393 results about "Fuzzy pid" patented technology

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 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 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 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 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 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 relates to the technical field of mechanical safety monitoring, and discloses a remote monitoring system for instability of a steel structure of hoisting machinery, which comprises the following modules: an inclination monitoring module for collecting inclination angle data of a crane in real time and carrying out signal preprocessing, and a control module for receiving the data and carrying out comparative analysis on the received data and a preset threshold value. And according to an analysis result, a control instruction is sent to the execution module and data is output to the remote transmission module. The execution module, the remote transmission module, the environment compensation module and the data management module are arranged. According to the invention, through multi-module cooperation and algorithm innovation, high-precision monitoring and active prevention and control are realized. A double-axis / three-axis tilt angle sensor is combined with Kalman filtering, the precision reaches + / -0.1 degree, and the reliability is improved through the redundant design of key part equipment. The control module realizes multi-stage alarm through fuzzy PID and a finite-state machine, a Transformer model predicts risks in advance by 10-30 seconds, environment compensation is realized, hybrid networking enhances adaptability, 5G + VR monitoring improves operation and maintenance efficiency, 60% of tipping accidents can be reduced, and multi-scene safety requirements are met.

The invention relates to the technical field of intelligent control, and particularly discloses a servo control method and a servo control system for a fast door, and the method comprises the following steps: a multi-modal data fusion step: collecting a mechanical state, an environment parameter and system health data, and constructing a multi-dimensional state vector; a self-adaptive control step: dynamically generating control parameters based on fuzzy PID and a deep learning model; a multi-stage energy consumption optimization step: switching a control strategy according to the operation stage of the door body, and introducing a regenerative braking and energy efficiency optimization algorithm; and a fault self-healing step: realizing control switching in an abnormal state through sensor redundancy and actuator backup. Compared with traditional single closed-loop control, the positioning precision is improved, and the anti-interference capability is improved through fusion of the mechanical state and the environmental data.

The invention discloses an ultrasonic transducer series resonant frequency tracking method and an ultrasonic driving power supply, and the tracking method comprises the steps: calculating a current phase difference between a voltage signal and a current signal, determining a phase difference error and an error change rate through the combination of a target phase difference, and determining the resonant state of an ultrasonic transducer according to the comparison of the phase difference error and an error threshold value; if the current driving frequency is in the resonance state, keeping the current driving frequency unchanged; and if in a non-resonant state, correcting the driving frequency through a fuzzy PID algorithm. In order to cope with the sudden change of the load, the tracking method introduces an AI model to identify the load and predict the optimal driving frequency, and then the predicted optimal driving frequency is used as the current driving frequency. According to the invention, the response speed and the adjustment precision in dynamic frequency tracking are improved, and adaptive resonance driving and frequency control of the ultrasonic transducer under variable load and variable working conditions can be realized.

The invention discloses a UPA pressure automatic compensation system and method for mask substrate polishing. The UPA pressure automatic compensation system comprises a pressure execution module, a distributed sensing feedback network and an intelligent control unit. Wherein the pressure execution module is used for directly polishing a mask base plate and comprises a force application shaft, a polishing head, an air source, a UPA air bag and an electric proportional valve, the distributed sensing feedback network comprises an air path pressure sensor and a tail end contact pressure sensor, and the intelligent control unit receives a collection signal of the distributed sensing feedback network and sends the collection signal to the polishing head. On the basis of a current process formula and process parameter coupling model, a data fusion algorithm, a feed-forward compensation algorithm and a fuzzy PID algorithm are operated to obtain a feed-forward compensation amount and a fuzzy PID output amount, the feed-forward compensation amount and the fuzzy PID output amount are superposed, a total control voltage is generated and output to an electric proportional valve, and then the input air pressure of a UPA air bag is adjusted. Therefore, closed-loop control over the polishing pressure of the mask substrate is achieved. Through multi-sensor data fusion, fuzzy PID self-adaptive control and intelligent feed-forward compensation based on a process parameter coupling model, real-time, accurate and constant control of the polishing pressure is realized, so that the surface precision and yield of the mask substrate are improved.

