5751 results about "Control parameters" patented technology

The invention relates to the technical field of heat management of energy storage systems, and discloses a heat management method of an intelligent distributed liquid cooling energy storage system. The method comprises the following steps: acquiring operation state parameters of a plurality of single batteries and optimizing timestamp synchronization; and processing the temperature data by adopting small-batch time series decomposition, and extracting change trend characteristics and abnormal hot spot positions. And based on the information, optimizing a heat exchange control rule base parameter set through a non-dominated sorting strategy, and generating optimized control parameters and priority rules. And in combination with inlet temperature and flow parameters of the liquid cooling system, a tabu search mechanism is utilized to decide a cooling liquid flow distribution scheme, and a branch control instruction is output. And finally, a flow constraint boundary is dynamically adjusted by adopting an approximate gradient projection technology, and a branch and bound strategy is fused to discretize and optimize a distribution path, so that the branch flow is finely regulated and controlled, and the thermal management precision and reliability of the energy storage system are improved. According to the method, the accuracy, adaptability and stability of thermal management of the liquid cooling energy storage system are improved.

The invention relates to the technical field of ground mobile unmanned equipment control, and discloses a ground mobile unmanned equipment autonomous obstacle avoidance control system optimized by artificial intelligence. The system comprises an environment perception layer, a bimodal risk assessment layer, a dynamic decision-making layer, a trajectory optimization layer and a feedback optimization layer. The environment sensing layer adopts a retina fovea centralis imitating mechanism to perform non-uniform sampling on laser radar point cloud data to generate dynamic point cloud partitions; the bimodal risk assessment layer fuses two types of radar data to generate static and dynamic obstacle risk assessment diagrams; the dynamic decision-making layer establishes space-time mapping and generates an obstacle confidence coefficient matrix through a graph neural network; the trajectory optimization layer converts the matrix into a control parameter based on a multi-objective evolutionary algorithm, and issues the control parameter through a time-sensitive network protocol; and the feedback optimization layer monitors environment change, calculates deviation, generates an effectiveness index, and dynamically adjusts a point cloud acquisition strategy until the index is optimal. According to the system, the autonomous obstacle avoidance capability and adaptability of the ground mobile unmanned equipment in a complex environment are enhanced.

The invention discloses a multi-terminal mixed intelligent inspection and task scheduling method and system based on air-ground-person cooperation. The method comprises the following steps: constructing dynamic unknown environment active cognition; updating dynamic unknown environment active cognition; the candidate path segments are evaluated through a path cost evaluation function, path reachability judgment is performed based on a cost value, and an exploration guide item is introduced to actively guide an inspection carrier to enter an unknown area to realize synchronization of task execution and map expansion; in combination with semantic task description, real-time states of multiple inspection carriers and an updated global environment model, task dynamic allocation and path collaborative optimization are performed, and the emergency, execution efficiency and resource utilization rate of inspection tasks of multiple terminals are balanced; the operation state and environment cognitive quality of each inspection carrier are monitored in real time, and an MR teleoperation intervention mechanism is automatically triggered when it is detected that continuous planning fails, sensing confidence is lowered or a key unknown area is entered; projecting a global environment model, real-time sensing data and a planned path to an operator MR device in an immersive manner, guiding by an operator through gestures, gazing or voice, and finishing path correction or task redistribution by adopting a progressive sharing control strategy and fusing with a manual instruction and autonomous planning; a successful MR intervention process of an operator and a context environment thereof are recorded in a context memory library, and when a similar task or environment scene is encountered subsequently, a verified guide strategy or control parameter is actively recommended.

The invention relates to the technical field of automatic production control and industrial data processing, in particular to a PLC-based gypsum-based flame-retardant plate production line control system and method, and the system comprises a data collection module which collects sensor data in real time; the ideal state construction module is used for constructing a pure ideal state vector; the disturbance simulation module is used for generating a theoretical damaged state vector corresponding to a specific fault mode; the difference calculation module is used for generating real and theoretical deviation vectors; the coupling verification control module is used for calculating a similarity value between the actual deviation vector and the theoretical deviation vector; if the similarity value is larger than a preset judgment threshold value, it is judged that a real physical fault exists at present, and a PLC precise compensation instruction is generated and sent to a production line execution mechanism; otherwise, judging that non-physical noise exists at present, keeping the control parameters of the production line unchanged, and starting a dynamic filtering program; according to the method, the problem that physical faults and sensor clutters are difficult to distinguish in a high-noise environment is effectively solved, and missing report is avoided.

