436 results about "Feedback regulation" patented technology

The invention relates to the technical field of network operation and maintenance supervision, in particular to a network operation and maintenance environment risk assessment method and system based on a mimicry security technology, and the method comprises the steps: carrying out the collection and preprocessing of network operation and maintenance environment data, and generating standardized security data; abnormal feature data and disturbance response data are extracted through real mimicry analysis and active challenge mimicry analysis, and associated threat feature data are generated through fusion; based on the associated threat feature data, constructing a heterogeneous twin model, performing state modeling, threat modeling and resource modeling, and generating risk assessment data; and generating an optimal response strategy through a multi-objective optimization algorithm, including flow control, node isolation and resource scheduling, and dynamically updating a risk state through a feedback regulation mechanism. Through dynamic threat identification and real-time optimization response, the risk assessment accuracy and the security defense level of the network operation and maintenance environment are improved, and the method has the advantages of being efficient, intelligent and dynamic.

The present invention relates to the technical field of circuit control, and relates to a direct-current high-voltage generator regulation and control method and system based on intelligent feedback. A fuzzy control logic operation is performed on an output current flux value and an input voltage fluctuation factor of a load experimental object, so as to obtain the disturbance amplitude of a voltage fluctuation state for a current flux change trend; if a step-up transformer can execute a PID control technology by adjusting a transformation ratio, PID real-time adjustment parameters are calculated, and a crossover mutation operation of individuals is performed on the basis of a fitness function, so as to generate a first boost regulation and control scheme; and if the step-up transformer cannot execute the PID control technology by adjusting the transformation ratio, a calibrated state feedback controller is constructed on the basis of a state variable of the current step-up transformer and an adjustment signal output amplitude, so as to generate a second boost regulation and control scheme. The present invention achieves intelligent feedback regulation and control by independently analyzing components of direct-current high-voltage generators, improving the stability and accuracy of outputting high-voltage direct current.

The invention relates to the technical field of automatic irrigation control, in particular to a multi-sensor fusion agricultural precise irrigation system which comprises a leaf area analysis module, an environment evaluation module, a task arrangement module, an execution regulation and control module and a feedback regulation module. According to the method, the crop leaf area index is introduced to be compared with the growth stage target value, the dynamic expression of the group moisture demand is realized, the environmental evapotranspiration influence factor is generated in combination with illumination and temperature disturbance parameters, the timeliness of water demand driving force identification is enhanced, and the task execution sequence is judged by adopting regional water pressure disturbance data; a multi-region irrigation resource allocation process is optimized, a water quantity and pressure difference double-factor adjustment mechanism is utilized, the rationality and response accuracy of time length allocation are improved, water supply state analysis and task target deviation comparison are performed, dynamic supplementary irrigation regulation and control are realized, the closed-loop capability and supplementary irrigation accuracy of irrigation period control are improved, and the irrigation period control efficiency is improved. And the stable allocation capability and the irrigation response efficiency of the irrigation system under the influence of multiple variables are enhanced.

The invention provides a sewage treatment carbon source intelligent adding method, which comprises the following steps: collecting multi-parameter feed-forward and feedback signals of water inlet and an anoxic tank, constructing a dynamic model containing feed-forward compensation, model prediction control and feedback compensation, calculating the theoretical adding amount of a carbon source, inputting a predicted value and feedback parameters into an LSTM network for correction, and optimizing a network structure by a genetic algorithm. The adding amount is controlled in a closed-loop mode through a variable frequency pump, the LSTM weight is updated on the basis that the error is larger than 5%, and finally a control strategy is optimized by using an NSGA-II algorithm and integrating carbon source consumption, effluent total nitrogen and energy consumption. The dynamic self-adaptive carbon source adding method is constructed by fusing feedforward perception, LSTM prediction, feedback regulation and multi-objective optimization, so that quick response and accurate control on water quality fluctuation are realized, the denitrification efficiency and the carbon source utilization rate are improved, and the method has excellent engineering adaptability and popularization value.