The invention relates to the technical field of laser welding, and discloses a laser welding method for accurately controlling weld penetration. According to the invention, through fusion of coaxial multispectral sensing and a back monitoring unit, multi-dimensional information synchronous acquisition of weld front molten pool morphology, a temperature field and a back thermal radiation signal is realized, and limitation of single sensing is overcome; furthermore, a dynamic instruction is generated based on the extracted multi-characteristic parameters and an adaptive fuzzy PID algorithm, thereby realizing precise closed-loop control of laser energy and heat input, and improving real-time performance and robustness of control. Particularly, a digital twin model is introduced to carry out forward prediction, cross validation is carried out with back monitoring signals, a triggered lead compensation mechanism effectively overcomes hysteresis of traditional feedback control, finally, precision control and stable forming of weld penetration are achieved, the defects of incomplete penetration, burnthrough and the like are effectively avoided, and the welding quality is improved. And the device is particularly suitable for occasions with extremely high welding quality requirements.

The invention discloses an integrated circuit source measurement unit control method for improving a raccoon algorithm and optimizing fuzzy PID. The method comprises the steps that an output voltage control system of an SMU is set up; acquiring output end voltage data of the SMU system in real time; calculating a voltage error and a voltage error change rate; a fuzzy inference rule base is constructed, fuzzy controller parameters are dynamically adjusted based on the fuzzy rule base according to the voltage deviation value and the voltage error change rate, and then PID parameters are dynamically adjusted; a control signal is generated according to the set PID parameter, and the output voltage of the SMU is adjusted; and S4, optimizing the fuzzy PID voltage controller obtained in the step S4 by adopting an improved raccoon algorithm, selecting a decision variable as a parameter of a fuzzy controller, and assigning the optimized parameter to the fuzzy PID voltage controller to realize control on the output voltage of the SMU. According to the invention, the improved raccoon optimization algorithm and the fuzzy PID controller are combined and applied to the integrated circuit source measurement unit, so that the control performance of the SMU system is improved.

The invention relates to the field of coal mine geological disaster monitoring, in particular to a coal mine surface slope remote sensing data fusion monitoring method and system, and the method comprises the steps: collecting multi-source data through a satellite-unmanned aerial vehicle-ground three-stage network, and carrying out the cleaning, correction and standardization preprocessing; utilizing an improved LSTM model which introduces an attention mechanism and a geological parameter correction item to predict slope displacement and risk levels; distributing monitoring resources through a PSO (Particle Swarm Optimization) algorithm, dynamically adjusting an equipment state through a fuzzy PID (Proportion Integration Differentiation) algorithm, and fusing multi-source data through a Bayesian algorithm to generate a high-dimensional matrix; and finally, storing data based on the alliance chain and performing early warning based on a dynamic threshold value. The system correspondingly comprises six core modules. The problems of narrow coverage, poor timeliness and low data utilization rate of traditional monitoring are solved, millimeter-level precision monitoring is realized, the early warning response is less than or equal to 5 minutes, the cost is reduced by more than 40%, and the method is suitable for safety and ecological monitoring of various coal mine slopes.

The invention discloses an intelligent control method and system for a constant-temperature incubator based on deep learning. The method comprises the following steps: acquiring an equipment state vector in real time; analyzing the equipment state vector by using an improved model, and predicting an environmental parameter demand trend in a culture process to obtain a prediction result; modeling a control strategy into an MDP Markov decision process through a DRL deep reinforcement learning framework according to the prediction result, and dynamically optimizing power distribution of a heater, a refrigerator, a humidifier and a fan by taking minimization of energy consumption and temperature fluctuation as objective functions to obtain an initial control strategy; and adjusting the power of the actuator according to the initial control strategy by using a fuzzy PID algorithm, monitoring sensor data in real time through an isolated forest model, and adjusting the initial control strategy when abnormality is detected to obtain a target control strategy. The environment demand change is pre-judged in advance, and the hysteresis problem of traditional control is solved.