The invention belongs to the technical field of automatic control, and relates to a self-adaptive control method based on multi-physical modeling. According to the method, by collecting multi-source data of a controlled object, a thermal, electric and force coupling relation used for control analysis is established so as to describe dynamic responses under different operation conditions. And calculating stress, motor power, energy consumption and temperature rise change in the operation process based on a coupling relation to obtain system performance data, verifying stability and safety of different control parameter combinations in a simulation environment, and obtaining performance indexes including operation retardation risk, overload safety margin and safety response time limit. And according to a simulation result, under the condition of meeting safety constraints, taking energy consumption and temperature rise as optimization targets, adjusting control parameters, generating optimized control parameter configuration data, and feeding back the optimized control parameter configuration data to a control unit, so that closed-loop adaptive control and performance optimization are realized. According to the invention, through multi-physical coupling modeling and simulation optimization, the adaptability and reliability of the automatic control system are improved.

The invention relates to an automobile seat adjusting method and device, a vehicle and a storage medium, and the method comprises the steps that multi-dimensional biomechanical data including body pressure distribution data of a driver and passengers and deep muscle group activity spectrum information are obtained; the method comprises the following steps: collecting motion state data and pavement environment data of an automobile, generating an automobile passenger environment coupling data set according to the motion state data and the pavement environment data of the automobile, and establishing a driving environment dynamic parameter library of the automobile according to the automobile passenger environment coupling data set; and based on the multi-dimensional biomechanical data and parameters in the driving environment dynamic parameter library, using a preset digital twin support model to determine control parameters of the automobile, generating an adjusting instruction of the automobile seat according to the control parameters, and adjusting the automobile seat according to the at least one seat adjusting action. Therefore, the problems that in the prior art, the intelligent degree and the individuation degree of seat adjustment are low, and continuous and comfortable sitting and riding experience is difficult to provide for a driver and passengers are solved.

The invention relates to the technical field of inverter control, and provides an MBD-based reinforcement learning inverter control algorithm optimization method, which comprises the steps of S1, establishing a state space model of an inverter, designing a baseline controller of double-loop control, and constructing an MBD simulation platform containing multiple load models; s2, defining a state space of the 12-dimensional state vector, designing a continuous action space, and constructing an adaptive multi-target reward function; s3, training a control parameter optimization strategy by adopting an Actor-Critic algorithm architecture in combination with an experience playback mechanism and a hybrid exploration strategy; s4, establishing a security constraint mechanism of three-layer security protection, designing a fault detection and isolation strategy, realizing a closed-loop online learning strategy of pre-training-transfer learning-online fine tuning, and meanwhile, adopting a self-adaptive updating mechanism; and S5, verifying the optimization effect of the control algorithm through quantitative index evaluation, an experimental verification scheme and a real-time performance requirement test. And the upgrade of inverter control from model driving to data and model cooperative driving is realized.

The invention belongs to the technical field of tunnel engineering, and provides a multi-physics field real-time assimilation simulation and regulation method and system in a tunnel grouting process in order to solve the problem that real-time dynamic simulation and automatic regulation are lacked in existing tunnel construction, and the real-time assimilation simulation and regulation method and system in the tunnel grouting process are provided by utilizing ensemble Kalman filtering and combining real-time monitoring data in the tunnel grouting process. Dynamically correcting parameters of the multi-physical model; time correlation in the slurry condensation process is considered, a time-varying condensation model depicting physical property changes of slurry evolving along with time is integrated, the time-varying condensation model serves as an external function in the time step length to be embedded into the multi-physical field model in correction, and the slurry flowing state is adjusted in a self-adaptive mode through numerical simulation; and generating control parameters of tunnel grouting according to a dynamic simulation result, and realizing closed-loop regulation and control of tunnel grouting. Synchronous linkage of numerical simulation and on-site working conditions is realized.

