal demonstration data set; executing time alignment and normalization to form a standardized input sequence; action features, force features and visual features are extracted through the action modal coding network, the mechanical modal coding network and the visual coding network; fusing to form a multi-modal tensor sequence, and inputting the multi-modal tensor sequence into a Transform decoder to generate a joint time sequence representation; outputting a training action prediction vector from the joint time sequence representation through an action predictor, constructing a supervision loss function in combination with expert demonstration annotation to update each network, and obtaining an optimization control model; and processing the real-time input control driving execution mechanism based on the optimization control model. According to the method, the control model is trained and optimized through multi-modal sensing and time sequence modeling, the joint control instruction is generated in deployment, and stable execution of a complex interaction task is achieved.

The invention provides a heat storage heat pump system control method based on a physical information neural network, and belongs to the technical field of heat storage pump system intelligent control. Aiming at the problems that in the prior art, an algorithm is difficult to adapt to dynamic energy consumption requirements, engineering application of a model is difficult due to building space heterogeneity, high-order RC model prediction credibility is weak, engineering feasibility is poor and the like, a solution combining a physical information sequence to sequence neural network technology and a finite-state machine control strategy is provided. On the model level, a 2R2C resistance-capacitance RC model of building temperature change is established, and then a PI-Seq2seq prediction model is proposed based on the physical model. On the control flow optimization level, on the basis of an industrial and commercial time-of-use electricity price policy, an FSM control model is designed, a system state set is defined, parameters and a transfer function are input, and a control rule is constructed in combination with the working period of a building heat pump and the characteristics of a heat storage tank. And finally, energy consumption cost optimization and indoor temperature stabilization under the peak-valley electricity price are realized.

The invention relates to the technical field of industrial robot control, and discloses a robot end trajectory optimization method and system based on industrial vision, and the method comprises the steps: obtaining a workpiece surface image in real time through a vision sensor, and obtaining deformation data through image enhancement and feature extraction; when the deformation quantity exceeds a threshold value, calculating a multi-axis coordination parameter by adopting an optimization algorithm to generate a trajectory correction instruction; integrating speed constraints by updating a control model, and determining a final trajectory by using Kalman filtering to fuse sensor feedback data; and the control parameters are iteratively adjusted in the deformation feedback loop, and a stable machining track is formed. The method can solve the problem that in the prior art, the robot tail end track precision is low.

The present invention belongs to the field of smart power grids and power system automation control. Disclosed is a load emergency control method based on a dynamic event triggering mechanism, which method comprises: on the basis of an indicator of safe operation of a system, performing region division on a distribution network; proposing an adaptive load level division method based on user response willingness, and by taking the minimization of the system operation cost of the distribution network as an objective, establishing a precise load control model of the distribution network; and using a dynamic event triggering mechanism to reduce communication between substations and a master station of the distribution network, so as to realize effective exchange and precise execution of load control policy information. The method of the present invention can precisely realize precise control of a load, and can effectively alleviate communication transmission pressure of a distribution network, thereby ensuring the stability of a power grid and the continuous supply of critical loads.

The invention relates to the technical field of injection molding, and discloses a plastic part injection mold regulation and control system and method based on a complex curved surface and a thin-wall structure, and the plastic part injection mold regulation and control system based on the complex curved surface and the thin-wall structure comprises a mold modeling module, a temperature modeling module, a control modeling module, a control optimization module and a control execution module; the plastic part injection mold regulation and control method based on the complex curved surface and the thin-wall structure comprises the steps that a mold three-dimensional model and thermal control area mapping are built, a heat conduction model is built, modal dimensionality reduction is conducted, a target temperature field and a performance function are combined, optimal temperature regulation and control are achieved through a predictive control algorithm, real-time feedback is matched, and a heating and cooling unit is driven. And dynamic closed-loop control of the mold thermal field is realized. According to the method, thermal control precision and energy consumption efficiency are optimized through Galerkin modal dimension reduction, model predictive control and a weighted performance function, global closed-loop temperature control is achieved by combining mapping of a three-dimensional area and a heating and cooling unit of the mold, and system safety and stability are enhanced.