The invention discloses a server mainboard regional distributed liquid cooling heat exchange adjusting method, which comprises the following steps of: acquiring a temperature parameter set and a power consumption parameter set of each monitoring region in real time, and generating a thermal load parameter feature vector; identifying a high heat load core area set according to the heat load parameter feature vector; generating a liquid cooling branch flow distribution instruction set based on the position distribution of the high heat load core area set; executing the liquid cooling branch flow distribution instruction set, and obtaining an adjusted temperature feedback parameter set; according to the deviation degree of the temperature feedback parameter set and the target temperature interval, dynamic feedback adjustment is carried out by applying the heat conduction compensation coefficient and the fluid viscosity-temperature compensation coefficient, and an updated liquid cooling branch flow distribution instruction set is generated. The method has the following advantages and effects that by sensing the thermal load space distribution characteristics of all areas of the mainboard in real time, cooling flow distribution is dynamically adapted, real-time compensation of the fluid viscosity-temperature effect is integrated, local hot spots are eliminated, and the heat dissipation effect is improved.

The invention relates to the technical field of oil filter control, in particular to an oil filter digital intelligent control system based on sensors, which comprises a fusion monitoring unit, an oil temperature control unit and a feedback regulation unit. The oil temperature control unit calculates the optimal heating rate and the target temperature range through a self-adaptive algorithm according to the environment and the initial temperature of oil, compares the optimal heating rate and the target temperature range with the actual oil temperature in the heating process, adjusts the heating power through pulse heating according to the result, judges the working load according to the specific heat capacity of oil and adjusts the frequency of a vacuum pump. And the feedback regulation unit evaluates the system stability based on the target operation frequency and the heating power of the vacuum pump, generates an optimization strategy and feeds back the optimization strategy to the oil temperature control unit, so that accurate oil temperature control is realized, the equipment stability and the energy utilization efficiency are improved, and efficient operation of the oil filter is guaranteed.

The invention relates to the technical field of intelligent equipment autonomous control, and discloses an AIGA-driven intelligent equipment autonomous control and feedback adjustment method, which comprises the following steps of S1, collecting equipment body state parameters and external environment data in real time through a multi-mode sensor array; s2, combining the collected data with a preset user instruction and a real-time environment semantic analysis result; s3, disassembling the task target into a continuous action instruction set; s4, executing the action instruction set through the layered real-time control architecture; s5, monitoring an action execution effect based on the multi-modal sensor array; and S6, analyzing the relevance between the evaluation result and the action instruction through a causal inference engine. According to the method, an equipment action execution result is fed back to an intention generation layer in real time, a deviation source is positioned through a causal inference engine, a control strategy is dynamically adjusted, closed-loop iteration from one-way instruction execution to perception-decision-execution-verification is achieved, and the effect of self-adaptability in a dynamic scene is achieved.

The invention discloses a deep water-rich weak-impact coal seam surrounding rock control method based on rapid tunneling, and relates to the technical field of surrounding rock control. The method comprises the steps that S1, a high-risk area is identified through address detection, a waterproof curtain is constructed, and the tunneling disturbance risk is reduced; s2, rapid tunneling and instant supporting are conducted, the surrounding rock exposure time is shortened, tunneling and supporting are completed synchronously, and deformation development is restrained; s3, sensing a surrounding rock-support response in real time, optimizing parameters and early warning risks, and realizing dynamic monitoring and feedback regulation and control; s4, later-stage continuous reinforcement maintenance is conducted, so that the roadway adapts to the rheological characteristics of deep surrounding rock, and the service cycle of the roadway is prolonged; according to the method, a high-risk area is identified through various detection technologies, and instant supporting is achieved through cutting process optimization and digging and supporting collaborative operation; real-time monitoring and dynamic feedback are achieved, and automatic optimization of supporting parameters is achieved; and later reinforcement and periodic maintenance are adopted to ensure that the surrounding rock supporting system can adapt to the rheological characteristics of deep surrounding rock for a long time.