The invention belongs to the technical field of charging detection, and particularly relates to a charging pile extreme environment test method based on composite stress simulation, a terminal and a storage medium, and the method comprises the steps: inputting an identifier corresponding to a target test scene through a human-computer interaction interface of a test cabin, and executing a query operation based on the input identifier, retrieving a preset parameter combination from a preset scenarized stress library by using the mapping function; in a closed test cabin, environment parameter data are collected in real time through a distributed sensor array and fed back to a central controller, and the central controller drives an execution component to cooperatively regulate and control parameters such as temperature and humidity, so that the environment parameters are stabilized in a preset range; a sequential stress scheme is generated based on a pre-stored dynamic loading algorithm, an execution component is controlled to realize stress superposition or stress alternation, and a charging load is synchronously adjusted to simulate a composite stress effect under an actual working condition; simulation of multi-factor combined stress and dynamic loading is realized, an actual use fault can be found in advance, and the matching degree of a test result and a real environment is remarkably improved.

The invention relates to the technical field of robot control and somatosensory interaction, and particularly discloses a knee joint exoskeleton robot control system based on somatosensory interaction. The system comprises a somatosensory signal acquisition and fusion module, a personalized dynamic model construction module, an adaptive intention decision module, a variable impedance compliance control module and a rehabilitation process evaluation and parameter self-tuning module. Through cooperative work of the modules, precise recognition of the motion intention of a wearer, online construction of an individualized biomechanical model and flexible and adaptive motion control are achieved, control parameters can be automatically adjusted according to the rehabilitation process, and therefore the flexibility and individuation level of man-machine interaction and the effectiveness of rehabilitation training are improved.

The invention discloses a coupling iterative algorithm library and a reactor multi-physical field coupling calculation program construction method. The coupling iterative algorithm library comprises an open source library layer used for integrating a plurality of numerical calculation libraries and providing an input / output interface for a reactor multi-physics field coupling framework and each numerical calculation library; the core algorithm layer is used for integrating a plurality of coupling iteration algorithms and a plurality of acceleration algorithms related to each coupling iteration algorithm; the interface layer is used for providing a data interaction interface between a nuclear reactor multi-physics field coupling calculation program and the core algorithm layer; and the iterative algorithm template layer is used for forming an iterative solution process control template function based on the open source library layer, the core algorithm layer and the interface layer, and the iterative solution process control template function is used for constructing a nuclear reactor multi-physics field coupling calculation program based on the process control parameters set in the input file. According to the coupling iterative algorithm library, the development efficiency and the development quality of a nuclear reactor multi-physics field coupling calculation program can be improved.

The invention discloses a machine learning multi-objective optimization control method based on coal quality characteristics and combustion state feedback, and belongs to the technical field of industrial process automatic control. According to the method, collaborative optimization control over the combustion efficiency, NOx emission and combustion stability is achieved under the multi-working-condition operation condition of the combustor. According to the method, coal quality characteristic parameters and combustion state data are collected, working condition clustering is carried out after data fusion, and a historical combustion operation data set coupling coal quality characteristics and combustion states is constructed; a combustion state evaluation model and a combustion control model are established by adopting machine learning, on the basis of identifying working conditions in real time and evaluating combustion performance, a control parameter adjustment amount is generated in combination with trend extrapolation and a multi-objective cost function, and a combustion state feedback rolling correction model and a control strategy are utilized to realize combustion control. The self-adaptive closed-loop collaborative optimization control of the combustion process under multiple coal types and multiple working conditions is realized, the boiler efficiency is improved, the NOx emission is reduced, and the operation robustness is enhanced.

