297 results about "Fuzzy pid control" patented technology

The invention relates to the technical field of automation, in particular to an automatic monitoring and optimizing system for a fine chemical production process, which comprises the following steps of: extracting time-frequency domain fusion characteristic quantity of stirring torque power time sequence fluctuation data in real time through an incidence matrix construction module, dynamically inverting a thixotropic index by combining a deep neural network model, and optimizing the stirring torque power time sequence fluctuation data; the problem that a traditional method is difficult to perceive material rheological characteristics in real time is solved. The dynamic coupling analysis module analyzes the material viscosity change rate based on the thixotropic index, fuzzy PID control is adopted to generate a stirring speed adjusting instruction dynamically matched with the viscosity and a jacket temperature compensation value, and the defects of uneven mixing and local overheating caused by lagging adjustment of process parameters are overcome; the multi-target collaborative optimization module locks the mass optimization weight in the viscosity sudden change stage, rapidly stabilizes the reaction condition through a feed-forward compensation algorithm, dynamically balances the stirring power consumption and the heat transfer efficiency based on Pareto frontier search in the steady state stage, and solves the conflict between the mass and the energy efficiency target.

The invention discloses a stress analysis method for bridge incremental launching construction, and particularly relates to the field of stress analysis. According to the method, a mechanical state matrix is generated by combining wavelet noise reduction and Kalman filtering through distributed optical fibers, a piezoelectric force sensor, a vibrating wire stress meter and other equipment and real-time data; a pushing system-temporary support-beam body coupling dynamic mechanical model is established, and a generalized alpha method is adopted for solving and Bayesian optimization calibration parameters are adopted; key parameters are identified on line through a three-level progressive framework, boundary conditions are dynamically updated in combination with a total station and a laser tracker, and supporting rigidity self-adaptive adjustment is achieved through fuzzy PID control. Model confidence evaluation adopts a residual norm-modal confidence criterion-Bayesian update three-level mechanism, finally, stress distribution, support reaction and displacement deviation are calculated based on a correction model, and a safety index, threshold alarm and risk analysis report is generated through a fuzzy comprehensive evaluation method and fed back to a construction control center in real time.

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 relates to the technical field of intelligent control, in particular to an intelligent power optimization scheduling method in a new energy grid-connected scene, which comprises the following steps of: acquiring a current load, a rated load, an adjustment rate and a temperature rise value through a data interface, and normalizing to generate a characteristic parameter matrix; the product of the adjusting rate and the temperature rise value is used for calculating the correlation degree with the aging factor to obtain an elastic weight, extracting the voltage stability and the weight, inputting the voltage stability and the weight into a Dijkstra algorithm to screen a path meeting the safety margin, monitoring the slope of an inertia change curve, adjusting a compensation coefficient through fuzzy PID control, and feeding back the compensation coefficient to correlation degree calculation. According to the method, multi-source parameters are fused through a rule tree structure, the load regulation rate and the aging factor weight are dynamically distributed, bus voltage stability and elastic weight path optimization are combined, a synchronous inertia margin safety correction system is constructed, the system response precision, the state cooperation efficiency and robustness are improved, local optimum is avoided, and the system reliability is improved. And the standby capacity configuration and the frequency advance correction capability are optimized.

The invention discloses an automatic batching and stirring control system based on an industrial computer, belongs to the field of new wall material production control systems, and aims to solve the problems that the real-time performance is poor, and the influence of raw material characteristic changes on a fall value cannot be pre-judged. Weighing, humidity, infrared flow velocity, temperature, pressure, torque and airflow sensors are integrated, multi-dimensional parameters such as material weight, flow velocity, humidity and air pressure in a bin are monitored in real time, the influence of environmental interference on the sensors is dynamically compensated through multi-parameter collaborative feedback, the real-time performance and reliability of data acquisition are improved, a fuzzy PID control algorithm and a machine learning model are fused, and the real-time performance of the system is improved. A nonlinear mapping relation is established through a training data set, the initial valve opening degree and the fall value are predicted, errors are corrected in real time, dependence on historical data is reduced, the first-time use error of a new formula is reduced, the response time for coping with sudden environment changes is shortened, a single adjustable-caliber electromagnetic valve is adopted, the inner wall is coated with a nano anti-sticking coating, and the service life of the valve is prolonged. And the opening degree is dynamically adjusted in combination with a PID algorithm.

