548 results about "Fuzzy pid" patented technology

Disclosed is a motor fuzzy PID parameter tuning method based on an improved whale algorithm. The method comprises: building a brushless direct-current motor speed control system model, and using a fuzzy PID controller to perform motor speed control. A conventional whale algorithm is optimized by using a chaotic convergence factor, a fractional order, and Levy flights, so as to obtain an improved whale algorithm. Secondly, the overshoot of the system is used as a component of an ITAE performance index to obtain an improved ITAE performance index, and the improved ITAE performance index is used as a fitness function for the improved whale algorithm. Finally, the improved whale algorithm is used to optimize input and output membership functions of a fuzzy controller, so as to obtain optimal ΔKp, ΔKi, and ΔKd values, and Kp, Ki, and Kd parameters of a PID controller are tuned to implement motor speed control. The present invention addresses the difficulty of tuning parameters of conventional PID controllers and solves the problem of low precision of motor speed control, has the advantages of high anti-interference capability, little overshoot, and short adjustment time, and improves the dynamic characteristics and robustness of the controllers.

The invention discloses a cable inspection robot attitude control method based on MPC and ADRC fusion, and the method comprises the steps: firstly, building a model prediction control (MPC) model, carrying out the prediction of the future state of a robot, solving an optimal control problem, and obtaining initial control input for achieving the internal dynamic compensation; secondly, an extended state observer (ESO) in an auto-disturbance rejection control (ADRC) model is adopted, and external disturbance is estimated in real time and used for compensating control input; thirdly, a fuzzy self-adaptive module based on fuzzy PID is introduced, the attitude error, the attitude error change rate and regeneration disturbance intensity generated based on estimation disturbance are collected in real time, the MPC weight and the ESO gain are dynamically adjusted, and the response speed and robustness of the system under variable working conditions are improved. According to the hierarchical control scheme, the defect that an existing single control method is difficult to keep the optimal control effect under variable working conditions is effectively overcome, and technical guarantee is provided for stable operation of the cable inspection robot in a high-risk and high-complexity environment.

The invention discloses a thermal power plant circulating water intelligent dosing method based on multi-module cooperation. The system is composed of a detection module, a control module, a chemical adding module, a sewage discharging module, a water supplementing module and an AI intelligent learning feedback module, key parameters such as the pH value, the conductivity, the temperature, the residual chlorine, the turbidity and the flow of circulating water are monitored in real time through a multi-dimensional sensing network, a dynamic water quality prediction model is established in combination with an AI algorithm, and precise closed-loop control over the chemical adding amount is achieved. The system adopts an intelligent decision-making mechanism coupled by a fuzzy PID algorithm and deep reinforcement learning (DRL), can adaptively adjust the dosing proportion of chemicals, and synchronously links a blow-down valve and a make-up pump to maintain the water balance of the system. The system solves the problems of large hysteresis quality and high manual dependence degree of a traditional dosing system, has the characteristics of real-time response, accurate control, energy conservation and consumption reduction, and provides an innovative solution for intelligent transformation of a circulating water system of a thermal power plant.

The invention discloses an aircraft wheel cooling method and device, and the method comprises the steps: collecting the temperature of a brake disc and the air pressure of a hub in real time through a temperature and pressure sensor, calculating the temperature change rate and the pressure difference through an embedded processing module, and generating a thermodynamic initial parameter set; calculating a thermal stress distribution value of the brake disc based on the initial parameter set, and generating a cooling liquid flow and spraying angle instruction set in combination with a material threshold database; the opening degree of an annular pipeline piezoelectric valve is regulated according to an instruction set, an injection area is regulated according to flow velocity-surface area heat exchange efficiency, and waste heat is synchronously recovered to optimize power consumption; the thermal infrared imager obtains temperature field distribution, the initial parameter tolerance range is compared, the spraying angle and the flow instruction are corrected through a fuzzy PID algorithm, and temperature gradient errors are eliminated; and based on the temperature drop rate, the pressure stability and the waste heat recovery efficiency, an efficiency index is generated by using a neural network scoring model, and algorithm weights and piezoelectric valve response parameters are dynamically adjusted. The cooling efficiency is effectively improved, and the thermal stress risk is reduced.