The invention discloses a visual perception and dynamic anti-collision intelligent closed-loop control method for a central control mechanical arm in a vehicle, and the method comprises the steps: S1, loading an improved YOLOv11 network detection model, and reading a video stream of a camera; s2, preprocessing each frame of image in the video stream, inputting the preprocessed image into the model, and performing target identification; s3, inputting an identification result into a dynamic safe area collision model, and performing coordinate conversion through a hand-eye calibration algorithm; calculating a relative position and a motion trend in combination with depth sensor data, and judging a risk level to generate a corresponding protection instruction; and S4, executing a hierarchical control strategy according to the risk level, updating the action track of the mechanical arm, and returning to the step S1 through a closed-loop feedback mechanism to adjust the safety threshold and the control parameters to form a complete'perception-decision-execution-optimization 'autonomous evolution closed loop. According to the method, the visual perception robustness and the target detection precision under the complex illumination condition are improved, and real-time dynamic anti-collision response and millisecond-level rapid obstacle avoidance are realized.

The invention relates to a motor energy-saving control method for adaptive parameter adjustment. The method comprises the following steps: constructing an operation parameter sequence and comparing the operation parameter sequence with a preset reference model by collecting the rotating speed, the torque and the current signal of a motor in real time to generate a parameter deviation vector; based on a deviation threshold comparison result, carrying out noise processing on the overrun deviation to obtain a filtered sequence, and calculating a time delay degree in combination with historical parameters; calculating a compensation gain according to the time delay, fusing the compensation gain with the filtered deviation to generate a control instruction sequence, correcting the compensation gain through fuzzy control to obtain a stabilization parameter, and further generating a correction control instruction sequence; and finally, acquiring load data, fusing the load data with the correction instruction to obtain a system operation characteristic value, and generating an optimization state identifier when a judgment standard is met. By adopting the method, adaptive adjustment of the control parameters can be realized, the dynamic working condition is accurately responded, and the energy-saving efficiency of the motor under the variable-load working condition is improved.

The invention relates to the technical field of industrial process control, and discloses a denitration intelligent monitoring method and system based on data analysis and a storage medium. The method comprises the following steps: performing working condition identification by using an improved K-means algorithm to obtain stable operation, fire change preparation, fire change proceeding and fire change recovery modes; matching a segmented control strategy according to an identification result, and carrying out weighted fusion on feedforward and predictive control in a fire exchange preparation stage to generate a differentiated liquid ammonia regulation instruction; the disturbance feed-forward compensation amount is calculated in combination with the combustion state of the kiln, the adjusting instruction is restrained, and a liquid ammonia control instruction is obtained; the execution effect is monitored in real time through standardized deviation detection, manual intervention is switched in case of abnormity, and control parameters are fed back and updated in case of normal. The problems that NOx control lags behind and liquid ammonia consumption is not economical under the fire exchange working condition are solved, and the control precision and the self-adaptive capacity of the denitration system are improved.

The invention relates to the technical field of power supply equipment power control. The load power track simulation control method comprises the steps of obtaining voltage information and current information of target power supply equipment, determining power deviation information and a dynamic target voltage range generated on the basis of a preset load behavior prediction model, constructing a combined regulation and control weight vector on the basis of the power deviation information, and obtaining a load power track simulation control result. According to the combined regulation and control weight vector and the dynamic target voltage range, constructing a target power evolution trajectory curve by using a physical response inversion mechanism, executing time discretization processing on the target regulation and control trajectory curve, extracting voltage change values and current change values corresponding to each time step, and calculating the target power evolution trajectory curve; and combining the voltage change value and the current change value according to a time sequence to form a control parameter sequence, and generating an analog control instruction matched with the target power evolution trajectory curve according to the control parameter sequence. The method has the effect of improving the real-time response capability of the power supply equipment under complex working conditions.

The invention relates to the technical field of automatic control, and discloses a thermal power generating unit cooperative control method based on digital twinning, which comprises the following steps: firstly, off-line executing benchmark construction, and obtaining a benchmark digital twinning model and a benchmark cooperative control parameter set which represent the ideal state of a unit; a twinborn deviation vector is obtained by comparing real-time output of a physical unit with theoretical output of a model, a deviation characteristic vector of quantitative characteristic deviation is obtained through processing of a deconstruction algorithm, then historical deviation characteristics are analyzed by adopting a time sequence prediction model, a prediction output vector representing a future evolution trend is calculated, and then a prediction result is obtained. And calculating a prospective control parameter correction vector based on the prediction vector, and combining the prospective control parameter correction vector with a reference parameter set to generate a dynamic cooperative control parameter set. According to the method, the slow time-varying drift of the dynamic characteristics of the unit can be actively compensated, the continuous optimization of a control strategy is realized, and the stability and the operation economy of a control system are improved.