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 electrochemical testing, in particular to an electrochemical impedance spectroscopy testing system based on multi-model combination, which comprises a data acquisition unit, a deep learning analysis unit, a frequency control unit and a feedback regulation unit. The deep learning analysis unit adopts a convolutional neural network and a long-short-term memory network to construct a model, after training, features are extracted, trends are analyzed, and dynamic frequency priority ranking is carried out, the frequency control unit drives the electrochemical workstation to adjust the frequency according to a weight generation strategy, and the feedback adjustment unit calculates predicted and actually measured impedance errors. When the threshold value is exceeded, parameter correction is carried out in each unit, the test frequency is intelligently optimized according to the dynamic change of the electrochemical process, key information is accurately captured, resource waste is avoided, the test efficiency and accuracy are improved, the stability and reliability of the system are ensured through a feedback regulation mechanism, and the research and analysis capability of the electrochemical system is effectively improved.

The invention provides a muscle stimulation adjusting method and system based on gait detection. The method comprises the following steps: acquiring lower limb biomechanical data; performing phase recognition according to a dynamic programming algorithm, and constructing a pressure center track to perform feature extraction to obtain gait features; carrying out feature analysis on the gait features through a reverse dynamics model and a machine learning model, and carrying out output judgment according to a double-model collaborative decision-making mechanism to obtain a stimulation parameter decision-making result; generating an electrical stimulation waveform according to the decision result, and applying differential electrical stimulation to the target muscle in the gait cycle to obtain a stimulation scheme; and monitoring stimulated myoelectricity feedback signals and gait changes, and carrying out parameter updating by calculating a signal change rate and analyzing a motion track error to obtain an updating scheme. According to the method, through a double decision-making mechanism of the reverse dynamics model and the machine learning model and a real-time feedback regulation mechanism, the gait phase recognition precision is improved, and precise regulation and control of stimulation parameters are realized.

The invention provides a wide-voltage self-adaptive closed-loop vector swimming pool pump anti-interference method, and relates to the technical field of motor control and power electronics, and the method comprises the steps: collecting three-phase power supply voltage fluctuation characteristic parameters and motor operation state parameters in real time; performing amplitude compensation and phase synchronization correction on the voltage parameter based on the dynamic voltage compensation factor; a closed-loop vector anti-interference control model is constructed, and model parameters are adjusted in real time through a vector immunity coefficient; dynamically correcting control output by adopting a multivariable feedback regulation mechanism; executing an anti-interference instruction and synchronously updating PWM modulation parameters, and verifying a control effect in combination with hardware-in-the-loop simulation; and parameter self-learning optimization is realized through a genetic algorithm. The dynamic anti-interference capability under power grid voltage fluctuation and harmonic interference is improved, intelligent thermal management is realized through a temperature-voltage-current multi-dimensional coupling model, full-life-cycle self-adaptive optimization of the system is guaranteed, and the problems of compensation lag, insufficient harmonic suppression and poor long-term stability are effectively solved.

The invention provides an intelligent control method and system for the zinc layer thickness of continuous hot galvanizing, and relates to the technical field of data processing, and the method comprises the steps: collecting the historical production data of a production line, and constructing a zinc layer thickness and air knife pressure prediction model; the two models are trained according to historical data, and a setting table of the distance and height of the air knife is obtained through statistical analysis; acquiring real-time production data, determining air knife parameters according to the setting table, and inputting the air knife parameters into the trained air knife pressure prediction model to obtain an air knife pressure set value; and inputting the real-time data and the air knife set value into the zinc layer thickness prediction model to obtain a first zinc layer thickness prediction value. If the predicted value meets a preset standard, current air knife parameters are stored, otherwise, a feedback adjustment mechanism is used for adjusting air knife pressure, and a second zinc layer thickness predicted value is calculated; if the adjusted predicted value meets the requirement, air knife parameters are stored; if the requirement is not met, the adjusting step is repeated; and intelligent control is carried out through the stored air knife parameters.

