1563 results about "Dynamic control" patented technology

The invention discloses a truss structure wind-induced dynamic response prediction method and system based on physical enhancement. The method comprises the following steps: carrying out feature extraction and alignment fusion on input data containing condition parameters and wind speed time sequence data by utilizing a long short-term memory network and a physical enhancement attention mechanism; extracting multi-scale features from the fusion features through expansion convolution, and performing weighted aggregation on the multi-scale features; the physical priori knowledge of structural vibration is fused into position coding and a self-attention mechanism so as to carry out response prediction; and integrating physical model information of the truss structure and a dynamic control equation into a loss function, and calculating physical information residual loss so as to improve the physical interpretability of a prediction result. According to the method, data heterogeneity can be eliminated, complementary information can be fused, the multi-scale characteristic of wind-induced response is coped with, the accuracy and efficiency of wind-induced dynamic response prediction of the truss structure are effectively improved, and the physical interpretability and generalization ability are enhanced.

The invention relates to the technical field of battery management, and particularly discloses a dynamic control method for an intelligent balance management system of a lithium battery module, which comprises the following steps of: acquiring load current of the battery module and end voltage signals of each monomer in real time; calculating instantaneous internal resistance and polarization voltage parameters of each single battery in real time; the terminal voltage is compensated in real time, and a purified open-circuit voltage signal is generated; fusing the charge state reference value with the current integral result, dynamically calculating a confidence index according to a current standing condition and voltage stability so as to perform adaptive weighting, and outputting a smooth charge state estimation value; calculating a balance urgency degree index by combining the estimated value, the real-time temperature and the inconsistency change trend, and generating a differentiated balance control instruction; an energy transfer path is established and controlled according to the instruction, the rate is dynamically adjusted in the process, and operation is terminated when the duration or state consistency condition is met; according to the method, the stability of state estimation and the accuracy, safety and efficiency of balance control under the dynamic working condition are improved.

The invention relates to the technical field of computer intelligent operation and maintenance and artificial intelligence, in particular to an operation and maintenance scheme generation method, system and device based on cooperation of a large language model and a knowledge graph and a medium. The method comprises the following steps: denoising and abstracting multi-source heterogeneous operation and maintenance data by using a large language model, extracting key fact dimensions, further identifying a fault entity, mapping the fault entity to an operation and maintenance knowledge graph to position an initial anchor point, executing two-stage collaborative reasoning based on the anchor point, and generating a plurality of candidate traceability paths by alternately performing relationship exploration and entity exploration; and finally, reordering the candidate paths based on path length perception and semantic correlation, dynamically controlling the termination and understanding strategy of reasoning by using a comprehensive confidence score, outputting a final solution, effectively overcoming the problems of information redundancy interference reasoning and large model illusion in a complex operation and maintenance scene through the cooperation of a large model and a knowledge graph, and improving the reliability of the system. And the accuracy of root cause positioning and the executable performance of the solution are obviously improved.

The application discloses a processing method of gel candies, and belongs to the technical field of gel candies. The method comprises a control unit with multiple independent pouring units, each pouring unit comprising a pouring head and a temperature control module, and the following steps are performed: S1: initial sensing and parameter setting; S2: a staged dynamic pouring step; S201: a starting stage, in which pouring is started at P0 and the F0, and lasts for a predetermined starting time Δt1; S202: a stable stage, in which after Δt1 ends, a control mode based on real-time viscosity feedback is switched to, the heating power is adjusted to maintain the real-time viscosity μ of the material in a target range [μ min , μ max ], and pouring is performed at a basic pump pressure F base . Through the staged dynamic control, the application realizes the precision and high efficiency of the gel candy pouring process.