The invention relates to the technical field of energy storage battery control, in particular to a high-reliability low-attenuation energy storage battery management system based on intelligent control, and the system comprises a data collection module which is used for collecting battery temperature, environment temperature, SOC (state of charge), SOH (state of health) and external mechanical pressure information; the model construction module is used for constructing a battery attenuation risk dynamic coupling evaluation model and generating a battery safety and attenuation risk dynamic evaluation diagram; the self-adaptive adjustment module adopts a cross-dimension multi-stage cooperative control strategy algorithm, temperature compensation current control and SOH grading current limiting linkage are combined, and SOC dynamic charging optimization and mechanical pressure early warning are coupled; and the evaluation report module adjusts control parameters through a dynamic optimization algorithm, generates a battery health state evaluation report, synchronously uploads the battery health state evaluation report to a cloud database, and establishes a full-life-cycle battery attenuation risk file. Therefore, the problems that in the prior art, a risk assessment model is large in limitation, a control strategy lacks collaboration, and attenuation risk prevention and control lags are solved.

The invention relates to an abnormity alarm method and system for a feed production control system, and the method comprises the following steps: collecting multi-field data related in a feed production process in real time, the multi-field data at least comprising production equipment operation parameters, material characteristic parameters, environmental parameters and process control parameters; preprocessing the collected multi-field data, and fusing to generate a multi-field description feature vector representing the current production state; inputting the multi-field description feature vector into a pre-trained fault prediction model, wherein the fault prediction model respectively calculates the confidence of the fault type prediction result and the confidence of the fault point prediction result; wherein the confidence coefficient is in positive correlation with the distance from the feature vector to a known fault mode clustering center or the distance from the feature vector to a decision boundary; and when the fault prediction result indicates that an abnormal risk exists and the corresponding confidence exceeds a preset threshold, triggering a corresponding alarm signal.

The invention discloses a self-adaptive harmonic current suppression method for harmonic waves of a photovoltaic inverter, which belongs to the technical field of power quality control of a power system and comprises the following steps of: compensating a control strategy through a self-adaptive harmonic component of the photovoltaic inverter by adopting a'detection-reverse injection 'framework; the difference between the residual apparent capacity of the inverter after fundamental wave power supply and the borne harmonic power is used as an adaptive quantity, and the total harmonic distortion rate of the grid-connected point is reduced to the maximum extent on the premise of no overload; the method comprises the following steps: firstly, constructing a real-time harmonic detector based on a cascade generalized integrator; secondly, an inverter impedance model is established from the aspect of impedance stability, and a control parameter stability boundary is determined; then, a self-adaptive harmonic control strategy is introduced into the single-inverter grid-connected system, and a reasonable parameter setting method is provided on the basis of considering stability limitation; afterwards, a capacity-matched harmonic power distribution algorithm is provided for a multi-inverter parallel system, and the efficiency and operation stability of a photovoltaic grid-connected power generation system are improved.

The invention relates to the technical field of process optimization, in particular to a collaborative optimization analysis method for multi-station tower drum welding. In order to solve the problem that cooperative control of tower drum multi-station welding is difficult, firstly, multi-mode sensing data of welding seam vision, arc sensing, thermal field distribution and welding voiceprint are loaded, a fusion evaluation model is constructed to obtain a welding seam deviation value and a sensor confidence score, and the health state of a sensor is judged according to a threshold value; and a corresponding fault-tolerant reconstruction control mode is triggered to obtain welding tracking control parameters, then a multi-station welding heat source model is loaded, thermal interference is decoupled in combination with the control parameters and thermal field data, multiple stations are cooperatively regulated and controlled, a cooperative control instruction is output and executed, and welding process optimization is achieved.