The invention relates to an automatic identification control method and system based on heat dissipation of a liquid cooling plate in a heat exchange system. The method comprises the following steps: acquiring real-time operation data of a target liquid cooling plate; establishing a dynamic thermal resistance network model, and calculating a current thermal resistance value; refrigeration demand parameters are obtained, and a demand weighted thermal resistance value is generated in combination with the current thermal resistance value; thermal resistance deviation is obtained based on the demand weighted thermal resistance value, and the opening degree of a cooling liquid flow valve in the heat exchange system is dynamically adjusted through a fuzzy PID controller; the PID parameters are updated through a preset optimization algorithm, and the fuzzy PID controller is optimized; according to the automatic identification control method and system based on heat dissipation of the liquid cooling plate in the heat exchange system, real-time dynamic adjustment of the flow of the cooling liquid is achieved by establishing the dynamic thermal resistance network model and combining fuzzy PID control, the problems that a traditional method lags in response and is insufficient in control precision are solved, and the control efficiency is improved. The method has the effects of improving the dynamic response speed, improving the control precision and optimizing the energy efficiency.

The invention provides a high-efficiency control method and system for a central air conditioner refrigeration machine room, and relates to the technical field of automatic control, and the method comprises the steps: 1, obtaining chilled water supply and return water temperature difference and flow data, and calculating the dynamic cold load demand of a building in combination with the personnel distribution thermal imaging data of each floor; and 2, on the basis of the dynamic cooling load requirement, a dynamic adjustment model for the number of running water chilling units is built, matching degree calculation is conducted on the actual cooling load value and the single refrigerating capacity of the water chilling units through a fuzzy PID control algorithm, and a final unit running combination scheme is generated. By calculating the cooling load requirement, dynamically adjusting the operation parameters of the water chilling unit, the chilled water pump and the cooling tower and balancing the equipment operation time, the system energy efficiency is effectively improved, the energy consumption and the operation and maintenance cost are reduced, and stable operation of the refrigerating system is guaranteed.

The invention discloses a carbon fiber composite material surface modification spraying system and a control method thereof. The system comprises a multi-axis robot, a plasma spray gun, a contact angle measuring probe, a 3D line laser scanner, a hyperspectral imager, an environment sensor and a controller. According to the method, the plasma power and the robot speed are adjusted in real time through contact angle measurement and plasma treatment feedback control, so that the CFRP surface can accurately reach a target value, and the coating adhesive force is improved; 3D line laser scanning and hyperspectral imaging are combined, and the posture, the distance, the wet film thickness and the component uniformity of the spray gun are monitored in real time; based on a prediction model fusing a physical model and a neural network, a self-adaptive fuzzy PID control algorithm is adopted, and the coating flow and the track posture of a spray gun are accurately regulated and controlled in real time. The problems of weak coating binding force, uneven thickness, orange peel, sagging and serious coating waste in traditional spraying are effectively solved, and self-adaptive, high-quality and green spraying of workpieces with complex curved surfaces is achieved.

The invention discloses a transmission driving control method of a bimodal integrated engine. The transmission driving control method comprises the following steps: S1, state sensing; s2, mode decision making; S3, dynamic adjustment; s4, mode switching; S5, power optimization distribution; s6, adaptive learning is carried out; and S7, fault-tolerant guarantee. The invention further discloses a bimodal integrated engine transmission system which comprises the following modules: a power input module; a transmission adjusting module; a state sensing module; an intelligent control module; and a fault-tolerant guarantee module. Parameters are dynamically adjusted through fuzzy PID control according to speed deviation, meanwhile, real-time correction is conducted in combination with a multi-mode intelligent algorithm, speed fluctuation is precisely controlled within + / -3 km / h, flight stability is guaranteed, a model prediction control and multi-mode intelligent control cooperation mechanism is adopted, an optimal power output sequence is calculated in advance, coordinated distribution is completed, and the optimal power output sequence is controlled to be within + / -3 km / h. The dynamic response speed is increased by 40%, and the high-dynamic task requirement is met.