The invention discloses an engineering plastic extrusion control system, and relates to the technical field of plastic processing, and the engineering plastic extrusion control system comprises the following steps: obtaining basic configuration information and a denaturation threshold value of target engineering plastic; determining a second constraint condition based on basic configuration information by taking a denaturation threshold as a first constraint condition, and supervising and training an optimization control module in combination with the basic configuration of the extruder; an extrusion task is read, extrusion full-cycle control analysis under constraint condition limitation is carried out in combination with an optimization control module, and a dynamic extrusion scheme is determined; executing a dynamic extrusion scheme, and determining a feedback regulation threshold value; carrying out extrusion state evaluation on an extrusion channel of an extruder, and positioning a feedback regulation point by combining with a feedback regulation threshold value with mold-based molding and curing state evaluation; and based on the feedback regulation point, performing feedback extrusion regulation based on the extrusion control system. The technical problems of low control precision, poor stability and low intelligent degree in the prior art are solved, and the technical effect of improving the engineering plastic extrusion quality and efficiency is achieved.

The invention relates to the technical field of biomedical engineering, and discloses an acute respiratory distress syndrome mechanical ventilation adaptive adjustment control system, which comprises a data acquisition module, a signal preprocessing module, a physiological parameter calculation module, a prediction module, a decision and control logic module and the like. Wherein the prediction module predicts a dynamic lung compliance change trend by using a long short-term memory neural network model, and the decision and control logic module generates a ventilation parameter adjustment instruction according to a prediction result and a clinical safety rule. By adopting the technical scheme, the system overcomes the hysteresis of traditional feedback regulation, reduces the risk of breathing machine related lung injury, optimizes oxygenation and carbon dioxide removal efficiency, and provides an accurate, safe and individualized mechanical ventilation treatment scheme.

The invention discloses an aquaculture comprehensive guarantee method and system based on multi-modal data acquisition, and relates to the technical field of intelligent aquaculture, and the method comprises the steps: collecting aquaculture multi-modal data and an aquaculture image of an aquaculture region, carrying out the aquaculture feature capture of the aquaculture image, and outputting a visual feature group; inputting the visual feature group into a MobileNet lightweight model, and outputting a health state label and a confidence score; based on the health state label and the confidence score, outputting disease type data through a residual network structure optimized by transfer learning; constructing a graph neural network according to the disease type data, and generating a dynamic regulation and control scheme; and the control center executes the dynamic regulation and control scheme, carries out regulation and control effect detection and target comparison on the aquaculture area of the dynamic regulation and control scheme, judges whether the regulation and control effect is effective or not, and generates a feedback regulation instruction. According to the invention, through multi-modal data fusion and intelligent decision closed loop, dynamic and accurate guarantee of aquatic product health management is realized.

The invention provides an AI-driven industrial decarburization process control method and system based on feedback regulation, and relates to the technical field of industrial control, and the method comprises the steps: executing industrial decarburization process monitoring, and obtaining a process operation data stream sequence; performing process working condition time sequence iterative analysis; matching is carried out in a pre-constructed causal knowledge base; traversing the associated causal clue set to perform historical data backtracking exception investigation to obtain an exceptional associated causal clue set and an exception degree set; and performing feedback control scheme identification to obtain a target feedback control scheme, generating a feedback adjustment instruction, and issuing the feedback adjustment instruction to the control execution module to execute the target feedback control scheme. According to the method and the device, the technical problem that in the prior art, due to the fact that static model prediction or empirical rule-based judgment is mainly adopted, real-time tracing and dynamic intervention of process-level carbon emission abnormity are difficult to achieve, and the carbon emission control effect in a complex environment is not ideal is solved, and the control effect of the industrial decarburization process is improved.

