1755 results about "PID controller" patented technology

The invention relates to the technical field of marine monitoring, in particular to a marine target visual servo tracking method, which comprises the steps of multi-modal sensor fusion, a self-adaptive visual tracking algorithm, a servo control and visual collaboration mechanism and a shielding processing and target re-identification strategy. The problem that tracking is unstable under the conditions of illumination change, ship body shaking, target shielding and the like in a traditional method is solved. The IMU, the GNSS and the visual data are fused through extended Kalman filtering, ship body shaking is compensated, and the target state estimation precision is improved; the improved D-Fi ne target detection model is combined with an online feature updating mechanism to dynamically adapt to the appearance change of the target; the prediction and correction control strategy and the double-closed-loop PI D controller cooperate to adjust the camera holder, and the tracking delay is reduced; the multi-clue shielding detection and space-time joint feature matching technology ensures accurate re-identification of the target after shielding is removed. The real-time performance and robustness of the tracking system on an embedded platform are improved, and an efficient and stable target tracking solution is provided.

The invention provides an intelligent incubation bin anti-interference control method and system based on error self-learning, and relates to the technical field of intelligent control, and the method comprises the steps: respectively calculating a temperature deviation value and a humidity deviation value according to an optimization parameter sequence of a temperature prediction error and an optimization parameter sequence of a humidity prediction error; performing normalized weighted summation on the temperature deviation value and the humidity deviation value, and performing dynamic scaling through a Gaussian kernel function to obtain a correction factor; dynamically adjusting the activation function slope of a neural network hidden layer according to the correction factor and a BP neural network, reconstructing the weight of the fuzzy rule base based on the numerical distribution characteristics of the correction factor, and generating the corrected weight of the fuzzy rule base; and based on the corrected fuzzy rule base weight, constructing a three-dimensional parameter adjustment curved surface, and dynamically adjusting proportion, integral and differential parameters of a PID controller through a curved surface gradient search algorithm to generate a control signal. According to the invention, the control accuracy is improved.

The invention relates to the technical field of temperature equalization control, and discloses a temperature equalization control method and device for a liquid cooling over-charging system. The method comprises the following steps: carrying out grid division on the liquid cooling overcharge system, inputting thermophysical parameters, and obtaining a temperature field calculation matrix through finite difference time domain analysis; establishing a two-layer distributed control network of a station-level control layer and a pile-level control layer based on the temperature field calculation matrix; acquiring real-time temperature data of the two layers of distributed control networks, and performing temperature field mapping to obtain temperature field distribution data; performing linear prediction and nonlinear prediction calculation on the temperature field distribution data to obtain a temperature change trend and a temperature extreme point; the target temperature is calculated according to the temperature change trend and the temperature extreme point, the cascade PID controller is adopted to adjust the rotating speed of the cooling liquid pump, and the temperature balance control parameters are obtained, station-level and pile-level cooperative control is achieved, cooling resources among the multiple charging piles are reasonably distributed, and the overall cooling efficiency of the system is improved.

The invention discloses a DC-DC power supply dynamic response optimization system based on digital PID, which relates to the technical field of DC-DC power supply dynamic response optimization, and comprises a voltage and current sampling module, a reference signal generation module, an output signal generation module, a digital signal generation module, a control module and an output signal generation module, the digital PID controller calculates control output according to errors, the PWM signal generation module generates corresponding PWM signals according to the control output, the power switch driving module amplifies the signals to drive a switch tube, the load monitoring module monitors load changes in real time, output of the load monitoring module can be fed back to other modules, and power supply dynamic response optimization is achieved. According to the invention, the dynamic response performance of the DC-DC power supply is remarkably improved, the output fluctuation is reduced, the recovery time is shortened, the power supply efficiency is improved, the stability is guaranteed by adopting multiple anti-interference design, peripheral elements are reduced, miniaturization is realized, the cost is reduced, fault diagnosis is realized, and the service life is prolonged.