The invention discloses an intelligent driving evaluation method and equipment. The method comprises the following steps: acquiring environment perception data recorded by a real vehicle and a corresponding recorded vehicle pose; performing data transformation on the environment perception data according to a pose difference between the vehicle model pose and the recorded vehicle pose so as to generate transformation perception data corresponding to a vehicle model view angle; generating a prediction track based on the transformation perception data, iteratively updating the pose of the vehicle model according to the prediction track, and recording dynamic evaluation data; the dynamic evaluation data comprises an iteratively updated vehicle model pose, transformation perception data and a prediction track; and determining an evaluation performance index of the intelligent driving system based on the dynamic evaluation data. Therefore, a dynamic closed-loop evaluation mechanism under a real data condition is constructed, self-adaptive association of perception input and a vehicle motion state is realized, and an intelligent driving system can generate continuous decision feedback in an evaluation process, so that the time sequence consistency and stability of the system in dynamic control are accurately reflected.

The invention relates to the technical field of automobile dynamics control, in particular to an ambulance suspension intelligent agent based on deep reinforcement learning and an optimization method thereof, and the optimization method comprises the steps: S1, initializing an evaluation network and a control strategy network; s2, adopting a swarm intelligence algorithm to initialize a population; s3, the number M of evolution generations of the controller and the time step T of each generation are set, in each time step, the initial fitness of the controller is set to be 0, and the fitness of the controller is updated according to the reward value and the auxiliary reward of the current time step; s4, after each intergeneration is finished, updating the population according to the fitness by adopting a swarm intelligence algorithm, and training the evaluation network and the control strategy network to update network parameters; and S5, after M generations of evolution are completed, screening out the controller with the highest fitness from the controllers, and setting the network parameters corresponding to the controller in the control strategy network. The invention is at least beneficial to improving the adaptability of the ambulance to different working conditions.

The invention relates to the technical field of information processing, and discloses a method for dynamically regulating and controlling production parameters of a plastic film by utilizing program process scheduling. The method comprises the following steps: converging a real-time parameter sequence to a digital twinborn body to establish virtual mapping, and constructing a multi-dimensional data model; analyzing a material state evolution trend in the digital twinborn body, synchronizing with a physical production line state, fusing historical data to predict a potential influence range, and generating a regulation and control demand report; generating an adjustment scheme draft, performing priority ranking on simulation tasks in the adjustment scheme draft by adopting a program process scheduling algorithm, dynamically allocating computing resources, executing the simulation tasks in parallel, and determining an optimization parameter set in combination with a gradient descent algorithm; and iteratively correcting the parameters in the digital twinborn body through feedback circulation to generate a final coordination control instruction, inputting the final coordination control instruction into the digital twinborn body, confirming synchronism and outputting a report. According to the invention, the precision of dynamic regulation and control of the production parameters of the plastic film is improved.

The invention discloses a self-adaptive coordination dynamic voltage control method and system based on a micro-grid, and relates to the field of micro-grid self-adaptive coordination dynamic voltage control. In order to solve the defects that an existing micro-grid model cannot adapt to complex disturbance and dynamic load and cannot accurately reflect the actual network state when regulating and controlling the dynamic voltage of the micro-grid, the current operation mode of the micro-grid is analyzed; when the synchronization error is smaller than the tolerance, the PCC is closed and switched to a grid-connected operation mode; if the current large power grid is normal, the grid-connected operation mode is maintained; if the current large power grid is abnormal, starting island detection and judging that the current microgrid is in an off-grid operation mode; executing a corresponding grid-connected control strategy, an off-grid control strategy or a switching pre-control strategy according to the voltage value; and when the execution of the current control strategy is detected to be abnormal, automatically terminating the execution of the current control strategy, rolling back to the previous safe state, and starting a standby plan at the same time. The method is mainly used for carrying out adaptive coordination dynamic control on the output voltage of the micro-grid.

The invention relates to the technical field of drilling parameter optimization control, and provides a petroleum drilling parameter optimization method and system and a storage medium. Comprising the steps that torque fluctuation amplitude and bit pressure response delay data are collected in real time, an original data sequence is constructed, and parameter trend data are obtained; analyzing the parameter trend data to identify a stratum change mode, extracting trend feature information, and when the trend feature information meets a preset condition, determining a corresponding abnormal mode identifier; generating a parameter adjustment direction of drilling control according to the abnormal mode identifier, and optimizing the parameter adjustment direction in combination with parameter weight analysis and a real-time feedback mechanism to obtain final optimization guidance data for guiding drilling operation; and mapping the final optimization guidance data to a control interface, and generating a dynamic adjustment instruction for drilling operation. The problems that in the existing petroleum drilling process, parameter adjustment lags behind, and stratum changes are difficult to adapt are solved, and intelligent optimization and dynamic control over drilling parameters are achieved.