The invention relates to the technical field of desulfurization towers, in particular to an ultrasonic agglomeration dedusting and demisting device of a desulfurization system and an operation method thereof, and aims to solve the problems that in the prior art, fine particles in flue gas are not subjected to agglomeration pretreatment, a targeted cleaning mechanism is lacked, and a demister is easily blocked due to filter residue attachment after long-term operation; and finally, the dedusting and demisting efficiency of the desulfurizing tower is reduced. According to the invention, the ultrasonic generation module is started, parameters are dynamically optimized based on a PLC and a fuzzy PID algorithm to induce agglomeration of PM2.5 and fog drops, the particle size is increased through a depressurization ring, and then efficient gas-liquid separation is carried out through a dual separation mechanism; when filter residues are attached, the motor drives the spray pipe to rotate and spray, the cleaning assembly is matched for wiping, the guide assembly guides waste liquid, the intelligent system adjusts in real time and triggers a cleaning program, a pretreatment and cleaning mechanism is established, blockage is eradicated, stable operation of parts is ensured, and the dust and mist removal efficiency is improved.

The invention discloses a complex wind field simulation method suitable for a small and medium-sized unmanned aerial vehicle wind resistance test, and relates to the technical field of intelligent wind control, and the method comprises the steps: based on a plasma exciter control instruction set, generating a directional micro vortex group in a wind tunnel through a plasma exciter array, meanwhile, the spatial distribution of the directional micro vortex group is regulated and controlled by adopting an ultrasonic interference method, and a three-dimensional turbulent flow field is generated; an unmanned aerial vehicle anti-risk test is executed, anti-wind performance data is collected and combined with PIV to verify the stability of the nest ring structure, and dynamic adjustment is carried out through a fuzzy PID control algorithm; and constructing a judgment matrix based on the adjusted wind resistance test data, calculating a wind resistance performance index of the unmanned aerial vehicle by adopting a dynamic time warping algorithm, and generating a performance evaluation report. According to the invention, through vortex ring stability feature extraction and fuzzy PID dynamic adjustment based on the deep space-time convolutional network, closed-loop stability control in a complex wind field environment is realized.

The invention relates to the technical field of industrial environmental protection, in particular to a double-hearth lime kiln low-emission collaborative dust removal system and a control method. The control method comprises the steps that a flue gas sensing module collects real-time flue gas parameters and kiln body working condition data; the working condition linkage regulation and control unit dynamically adjusts operation parameters of the dust removal equipment through a fuzzy PID working condition matching algorithm; the regional collaborative dust removal assembly performs differential dust removal according to flue gas characteristics and treatment requirements of different dust raising points, and flue gas flow guide is performed through a double-hearth time sequence valve group during kiln body switching; the noise elimination and vibration isolation cooperation unit adjusts the noise elimination amount and the vibration isolation damping according to the rotating speed of the draught fan and controls noise and vibration. The tail gas deep purification unit evaluates the dust removal state through a flue gas and dust coupling model, carries out deep treatment on the dedusted tail gas and controls the tail gas emission concentration; the anti-blocking optimization unit carries out cooperative anti-blocking through dynamic rapping and airflow purging based on historical operation data and real-time working conditions. Therefore, the problems of poor dust removal stability, high operation energy consumption and the like in the prior art are solved.

The invention discloses a method for inhibiting temperature fluctuation of an oriented silicon steel BAF furnace, which comprises the following steps: adding 0.005-0.01 wt% of Nb, and forming a nanoscale Nb (C, N) precipitate; a three-dimensional heat conduction model is established in a mode of linkage of infrared thermal imaging and electromagnetic induction heating in combination with a finite element method, and real-time monitoring and accurate control of a temperature field in the furnace are achieved. On the basis of a fuzzy PID hybrid control strategy, the optimal heating power and scanning path are predicted in combination with a convolutional neural network, and closed-loop control of local temperature compensation is achieved; a random forest model with the temperature gradient delta T, the Als content and the Nb precipitated phase density as input parameters is constructed, technological parameters are iteratively optimized in combination with a genetic algorithm, and dynamic self-adaptive adjustment of an annealing temperature curve is achieved. According to the method, uniform growth of Gaussian grains in the secondary recrystallization process is achieved, and the effects of reducing annealing temperature fluctuation, improving secondary recrystallization uniformity and optimizing grain orientation distribution are achieved.