The invention discloses a distribution network cable fault early warning method and system based on multi-terminal synchronous traveling wave detection, and relates to the technical field of cable fault detection. The method comprises the following steps: deploying at least three synchronous detection ends at each branch end point of a cable, and synchronously triggering a sampling rate; establishing a master-slave synchronous network, and performing frequency spectrum correction on the acquired data; constructing a real-time correction model based on the data of the distributed temperature sensor; extracting early fault features, and constructing a three-dimensional feature tensor; carrying out preliminary diagnosis on edge nodes and carrying out feature classification by adopting a transfer learning optimization network; and in combination with the cable load rate and the environment temperature and humidity, an early warning threshold self-adaptive model based on fuzzy PID (Proportion Integration Differentiation) is established. Through multi-terminal cooperative perception and intelligent analysis, the scheme converts a fault processing mode from passive first-aid repair to active defense, improves the manual inspection efficiency of the distribution network cable, and finds early fault hidden dangers in advance.

The invention relates to the field of mine ventilation, in particular to an intelligent adjusting method and system for mine ventilation. The method comprises the following steps: constructing a dynamic topology model taking a roadway as an edge and taking a fan and an air door as nodes based on a mine CAD drawing; acquiring the actual wind pressure and the actual wind volume of each roadway branch in real time, performing inversion to obtain the current wind resistance, and updating the wind resistance attribute of the edge in the dynamic topology model by using the current wind resistance; calculating the optimal air volume of the roadway branch by using the updated dynamic topology model; based on the optimal air volume, target regulation and control parameters of the draught fan and the air door are obtained through solving by means of a particle swarm algorithm; and according to the target regulation and control parameters, the fan and the air door are adjusted through a fuzzy PID algorithm. The problem that in the prior art, mine ventilation adjustment is not intelligent and safe enough is solved, the theoretical optimal air volume is obtained through deep analysis of the mine, then ventilation adjustment is conducted step by step, and the scientificity and efficiency of ventilation adjustment are improved.

The invention discloses a molten salt heat storage and heat exchange efficiency optimization system and method in a whole-plant technological process. The system comprises a high-temperature molten salt storage tank, a low-temperature molten salt storage tank, a temperature sensor group, a variable-frequency pump group, a multi-stage heat exchanger, an intelligent control unit, a heat buffer module and the like, and the high-temperature molten salt storage tank and the low-temperature molten salt storage tank are connected through a conveying pipeline to form a circulation loop. The intelligent control unit regulates and controls the rotating speed of the pump set and the valve opening degree based on a fuzzy PID algorithm, the heat buffering module reduces heat loss, and the multi-stage heat exchanger enhances heat exchange. The method comprises the steps of temperature and flow data collection, efficiency deviation value calculation, fuzzy PID control, thermal buffer optimization, waste heat feedback and the like. Besides, the system also has the functions of digital twinborn prediction, multi-objective optimization, abnormal working condition self-healing and the like, can be connected with a DCS (Distributed Control System) to obtain energy consumption data and dispatch stored energy, and also can predict equipment faults. The fused salt heat energy storage and heat exchange efficiency is effectively improved, reasonable energy allocation and cost control are achieved, and the reliability and stability of the system are enhanced.