The invention discloses a flux linkage tracking full-speed-domain high-precision predictive current control method for a permanent magnet synchronous motor, and the method comprises the steps: sampling a phase current signal, a position signal and a speed signal of the permanent magnet synchronous motor, and obtaining a dq sampling current through coordinate transformation; calculating a predicted current according to the established tracking discrete model; the sinusoidal variation characteristic and the dynamic transient characteristic of the resistance voltage drop are accurately considered in the current prediction and reference voltage calculation stage, and the resistance voltage drop is accurately represented through accurate feed-forward compensation of predicted current and reference voltage; a driving signal of the inverter is obtained through coordinate transformation and space vector modulation, and full-speed-domain high-precision fast-response current prediction control over the permanent magnet motor is achieved. According to the method, the current flow steady state control error caused by the model error is reduced, the current control precision and the dynamic response performance of the permanent magnet synchronous motor in the full-speed domain are effectively improved, and a high-precision model basis is provided for high-precision position-free control and parameter online identification.

The invention provides an electromagnetic quasi-zero stiffness vibration isolator and a control method, and belongs to the technical field of vibration isolation. The vibration isolator comprises a base, a positive stiffness mechanism, a negative stiffness mechanism, a positive and negative stiffness connecting mechanism and a dynamic control module. The positive stiffness mechanism adopts a hydraulic spring to provide basic positive stiffness; the negative stiffness mechanism comprises a plurality of stator units distributed on the base in an annular array and a rotor suspended in the center of a magnetic field of the stator units, and is used for generating nonlinear negative stiffness; the top end of the hydraulic spring supports a load through the bearing table and keeps a preset gap with the inner wall of the rotor; the positive and negative stiffness connecting mechanism realizes displacement transmission and cooperative adjustment; the dynamic control module comprises a displacement sensor and a controller, the sensor collects displacement signals, and the controller adjusts excitation currents of the stator units according to the signals and dynamically adjusts electromagnetic force, so that the vibration isolator is kept in a quasi-zero stiffness state. The quasi-zero stiffness state can be still kept when the load changes, load self-adaption low-frequency vibration isolation is achieved, and the system stability and the vibration isolation performance are improved.

The invention discloses a digital workshop multi-equipment collaborative operation control system based on industrial Internet of Things, which relates to the technical field of industrial Internet of Things collaborative control and comprises a data acquisition module, a health degree evaluation module, a collaborative scheduling optimization module, an instruction control module, a dynamic rescheduling module and a verification updating module. According to the method, a dynamic equipment association network is constructed, so that health degree evaluation considering the coupling influence between equipment is realized; and based on multi-source data fusion monitoring and real-time deviation analysis, a closed-loop dynamic rescheduling scheme subjected to physical and security verification is triggered and generated, and meanwhile, a system knowledge base is continuously updated by utilizing operation process data. According to the scheme, equipment isolation evaluation is converted into system coupling evaluation, so that static scheduling is upgraded to closed-loop dynamic control with self-adaption and continuous optimization capabilities, and the overall toughness and operation efficiency of multi-equipment collaborative operation are enhanced.

The invention provides a cell acoustic characteristic parameter estimation method and system based on a full-connection neural network and a computer medium, and relates to the technical field of biomedical engineering and micro-nano operation, and the method comprises the steps: constructing a three-layer spherical shell model of a target cell, defining the radius, density and sound velocity of each layer and a surrounding medium, and constructing a three-layer spherical shell model of the target cell; the acoustic impedance and the wave number of each layer are calculated; obtaining a transducer driving parameter, a displacement speed of the cell or equivalent particle solid sphere particle, and physical parameters of the cell and a medium; inputting the input feature data into a pre-trained full-connection neural network model to output acoustic characteristic parameters such as a scattering coefficient and an internal refraction coefficient; and calculating acoustic radiation force based on the acoustic characteristic parameters to realize dynamic control. Based on the full-connection neural network and the three-layer spherical shell model, the cell acoustic parameters are efficiently and accurately estimated, the problems that a traditional method is complex in calculation and insufficient in precision are solved, and accurate operation of the acoustic tweezers on the cells is achieved.