The invention discloses a grid-connected inverter control method based on second-order linear active disturbance rejection, and particularly relates to the field of grid-connected inverter control. Voltage and frequency error signals between the output side of the inverter and the power grid are obtained, a pre-synchronization control model is constructed, and voltage amplitude and frequency synchronization is achieved; a second-order linear active-disturbance-rejection controller is introduced in the frequency synchronization control process, an extended state observer is adopted to estimate the output frequency, the frequency change rate and a disturbance term, and a state error feedback control law is combined to generate a frequency regulation control quantity, so that accurate frequency control is realized; after grid connection, a dual-channel LADRC power regulation controller is constructed, and decoupling tracking control is carried out on active power and reactive power; meanwhile, on the basis of fast Fourier transform processing as a stable grid-connected state criterion, the method has the advantages of being high in control precision, high in anti-interference capability, good in current waveform quality, high in adaptability and the like, and is suitable for various grid-connected application scenes.

The invention discloses a sewage treatment whole-process operation regulation and control calculation system based on deep learning, and relates to the technical field of intelligent control, and the system comprises a strategy optimization module which optimizes a future pollution trend prediction sequence and a regulation and control factor weight table by using a reinforcement learning strategy network in combination with a genetic algorithm optimizer, dynamically generating an aeration, dosing and backflow operation parameter combination aiming at a regulation target to form a dynamic regulation instruction set; the instruction execution module is used for issuing a dynamic regulation and control instruction set to the programmable logic controller by utilizing an industrial control interface, and collecting response information and real-time effluent quality data as execution feedback information; and the model updating module carries out error comparison on execution feedback information and a future pollution trend prediction sequence, constructs a weighted error sequence, and dynamically corrects deep network parameters through an online fine tuning strategy to form a prediction control model. According to the method, the generalization ability and robustness of the predictive control model are remarkably improved.

The invention relates to the technical field of intelligent power distribution and electrical equipment state monitoring, in particular to an intelligent power distribution cabinet data acquisition control system and method, and the method comprises the steps: presetting a temperature sensor and a Hall sensor at the lap joint of a circuit breaker contact and a copper bar of an intelligent power distribution cabinet; calibrating inherent parameters of the system; in response to starting operation of the intelligent power distribution cabinet, the following steps are triggered: obtaining real-time load current, real-time environment temperature and actual temperature through filtering processing; generating a theoretical healthy temperature through a thermoelectric coupling evolution equation; calculating aging resistance increment through dynamic impedance differential evolution processing; generating an aging resistance estimated value through contact state multi-dimensional evaluation; generating a dynamic current limit value through an intelligent current limiting control model; outputting a control signal to adjust the operation state of the intelligent power distribution cabinet; according to the invention, the problem that a traditional single threshold scheme cannot accurately identify a real health state is solved, and the sensitivity and accuracy of contact aging detection are remarkably improved.

The invention belongs to the field of robot control and bionic machinery, and particularly relates to a sensing-driving-control integrated dexterous hand single-finger modularization control method which comprises the following steps that a series-parallel connection hybrid three-degree-of-freedom mechanical structure is adopted, a modularization single-finger mechanism is constructed, and a single-finger driving control circuit board is designed on the modularization single-finger mechanism; a motor outputs joint driving torque through a lever transmission mechanism, and the joint motion state is fed back in real time through a magnetic encoder. Collecting a contact force vector F in real time through a sensing system; the single-finger driving control circuit board adopts a layered control framework to sequentially execute current closed-loop control, speed closed-loop control and position closed-loop control; based on the contact force vector F, generating a joint angle compensation amount through an admittance control model; a target joint angle instruction is received through a bus, and multi-module cooperative control is achieved. According to the invention, a current-speed-position three-ring nested control architecture is designed, and the disturbance rejection ratio is effectively improved.

The invention discloses an internal and external network security service passing method and system based on edge computing, and belongs to the technical field of network security, and the method comprises the steps: obtaining multi-dimensional attribute information of an edge node, generating a security policy basic data set, generating a traffic feature fingerprint and a self-adaptive environment adaptive fingerprint, and calculating a fusion matching degree; edge security entity nodes are generated in combination with service scene adaptive threshold clustering, and then a security policy meta-model with a security policy blueprint as a core is constructed; analyzing the security policy meta-model through a multi-modal semantic analysis engine, generating a dynamic security enhancement model, and mapping the dynamic security enhancement model to a hierarchical security control model; based on the hierarchical model, outputting edge node internal and external network safety passage configuration and a corresponding safety passage ledger through a scenarized configuration generation algorithm; according to the method, adaptive generation, dynamic optimization and accurate execution of the security policy are realized, and the security, the automation level and the operation and maintenance efficiency of internal and external network passing in the edge computing environment are effectively improved.