The invention relates to the technical field of intelligent dimming control, and discloses an intelligent dimming control method and system based on an LED driver, and the method comprises the steps: carrying out the current and light intensity data collection of the LED driver under different dimming depth values, and obtaining a response characteristic data set; inputting the response characteristic data set into a convolutional block attention module and convolutional neural network combined architecture for training to obtain an LED response characteristic prediction model; the ratio of the light intensity change rate to the current change rate is calculated, and a piecewise linear dimming sensitivity index is obtained through piecewise linear function fitting; constructing a first-order inertia controller and a brightness feedback loop, and generating a brightness self-compensation control strategy; according to the LED driver, the brightness change response is predicted, and the PWM dimming control signal is generated according to the trajectory smoothing algorithm and the PID controller, so that the adaptive capacity of the LED driver to external interference and parameter change is enhanced, and the stability and reliability of the LED driver are improved.

The invention discloses a constant-temperature and constant-humidity test box temperature control system based on a multistage PID algorithm and a control method thereof. The constant-temperature and constant-humidity test box temperature control system comprises an environmental parameter detection module, an in-box parameter detection module and a multistage PID controller, wherein the environmental parameter detection module is used for collecting temperature, humidity and air pressure data outside a test box in real time; the in-box parameter detection module is used for collecting multi-dimensional environmental parameters inside the test box; when the failure of any sensor is detected, starting a fault self-diagnosis module, replacing the data of the failure sensor with a data interpolation method of a side sensor, and recording the deviation compensation amount at the same time. A multi-stage PID control architecture is adopted, and feedforward compensation, parameter adaptive optimization and fault redundancy strategies are combined, so that high-precision and anti-interference control of the temperature and humidity of the test box is realized.

The invention discloses an electric heating cooker uniform temperature control method and system for gradient temperature difference regulation and control, and relates to the technical field of temperature control, the method comprises the following steps: dividing an internal heating area of a target electric heating cooker, arranging temperature sensors, and acquiring meal temperature data of N subareas in real time through a subarea temperature sensor array; constructing a gradient temperature difference model based on meal heat conduction information and food material characteristic information, and performing deviation calculation on the meal temperature data of the N sub-regions based on the gradient temperature difference model to obtain temperature deviation data of the N sub-regions; and a PID controller is adopted to perform heating power analysis on the temperature deviation data of the N sub-regions, and the heating power adjustment amount of the N sub-regions is determined to control the target electric heating cooker to perform uniform-temperature heating control. The technical effects of realizing uniform temperature control of the electric heating cooker, effectively solving the problem of local overheating or half-cooking caused by a traditional heating mode, ensuring uniform heating of meals and improving the heating quality are achieved.

The invention relates to the technical field of control systems, in particular to a sorbitol-based production parameter intelligent regulation and control system, which comprises a real-time feedback regulation and control module for continuously acquiring data, calculating real-time deviation and generating real-time deviation data based on real-time monitoring of temperature, pressure and concentration in a current reaction kettle. According to the invention, through real-time monitoring and dynamic calculation of parameters in the production process, variable deviations such as temperature, pressure and concentration are obtained and adjusted, and quick response processing of multi-dimensional production data is realized. By optimizing a dynamic adjustment strategy of PID controller parameters, proportion, integral and differential parameters are optimized according to real-time feedback, and the problem of adjustment lag caused by fixed parameters is solved. The current reaction condition and the raw material consumption rate are comprehensively considered in dynamic adjustment of the feeding flow, and meanwhile, the flow value matched with the current reaction demand is calculated and obtained, so that the condition that feeding and demand are disjointed in traditional regulation and control is avoided.

The invention relates to the technical field of intelligent control, and particularly discloses a linkage control method for energy-saving ventilation equipment, which comprises the following steps of: acquiring indoor and outdoor temperature, indoor carbon dioxide concentration and personnel occupancy state data in real time; according to the method, deep collaborative analysis is carried out on the indoor personnel occupancy state and the deviation between the carbon dioxide concentration and a target set value, so that joint characterization of the current indoor fresh air volume regulation and control requirement is achieved, control parameters of a fresh air volume PI D controller are dynamically optimized accordingly, and a basic fresh air volume regulation instruction is calculated. On the basis, thermal disturbance introduced by fresh air is further quantified based on the outdoor and indoor temperature difference, and the thermal disturbance serves as a feedforward signal to be compensated to an air conditioner load adjusting loop, so that dynamic adjustment of the air conditioner load is achieved. In this way, cooperative control over ventilation and air conditioning systems can be achieved, the fresh air-air conditioning equipment energy consumption coupling conflict in traditional control is restrained, and therefore the building energy consumption is reduced while the indoor environment quality is guaranteed.