The invention belongs to the technical field of civil engineering, and discloses a shield tunneling dynamic control method based on propulsion-settlement-grouting closed-loop linkage, which comprises the following steps: S1, layout of settlement monitoring points and acquisition devices and data acquisition; s2, settlement data analysis and multi-stage early warning identification; s3, grading response adjustment of synchronous grouting parameters is carried out; s4, propulsion parameter coupling optimization control; s5, grouting effect evaluation and parameter correction; and S6, secondary grouting and emergency plugging mechanism. According to the shield tunneling dynamic control method, three elements of propulsion, settlement and grouting are brought into a unified dynamic closed-loop control framework for the first time, multi-source monitoring data, an early warning mechanism and a parameter collaborative optimization strategy are fused, and intelligent coupling control of settlement prediction, response adjustment and feedback evaluation in the shield tunneling process is achieved; and pertinence, real-time performance and construction safety of grouting regulation and control are effectively improved.

The invention relates to the field of industrial automatic control systems, and discloses a mine hoisting cage positioning information and emergency plan linkage control system, which comprises a conventional dynamic control unit and an absolute safety anchor point unit, the anchor point unit latches a continuous positioning value of a conventional unit when a binary anchor point sensor is triggered, and the absolute safety anchor point unit locks the continuous positioning value of the conventional unit when the binary anchor point sensor is triggered. The method comprises the following steps of: acquiring a theoretical reference value, comparing the theoretical reference value with the theoretical reference value to generate a drift calibration value, sending the drift calibration value to a conventional unit, and correcting a positioning reference by the conventional unit by using the drift calibration value. The online self-calibration of a control system is realized by using a safety anchor point, so that the safety unit is converted into an active calibrator; the accumulated drift of the high-precision sensor can be avoided during operation.

The invention discloses a power system cascading failure chain modeling and blocking method, device and system considering source-load double-end uncertainty, and a medium. The method comprises the steps of constructing source-load uncertainty input distribution (wind power introduces Weibull distribution based on a wind speed segmented power model, photovoltaic introduces Beta distribution based on an irradiance output model, and a load side introduces a normal distribution fluctuation model); an MSFF method is adopted to construct a unified source-load stochastic power flow model, and a multi-scene power flow state set is obtained; introducing a power flow out-of-limit criterion and a hidden fault probability function, building a cascading fault propagation model fusing wind-solar load uncertainty, and dynamically simulating fault chain evolution; and formulating a staged collaborative blocking strategy (cutting off non-critical load emergency control, quickly responding a battery energy storage system and dynamically adjusting a critical line protection constant value) to realize real-time fault suppression. Simulation shows that the method is suitable for power system security defense in a high-proportion new energy penetration scene, and has good engineering adaptability and dynamic control performance.

The invention provides a display screen dynamic control method and system, and relates to the technical field of data processing, the method comprises the following steps: constructing a space coordinate system comprising a plurality of entity nodes, the entity nodes comprising a display screen preset angular point, a user binocular vision center and a handheld interaction device; analyzing a relative pose relationship among the nodes by calculating a projection dot product of a space vector corresponding to each node; constructing a dynamic topology network based on the relative pose relationship, and synchronously collecting environment illumination intensity, user fixation point coordinates and user pose data; performing time sequence synchronization and Kalman filtering preprocessing on the acquired multi-modal data to obtain an initial feature set; and generating a spatial calibration coefficient according to the distribution characteristics of the dynamic topology network, and performing normalization compensation on the initial feature set through the calibration coefficient to obtain a calibrated multi-modal sensing feature vector. According to the invention, accurate and intelligent dynamic control is realized.