The invention provides a sleep-aiding medicine pillow dynamic regulation and control method and system based on physiological parameter analysis. According to the method, pressure data and sleep bio-electricity signals of a contact area of a user are collected in real time, a characteristic frequency band proportion and a heart rate fluctuation parameter are analyzed, and a correlation model is established; calculating a bioelectricity signal energy integral value of the characteristic frequency band based on the model, and quantizing to generate a bioelectricity intensity index; the heat conduction efficiency is changed by adjusting the molecular density of the temperature-sensitive material, and a contact layer heat transfer dynamic instruction is generated; pressure feedback and instruction waveform characteristics are combined, the micropore on-off duty cycle is adjusted, and the opening duration of unit time is controlled; the drug release rate is synchronously adjusted, so that the response waveform and the heat conduction instruction are dynamically balanced, and closed-loop regulation is formed. According to the application, through multi-parameter dynamic analysis and feedback adjustment, accurate cooperative control of heat conduction and medicine release of the medicine pillow is realized, and the matching degree of sleep-aiding substance release and sleep rhythm is optimized.

The invention discloses a hydrogen purification pressure optimization system and a control strategy for hydrogen production through electrolysis of water, and relates to the technical field of hydrogen production through electrolysis of water, and the hydrogen purification system comprises a hydrogen purification unit which comprises a plurality of adsorption towers arranged in parallel and is used for adsorbing and removing impurities in hydrogen; the pressure optimization system comprises a pressure sensor, a pressure regulating valve and a controller, the pressure sensor is used for monitoring the pressure in the adsorption tower in real time, the pressure regulating valve regulates the pressure according to an instruction of the controller, and the controller is used for regulating the pressure according to the adsorption characteristics of the adsorbent and the requirements of the hydrogen purification process. Setting pressure control parameters to adjust the pressure in the adsorption tower in real time; and the control unit is used for performing coordinated management on the temperature and the flow of the pressure optimization system, and forming closed-loop feedback regulation of parameters by comparing the actual purity of the purified hydrogen with a preset purity standard. The hydrogen purity is improved, the adsorbent works under the optimal pressure by optimizing the pressure control strategy, the hydrogen purity is effectively improved, and the energy consumption is reduced.

The invention discloses a new energy consumption multi-objective decision reasoning method and system based on a knowledge graph. The method comprises the following steps: completing data acquisition and preprocessing; extracting the relationship between the core entities and the entities to generate a structured triple, and further constructing an energy field knowledge graph; designing a loss function by taking economy and stability as optimization targets, training a GNN integrated with power grid topology perception in combination with historical scheduling data, and completing model construction; entity change and relation update in the new data are identified through an incremental learning method, and the energy field knowledge graph is automatically supplemented or corrected; according to a current scheduling demand, extracting a corresponding associated sub-graph from the updated knowledge graph, inputting the trained GNN model, and outputting a scheduling decision scheme; and accessing the scheduling decision scheme output by the GNN to a power grid digital twinborn simulation platform to complete analogue simulation and feedback regulation. And efficient and safe consumption of new energy in a dynamic environment is ensured.

The embodiment of the invention provides a user state adjusting system based on multi-sensory focusing and neural remodeling, and the system comprises a physiological data interaction module which is configured to collect a multi-modal physiological signal of a user; the intelligent decision-making module is internally provided with a pre-trained neural state evaluation model so as to evaluate the current neural physiological activation state of the user according to the multi-modal physiological signal and execute feedback regulation logic based on the evaluated current neural physiological activation state of the user so as to generate a multi-modal feedback control instruction; the multi-mode feedback output module is configured to generate at least one sensory regulation stimulus based on the multi-mode feedback control instruction; and the nerve focusing guide module is configured to position a nerve physiological reflex area strongly related to the user state based on the subjective state description of the user in combination with the multi-modal physiological signal of the user, so as to generate man-machine interaction guidance to focus the attention of the user on a target reflex area and form a closed-loop regulation link of'perception feedback and re-perception '.

The invention relates to a high-precision hydraulic charging regulation and control method and system based on a PID and MPC combined algorithm, and the method comprises the steps: collecting a pulse pressure waveform borne by a pressure-bearing part under a target working condition, and collecting a corresponding relation between a control instruction of the opening degree of a regulating valve and the pulse pressure waveform, constructing a pulse pressure waveform library to update a target waveform of the target working condition; creating a state space model to update dynamic characteristics of the hydraulic charging system, dynamically optimizing pre-regulated first-step control input through an MPC algorithm according to the dynamic characteristics, and updating a real waveform tracking target waveform of prediction output in a rolling manner until prediction similarity meets a set threshold value of the target waveform; and in response to the condition that the predicted similarity meets a set threshold value, selecting a PID algorithm to perform feedback adjustment and dynamic compensation on the control input of the real waveform, and outputting target control input simulating the target waveform under the target working condition. The precision, efficiency and flexibility of regulation and control of the hydraulic filling system can be improved.