The invention discloses an efficient refrigerating machine room air conditioner energy-saving intelligent control system and method, and relates to the field of air conditioner intelligent control. The method comprises the steps that air conditioner operation parameters, machine room equipment operation data and machine room environment data are collected and preprocessed; building a deep neural network by using a deep Q-learning reinforcement learning algorithm to obtain a thermal load demand of the machine room; introducing a nonlinear dynamic compensation function to calculate an air-conditioner dynamic energy efficiency coefficient and air-conditioner refrigeration power, and calculating the rotating speed of an air-conditioner compressor by referring to air-conditioner operation parameters; a PID (Proportion Integration Differentiation) controller is used for carrying out primary frequency conversion on a machine room air conditioner, and a multi-stage adjusting mechanism is used for correcting a primary frequency conversion result in real time according to machine room equipment operation data, machine room environment data change and frequency conversion conditions. According to the air conditioner energy-saving control method, efficient air conditioner energy-saving control is achieved by combining the deep Q-learning reinforcement learning algorithm, the nonlinear dynamic compensation function and the PID controller.

The invention discloses a three-temperature test method and device for a semiconductor chip, and relates to the technical field of semiconductor chip testing, and the method comprises the steps: inputting an initialization parameter set into an impedance temperature mapping model, carrying out the phase angle analysis and multi-band feature fusion through a broadband impedance spectrum analysis method, obtaining a chip junction temperature instantaneous value, and carrying out the measurement of the junction temperature instantaneous value. A PID controller is utilized to dynamically compensate a junction temperature instantaneous value of a chip, a fuzzy rule base inference engine is utilized to map the junction temperature instantaneous value into an execution instruction set, the execution instruction set is converted into a bidirectional voltage signal through pulse width modulation, a semiconductor chilling plate is driven to perform bidirectional temperature rise and drop control in a three-temperature environment, and real-time temperature change test data is synchronously acquired. According to the method, the junction temperature instantaneous value of the chip can be more accurately obtained by combining broadband impedance spectroscopy analysis with a multi-band characteristic fusion technology, and meanwhile, the PID controller is combined with the fuzzy rule base inference engine, so that a response can be quickly made under the condition that the temperature is quickly changed, and the response capability is greatly improved.

The invention provides an unmanned ship path tracking control method based on lightweight self-adaptive sight guidance. The method comprises a self-adaptive sight guidance algorithm, a foresight distance is adjusted in real time according to the current position and path deviation of an unmanned ship, ideal course information is obtained, and the path convergence speed and algorithm calculation efficiency are improved. And in the virtual simulation platform, marine environment information is combined, and unmanned ship dynamic model parameters are obtained through experiments. And constructing an objective function taking the path tracking error and the rudder angle change as evaluation indexes, and optimizing PID controller parameters by adopting an improved sparrow search algorithm. According to the algorithm, a dynamic additive updating strategy is introduced, so that the global search capability and the convergence stability are improved, and the optimal PID parameter is ensured to be obtained. The method has the advantages of being high in response speed, small in overshoot and high in path tracking precision in the complex marine environment, the real-time control performance and the environment adaptability of the unmanned ship can be remarkably improved, and the method is suitable for the unmanned ship path tracking control requirements in various task scenes.

The invention discloses a processing control method and system for a carbon fiber composite transmission shaft, and relates to the technical field of intelligent transmission shaft manufacturing, and the method comprises the steps: dynamically adjusting the control current of a magneto-rheological damper through a PID controller based on a process control parameter set, and recording tension fluctuation and temperature gradient data; inputting tension fluctuation and temperature gradient data into an ANSYS coupling simulation engine, establishing a three-dimensional temperature field model, calculating a resin flow compensation coefficient, generating a temperature-pressure cooperative control curve through an NSGA-III multi-objective optimization algorithm based on the resin flow compensation coefficient, and calculating the resin flow compensation coefficient and the temperature-pressure cooperative control curve according to the resin flow compensation coefficient and the temperature-pressure cooperative control curve. And integrating through a time sequence control engine to generate a solidification control instruction. According to the invention, a multi-source data fusion algorithm is combined with the industrial Internet of Things edge computing gateway, so that accurate monitoring and synchronous processing of the process control parameter set are realized, and the dynamic behavior of the carbon fiber composite material in the processing process is comprehensively reflected.