The invention provides an industrial air conditioner and waste heat recovery collaborative intelligent optimization control method and related equipment, and is applied to the technical field of data processing. Industrial air conditioner and waste heat recovery system data are fused through the Internet of Things and edge calculation, a standardized parameter sequence is generated through wavelet noise reduction and is converted into an energy flow network atlas, high-energy-consumption working conditions are subdivided through fuzzy clustering, and a dynamic energy efficiency coupling model is constructed. And based on the model, constructing an optimization equation by using an improved particle swarm algorithm, and obtaining the energy consumption of the key node in combination with an energy gradient utilization matrix. And an energy-saving potential coefficient is generated through the multi-dimensional feature matrix, a production plan grouping operation mode is combined, a self-adaptive optimization control model is constructed by using deep reinforcement learning, and finally a real-time regulation and control instruction and an energy efficiency scheme are generated to realize system collaborative optimization.

The invention relates to the technical field of industrial control, in particular to an industrial stacker intelligent control system based on machine vision, and the system comprises a state resolving module which is used for carrying out the feature region decoupling of original observation data, and resolving a position deviation matrix and a deformation state variable for a rigid subspace and a flexible subspace respectively; the dynamic modeling module is used for identifying the first-order inherent frequency and the damping ratio of the mechanical structure on line and establishing a second-order impedance contact model; the impedance control module is used for generating an expected speed based on the position deviation and adaptively adjusting an impedance parameter according to a deformation state so as to carry out compliant modulation on a speed vector; and the estimation shaping module is used for correcting system time lag by using a Kalman observer and inhibiting residual vibration in combination with a zero-vibration input shaper. Through multi-dimensional physical field fusion and dynamic modeling, the problem that a rigid control model is mismatched with a flexible operation object is solved, and self-adaptive compliant control and vibration-free accurate operation of the stacking machine in the unstructured environment are achieved.

The invention relates to the technical field of distributed resource scheduling, and discloses a power distribution network distributed resource cluster optimization regulation and control method, system and device considering power communication coupling and a medium, and the method comprises the steps: obtaining the related data of a power distribution network and a communication network, and building a comprehensive division index set based on a target communication network; performing cluster division according to the index set to obtain a plurality of power distribution network distributed resource clusters; an optimization regulation and control model is constructed based on the division result, a target function is oriented to the minimum cluster operation cost, and power flow, communication operation and resource operation characteristic constraints are comprehensively considered; solving the model by adopting a time and event dual-drive solving strategy; a rapid solving method based on multi-graph convolution is utilized to extract a data space dependency relationship, a mapping mechanism from an operation state to an optimal strategy is constructed, and solving efficiency is improved; and performing optimization regulation and control on the cluster according to the solved optimal strategy to realize efficient and stable operation of the power distribution network under power communication coupling.

The invention discloses a reconfigurable casting process module hierarchical control method, which comprises the following steps of: firstly, acquiring a multi-source sensor data stream generated in a casting task execution process, the data comprising key process parameters such as temperature, pressure, flow velocity and the like; based on this data stream, an initial condition feature set for characterizing the dynamic condition is determined. And then, carrying out segmented working condition optimization processing on the feature set by utilizing a hierarchical decoupling regulation model, and obtaining an optimized working condition feature set through system coupling dissociation and random disturbance stripping. And finally, performing feature reconstruction processing on the optimization feature set, and generating an optimization control instruction accurately matched with the current casting process to adjust the pouring speed, the cooling rate or the pressure parameter in real time, thereby realizing closed-loop optimization control of the casting process. According to the method, the control precision and the process stability under complex working conditions are effectively improved.