The invention provides a coordination control method and device of a photovoltaic energy storage inverter for automobile charging based on artificial intelligence, and belongs to the technical field of automobile photovoltaic energy storage coordination control, and the method comprises the steps: monitoring the output power of a photovoltaic array, the state of charge (SOC) of an energy storage system, the state of a power grid and the power demand of a load in real time; a reinforcement learning algorithm is introduced, weather forecast and historical load data are combined, photovoltaic output and load demands in a future time period are predicted, and a power distribution instruction is generated; deciding a current operation mode and generating a mode switching instruction; seamless switching between grid connection and grid disconnection is controlled, and after switching is completed, the charging and discharging proportion of the lithium battery and the super capacitor is coordinated; integrating the energy storage real-time state and the power distribution instruction, and optimizing and adjusting the control parameters of the inverter in real time; continuously monitoring a running state and a switching process, and starting a standby mode when a fault is detected; and the data is uploaded to a monitoring platform. The system operation is efficiently optimized, the service life is prolonged, and stability is guaranteed.

The invention discloses a hybrid energy storage capacity configuration method based on an energy storage characteristic and instruction difference dynamic matching strategy and a related device, and relates to the technical field of power system energy storage, and the method comprises the steps: carrying out the mode division according to the amplitude of a total power instruction signal of a hybrid energy storage system, and carrying out the configuration of the hybrid energy storage capacity based on the dynamic matching strategy; distributing the total power instruction signal to a super capacitor and a lithium battery, and calculating the charge states of the super capacitor and the lithium battery; constructing a control configuration integrated model by taking the total power command signal as input and the charge state as feedback; determining value range constraints of the configuration parameters and the control parameters; and under constraint conditions, solving the comprehensive multi-objective optimization function of the control and configuration integrated model by adopting a traversal algorithm to obtain an optimal configuration parameter and control parameter combination, and determining a hybrid energy storage capacity configuration scheme of the hybrid energy storage system. The problems of insufficient energy storage utilization and insufficient frequency modulation precision in a traditional method can be solved.

The invention discloses a copywriting generation method based on artificial intelligence. The method comprises the following steps: constructing a propagation technique knowledge base and a creation technique knowledge base of an advertisement copywriting according to demand information; retrieving a matched creative strategy combination from the creative technique knowledge base, and encoding the creative strategy combination as a first cue word fragment; outputting a Top-K sub-graph, and encoding the Top-K sub-graph into a second cue word fragment; splicing the first cue word segment, the second cue word segment and the target audience portrait and style control parameters, generating a complete cue word, and generating an advertisement copywriting first draft; performing quality evaluation and generating a hallucination probability graph; and outputting the finally generated advertisement copywriting until the illusion probability output by the rewritten advertisement copywriting meets the requirement. According to the method, a closed loop of copywriting generation-evaluation-optimization is realized, and the quality of the copywriting can be effectively improved through a small number of iterations.

The invention relates to the technical field of droop control, in particular to a voltage droop control optimization method and system based on parameter self-adaption and a medium, and the method comprises the steps: dynamically recognizing a voltage sensitive interval according to voltage fluctuation characteristics, determining critical voltage distribution data, and determining a switching trigger condition of a current power grid control mode according to the critical voltage distribution data; analyzing the correlation degree between the current power grid control mode switching event and the power grid stability based on the switching trigger condition to obtain a switching behavior risk level; reconstructing voltage droop control parameter adjustment direction logic based on the switching behavior risk level, and generating a stable direction vector; calibrating the original droop control output signal according to the stable direction vector, and generating a self-adaptive droop control output instruction; and distributing the adaptive droop control output instruction to each substation node for voltage regulation. The voltage droop control parameters are adaptively optimized through the real-time running state of the power grid, and parameter adaptive optimization of voltage droop control is achieved.

The invention is suitable for the technical field of intelligent control, and discloses a water cooling control method, system and device and a medium. Based on the preprocessed data, constructing a thermal load trend prediction model based on LSTM to obtain a cold load prediction value; based on the cold load prediction value, a PID adjusting signal is generated in combination with the temperature error value; based on the PID adjusting signal, a water pump target frequency instruction is obtained in combination with the cooling load predicted value; based on the water pump target frequency instruction, a valve target opening instruction is obtained in combination with the actually-measured water flow; based on the valve target opening instruction, the fault alarm and emergency instruction is generated, the preset emergency measures are executed, the PID control parameters are updated, the performance evaluation report and the feedback information are obtained, and the response speed, the operation stability and the autonomous decision-making capacity of the water cooling system under the dynamic load and abnormal working conditions are improved.