The invention discloses a charging pile intelligent heat dissipation method and device based on heat conduction line optimization and a medium, belongs to the technical field of charging pile heat dissipation, and aims to solve the technical problem of how to avoid the defects of high thermal resistance, response lag and large energy consumption in a traditional charging pile heat dissipation scheme and improve the heat dissipation efficiency and operation reliability of a charging pile. According to the technical scheme, a heat conduction structure is optimized; deploying a thin film thermocouple and constructing a three-dimensional temperature field model through infrared thermal imaging; establishing a thermal resistance-flow-power transfer function model by adopting a fuzzy PID (Proportion Integration Differentiation) and model predictive control hybrid algorithm; historical charging data are analyzed based on an LSTM neural network, a power peak value is predicted 300 ms ahead of time, and heat dissipation equipment is pre-started.

The invention discloses a construction monitoring system for a municipal asphalt road, relates to the technical field of road construction, and optimizes the road construction process through four modules of whole-process data acquisition, edge analysis and decision, dynamic quality detection and dynamic feedback control. And collecting data of each construction stage in real time. Real-time analysis is carried out through a BP multi-target decision network, the construction process is adjusted, quality detection is carried out on the surface layer and the deep layer of a road through a lightweight detection model, and defect marking and thermodynamic diagram display are carried out through a BIM platform. And on the basis of analysis and detection results, a fuzzy PID algorithm and a near-end strategy optimization algorithm are adopted, construction parameters are adjusted in real time, and construction quality and efficiency optimization are ensured. The problems that an existing sampling detection method is difficult to comprehensively cover a construction area, the quality problems of different levels cannot be visually displayed, and timely processing of constructors is affected are solved, and comprehensive monitoring, real-time analysis and intelligent adjustment of the road construction process can be achieved.

The invention discloses an energy efficiency management system for low-carbon and energy-saving operation of building electromechanical equipment, and belongs to the technical field of building energy saving and intelligent control. The system comprises a data acquisition module, an energy efficiency analysis module, an optimization decision module and an execution control module; the data acquisition module acquires information such as equipment operation parameters in real time through the heterogeneous sensor network and the LoRaWAN edge gateway; the energy efficiency analysis module performs multi-dimensional evaluation on the basis of a dynamic energy efficiency reference algorithm by fusing an equipment energy efficiency ratio, carbon emission per unit area and the like; the optimization decision module adopts a mixed integer nonlinear programming algorithm, simulates working conditions through a mixed digital twin model, and generates a control strategy for collaborative optimization of energy consumption, carbon emission and equipment life; the execution control module dynamically adjusts equipment parameters by using a fuzzy PID composite controller; the method breaks through the limitation of static evaluation and single-target optimization of a traditional system, the comprehensive energy efficiency can be improved by 15%-30%, carbon emission is reduced by 20% or above, the service life of equipment is prolonged, and the credibility requirement of carbon checking is met.

The invention relates to the technical field of arc discharge control, in particular to an arc discharge machine control method based on multi-parameter collaborative optimization, which comprises the following steps of: generating a first regulation and control instruction by adopting a fuzzy PID (Proportion Integration Differentiation) algorithm based on a deviation value between a dynamic parameter feedback signal and a preset threshold value, the axial feeding speed of the servo motor, the power output curve of the arc generator and the coolant spraying time sequence are synchronously adjusted, so that the arc energy density gradient and the workpiece heat conduction rate form dynamic balance, the mechanical resonance frequency in the machining process is monitored through the vibration spectrum analysis unit, and when it is detected that the resonance intensity exceeds a safety threshold value, the coolant spraying time sequence is started. And a second regulation and control instruction is generated by triggering a frequency compensation algorithm, and the driving frequency of the servo motor is corrected in real time, so that the control precision is improved, the arc discharge process is more stable, the response speed is increased, the arc discharge time is shortened, the adaptability is enhanced, the requirements of different materials and processes can be met, the energy consumption is reduced, and the production efficiency is improved.