The invention relates to the technical field of agricultural machinery control, in particular to a multi-variety vegetable harvester dynamic identification and feeding control system based on image processing, which comprises an image acquisition module, an environment sensing module, an image processing module, a data fusion module, a dynamic identification and tracking module and a feeding control module. Through binocular stereo vision and millimeter wave radar fusion, three-dimensional space positioning and dynamic tracking of the brassica oleracea corm are realized. Track prediction is carried out in combination with extended Kalman filtering and a long-short-term memory network, and the clamping path of the mechanical arm is optimized; the clamping force is adaptively adjusted through fuzzy PID control, and accurate feeding of the cabbages is achieved in combination with speed control of the conveying belt. The automatic cabbage harvesting machine improves the automation level of cabbage harvesting, effectively reduces the damage rate, improves the operation stability and harvesting efficiency, and is suitable for intelligent harvesting operation of a large-scale cabbage planting base.

The invention discloses an automatic control method and system for a bias point of a photoelectric modulator, and relates to the field of signal processing, and the method comprises the steps: generating a composite disturbance voltage signal through a digital signal processor; the bias electrode is injected into the photoelectric modulator and used for collecting the optical signal output by the photoelectric modulator and converting the optical signal into an electric signal; extracting a fundamental component amplitude and phase difference data of a second harmonic component, a third harmonic component and a fundamental wave; dynamic characteristic parameters are calculated through spectral analysis, and the mapping relation between the characteristic parameters and the offset of the offset points is established; outputting the offset direction and the offset level of the offset point through a deep learning model; a bias voltage compensation amount is generated through a fuzzy PID (Proportion Integration Differentiation) controller; and adjusting the bias electrode voltage, smoothing the filter, and eliminating high-frequency jitter. The method has the advantages that high-precision and stable bias point adjustment is realized through the composite disturbance signal, real-time spectrum analysis and deep learning optimization decision in combination with fuzzy PID control and a dynamic closed-loop mechanism, and the system performance and stability are remarkably improved.

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 robot track online compensation and precision control method and system based on multiple sensors, and belongs to the technical field of robot track control. The method comprises the following steps: acquiring target trajectory data and original data of each sensor, and performing time alignment on the original data of each sensor to generate a fusion data set comprising vibration acceleration data and pose data; performing time-frequency feature extraction based on the vibration acceleration data to obtain a dominant vibration frequency spectrum; constructing a mechanical transfer function model of the joint driving torque and the tail end vibration displacement; based on the mechanical transfer function model, constructing an adaptive notch filter to generate a feed-forward compensation sequence; according to the target trajectory data, the pose data and weight parameters, updated in real time, of the sensors, a trajectory fusion deviation value is obtained through calculation; and after the feedforward compensation sequence and the trajectory fusion deviation value are integrated, a joint driving current instruction is generated based on a fuzzy PID control algorithm. The robot trajectory control precision can be improved.

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.

The invention discloses an intelligent controllable road bridge vibration detection method based on magnetorheological fluid and a damping device, and belongs to the technical field of bridge engineering structure health monitoring and vibration control, and the method comprises the steps: calculating a comprehensive vibration strength index through multi-source signal fusion and adaptive Kalman filtering, and estimating the bridge speed; the damping force is mapped through the pre-calibration database, and a damper state verification mechanism is introduced; a gain self-adaptive ceiling algorithm is combined with a fuzzy PID control force algorithm for tracking to realize accurate control; finally, the health state of the system is evaluated through performance degradation indexes of temperature compensation, corresponding devices are integrated with a sensing network, a control unit and a distributed acquisition architecture, and the problems that a traditional system is single in state sensing, fixed in control strategy, lack of a self-diagnosis function, complex in engineering wiring and the like are solved. The comprehensive solution integrating intelligent monitoring, self-adaptive control and health self-diagnosis is realized, and the reliability, adaptability and maintainability of the bridge damping system are remarkably improved.