The invention relates to a multi-energy collaborative intelligent micro-grid control method, device and equipment and a medium. According to the method, a micro-grid frequency signal is collected in real time, the change rate of the micro-grid frequency signal is calculated, the inertia requirement of a system is accurately evaluated, the maximum virtual inertia capable of being provided by each unit is dynamically calculated in combination with the physical characteristics and operation constraints of each energy unit, and the system requirement and the unit capacity are dynamically matched through grey correlation analysis to generate an optimal distribution scheme. And then the special control models of all the units are driven to generate differentiated power regulation instructions, and finally power support is cooperatively executed through the power electronic converter, so that accurate matching and efficient cooperation of multi-energy virtual inertia capability are realized, the frequency stability of a high-proportion new energy power grid is remarkably enhanced, and the system performance is improved. The problems of resource allocation mismatch, poor control adaptability and the like in a traditional method are effectively avoided, and the dynamic safety of the system is guaranteed under the complex operation working condition.

In a thermal management system for a vehicle and a method for controlling the same, the thermal management system includes a fluid transfer device configured to execute thermal management of at least one object, to be thermally managed, through introduced ambient air, and a controller configured to determine a target air flow rate required for thermal management of the at least one object to be thermally managed, and to control the fluid transfer device based on an optimal control value determined using a control model for a predictive state value according to a current state value and the target air flow rate.

The invention belongs to the technical field of intelligent measurement, and discloses a mobile terminal automatic measurement method and system based on YOLO key point detection and an AR platform, and the method comprises the steps: obtaining multi-modal data, and carrying out the preprocessing; performing target detection and key point positioning based on the RGB image to obtain two-dimensional key point coordinates; based on the depth image and the pose information, a three-dimensional space coordinate system is established, the two-dimensional key point coordinates are converted into the three-dimensional space coordinate system, and in the conversion process, a self-adaptive weight fusion mechanism is adopted to perform fusion on the multi-modal data to obtain a fusion result; a nonlinear error propagation control model is adopted to suppress error accumulation in the process of converting the two-dimensional coordinates into the three-dimensional coordinates; and calculating morphological parameters of the target object based on the converted three-dimensional key point coordinates. According to the invention, the problems of low measurement efficiency, poor precision, high equipment cost, insufficient cross-platform adaptation and the like in the prior art are solved, and high-precision, real-time and low-cost automatic measurement of various types of target objects at the mobile terminal is realized.

The embodiment of the invention provides an unmanned ship cooperative hunting control method, and belongs to the technical field of control, and the method specifically comprises the steps: distributing a main hunting target for each chasing unmanned ship; for each chasing unmanned ship, an original observation vector containing a variable-length interaction sequence is formed according to the distributed main hunting target of the chasing unmanned ship; a variable-length interaction sequence in the original observation vector is converted into a standardized state vector of a fixed dimension through a two-stage attention mechanism; inputting the standardized state vector into a strategy network of the unmanned ship, and outputting a continuous action control instruction; and calculating an instant reward based on empirical data obtained after executing an action control instruction, and performing iterative updating on a strategy neural network and a corresponding value neural network by adopting a centralized training and decentralized execution mode based on a multi-agent depth deterministic strategy gradient framework to obtain a control model to generate a cooperative control scheme. Through the scheme disclosed by the invention, the control precision and adaptability are improved.

The invention relates to the technical field of power system control, in particular to a direct-current capacitor self-synchronizing grid-connected control strategy method, system, equipment and medium, a direct-current capacitor energy dynamic model is established based on an actual energy balance relation, a direct-current voltage signal is acquired in real time, and a synchronous compensation signal is calculated based on the acquired direct-current voltage signal; performing phase compensation and gain adjustment on the synchronous compensation signal through a correction link based on the calculated synchronous compensation signal, adjusting the internal frequency of the inverter based on the synchronous compensation signal after phase compensation and gain adjustment, controlling the DC voltage through the internal frequency of the inverter, and establishing a self-synchronization control model; and based on the self-synchronization control model, establishing an adaptive control algorithm, generating a damping coefficient and a virtual inertia coefficient through the adaptive control algorithm, and dynamically adjusting control parameters according to the damping coefficient and the virtual inertia coefficient.