The invention relates to the technical field of trajectory tracking control of piezoelectric motion tables, and discloses a trajectory tracking control method and device for a piezoelectric console, and the method comprises the steps: constructing an initial dynamic model of a piezoelectric motion table based on an asymmetric Bouc-Wen hysteresis model, and obtaining a to-be-identified parameter set; based on the root-mean-square error between the actual output displacement of the piezoelectric motion platform at the plurality of sampling time points and the predicted output displacement of the initial dynamical model, constructing a self-adaptation function of the parameter set to be identified; identifying and acquiring a target model parameter set by using a multi-modal Bayesian gradient optimization algorithm and taking the value convergence of the self-adaption function as an optimization target to obtain a target dynamic model; based on the target dynamic model, constructing a hysteresis state observer, and obtaining feed-forward compensation voltage; constructing a self-adaptive fuzzy PID feedback controller, and obtaining a feedback control voltage; and summing the feedforward compensation voltage and the feedback control voltage to generate a driving voltage so as to drive the piezoelectric motion table to perform trajectory tracking.

The invention discloses a radio frequency ablation electrode control method which comprises the following steps: S1, initializing an ablation temperature target value, a safety limit value and a power adjusting range, and loading a pre-stored anal fistula ablation three-stage feature library and impedance-temperature correlation degree thresholds of all stages; s2, collecting the electrode temperature, the electrode-tissue impedance and the ablation duration in real time, and judging the current ablation stage according to the matching relationship between the ablation duration and the three-stage feature library; s3, formulating a state judgment rule according to the current stage; s4, a fuzzy PID algorithm is adopted to calculate a power basic adjusting amount, a power dynamic compensation amount is calculated, and final output power is determined; and S5, repeating the steps S2-S4 to realize closed-loop control, triggering first-level early warning when the parameters are close to the boundary, triggering second-level alarm when the parameters exceed the safety limit value, and recording abnormal data. The stage adaptability is high, the problem of temperature adjustment lag can be solved, the adjustment precision is accurate enough, and the ablation effect can be improved.

The invention belongs to the technical field of metal surface treatment, particularly discloses an intelligent laser self-adaptive rust removal system and method based on machine vision, belongs to the field of metal surface treatment, and solves the problems that existing laser rust removal parameters are fixed, the quality is unstable, and a base material is prone to being damaged. The system comprises a central control module, and a machine vision acquisition module, a laser derusting execution module, a parameter storage module and an operation monitoring feedback module which interact with the central control module; the machine vision collects metal surface images and extracts corrosion characteristics, the central control module recognizes corrosion areas and grades through YOLOv8 and CNN algorithms, optimal laser parameters are calculated in combination with fuzzy PID, the laser rust removal execution module works according to the parameters, the operation monitoring feedback module monitors in real time to form a closed loop, and the parameter storage module stores data and supports optimization. The method comprises the six steps of initialization loading, corrosion collection and analysis, parameter calculation, self-adaptive rust removal, quality reinspection and data storage.

The invention discloses a temperature and humidity adaptive control system and method for drying flower traditional Chinese medicinal materials, and the system comprises a humidity gradient monitoring module which is used for deploying a 12-channel capacitive humidity sensor spiral array and a laser radar, generating a three-dimensional humidity field thermodynamic diagram in combination with a Kriging interpolation method, calculating a humidity gradient change rate in real time, and carrying out the early warning of local abnormality; the heat-humidity ratio regulation and control module is used for integrating humidity gradient monitoring data and a real-time result of the evaporation latent heat calculation module based on a fuzzy PID algorithm, dynamically adjusting the heating power and the rotating speed of a dehumidification fan, and meanwhile, integrating a PVDF piezoelectric film sensor to monitor petal stress; the evaporation latent heat calculation module is used for predicting a future evaporation rate by combining an LSTM neural network based on an edge calculation unit and providing key parameters for the energy supply matching module; the energy supply matching module is used for outputting a combined control instruction of the heating power and the air door opening degree; and the air volume balance optimization module is used for dynamically adjusting air volume distribution according to the humidity gradient monitoring data.