The invention provides an intelligent control method and system based on electro-hydraulic linkage. The method comprises the steps that multi-mode environment parameters are collected, a digital twin environment model is constructed, and data are synchronized with an electro-hydraulic servo system in real time. And establishing a nonlinear model, identifying parameters by using a least square method and a recursive least square method, verifying the precision through a step response test, and updating. And constructing an adaptive LSTM neural network inverse model to output a control voltage signal. And the signal is subjected to weighted fusion with the output of a PID controller, and the PID controller dynamically adjusts a gain parameter according to an error integral term to generate a control instruction to drive the system. According to the invention, the LSTM inverse model provides feed-forward compensation, the PID controller realizes feedback correction, and the LSTM inverse model and the PID controller are complementary. Meanwhile, based on a related nonlinear model and a parameter self-adaptive updating mechanism, the influence of working condition change is accurately described, and high-precision control of the electro-hydraulic servo system under the complex working condition is achieved.

Disclosed are a method and system for controlling a robotic arm pressing force based on a pulse wave signal. The method comprises: acquiring a pulse wave signal of a target; acquiring an interaction force between an end of a robotic arm and the target, and determining an expected pressing force according to a mapping relation between a pressing effect and the pulse wave signal; establishing an admittance control model to convert the expected pressing force into an expected trajectory of the robotic arm; and determining, on the basis of an error between an actual trajectory and the expected trajectory of the robotic arm, a control quantity of the subsequent movement of the robotic arm by using a PID controller, so as to achieve the tracking of the expected trajectory. The present invention achieves a low-impedance and compliant interaction effect when the target of pressing is moving.

The invention discloses a fuel cell output voltage control method and system based on fuzzy neural network PID, and the method comprises the steps: building an air-cooled fuel cell voltage model based on an MATLAB / Simulink simulation platform, and obtaining the temperature changes of an air-cooled fuel cell under different working conditions; establishing an air-cooled fuel cell test bench, and obtaining a polarization curve of output voltage changing along with current; according to experimental data, calibrating the semi-empirical equation by adopting a nonlinear least square method, and comparing experimental results to verify the accuracy of the model; the input hydrogen flow is changed through the PID controller, so that stable control of the output voltage of the galvanic pile is realized, and the power generation performance of the battery is improved; a fuzzy neural network is adopted to optimize a PID controller, control parameters of PID are adjusted in a self-adaptive mode according to the error between expected voltage and actual voltage and the error change rate, and therefore a better temperature control effect is achieved. The method has the advantages of high robustness, high response speed and the like, and ensures that the voltage output of the fuel cell stack is maintained at a fixed preset value.

The invention discloses a closed-loop optimization method for cerebral apoplexy electrical stimulation treatment, and belongs to the technical field of PID control optimization, and the method specifically comprises the steps: 1, constructing a cerebral apoplexy electrical stimulation control model; step 2, improving a sled dog optimization algorithm, wherein an improved strategy is as follows: D1, generating an initial population by using a chaotic mapping optimization strategy fused with a Halton sequence; d2, using a dynamic mixed collaborative search strategy to improve a mathematical model in an obstacle avoidance stage; d3, using an elite Levy dynamic navigation strategy to improve the mathematical model of the lost direction stage; 3, setting parameters of a PID controller module in the cerebral apoplexy electrical stimulation control model by using an improved sled dog optimization algorithm, and obtaining optimal Kp, Ki and Kd parameters through optimization; and step 4, inputting the obtained optimal Kp, Ki and Kd parameters into a cerebral apoplexy electrical stimulation control model, and optimizing a control effect.