The invention discloses a measurement and control system of gas heating equipment, which relates to the technical field of equipment measurement and control, and is characterized in that a flow feed-forward control module is used for determining a target mass flow value through a feed-forward compensation processing mechanism according to at least one parameter measured by a sensor measurement module; the temperature control module is used for determining PID control parameters of a cascade PID control structure comprising a main loop and an auxiliary loop according to the temperature parameters measured by the sensor measurement module and a preset temperature difference grading fuzzy PID control rule; and the power control module is used for determining the target heating power according to the target heating rate and the characteristic parameters of the rear-end purging object, and adjusting the heating power of the heater in combination with an anti-integral saturation mechanism configured by the temperature control module and the power control module. By adopting the system, the heating process can be accurately adjusted and quickly responded, and the temperature control stability and the equipment operation efficiency are improved.

The invention provides a field emission thruster extraction electrode indium deposition cleaning temperature control method and system, and relates to the technical field of space electric propulsion, and the method comprises the steps: obtaining multi-source temperature data and electric field intensity data of a target region, and carrying out the time-space fusion processing of the two data, and generating target feature information reflecting the deposition state of an indium material; based on the feature information, a fuzzy PID control algorithm is adopted to generate a power control signal, and then the embedded heating unit is adjusted; monitoring electrothermal characteristics to obtain thermal impedance parameters, and further generating heat flow distribution data; analyzing heat flow data by using a state observer to predict an indium deposition growth trend to obtain deposition rate information, finally determining a cleaning control strategy according to the growth rate, and controlling temperature change in a non-uniform heating mode to enable the indium material to reach a phase change state. According to the invention, the reliability and maintenance efficiency of on-orbit long-term operation of the field emission propeller are improved.

The invention provides an environmental protection evaluation method and system based on operation parameters of an integrated printer, and relates to the technical field of green printing, and the method comprises the steps: inputting the operation parameters of the integrated printer into a neural network algorithm, calculating environmental protection index scores of VOCs, wastewater, energy consumption, solid waste and the like, and generating a printing technology optimization suggestion based on the environmental protection index scores. And the self-adaptive fuzzy PID control algorithm is adopted to automatically adjust the operation parameters, and it is ensured that the environmental protection index score reaches the preset threshold value, so that environmental protection optimization of the printing process is achieved, the resource utilization efficiency is improved, and environmental pollution is reduced.

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 provides an intelligent algorithm optimization gate adaptive steady-state adjustment method, and relates to the field of water conservancy projects. The intelligent algorithm optimization gate self-adaptive steady-state adjustment method comprises gate adjustment system model building, gate feedback adjustment algorithm definition, complex system transfer function fitting, MATLAB / Simulink simulation verification analysis and one-dimensional hydraulic model verification, and specifically comprises the steps of firstly building a gate self-adaptive steady-state adjustment model, defining a gate feedback adjustment algorithm, and performing one-dimensional hydraulic model verification; considering that a gate adjusting system is a large-time-delay and strong-coupling system, fitting is conducted on a transfer function according to actually-measured water level and flow data, then an IPSO-Fuzzy PID control model is established according to the principle and characteristics of an IPSO optimization algorithm, three simulation models of PID control, Fuzzy PID control and PSO-Fuzzy PID control are established based on MATLAB / Simulink, and a simulation model is established for verifying the self-adaptability of the optimization model. Step signals are applied to simulate external interference, simulation comparison is carried out, the built model is utilized, actual measurement data are input, and optimal control over the gate self-adaptive steady-state adjusting system through an intelligent algorithm is achieved.

The invention belongs to the technical field of hydroelectric generating set monitoring, and relates to a water turbine draft tube pressure pulsation online monitoring and stability optimization method and system. Pressure pulsation signals of different positions of the draft tube are obtained; performing pressure pulsation characteristic parameter identification on the pressure pulsation signal by adopting a hybrid optimization algorithm to obtain an identified pressure pulsation characteristic parameter; establishing a dynamic air supply amount calculation model based on the real-time operation parameters of the unit; according to a fuzzy PID control method, dynamically correcting an output value of the dynamic air supply amount calculation model to obtain a dynamic air supply adjustment parameter; and performing multi-objective optimization matching on the identified pressure pulsation characteristic parameters and the dynamic air supply adjustment parameters, and generating a control instruction according to an optimization matching result. According to the method, premature convergence is effectively avoided in the parameter identification stage, the feature extraction precision is improved, adaptive adjustment of the air supply amount is facilitated, stable operation of the unit under different working conditions is facilitated, and optimization of the energy consumption efficiency is facilitated.