The invention relates to the technical field of robot joint control, and discloses a robot dog mechanical joint control method. The method comprises the following steps: acquiring a whole-body joint real-time sensing data set which covers the torque reading of a multi-axial force sensor, the angle data of a joint encoder and the body attitude data of an inertial measurement unit; motion state compensation is carried out on the sensing data based on a dynamic window mechanism, and a dynamic compensation matrix containing kinematics compensation parameters is generated; and inputting the multi-modal motion control data set into a gradient descent model under space-time constraint to complete multi-modal motion feature fusion so as to obtain a fusion motion control data set. Joint behavior abnormity is detected through a Lyapunov stability analysis algorithm, and abnormal nodes are identified; and iteratively optimizing control parameters through a fuzzy PID optimization algorithm to generate an optimized control parameter set, finally constructing a three-dimensional virtual motion space model, and establishing a dynamic mapping relationship between a virtual track and an actual mechanism.

The invention discloses a time sequence-based fuzzy PID (Proportion Integration Differentiation) control algorithm dynamic thermal assistance method, which comprises the following steps of: acquiring temperature data of a drilling and riveting area in real time through an infrared sensor, mapping temperature deviation and a change rate thereof to an adjustment weight of a PID parameter based on a fuzzy rule base, and solving the problem of parameter mismatch of the traditional PID in a nonlinear system. Process parameter database matching: establishing an association rule base of a material combination such as a metal-composite material and a preset temperature threshold value, and dynamically setting a temperature control target by combining a thermal physical property heat conductivity coefficient and a melting point of the material so as to realize differential regulation and control of dissimilar materials; the temperature-vibration coupling model based on the LSTM neural network is constructed to precisely and cooperatively regulate and control internal correlation of temperature and vibration, vibration disturbance brought in the temperature rising process is accurately obtained, and dynamic adjustment of the temperature and vibration in the drilling and riveting process is achieved by finely adjusting the power of the active damper.

The invention relates to a multi-modal fusion corn harvester header height adaptive adjustment method and system, and belongs to the technical field of automation control, the method comprises the following steps: using a binocular camera to collect crop images, and preprocessing the crop images to obtain a depth map; the image is input into a YOLOV8n model, and coordinates of a center point at the bottom of a bounding box of the corncob are obtained through model operation; performing plane fitting on the depth map to obtain a ground height; calculating a corn ear height initial value based on the ground height and the center point coordinate; vibration data of working of the harvester is collected to perform vibration compensation on the initial height value to obtain a height correction value; and dynamically adjusting the header height of the harvester by adopting a dynamic fuzzy PID algorithm based on the correction value. According to the method, the target detection model and the dynamic fuzzy PID algorithm are combined, so that the problems of low visual detection precision and control response lag of existing agricultural machinery in a complex field scene are solved, and crop loss in harvesting operation is effectively reduced.

The invention relates to the technical field of automatic control, in particular to a coating machine transverse closed-loop control method and system based on fuzzy PID. The method comprises the following steps: arranging a plurality of pressure sensors on the inner side of the boundary of a coating head to collect the pressure value of each measuring point in real time, and calculating the pressure difference value between the adjacent sensors to form a pressure gradient sequence; the abnormal degree of the boundary flow state is recognized according to the distribution mode of the pressure gradient sequence, when the pressure gradient exceeds a normal fluctuation range, a boundary disturbance event is marked, and boundary state monitoring data is obtained; and measuring the actual coating thickness of each transverse position, and comparing the actual coating thickness with a preset target thickness to calculate a thickness deviation value. According to the method, hysteresis and rigid constraints of traditional PID control are broken through, restrictions of difficulty in boundary disturbance modeling, control feedback delay and frequent manual parameter adjustment are overcome, and transverse closed-loop control based on data perception and intelligent judgment is realized.