The invention provides an automatic generation control (AGC) collaborative optimization method based on time sequence deep reinforcement learning PID (Proportion Integration Differentiation) control. Comprising the following steps: establishing a three-area multi-source interconnected power system with participation of hydroelectric power, thermal power, wind power, photovoltaic power and energy storage, and bringing wind and light storage resources into an automatic power generation control loop. The invention provides a novel AGC adaptive PID controller based on time sequence deep reinforcement learning by utilizing the time sequence perception capability of a long short-term memory neural network and combining a double-delay depth deterministic strategy gradient algorithm, and provides an efficient time sequence deep reinforcement learning solution for intelligent power grid frequency control.

The invention relates to a deformation online measurement and control method for multi-robot collaborative assembly. The method comprises the steps that multi-view surface images of workpieces in the assembly process are collected in real time through distributed robots and cameras arranged at the tail ends of the distributed robots; each view angle surface image is input into a deep learning model trained based on a digital image related technology, the optimal pixel displacement corresponding to each view angle surface image is output, and the optimal pixel displacement corresponding to each view angle surface image is converted into a spatial displacement label; the deep learning model takes an encoder-decoder as a trunk network; fusing the spatial displacement labels corresponding to the view angle surface images to obtain a fused displacement field; based on the fusion displacement field and the nominal path planning point, the tail end pose of the distributed robot is determined; the assembly error is calculated based on the reference target positioning and the tail end pose, and the PID controller adjusts the joint space of the distributed robot based on the assembly error. According to the method, the calculation overhead is remarkably reduced, and the measurement precision and robustness are improved.

The invention belongs to the technical field of intelligent home control, and particularly relates to an electric blanket intelligent temperature control system, which analyzes the matching relationship between the heating power and the temperature response in the historical operation cycle of a plurality of independent subareas of an electric blanket, fuses the thermal coupling effect between the subareas, and independently quantifies the dynamic thermal inertia parameter of each subarea. Based on the independent temperature change trend and the real-time temperature deviation of each partition, a control state including a steady state, a transient state or a strong compensation state is decided and maintained for each partition, the dynamic thermal inertia parameter, the real-time temperature deviation and the current control state are integrated, differentiated feed-forward compensation amounts are generated and superposed to the output of a PID controller of the corresponding partition, and the control state of the corresponding partition is calculated. A final power control instruction is generated after amplitude limiting and smooth filtering, temperature response lag caused by thermal inertia is actively predicted and counteracted, precise control is achieved in combination with partition state recognition, and the system response speed and control stability are remarkably improved.

The invention discloses a blowout preventer remote automatic grease injection system based on PID control and a control method, and relates to the technical field of grease injection, and the control method comprises the following steps: an initialization stage: automatically matching the specification of a choke tube according to the diameter of a cable, and preheating sealing grease to a set temperature; in the dynamic control stage, wellhead pressure and grease injection pressure are monitored in real time, pressure errors are calculated, the flow of a grease injection pump is adjusted according to a PID algorithm, and when the cable lowering speed suddenly changes, pressure fluctuation is inhibited in advance through a differential link; and according to the safety redundancy mechanism, when the main PID controller fails, the standby PID controller is automatically switched, and emergency locking is triggered when the wellhead pressure exceeds the limit. The flow of a grease injection pump is adjusted in real time through an incremental PID algorithm and a self-adaptive adjustment method, the grease injection amount is accurately controlled, pressure fluctuation is reduced, sudden change of the cable lowering speed is predicted through a differential link, the response precision is improved, stable operation of the system is ensured through a redundant controller and an emergency locking function, PID output is corrected through a dynamic temperature-viscosity model, and the reliability of the system is improved. The system service life is prolonged.