A predictive fuzzy PID control method for an intelligent greenhouse includes: acquiring greenhouse real-time data, where the real-time data includes temperature data and humidity data; obtaining fuzzy quantity, control quantity and error data based on the real-time data, where the error data includes an error value and an error change rate; obtaining a target value based on the error data and a predictive functional control; controlling the target value and the error data for fuzzy inference, determining and adjusting parameters, and generating a control signal based on the parameters; and controlling greenhouse equipment adjustment based on the control signal to enable an output value of the equipment to approach the target value, and repeating the above steps to achieve continuous greenhouse environment control. This method achieves more accurate and efficient control of environmental parameters such as temperature, humidity, and illumination, and provides an optimal growing environment for plants.

The invention relates to the technical field of plasma instant freezers, in particular to an intelligent control method of a multi-refrigeration-unit combined plasma instant freezer, which comprises the following steps of: sequentially starting a plurality of refrigeration units in a delayed manner after electrifying, and operating the refrigeration units at full power after starting; the number of the placed blood bags is collected according to a weight sensor; acquiring load capacity and load temperature data in the instant freezer in real time; generating a rotating speed adjusting instruction by adopting a PID control algorithm based on fuzzy logic; when the load is increased or the temperature rises, the control unit increases the rotating speed of the refrigeration unit; when the load is reduced or the temperature is close to a set threshold value, the control unit reduces the rotating speed of the refrigeration unit; dynamically distributing the work cycle and the start-stop sequence of each refrigeration unit; and through a closed-loop feedback mechanism, the rotating speed adjusting parameters are dynamically corrected according to the actual refrigeration effect. According to the method, high-precision temperature tracking of a fuzzy PID control algorithm is combined, and it is ensured that the biological activity of plasma is not damaged in the quick freezing process.

The invention discloses a decision control system and method based on temperature field and atmosphere analysis, and relates to the technical field of intelligent control. A monitoring module in the system obtains a temperature set and a gas concentration set; the trend analysis module constructs a three-dimensional model and establishes a coordinate system, a variation function is combined with a Kriging equation to supplement a temperature set to generate an expanded temperature set, the three-dimensional model is discretized into a plurality of nodes and is matched with points in the expanded temperature set through nearest neighbor search, a heat conduction equation and a convection boundary are set, and a finite element matrix equation set is constructed; solving to obtain a temperature field model and carrying out statistics to generate a temperature trend curve; a decision module compares the temperature trend curve, the gas concentration set and a process standard to obtain an abnormal section number, and analyzes a section temperature difference and a concentration error vector of the abnormal section number by adopting a fuzzy PID control algorithm to generate an adjustment vector; and the execution module performs automatic adjustment based on the adjustment vector, so that intelligent decision adjustment based on temperature field and atmosphere conjoint analysis is realized.

The invention provides a tunnel fire monitoring and ventilation and smoke exhaust linkage control system. The tunnel fire monitoring and ventilation and smoke exhaust linkage control system comprises a tunnel environment and fire monitoring module, a data fusion processing module, a tunnel ventilation and smoke exhaust linkage control module and a remote monitoring and control management system module which are connected through a communication network. The data fusion processing module is configured with a D-S evidence theory algorithm and is used for receiving multi-source environment data, calculating basic probability distribution of data of each sensor, quantitatively evaluating a fire occurrence probability through a combination rule and generating a fire state judgment result; the tunnel ventilation and smoke exhaust linkage control module is used for receiving the fire state judgment result and determining the target wind speed according to the fire scale; a fuzzy PID control unit and a hysteresis control unit are arranged in the tunnel ventilation and smoke exhaust linkage control module. According to the method, the problem of high false alarm rate of traditional monitoring is solved, the engineering problem that discrete ventilation equipment is difficult to continuously and accurately control is solved, and the disaster prevention and reduction capability of the tunnel is remarkably improved.

The invention provides a composite material machine tool machining precision real-time measurement and thermal deformation compensation control method and system, and relates to the technical field of composite material machining. Collecting data through a temperature sensor and a displacement sensor; establishing a multi-dimensional thermal deformation prediction model; measuring the position of the cutter in real time by adopting a laser interferometer; constructing a self-adaptive compensation controller, and generating a compensation instruction by adopting a fuzzy PID control algorithm; and sending the compensation instruction to a numerical control system to realize real-time compensation of the processing track. Closed-loop control over the machining precision of the composite machine tool is achieved, and the machining precision is improved.