The invention provides a vertical axis wind turbine power prediction method based on multi-dimensional environment data, and relates to the technical field of vertical axis wind turbine power based on the multi-dimensional environment data. Multi-dimensional data are collected in real time based on a sensor network, and the multi-dimensional data comprise environment parameters such as wind speed, turbulence intensity and temperature and humidity, and operation states such as rotor rotating speed and pitch angle; secondly, performing empirical mode decomposition on a vibration acceleration signal, constructing a physical information neural network, taking environmental data and an aerodynamic load frequency domain energy spectrum as input, outputting a real-time wind energy utilization coefficient and an aerodynamic coefficient, and finally, determining the aerodynamic load based on an aerodynamic load time domain signal. A load cyclic spectrum is extracted by adopting online rain flow counting of dynamic window length, an accumulated damage value is calculated in combination with a fatigue damage model of humidity correction, and when the residual life is lower than a threshold value, load peak suppression and power generation efficiency balance are realized through cooperative control of magnetic resistance torque linear attenuation and flap fuzzy PID (Proportion Integration Differentiation).

The invention is suitable for the field of crop fertilization control, and provides a fertilization control method and system based on corn protective farming, and the method comprises the steps: synchronously collecting time-space multi-dimensional data of a target planting region, and constructing a multi-modal data cube; generating a seed fertilizer ratio decision vector; according to the seed-fertilizer ratio decision vector, controlling the multi-channel ratio of mechanical precision seed-fertilizer simultaneous sowing equipment, so as to realize the dynamic ratio and variable fertilization of the seed fertilizer; a multi-spectral image and soil moisture content data are collected through an unmanned aerial vehicle group, and a space-time continuous crop growth monitoring network is constructed; dynamically adjusting the topdressing amount and the water and fertilizer concentration of the unmanned aerial vehicle group based on the growth monitoring network data; according to the method, by constructing a multi-modal data cube, the dynamic proportioning and variable fertilization of seeds and fertilizers are realized, the proportioning precision is improved by utilizing fuzzy PID and machine vision, a space-time monitoring network is constructed by virtue of an unmanned aerial vehicle group, the dynamic adjustment of topdressing is realized, and a data-driven closed-loop fertilization system is formed.

The invention discloses an outdoor workstation air quality monitoring and intelligent ventilation system and method, and belongs to the technical field of environment monitoring and intelligent ventilation control. The system is composed of an environment sensing module, an intelligent control unit and a ventilation execution device. And the environment sensing module realizes pollutant concentration field reconstruction through a multi-dimensional data fusion algorithm. The intelligent control unit constructs a dynamic ventilation model based on an improved fuzzy PID algorithm, and generates an optimal ventilation strategy in combination with meteorological prediction data and workstation structure parameters. And the ventilation execution device adopts a modular design to realize directional air supply and pollutant blocking. The method comprises four steps of real-time environment data acquisition, dynamic risk assessment, ventilation parameter optimization and execution mechanism linkage control, and remote intelligent regulation and control are realized through cooperation of edge computing and a cloud platform. According to the scheme, a passive response mode of a traditional ventilation system is broken through, and the technical advantages of accurate pollutant tracing, dynamic optimization of ventilation efficiency and intelligent adjustment of equipment energy consumption are achieved.

The invention relates to an intelligent sleep adjusting method and system based on closed-loop acousto-optic brain wave entrainment, electroencephalogram (EEG) signals are collected and preprocessed in real time through a wireless dry electrode, and high-precision sleep staging is achieved through a deep learning model. The system adopts a fuzzy PID (Proportion Integration Differentiation) controller to dynamically adjust acousto-optic synergistic stimulation: an acoustic module generates binaural beat signals, an optical module outputs blue light (470nm) and amber light (590nm) pulses, and the brain wave entrainment efficiency is enhanced through phase synchronization (the phase difference is less than or equal to 10 degrees) and golden section frequency coupling (fL is equal to 1.618 fA). Parameters are updated every 30 seconds through closed-loop feedback, the sound pressure level (30-50 dB) and the light intensity (10-100 lux) are adjusted according to the Weber-Fechner law, and the response delay is lt; the time is 200 ms. A three-level safety mechanism monitors gamma wave abnormity, epilepsy sample discharge and impedance overrun in real time, and triggers graded protection (alarming, cutting off light stimulation and shutdown). Clinical verifications show that the entrainment success rates of the delta wave and the theta wave respectively reach 71% and 68%, the sleep improvement effect is good, and a safe and efficient intervention scheme is provided for sleep disorders.