The invention relates to a four-axis unmanned aerial vehicle PID parameter setting method based on a whale optimization algorithm, and belongs to the technical field of unmanned aerial vehicle control. The method aims at solving the problems that in the traditional PID parameter setting process, experience is relied on, time is consumed, and optimal parameters are difficult to obtain. According to the method, the whale optimization algorithm is introduced, and the characteristics of high global search capability and high convergence speed are utilized, so that the parameters of the four-axis unmanned aerial vehicle PID controller are automatically optimized. The method comprises the steps that firstly, a kinetic model of the four-axis unmanned aerial vehicle is established, a control target of a PID controller is determined, PID parameters serve as optimization variables, and a fitness function is defined to evaluate control performance; then, performing global search in a parameter space by using a whale optimization algorithm, and gradually optimizing PID parameters by simulating a whale predation behavior; the optimized PID parameters are applied to a flight control system of the four-axis unmanned aerial vehicle, and stable attitude, height and position control is achieved. The method has the advantages that PID parameters are automatically set through the whale optimization algorithm, the low efficiency and subjectivity of a traditional trial and error method are avoided, and the parameter setting efficiency and precision are remarkably improved; meanwhile, for an unmanned aerial vehicle control system with high nonlinearity and complexity, the WOA can adjust parameters based on actual feedback under the condition that accurate modeling of the system is not needed, and dependence on system modeling is reduced.

The invention relates to the technical field of industrial process automation control, and discloses an intelligent adjusting method for an industrial waste liquid low-temperature evaporation process, which comprises the following steps of: superposing pressure step test excitation to a vacuum adjusting valve, synchronously acquiring temperature response data flow, calculating absolute error integral and SIAE and reply time characteristics of a response curve, and calculating the temperature response data flow according to the absolute error integral and SIAE and reply time characteristics of the response curve; substituting into an inverse model to solve an equivalent process gain and a time constant of the controlled object; based on the internal model control principle, PID controller parameters are dynamically calculated and updated through the calculated gain and the time constant, a control law is reconstructed to generate an execution instruction, and the problem of control divergence of a non-linear time-varying object under model mismatch is solved through active energy feature identification and a pole assignment mechanism; decoupling control and self-adaptive adjustment of fluid viscosity sudden change and equipment scaling are achieved, and the stability and control precision of the system are improved.

The invention relates to a system and method for rapidly establishing steady state vacuum chamber pressure. According to the system, the set value of the vacuum valve is fixed, and the driving current of the MFC electromagnetic valve is used for adjusting the pressure of the cavity, so that the pressure is stabilized more quickly. The system can operate in a training mode and an inference mode. In the training mode, a PID controller is used for determining set values of an MFC and a vacuum valve; in an inference mode during substrate processing, these settings are quickly invoked and executed, thereby achieving efficient and stable pressure control.

The invention discloses a performance testing method and device, electronic equipment and a computer readable storage medium, and relates to the technical field of performance testing, and the method comprises the steps: generating a target load of a current cycle according to the cycle sequence of the current cycle through a constructed load generation model; or, the target load of the current period is obtained through the PID controller according to the target load and the actual load of each historical period, the target load of each period can be dynamically generated, and the dynamic target load better fits the load of the real operation environment, so that the load of the current period can be dynamically generated. The problem that the stability of the server for deploying the AI accelerator cannot be effectively evaluated when the load with the highest power consumption is used for testing can be solved, and the technical effect that the stability of each performance index can be truly, effectively and accurately evaluated is achieved. In addition, the target test task is generated through the load of the period, the execution process of accelerated calculation of the target test task is relatively close to the real operation condition, and the obtained multiple performance indexes are relatively close to the real operation result.

The invention provides a bioreactor aeration self-adaptive control method and system based on machine learning, and the method comprises the steps: collecting the water inlet flow, ammonia nitrogen concentration, dissolved oxygen concentration, water temperature and chemical oxygen demand data of a bioreactor in real time through a sensor, and carrying out the water balance verification and the Kalman filtering pretreatment containing a water temperature compensation item; generating an aeration instruction by adopting multi-model collaboration; outputting a reference aeration rate by a deep reinforcement learning DRL model; the self-adaptive PID controller dynamically adjusts the proportionality coefficient according to the dissolved oxygen deviation and calculates the correction amount; and the LSTM network generates a fusion weight, and combines the seasonal compensation factor and the safety coefficient to synthesize the final aeration rate. The system passes mutation rate constraint and nitrification efficiency verification, an ASM1 model is triggered to recheck when the deviation exceeds the standard, and after passing, an air blower with the precision of + / -2% executes an instruction and performs closed-loop feedback in a period smaller than or equal to 10 seconds. After the method is implemented, the aeration energy consumption is reduced to 0.38 kWh / m, the effluent ammonia nitrogen standard reaching rate is improved to 98.5%, the dissolved oxygen fluctuation range is reduced to + / -0.3 mg / L, and the impact load response time is shortened to be within 15 minutes.