The invention relates to the technical field of medical data processing and human-computer interaction, and particularly discloses a rehabilitation training feedback adjusting system based on a brain-computer interface and myoelectricity sensing. The system comprises a multi-modal physiological signal acquisition module, a signal fusion and intention decoding module, a rehabilitation state dynamic evaluation module, a self-adaptive feedback strategy generation module and a multi-channel feedback execution module. By synchronously collecting and fusing the electroencephalogram signals and the electromyographic signals to decode the motion intention, dynamically evaluate the rehabilitation state and predict the trend, a multi-sensory collaborative feedback strategy is adaptively generated, personalized and dynamic precise adjustment of rehabilitation training is achieved, and the training effect and the patient participation degree are improved. The system can autonomously switch between a reinforcement learning strategy and an auxiliary guiding strategy according to the real-time performance and the long-term trend of a patient, and dynamically optimize various feedback parameters.

The invention provides a low-load combustion optimization control system for a unit, and the system comprises a sensing network module which collects key parameters of the unit in a combustion process; the data preprocessing module is used for preprocessing the collected key parameters; the index analysis module is used for analyzing the energy balance deviation degree, chemical balance and combustion uniformity evaluation indexes of the current unit based on the preprocessed key parameters; the control strategy execution module is used for carrying out threshold value judgment on the result of each index and correspondingly executing preset energy balance, chemical balance and combustion uniformity strategies; the feedback regulation module is used for acquiring the executed feedback signal and performing new index analysis and cyclic regulation to form a closed-loop feedback mechanism; energy balance, chemical balance and combustion uniformity in the combustion process are analyzed and controlled, so that combustion stability and combustion efficiency are improved, pollutant emission is reduced, the service life of equipment is prolonged, and meanwhile, the adaptability of a unit to a power grid is enhanced.

The invention provides a biofeedback type electrical stimulation regulation and control device based on multi-mode physiological signal monitoring and a control method, the regulation and control device comprises an electronic component layer, a circuit layer, a man-machine interaction part and an adhesion layer, physiological signal acquisition and electrical stimulation regulation technologies are utilized, and a multi-source biosensor and an intelligent processing module are integrated, so that the regulation and control of the biofeedback type electrical stimulation regulation and control device is realized. Dynamic monitoring and real-time analysis of physiological signals are achieved, self-adaptive adjustment of personalized parameters is conducted based on signal characteristics, and a non-invasive biological feedback regulation and control system is formed. Through integrated multi-mode detection and intelligent regulation and control functions, a non-invasive and convenient regulation and control means is provided for wearers with abnormal nerve or physiological states; a portable and comfortable attached structure is adopted, the device can be worn at the Neiguan point for a long time, physiological signal continuous monitoring and electrical stimulation closed-loop feedback adjustment are achieved, and the personalized health management requirement of a user is met.

The invention relates to the technical field of automatic control, and provides an intelligent detergent putting control method and system for automatic car washing equipment. The method comprises the following steps: acquiring a vehicle surface stain image through a high-precision camera, and identifying stain features to generate a multi-dimensional feature set; performing clustering division on the feature set to obtain a stain area feature set, and calling a car washing scheme parameter space; analyzing the region feature set based on the parameter space, and determining cleaning scheme parameters of each region; a cleaning agent putting part control logic is associated, and area putting control parameters are generated; the environment temperature and humidity are monitored in real time, and cleaning agent putting and feedback regulation and control optimization are carried out in combination with control parameters, so that the technical problems that existing automatic car washing equipment lacks pertinence during cleaning agent putting, the cleaning agent putting amount cannot be accurately controlled according to the actual stain condition of a car and environment changes, the car washing effect is poor, and cleaning agents are wasted are solved. The technical effects of improving the car washing quality, reducing the waste of the cleaning agent and achieving intelligent and accurate control are achieved.