The invention belongs to the technical field of industrial flue gas purification, and discloses an ammonia desulfurization optimization control system based on a machine learning algorithm, which comprises a feature extraction module, a working condition clustering module, a dynamic optimization module and a self-adaptive feedback module, the feature extraction module is used for collecting multi-dimensional operation parameters in a coal burning process, performing data dimension reduction through a PCA algorithm, and extracting key working condition features; the working condition clustering module identifies different operation working condition modes; the dynamic optimization module can construct a multi-modal optimization neural network based on different operation condition modes, and generates optimal control parameters in real time. Through PCA dimension reduction processing of the feature extraction module and in combination with a working condition clustering algorithm, automatic mode recognition and strategy switching under complex working conditions are achieved, fluctuation of desulfurization efficiency is reduced, and meanwhile the problems that the ammonia water adding amount depends on experience setting, raw material waste and secondary pollution are likely to be caused, and the operation and maintenance cost is large are solved.

The invention provides a control method, which is applied to an artificial intelligence experiment system, at least comprises a robot hardware platform, a controller, a wheeled robot chassis and an industrial mechanical arm, and the method comprises the following steps: starting an operation system and executing hardware self-inspection; loading a large language model service and a traditional AI model, wherein the traditional AI model comprises a speech recognition model, a target detection model and a semantic analysis model; a natural language instruction of a user is received, voice is converted into a structured text through a voice recognition model, an instruction intention is analyzed through a large language model and a semantic analysis model, robot control parameters are generated, and a multi-modal interaction control signal is obtained; based on the multi-mode interaction control signal, a camera is called to collect image data, the position and category of a target object to be grabbed by the robot are recognized through a target detection model, the moving path of the robot and the grabbing track of the mechanical arm are calculated in combination with laser radar data, and the wheeled robot chassis and the industrial mechanical arm are controlled to execute coordinated actions.

The invention relates to a micro-pressure oxygen cabin self-adaptive pressure control method and system based on iterative optimization, belongs to the technical field of pressure control, and aims at solving the problems that existing micro-pressure oxygen cabin pressure control depends on manual repeated debugging, the efficiency is low, and self-adaptive optimization cannot be achieved. The method comprises the steps that in the pressure stabilizing stage of the micro-pressure oxygen cabin, the pressure in the cabin is monitored in real time, and when the pressure exceeds the upper limit or the lower limit of a preset pressure interval, a pressure stabilizing wave crest section or wave trough section opening degree adjusting algorithm is executed so as to adjust the opening degree of an electric exhaust valve. And after the single oxygen therapy process is finished, executing an iterative optimization step: automatically analyzing the pressure data of the current pressure stabilization stage by the system, and iteratively updating the control parameters for the next oxygen therapy process according to the pressure data. According to the method, self-learning and evolution of control parameters are realized through a closed-loop mechanism of real-time adjustment and redisk optimization, the parameters can be quickly converged to the optimal state within multiple times of operation, the debugging period is shortened, and the stability of pressure control is improved.

The invention discloses a power electronic networking equipment stability analysis method, system, equipment and medium, and the method comprises the following steps: building a full-system state space model, carrying out the cross validation of the full-system state space model through employing a modal analysis method, an impedance analysis method and a complex torque analysis method, and obtaining the stability of the full-system state space model; obtaining a multi-mode coupling stability analysis result; according to the multi-mode coupling stability analysis result, constructing a power grid s domain node admittance matrix; establishing a virtual power angle dynamic equation of the network-forming converter, and optimizing control parameters according to an evaluation result; an inertia center reference system is adopted, a dynamic interaction model of the synchronous machine and the network-forming converter is established, and dynamic coupling characteristics between devices are analyzed through a relative motion equation. According to the method, the comprehensiveness, the precision and the engineering applicability of dynamic stability analysis of the novel power system are improved through fusion of a multi-dimensional collaborative evaluation framework and an innovative technical system.