The invention relates to a kitchen waste sewage accurate aeration control method, system and device based on fuzzy PID and deep learning and a medium, and the method comprises the steps: collecting parameters, and processing the parameters to obtain preprocessed data; predicting the oxygen demand through a deep learning time sequence prediction model, and generating a dissolved oxygen set value interval in combination with multi-target reinforcement learning; based on the deviation and the deviation change rate of a dissolved oxygen set value interval and a real-time measured value, an evolutionary fuzzy reasoning system is used for adjusting gain parameters of an aeration controller, PID control quantity is output and converted into an aeration equipment driving signal, and multi-area aeration intensity dynamic distribution is implemented in combination with a hydrodynamic mapping relation and preprocessed data. Through the synergistic effect of the technologies, the core defects of single sensing dimension, prediction lag, control target conflict, extensive execution and the like of a traditional method are systematically solved, and collaborative optimization of degradation efficiency, energy consumption economy and equipment operation life in the kitchen waste sewage treatment process is realized.

The invention relates to an automatic printing calibration method and system combining vision and AI and a storage medium, a multi-mode sensing unit integrated on a printing head assembly synchronously collects two-dimensional images, three-dimensional shapes and working spacing data of a printing area in real time, and data fusion is carried out by using a model based on a neural radiation field; generating a high-fidelity multi-dimensional digital mapping; carrying out defect identification and positioning by adopting a pre-trained convolutional neural network CNN model, and predicting an optimal adjustment parameter according to the defect information and the historical sequence through a long short-term memory network LSTM model; the edge calculation and control unit is combined with a material characteristic database to generate a hierarchical calibration instruction in a millisecond level, adopts fuzzy PID self-optimization control to dynamically set PID control parameters, and is combined with feed-forward compensation of the inertial measurement unit to perform accurate, smooth and robust control on an execution mechanism, so that the accuracy of calibration is improved. And full-automatic, high-precision and real-time online calibration of the printing process in a complex industrial environment is realized.

The invention relates to the technical field of industrial furnace temperature control, and discloses a tunnel furnace fuzzy PID temperature control process with strong anti-interference adaptive ability, which comprises the following steps: temperature signal acquisition: acquiring actual temperature values of each area in real time through temperature sensors arranged in different heating areas of a tunnel furnace, and calculating temperature signals of the actual temperature values; the collected temperature signal is converted into a digital signal to be transmitted to the temperature controller; and interference signal identification and classification: calling a preset interference identification model by the temperature controller, and carrying out fluctuation characteristic analysis on the acquired temperature signal. By adopting a dual-redundancy temperature acquisition mode combining distributed optical fibers and K-type thermocouples and combining an interference identification model based on an LSTM neural network, accurate identification and classification of interferences such as feed quantity fluctuation, power supply fluctuation and airflow disturbance in a kiln are realized, and through dynamic correction of fuzzy PID (Proportion Integration Differentiation) parameters and an interference compensation mechanism, the disturbance of the kiln can be accurately identified and classified. And the anti-interference capability of the tunnel kiln temperature control system is obviously improved.

The invention provides an energy-saving compressor intelligent self-adaptive control system based on multi-sensor fusion, which belongs to the technical field of industrial compressor intelligent control and comprises a data acquisition module, a signal conditioning module, a multi-sensor information fusion module, an intelligent control decision module, an execution driving module and a system power supply management module. A state display alarm and remote communication module can be additionally arranged. Multiple sensors collect operation and environment parameters of the compressor, and after conditioning, a comprehensive state evaluation result is generated through D-S evidence theory or fuzzy logic fusion; the intelligent module generates a control instruction based on a load prediction algorithm and a fuzzy PID self-tuning strategy, and the execution module drives the compressor to act; the power supply module supports solar power supply and multi-power supply switching. The system overcomes the limitation that a traditional compressor is single in sensing, rigid in control and the like, comprehensive sensing, accurate evaluation and self-adaptive energy-saving control are achieved, the operation stability is improved, and the energy consumption and the operation and maintenance cost are reduced.