The invention discloses a grouting splitting pressure simulation device under high-ground and high-water-pressure conditions, and relates to the technical field of tunnel and underground engineering model tests. A first water permeable unit controlled by the water injection unit and a second water permeable unit controlled by the confining pressure unit are respectively arranged at two ends in the length direction, so that water pressure and stratum confining pressure are independently applied. The integrated grouting unit of the device is used for grouting soil in the box and is provided with a comprehensive detection unit, the comprehensive detection unit monitors various parameters such as grouting pressure, water pressure, confining pressure, flow, soil resistance and wave velocity in real time, and independent loading and accurate dynamic control of the grouting pressure, formation water pressure and formation stress can be achieved; therefore, the grouting process of the deep-buried tunnel under the conditions of different burial depths, high water head differences and complex grout characteristics can be simulated in a laboratory with high precision, and the technical bottleneck that the complex environment of high ground stress and high water pressure coupling is difficult to reproduce in a traditional test method is effectively solved.

The invention discloses a hydropower station time-sharing power generation income dynamic control method fusing a power grid load demand, and the method comprises the steps: obtaining hydropower station multi-dimensional operation data and power grid time-sharing load and electricity price data, building a data association model based on an attention mechanism, generating a fusion feature data set through dynamic weight distribution, and carrying out the dynamic control of the hydropower station time-sharing power generation income. Time-sharing power generation income maximization serves as an objective function, reservoir water balance, unit output limitation and downstream ecological flow constraint are combined, a deep reinforcement learning algorithm is introduced to construct a dynamic control model, historical and simulated data are utilized to train the model and optimize parameters, and then real-time data are input to generate an optimal time-sharing power generation control scheme. Issuing an instruction and monitoring output deviation to realize dynamic control; according to the method, the power grid load and the time-of-use electricity price can be accurately matched, the power generation income is improved while multiple constraints are met, and the method is suitable for various hydropower station dispatching scenes needing to consider power grid response and income optimization.

The invention belongs to the technical field of energy consumption management, and particularly relates to an energy consumption optimization control method and system for heating and ventilation equipment, and the method comprises the steps: constructing a distributed pump set architecture, and converting a transmission and distribution system from a centralized regulation and control mode into a distributed regulation and control mode; developing adaptation technology and equipment; establishing a dynamic hydraulic balance regulation and control model by combining a digital twinning technology and an intelligent algorithm; the dynamic regulation and control model is used for optimizing the pump set operation state and the valve opening degree in real time; by constructing a distributed pump set framework, the system can independently adjust the rotating speed of the pump units according to the actual requirements of different sub-areas, the adaptability of the system to different working conditions and load changes is greatly improved, and the heating and ventilation requirements of all the areas can be more accurately met; the problem of excessive energy supply or insufficient energy supply of partial areas caused by overall adjustment in a centralized regulation and control mode is avoided, so that the overall operation efficiency and comfort of the system are effectively improved.

The invention discloses a privacy computing arrangement method, system and device based on dynamic trust and a medium, and relates to the technical field of data security. The method comprises the following steps: receiving a data processing request, and collecting context data including user trust, environmental risk and data sensitivity; calculating a dynamic trust score based on the context data, and dynamically selecting a target privacy calculation technology according to the score; scheduling tasks based on the selected technology and deploying a guard agent in the computing container; running state data is periodically collected through a guard agent to serve as heartbeat for verification, and if verification fails, key interruption and memory erasing and fusing tasks are triggered; after calculation is completed, packaging a result and an access control strategy into a security capsule; and in response to the capsule opening request, decrypting the rendering result in the memory after verifying that the terminal environment conforms to the strategy. According to the method, full-life-cycle dynamic control from calculation request to result use is realized, and the problem that authorization is out of control is solved.