The present invention relates to the technical field of water distillers, and disclosed are a thermocompression multi-effect water distiller system and a control method therefor. The thermocompression multi-effect water distiller system comprises a distillation unit and a control unit; the distillation unit comprises a multi-effect evaporator; the multi-effect evaporator is a horizontal tube falling film evaporator and has a side-by-side configuration, and the shell pass of a preceding effect evaporator is communicated with the tube pass of a following effect evaporator; the control unit controls the distillation unit during working, and the control unit is a feedforward-cascaded fuzzy PID model predictive control system and comprises an inner-loop control system and an outer-loop control system; the inner-loop control system uses fuzzy PID control, and the inner-loop control system is provided with a control system for fluctuations in industrial steam pressure Psteam; and the outer-loop control system uses model predictive control. In the present invention, by using the thermocompression multi-effect water distiller system and the control method therefor, the heat exchange efficiency is greatly improved; and a novel control system is provided, enabling normal operation within a range of 40-200% of a set operating condition.

The invention discloses a self-adaptive dynamic control method and system for the machining process of a numerical control machine tool, and relates to the field of intelligent control, and the method comprises the steps: collecting machining data in real time through physical and virtual sensors, and constructing a standardized data set after layering preprocessing; a CNN-LSTM hybrid model is utilized to extract spatial-temporal characteristics to realize working condition classification, and an NSGA-II algorithm is combined to solve a multi-objective optimization problem to generate an optimal control parameter solution set; parameters are dynamically adjusted through fuzzy PID, and a GRU model is adopted to predict machining errors for feed-forward compensation, so that closed-loop control of perception-decision-execution-feedback is formed. The system continuously monitors the actual machining deviation, parameters are optimized again when the actual machining deviation exceeds a threshold value, and cooperative improvement of machining precision and efficiency is achieved. The method has the advantages that NSGA-II multi-target optimization, fuzzy PID correction and GRU error prediction compensation are recognized through CNN-LSTM working conditions, closed-loop feedback iteration is combined, the machining precision and efficiency are improved in a balanced mode, the service life of a tool is prolonged, and the method is suitable for complex working conditions.

The invention relates to an energy accumulator durability test method and system, and the system comprises an extreme working condition simulation module which can achieve high-voltage, wide-temperature-range and high-frequency dynamic loading; the multi-sensor monitoring network integrates a fiber bragg grating strain sensor, a three-axis acceleration sensor and an acoustic emission sensor to realize collaborative monitoring of strain, vibration and microcracks; the intelligent control and energy recovery module is combined with a fuzzy PID and reinforcement learning algorithm, a magnetorheological fluid generator and a super capacitor bank, so that the control precision and the energy utilization rate are improved; and the data analysis and life evaluation platform accurately predicts the residual life by using an improved Bayesian network model and a real-time health index calculation module. According to the test method, a loading mode is dynamically adjusted through a real-time health index, an energy recovery strategy is optimized, and life early warning is triggered. The invention breaks through the limitation of the traditional test and provides an integrated solution.