The invention discloses a mountainous area sudden change wind field active control wind tunnel simulation method based on deep learning, and relates to the technical field of wind tunnel experiment research, and the method comprises the steps: obtaining a target wind field parameter in a mountainous area target region, constructing an MLP input vector according to the target wind field parameter in combination with wind tunnel real-time sensor data, a deep learning control model comprising an MLP forward mapping model and a Transform time sequence feedback model is established, the MLP forward mapping model generates a multi-fan array control signal according to the MLP input vector, a multi-fan array is controlled to generate a simulated wind field, the Transform time sequence feedback model generates a multi-fan array control correction signal according to a wind field difference value between the simulated wind field and a target wind field, and the multi-fan array control correction signal is controlled to generate a multi-fan array control correction signal according to a wind field difference value between the simulated wind field and the target wind field. The simulated wind field parameters are dynamically corrected in real time, and target wind field parameters are approached; according to the method, high-fidelity dynamic reproduction and intelligent adaptive control of the complex wind field in the mountainous area are realized through MLP feedforward generation and Transform feedback correction.

The disclosed method for training a robot control model includes performing, based on a plurality of multi-view images that have been masked, one or more operations to train a first untrained machine learning model to generate a first trained machine learning model that comprises a trained encoder, where the first trained machine learning model is trained to generate a plurality of reconstructions of the plurality of multi-view images prior to being masked; and performing, based on robot demonstration data, one or more operations to train a second untrained machine learning model that comprises the trained encoder to generate a second trained machine learning model, where the second trained machine learning model is trained to control a robot to perform at least part of a task.

The invention discloses a double-arm welding robot collaborative welding seam tracking control method and system, and the method comprises the steps: taking a near-end strategy optimization algorithm as a basis, introducing a task modulation mechanism, and enabling a control model to achieve differential response according to different welding seam type characteristics; thirdly, designing a segmented training mechanism under precision driving, and gradually improving the tracking precision and keeping convergence stability in the training process through a dynamic precision constraint guide strategy; and finally, precision correlation adjustment is carried out in the strategy and value objective function optimization process, and the balance between the control performance and the learning efficiency is realized. According to the method, the dependence on model precision and parameter setting is remarkably reduced, the robustness of the control method is improved, stable transition from millimeter-level tracking precision to submillimeter-level tracking precision is realized, and the generalization of the strategy and the engineering practicability are enhanced while high precision is ensured.

The invention discloses a composite joint control method based on a frequency conversion air source heat pump system, and relates to the technical field of air source heat pump systems, and the method comprises the steps: building an HPF control framework, building a multi-input and single-output control model through machine learning, and coupling a PID feedback signal to generate a composite control basic signal; building load single-step forward prediction is carried out by adopting an LSTM neural network, and a building load prediction value is output through training and calculation of the neural network; the heat pump compressor is subjected to staged joint control; forward control is carried out on the load side circulating water pump; a differential switching mechanism is arranged, and water supply temperature deviation is monitored in real time. According to the variable-frequency air source heat pump composite joint control strategy, an HPF control framework, a compressor staged joint control and differential switching mechanism, load side water pump forward control and equipment cooperative linkage are integrated, model dynamic updating and switching parameter optimization are introduced, and the limitation of a traditional single control mode is broken through.

The invention discloses an interpretable high-speed ramp intelligent driving vehicle confluence control method. The method comprises the steps that a state space is constructed, wherein the state space comprises the position and speed of a vehicle, lane information and the relative position and speed of adjacent vehicles; human-like decision guidance: training a human-like behavior discriminator based on real driving data by using a random forest model, screening action selection of reinforcement learning, and constructing a sample set containing state vectors and human-like action labels; reinforcement learning control; and carrying out interpretability analysis. Through an SHAP analysis method, an explainable DQN longitudinal acceleration control model in an automatic driving vehicle ramp merging process is provided, and an explainable automatic driving vehicle ramp merging control model is constructed in combination with an merging decision-making model constructed based on a random forest. According to the explainable high-speed ramp automatic driving vehicle confluence control method based on the confluence urgency degree and the environmental historical information, traffic conflicts during highway confluence can be relieved, and therefore the road passing efficiency is effectively improved.