The invention belongs to the technical field of laser additive manufacturing process control, and particularly relates to a temperature feedforward control method for a laser additive manufacturing molten pool. Comprising the following steps: step 1, acquiring a surface temperature field of an Nth layer after solidification through a thermal imaging system as an initial state T0; step 2, acquiring a scanning path of an (N + 1) th layer from a CAM system, calculating an equivalent heat dissipation index of each point on the path, and generating an EHDI sequence; 3, inputting the T0 and EHDI sequences into a pre-trained LSTM prediction model, and predicting the dynamic change of the molten pool temperature; and step 4, based on a model prediction path integral control algorithm, optimizing and generating an optimal laser power feed-forward control sequence of N + 1 layers by taking minimization of a cost function including a temperature tracking error, a power change and a power size as a target. According to the method, the path information is quantified into the EHDI sequence, the thermal dynamic state is accurately predicted through the LSTM model, the MPPI controller can'predict 'thermal disturbance before scanning is started, the optimal power scheme is generated, and feedforward accurate control over the temperature of the molten pool is achieved.

The invention relates to the technical field of robot attitude balance control, and provides a robot attitude balance control method and system. Before the feet of the robot make contact with the supporting face or at the initial stage of contact, preset micro-force excitation is actively applied to the supporting face for detection, and mechanical response information and foot displacement information between the feet and the supporting face are collected in real time; based on the information, the system can estimate the local physical characteristics of the supporting surface in real time, and dynamically control and adjust the balance control strategy of the robot according to the estimated local physical characteristics. Through active detection and foresight adjustment, the characteristic change of the supporting surface can be identified and adapted before or at the initial stage of instability, so that the control conflict and vicious circle possibly brought by passive compensation are avoided.

The invention discloses an electronic film cutting control method and system based on digital twinning. The method comprises the steps that tool and material interaction data are collected and denoised, time domain and frequency domain characteristics of the tool and material interaction data are extracted, spectral distribution is calculated through Fourier transform, and material characteristic fluctuation mode data are determined; if the threshold value is exceeded, judging that the toughness of the material is changed, obtaining time sequence data, obtaining a material difference feature vector, inputting the material difference feature vector into a support vector machine model for classification, matching a material database, and determining an optimized control parameter set; a deep neural network is adopted to predict the optimized control parameter set, and environment variables are fused to obtain a final dynamic control instruction set; and if the final dynamic control instruction is not matched with the current cutter state, a stable machining path is obtained through closed-loop feedback cycle updating. According to the method, the digital twin of the cutter and the material is constructed, the physical machining process is mapped in real time, the machining precision and adaptability can be remarkably improved, and intelligent response to the material difference is achieved.

The invention discloses a server test environment guarantee method and system based on dynamic simulation, a storage medium and computer equipment. The method comprises the steps that the air inlet temperature, real-time power consumption and heat dissipation strategy signals of a tested server and the actual environment temperature of an incubator are collected in real time; dynamically setting the temperature of the air inlet of the tested server as the target environment temperature of the incubator, and calculating the real-time temperature deviation between the actual environment temperature and the target environment temperature; if the real-time temperature deviation exceeds a preset threshold value, a dynamic PID control algorithm is triggered, a basic control quantity is obtained through calculation according to the real-time temperature deviation through the dynamic PID control algorithm, a dynamic compensation quantity is obtained through calculation according to the target environment temperature, the real-time power consumption and the heat dissipation strategy signal, and the heat dissipation strategy signal is obtained based on the basic control quantity and the dynamic compensation quantity. Generating a temperature control instruction of the incubator; according to the temperature control instruction, a refrigerating or heating device of the incubator is dynamically adjusted so as to guarantee the test environment of the tested server.

The invention discloses a beam guidance optimization method based on position prediction in unmanned aerial vehicle communication, and relates to the technical field of unmanned aerial vehicle communication optimization, and the method comprises the steps: obtaining the flight path data of an unmanned aerial vehicle, carrying out the adaptive segmentation according to the flight path data, and constructing a state evolution model for each flight path; performing short-term prediction and medium-term residual error calibration prediction according to the state evolution model, and constructing a prediction correction term to generate a prediction point set; mapping the prediction point set to a direction coordinate system of a ground communication array to construct an angle evolution tensor, and screening to obtain a candidate beam direction set; and screening a standby beam direction with the maximum path coverage redundancy from the candidate beam direction set. According to the method, a beam guiding optimization mechanism with state identification, adaptive prediction, space tolerance, historical correction and multi-path dynamic regulation and control capabilities can be realized, and the beam control precision, the failure recovery capability and the communication link stability in a complex airspace motion state during communication of the unmanned aerial vehicle are remarkably improved.