The invention particularly relates to a grinding force closed-loop control and flutter suppression system for crankshaft variable stiffness machining, and aims to solve the problems of unstable grinding force, flutter and surface defects caused by time-varying stiffness characteristics of a crankshaft due to a multi-crank throw and cantilever supporting structure. The system comprises a grinding force detection module, a control module and an execution module. The grinding force detection module is arranged in a grinding wheel and crankshaft contact area and collects grinding force signals in real time. The control module receives an actual grinding force signal, compares the actual grinding force signal with a target value, and outputs a compensation instruction and a suppression instruction; the compensation instruction is generated by adopting a self-adaptive PID or fuzzy PID algorithm based on force deviation, and the grinding wheel feeding speed or the main shaft rotating speed is dynamically adjusted to stabilize the grinding force; and extracting a high-frequency component of the force signal as a flutter feature, identifying a flutter bud through time-frequency analysis, and generating an anti-phase displacement instruction to suppress flutter. The execution module adjusts feeding or rotating speed according to the compensation instruction and adjusts radial displacement of the crankshaft or the grinding wheel according to the suppression instruction.

The invention discloses a computer chip cooling heat dissipation control method and system, and relates to the technical field of computer heat dissipation. The method comprises the following steps: acquiring multi-modal detection data of a target computer chip; constructing a self-adaptive thermal model; the adaptive thermal model is constructed based on a dynamic thermal resistance network, a depth Q network algorithm and a fuzzy PID algorithm; the dynamic thermal resistance network is used for describing thermal resistance parameters of the chip; inputting the multi-modal detection data into the self-adaptive thermal model, and performing dynamic hierarchical regulation and control by taking heat dissipation energy consumption, temperature fluctuation and chip life as optimization targets to obtain a hierarchical self-adaptive regulation and control strategy; the hierarchical adaptive regulation and control strategy comprises three levels of responses and corresponding control thresholds; the control threshold value is dynamically adjusted according to the performance attenuation trend of the heat dissipation system. The cooling and heat dissipation efficiency and effect of the computer chip can be improved, and the service life of the computer chip is further prolonged.

The invention discloses a new energy automobile high-voltage connector thermal management system and an optimization control method, and relates to the technical field of connecting piece thermal management. The multi-dimensional temperature sensing module is used for acquiring and fusing data by using a thermocouple, an infrared sensor and the like; the combined type heat dissipation execution module adopts a bionic liquid cooling plate, a nano coating fin and a semiconductor chilling plate to dissipate heat; the intelligent heat management control module is used for adjusting heat dissipation by means of an MPC algorithm and fuzzy PID; the fault diagnosis and safety protection module can give an alarm, cut off power and guarantee the reliability of the sensor; and the data interaction and optimization module is used for realizing data interaction and strategy remote optimization. Through multi-module cooperation and intelligent control, the working temperature of the connector is reduced, the heat dissipation efficiency and the control precision are improved, the response time is shortened, and the energy consumption and the service life are balanced; and meanwhile, the fault diagnosis capability is enhanced, the maintenance frequency is reduced, strategy optimization is realized by means of a digital technology, and safe and efficient operation of the high-voltage connector is guaranteed.

The invention provides an electro-hydraulic servo pump control method and system for controlling hydrogen compression based on fuzzy nerve, and relates to the technical field of hydrogen compression, and the method comprises the steps that the current state of a hydraulic cylinder controlled by an electro-hydraulic servo pump is determined, and an input control variable is obtained; determining an output control variable, establishing a fuzzy control rule, and dividing a basic discourse domain; performing composite defuzzification, and obtaining a corrected weight coefficient through a feedforward neural network; the discrete rule amplitude correction factor and the weight coefficient correction are dynamically optimized and corrected; neural network training is established, a loss function evaluation model is utilized, and hydrogen compression control is achieved through a double-buffering mechanism. Fuzzy PID control is adopted, composite defuzzification is carried out according to the real-time state change of a system in combination with fuzzy logic, a membership function output by the fuzzy PID is divided according to regions, a control signal of a driver is synthesized, and hydrogen compression control is achieved; system output is adjusted according to different working conditions, power loss is reduced, and system control precision is improved.