The invention discloses a dissolved oxygen closed-loop regulation and control method used in a sewage treatment process. The method comprises the following steps: acquiring real-time dissolved oxygen concentration in a reaction tank through a distributed sensor array, and performing temperature compensation; an online water quality analyzer is used for collecting water inlet flow, chemical oxygen demand, ammonia nitrogen and water temperature to construct a comprehensive input feature vector; establishing a nonlinear dynamic mapping model based on a deep neural network, and outputting a target dissolved oxygen set value changing along with working conditions; an aeration adjusting instruction is generated by adopting a feedforward-feedback composite adaptive fuzzy PID control algorithm; dynamically adjusting the air volume of an air blower and the opening density of a partition aeration disc to match the aerobic rate of microorganisms; and the control performance is evaluated regularly, and a model parameter online optimization mechanism is triggered to continuously improve the setting precision. According to the method disclosed by the invention, the dissolved oxygen can be accurately controlled, so that the aeration energy consumption is reduced by not less than 18%, the control precision is within + / -0.3 mg / L, and the robustness, the automation level and the low-carbon operation capability of the system are remarkably improved.

The invention relates to the technical field of data processing, and discloses a hydropower station fish gathering system inlet flow field adaptability optimization method and system. The method comprises the following steps: grading target fishes according to swimming speed and body length, setting a flow velocity adaptation threshold value, monitoring a flow field through an acoustic Doppler current meter, fusing data by using a Kalman filtering algorithm, calculating a fish rheotaxis index, and carrying out matching analysis to evaluate the adaptability of the flow field, according to the evaluation result, the flow guide pier and the microbubble curtain are controlled to conduct multi-stage linkage regulation and control, and a fuzzy PID control algorithm is adopted to achieve self-adaptive flow field control with the fish hydrotaxis index as a feedback signal. The problem that flow field optimization lacks fish behavior response feedback and multi-device coordination control in the prior art is solved, and the biological adaptability and the regulation and control precision of the inlet flow field of the fish gathering system are improved.

The invention discloses a large-span space truss roof sliding construction rail precision control and deviation correction method, and belongs to the technical field of steel structure construction. According to the method, a three-stage closed-loop control system is adopted, wherein in first-stage reference control, high-precision positioning of the pre-buried track is driven by + / -0.5 mm through a BIM model; in the second-stage dynamic monitoring, a multi-source sensor network comprises a laser tracker, a tilt angle sensor and a strain gauge array, track displacement, attitude and stress data are collected in real time, and millimeter-level monitoring is achieved in combination with a Kalman filtering algorithm; a hydraulic servo system is adopted for three-stage active deviation correction, the deviation trend is predicted based on an LSTM neural network, a hydraulic execution mechanism is driven through a fuzzy PID control algorithm, the response time is shorter than 8 seconds, and the deviation correction precision is + / -0.1 mm. According to the method, the construction precision is improved to + / -1.5 mm, the deviation rectifying efficiency is improved by 90%, and the method is suitable for large-span projects such as stadiums and airport terminals.

The invention relates to a PID (Proportion Integration Differentiation) control inertial servo system and a control method. The PID control inertial servo system comprises a controller, a control circuit, a driving circuit, a motor, a potentiometer, a system clock, a timer, an analog-to-digital converter, a plurality of serial ports, a communication bus, main control equipment, a steering engine, a fuzzy PID control module, a sampling module, a current sensor, a linear quadratic regulator, a PWM (Pulse-Width Modulation) duty ratio module and an integration module, the control circuit comprises a control chip; the driving circuit comprises a first driving circuit and a second driving circuit; the first driving circuit comprises a driving chip; and the second driving circuit is electrically connected with the motor and is provided with a plurality of switches. On the basis of position closed-loop feedback, with the fuzzy PID control algorithm as the core, optimal system parameters are obtained through a linear quadratic regulator, and compared with traditional PID control, fuzzy PID control has higher control precision, stability and response speed.