The invention relates to the technical field of water heater control, and discloses a control method of a gas water heater, a controller and the gas water heater, and the control method comprises the steps that in response to a starting request of the gas water heater, the gas water heater is controlled to operate based on a preset feedforward control algorithm; real-time water flow of the gas water heater is obtained, and first delay duration corresponding to the real-time water flow is determined based on the real-time water flow and a first preset relation table; and if the duration for controlling the gas water heater to operate based on the preset feedforward control algorithm reaches the first delay duration, the gas water heater is controlled to operate based on the preset feedback control algorithm. According to the invention, the opportunity of switching to the preset feedback control algorithm from the preset feedforward control algorithm is dynamically determined based on the real-time water flow, so that the temperature of the system can be rapidly raised to be near the set temperature through feedforward control after the system is started, and then the system is switched to the preset feedback control algorithm in time for feedback adjustment; therefore, the constant-temperature precision can be timely improved while the response speed is guaranteed, and the constant-temperature effect is improved.

The invention discloses a real-time feedback method and system for a hydrogen peroxide production process, and relates to the technical field of intelligent feedback. The real-time feedback method and system for the hydrogen peroxide production process comprises the following steps: S1, preprocessing production process data and historical process data; s2, self-adaptive time sequence prediction feedback regulation and control analysis is carried out, the rotating speed of a servo motor is adjusted, and a sampling flow error value is corrected; s3, performing regulation and control characteristic attribution analysis, and performing mixed solution transfer; s4, performing process equipment self-diagnosis for efficient emulsion separation and multi-parameter mahalanobis distance discrimination; and S5, carrying out process parameter optimization and risk early warning. The problems that an existing system is difficult to achieve automatic continuous sampling and flow self-adaptive regulation and control in the hydrogen peroxide production process, and high-precision real-time monitoring and intelligent early warning are difficult to achieve are solved.

The invention discloses an artificial intelligence-based intelligent regulation and control method and system for a performance test of a valve actuating mechanism, and belongs to the technical field of industrial equipment automation control. The method comprises the following steps: firstly, collecting multi-source time sequence data in a testing process of a valve actuating mechanism, constructing a long short-term memory (LSTM) network model, and further predicting performance index degradation of the valve actuating mechanism; secondly, test parameter dynamic optimization of reinforcement learning RL is carried out, an optimal strategy is solved, and function updating is carried out; and finally, fault early warning and diagnosis are carried out, closed-loop feedback regulation and control are realized, and a closed-loop intelligent regulation and control framework for the performance test of the valve actuating mechanism is formed. The method is realized based on a data acquisition module, an edge calculation module, an intelligent decision module and an early warning diagnosis module. According to the invention, the fault early warning and diagnosis capability of the valve actuating mechanism can be improved, and the test regulation can be adjusted in real time according to the optimal strategy generated by the valve actuating mechanism, so that the adaptive test is realized.

The invention belongs to the technical field of agricultural ecological monitoring, and discloses a cold region corn continuous cropping straw total returning full plough layer fertilization monitoring and adjusting system and method, and the system comprises a monitoring subsystem and a feedback adjusting subsystem which comprises a plurality of monitoring nodes. Each monitoring node comprises a plurality of sensor groups buried in different layer depths of soil; the feedback regulation subsystem comprises a data processing module and a diagnosis module; the data processing module is configured to generate a first monitoring parameter and a second monitoring parameter; the diagnosis module is configured to receive the first monitoring parameter and the second monitoring parameter and execute diagnosis conditions according to the planting stages of the first monitoring parameter and the second monitoring parameter; if the monitoring is in the planting period, executing a first preset adjustment measure; if the monitoring is in a non-planting period, executing a second preset adjustment measure in the monitoring; the system further comprises a storage module and a central processing unit. According to the invention, automatic and intelligent fertilization adjustment is realized.