The invention discloses a cooperative control method and system for combined optimization of an AGV and a manipulator, and the method comprises the steps: carrying out the unified modeling of the motion freedom degree of the AGV as the freedom degree of a base of the manipulator, and introducing environment constraints, and forming a combined control model; adopting a preset optimization method and introducing a joint optimization objective function to optimize the joint control model; in the operation process of the automatic guided vehicle and the manipulator, motion data of the automatic guided vehicle are collected in real time, posture change data of the automatic guided vehicle in a preset time window are predicted, and feedforward compensation control is conducted on the track of the manipulator according to a prediction result; and when the trajectory error or the motion singularity of the manipulator end effector exceeds a preset threshold value, the automatic guided vehicle executes active compensation pose adjustment to cooperate with the manipulator to complete the operation task. According to the invention, real-time adaptive optimization control of a dynamic environment and an unstructured operation scene can be realized by combining dynamic environment prediction, flexible force feedback and adaptive tail end rigidity adjustment.

According to the engine emission digital design system and method based on the virtual whole vehicle, the engine, the temperature control loop, the dynamometer and the emission analysis module are arranged on the bench subsystem, multiple circulation working conditions are pre-stored through the circulation working condition model in the control subsystem, and the main control model calls the target circulation working condition according to the design requirement; the driving control model simulates a vehicle driving state; obtaining a road resistance curve through a road resistance model; the engine compartment temperature control model controls the temperature control loop to realize temperature control; the engine working condition model controls the engine to work under the target engine working condition; the emission analysis module receives emission of an engine and analyzes emission components, and the master control model receives the emission components and judges whether the current emission of the engine reaches the standard or not. According to the scheme, engine emission design development under the condition that no physical whole vehicle exists can be achieved, the whole vehicle development period is greatly shortened, the development efficiency is improved, and the development cost is saved.

The invention discloses a laser wireless energy transmission system based on a vertical tracking structure and double-stage global optimization and a control method, and belongs to the technical field of photoelectric energy conversion and intelligent control. Aiming at the technical bottlenecks of high reflection loss of a photovoltaic array and MPPT failure caused by multi-peak characteristics due to non-uniform irradiation in the existing laser wireless transmission system, the invention provides three innovations: (1) designing a two-degree-of-freedom vertical tracking type photovoltaic array, and adjusting the pitch angle and azimuth angle of a photovoltaic panel in real time through a servo motor driving holder; a silicon nitride anti-reflection coating (the refractive index is smaller than or equal to 1.8) and a laser positioning sensor are combined to construct an incident angle deviation dynamic compensation model, and the reflection loss rate is reduced to be lower than 5%; (2) putting forward a global MPPT dual-stage optimization algorithm, dividing a voltage interval based on irradiation non-uniformity in a coarse positioning stage and generating an optimal candidate solution, and adopting an improved particle swarm algorithm (introducing an inertia weight dynamic adjustment mechanism and a local extreme value escape strategy) in a fine search stage to realize that the global maximum power point tracking precision reaches 99.2% in a multi-peak scene; and (3) establishing a reflection loss-power coupling control model, and solving the problems of tracking delay and energy oscillation of a traditional system through angle adjustment of the photovoltaic panel and closed-loop feedback of MPPT (Maximum Power Point Tracking) output. Compared with the prior art, the comprehensive efficiency of the system is improved by 18.6% under the dynamic irradiation condition, and the system is suitable for space laser power supply and mobile equipment wireless charging scenes.

The invention discloses a global cooperative control method and system for unbalanced fault ride-through of a multi-terminal DC power grid, and the method comprises the steps: constructing a fault detection model based on the positive sequence voltage drop depth and the negative sequence voltage size of the AC side of each converter station, automatically judging an unbalanced fault, and carrying out the fault detection when the fault is detected. Calculating the maximum allowable active current based on a fault station unbalanced current control model to carry out current limiting control on the converter station; and carrying out output power adjustment on the DC voltage deviation of the non-fault station based on the non-fault station power cooperation model. Asymmetric fault ride-through is carried out by using a constrained and optimized current instruction generation algorithm, and the capacity of a converter is dynamically and accurately distributed and utilized in a limited manner, so that the voltage support capability of a fault alternating-current power grid is maximized, and globally optimized adaptive power distribution is realized; the power of the non-fault station is calculated and distributed in a centralized manner and is dynamically adjusted according to the real-time available capacity, so that the self-adaption and global optimization of the running state change of the power grid are realized, and the fault handling toughness and reliability of the power grid are improved.