The invention discloses an oxygen-enriched destressing crystal growth method and a crystal growth furnace, and relates to the technical field of crystal growth, and the oxygen-enriched destressing crystal growth method comprises the following steps: step 1, pre-treating a PMNT polycrystal material, and putting the pretreated PMNT polycrystal material into a crucible; 2, pre-flushing the growth furnace and creating an oxygen partial pressure environment; 3, raising the temperature in the growth furnace to melt and homogenize the PMNT polycrystal material in the crucible; step 4, the crucible moves downwards for directional solidification of the crystal, a solid-liquid interface is formed and stably moves upwards, and in the process, a dynamic regulation and control strategy is adopted in the growth furnace to control an oxygen-enriched environment to promote crystal growth, so that the crystal digests growth stress in the growth process, and formation of oxygen vacancies is inhibited; and step 5, after crystal growth is finished, gradient cooling is performed to room temperature in an oxygen-enriched environment, and a crystal ingot is obtained. Oxygen is used as a regulation and control tool, the stress problem caused by oxygen vacancy is physically solved, the color center and performance problems caused by oxygen vacancy are chemically solved, and the crystal growth quality is improved.

The invention belongs to the field of power grid system control, and discloses a power grid system virtual impedance dynamic control method, system and device and a medium. The control method comprises the following steps: judging whether the power grid system breaks down or not by acquiring power characteristics of electronic equipment in the power grid system, including content proportions of voltage harmonics and current harmonics, current amplitude changes and positive and negative sequence voltage amplitudes. When a fault occurs, normal virtual impedance is calculated and combined with response current of harmonic waves to obtain dynamic virtual impedance, and then self-adaptive virtual impedance is comprehensively obtained and switched to the impedance value. According to the method, rapid and accurate dynamic current limiting and reactive power supporting under the complex fault working condition are achieved, the adaptability and stability of power electronic equipment to power grid faults are improved, fault current is effectively restrained, equipment damage is prevented, and meanwhile the transient characteristic and reliability of a system are enhanced.

The present specification provides a warehouse scheduling method and device, wherein the warehouse scheduling method comprises: determining the occupancy proportion of the buffer sites in the container buffer area; if the occupancy proportion of the buffer sites exceeds a set proportion threshold, it indicates that too many containers are stored in the container buffer area, and the buffer sites for performing container handover may be insufficient; at this time, the container scores of the containers stored in each buffer site in the container buffer area are determined to determine the buffer sites to be released in the container buffer area, and a corresponding return task is generated to return the containers to be returned in the buffer sites to be released to the container storage area, so as to dynamically control the occupancy proportion of the buffer sites in the container buffer area and ensure that there are sufficient buffer sites in the container buffer area for container handover. In this way, the container buffer area can be used for container storage in addition to container handover, thereby improving the container reuse rate and further improving the container outbound efficiency and target object picking efficiency, greatly saving the transportation resources of the robot.

The invention discloses a reinforcement learning biped robot gait control method based on a multilayer adaptive exploration mechanism, and belongs to the field of biped robot reinforcement learning control. The method aims at solving the problems that in existing gait control based on a near-end strategy optimization (PPO) algorithm, strategy convergence is unstable, and exploration and utilization balance is insufficient. According to the method, a multi-layer adaptive exploration mechanism is introduced on the basis of a standard PPO framework: firstly, a state-dependent standard deviation network is designed to realize adaptive noise adjustment of a space layer for dynamically controlling action disturbance intensity; secondly, constructing an entropy regularization mechanism based on advantage weighting, and realizing exploration intensity adaptive control of a direction layer; thirdly, a dynamic entropy coefficient is designed according to the uncertainty of a value function, and exploration rhythm self-adaptive adjustment of a time layer is achieved. Through the synergistic effect of three layers of mechanisms, the strategy realizes dynamic balance of exploration and convergence in different training stages, so that the stability of the training process and the strategy convergence quality are improved.