The invention relates to the technical field of environmental engineering water treatment automatic control, and discloses a landfill leachate treatment control method based on cooperative control. The method comprises the following steps: acquiring real-time monitored water quality parameter data streams including a pretreatment stage pH value, a main treatment stage COD concentration, a deep treatment stage membrane pressure difference and the like in a landfill leachate treatment system, inputting the data streams into a cooperative control framework, and dynamically correcting PID controller parameters through a nonlinear gain function to generate a primary adjustment instruction; evaluating and generating a strategy optimization instruction based on the reinforcement learning strategy network; inputting the two into a grey prediction model and outputting a cooperative control signal containing a feedforward compensation amount; and finally, dynamically adjusting the coagulant dosage, the aeration intensity and the working pressure of the membrane component according to the signal to form a closed-loop control loop. The method can master the water quality change in real time, accurately regulate and control the treatment process, improve the stability and reliability of the treatment system, and effectively improve the landfill leachate treatment effect.

The invention discloses a focusing control method and related equipment, and the method comprises the steps: synchronously collecting the data of a multi-modal sensor, extracting features and parameters, inputting a multi-modal fusion model subjected to joint optimization to generate a motor control instruction, and achieving the focusing through the hierarchical control of rough adjustment, fine adjustment and dynamic tracking. The dynamic weight fusion network deploys the weight of each modal data according to the environmental sensitivity coefficient, and combines the historical memory and trajectory pre-judgment of the alignment trajectory prediction network to reduce invalid calculation and repeated search and greatly improve the focusing speed; the multi-mode sensor works cooperatively to replace a special hardware function, so that the cost and the power consumption are reduced; the dynamic weight fusion network can adjust the modal confidence according to the environment and enhance the anti-interference capability in the complex light environment; the alignment trajectory prediction network and the fuzzy self-adaptive PID controller are in hierarchical cooperation, the problem of erroneous judgment of a complex scene is solved, and the micro-distance and dynamic tracking precision is improved; the model is subjected to pruning, distillation and quantitative optimization, light weight is achieved, and low-cost equipment is adapted.

The invention relates to the technical field of control systems, in particular to an organic fertilizer fermentation intelligent aeration control system applying a nano-molecular film. The system comprises a data acquisition module, a delay analysis module, a gas analysis module and a controller adjusting module. Wherein the delay analysis module is used for determining the response delay of the aeration control system in combination with the change condition of temperature, the regulation hysteresis of the aeration control system and the activeness of microorganisms; the gas analysis module is used for determining the comprehensive gas exchange rate of the fermentation heap according to the humidity change condition, the carbon dioxide concentration and the ammonia gas concentration of the fermentation heap; the controller adjusting module is used for determining a correction coefficient of the aeration control system; and taking the proportional gain corrected by the correction coefficient as the proportional gain of a PID controller in the aeration control system. According to the invention, the response speed and the control precision of the aeration control system are effectively improved.

The invention relates to the technical field of annealing furnace control, in particular to an annealing furnace pressure control method and system.The method comprises the steps that pressure data in all furnace sections of a continuous bright annealing furnace, temperature data of furnace walls at all the furnace sections and flow data at outlets of pressure regulating valves used at all the furnace sections are collected in real time; presetting an adjusting period of a proportion parameter in a PID controller used by a pressure adjusting valve used by each furnace section to obtain each adjusting moment of the proportion parameter; obtaining each synchronous change moment of each furnace section in a time period between each adjustment moment and an adjacent previous adjustment moment, and obtaining a pressure characteristic value of each furnace section at each adjustment moment; obtaining a trend similar characteristic value of each furnace section at each adjustment moment; and obtaining the value of the proportion parameter in the PID controller in the time interval between each adjustment moment and the next adjustment moment adjacent to the adjustment moment so as to control the furnace pressure in each furnace section. The invention aims to improve the control precision of the furnace pressure.