The invention discloses a UV adhesive viscosity real-time regulation and control system based on edge calculation, and particularly relates to the technical field of UV adhesive production and processing intelligent regulation and control, and the UV adhesive viscosity real-time regulation and control system comprises a viscosity sensing unit, an edge calculation node, an execution unit and a far-end cloud platform in a production field. The viscosity sensing unit collects seven types of parameters such as UV glue temperature, shearing rate and solid content, and the self-cleaning assembly is matched to guarantee the detection precision; a self-adaptive fuzzy PID algorithm is built in the edge computing node, a viscosity value and deviation are calculated in real time, PID parameters are dynamically adjusted, disturbance compensation is introduced, and a control instruction is generated; the execution unit preferentially starts component regulation and control or environment regulation and control according to the viscosity deviation and cooperates with an instruction to realize linkage; the cloud platform stores data, performs remote monitoring, optimizes algorithm parameters through federal learning, and pre-warns equipment faults through a built-in predictive maintenance module. According to the system, the real-time performance and precision of regulating and controlling the viscosity of the UV adhesive are improved, the failure outage rate is reduced, and the large-scale production requirement is met.

The invention discloses a control method based on coal dressing intelligent filter pressing, and belongs to the technical field of coal dressing, and the control method specifically comprises the following steps: pre-grading original coal slime through an intelligent multi-stage cyclone, collecting coal slime property data of each section in real time by using online detection equipment, and transmitting the coal slime property data to an intelligent control system; through a dual optimization mechanism, coal slime properties and filter pressing grades are matched, and optimal filter pressing parameters are generated; a multi-machine cooperative control strategy is adopted to drive a three-stage filter press to operate step by step, a virtual model of the filter press is constructed through real-time working state parameters of a sensor, and an abnormal state is dynamically corrected in combination with the real-time working state parameters and a fuzzy PID algorithm; carrying out resource processing on a filter cake generated by filter pressing, and meanwhile, carrying out grading treatment on filtrate; intelligent control over the whole process of coal slime grading filter pressing is achieved, and the coal slime treatment efficiency and the resource utilization rate are improved.

The invention discloses a self-adaptive pipetting system and method based on multi-mode sensing fusion. The system comprises a PLC (Programmable Logic Controller), a fusion multi-mode sensing module, a high-throughput execution module, a self-adaptive protocol matching module and a man-machine interaction module. According to the invention, through multi-channel cooperation and continuous transmission, the experiment efficiency is greatly improved; multi-modal sensing data are fused, and real-time closed-loop control and dynamic compensation are performed by adopting algorithms such as fuzzy PID (Proportion Integration Differentiation) and the like, so that the pipetting volume precision reaches + / -1%, and the repeatability is improved by 20-30%; a protocol feature library and an intelligent matching algorithm are built in, and experimental parameters can be automatically analyzed, matched and optimized; equipment state and process parameters are monitored in real time, faults and pollution risks are automatically early warned, and the operation safety and reliability are remarkably improved in combination with automatic anti-pollution design; a PLC is deeply fused with a sensing module and a decision module, a sensing-decision-execution intelligent closed loop is constructed, and an accurate and reliable integrated solution is provided for automatic pipetting.

The invention discloses a hydraulic servo control system and method for a mining guniting robot. Belongs to the technical field of servo control. Comprising a hydraulic driving unit, a sensing and feedback unit, an electrical control unit, an embedded host and a man-machine interaction unit, wherein the electric control unit adopts a fuzzy PID (Proportion Integration Differentiation) controller for controlling the joint position of the mechanical arm according to the information of the sensing and feedback unit; aiming at linear synchronous control of chassis walking, an anti-integral saturation speed feedforward PI controller is adopted to adjust the opening size and direction of a hydraulic proportional valve in real time, and closed-loop control of the position is achieved.