58 results about "Intelligent adaptive control" patented technology

The invention discloses a network construction type inverter for a photovoltaic energy storage system and a virtual synchronous machine control method. The network construction type inverter comprises an inverter power module and a central controller. The central controller is used for collecting power grid operation data in real time and generating a power grid impedance state vector through online impedance identification; and dynamically adjusting virtual inertia and damping parameters of the inverter power module according to the impedance state vector by using an adaptive mapping function so as to realize cooperative control and circulating current suppression between the modules. Performing stability boundary evaluation based on the adjusted parameters, then performing intelligent early warning of the photovoltaic energy storage system, and smoothly switching the operation mode of the inverter power module; according to the invention, through deep integration of a modular hardware architecture and intelligent adaptive control, the inverter has strong autonomous network construction capability, the problems of weak power grid adaptability and transient stability are effectively solved, and the dynamic response and reliability of a photovoltaic energy storage system are improved.

The invention relates to the technical field of temperature control, and discloses an intelligent welding temperature control method and system based on multi-sensor fusion, and the method comprises the steps: obtaining a welding spot temperature distribution matrix, contact pressure data and wetting angle data; performing environment interference identification and compensation to obtain an environment correction temperature matrix; performing thermal resistance calculation to obtain a dynamic contact thermal resistance value; identifying a solder phase change state identifier; according to the method, the control gain parameters are adjusted in real time according to the solder phase change state identification and the dynamic contact thermal resistance value, intelligent self-adaption of the control strategy is achieved, and the problem that adaptability of fixed parameter control in different welding stages is insufficient is solved.

The invention provides an intelligent self-adaptive control method and system for sewage aeration, and is applied to the field of intelligent control of activated sludge process sewage treatment. The method comprises the steps that inlet water quality parameters are collected through a sensor array, abnormal fluctuation is recognized through a support vector machine, the sudden increase probability of pollutant concentration is calculated by combining a neural network with a historical mode, and when a threshold value is exceeded, a response delay index is calculated based on inlet water flow and biochemical kinetic parameters; generating an oxygen injection sequence according to the real-time dissolved oxygen data so as to regulate and control aeration equipment, and performing closed-loop feedback regulation according to the real-time dissolved oxygen data; by means of the scheme, pollution impact can be predicted in advance, system lag can be compensated, accurate matching of oxygen supply and loads on the time sequence is achieved, the quality stability of outlet water is effectively improved, and energy consumption is reduced.

The invention provides a tunnel construction method, system and device based on intelligent self-adaptive control and a storage medium, and the method comprises the steps: obtaining environment perception data of an unexcavated area of a tunnel, constructing and dynamically updating a virtual geologic model of the unexcavated area of the tunnel according to the environmental perception data based on a digital twinborn technology, and predicting to obtain a potential geologic risk; generating a corresponding support scheme based on the virtual geologic model and the potential geologic risk so as to implement advanced or radial support operation; actual tunneling data in the excavation process are obtained, and target control parameters of a current tunneling cycle are dynamically generated through advanced analog simulation based on the virtual geologic model and the actual tunneling data; and based on the deviation between the target control parameter and the actual tunneling data, dynamically adjusting the tunneling speed of the tunneling mechanism and the rotating speed of the cutterhead through a simulated PID algorithm. According to the self-adaptive tunnel construction method, high-precision construction control can be achieved on the premise that construction safety is guaranteed, and the tunneling efficiency is improved.

The invention discloses a multi-stage uniform stirring preparation device and method for multi-element solid waste paste, belongs to the technical field of metal mine tailing filling, and solves the problems that an existing paste preparation method is not high in stirring applicability for the multi-element solid waste paste, the stirring efficiency is poor, and slurry is not uniform. The device comprises a three-stage gradient uniform stirring mechanism, a dynamic rheological regulation and control module and an intelligent adaptive control unit, multi-stage uniform stirring and multiple stirring modes are utilized, corresponding stirring modes are adopted for materials with different particle sizes, multi-element solid waste generated by a mine is fully utilized, discharge of the mine solid waste is greatly reduced, the cost of a mine filling process is reduced, and finally paste filling slurry which is uniform in stirring and good in performance is formed. The method is suitable for recycling mine solid wastes such as metal and nonmetal and goaf treatment.

The invention relates to the technical field of photovoltaic power stations, and particularly discloses a neural network enhanced photovoltaic power station multi-modal data monitoring and early warning method and system. The method comprises the following steps: performing multi-modal data acquisition on renewable energy equipment under an extreme environment condition; parameters in the environment subsets are fused, influence coefficients are calculated, and fusion vectors are obtained; predicting an equipment performance trend and judging a strategy adjustment direction to obtain a decision instruction; extracting more gradient features to update response demand features; recalculating the influence coefficient, predicting the performance trend of the equipment and judging the adjustment direction of a new strategy; if the new strategy adjustment direction indicates the specific load scene, updating and generating a new decision instruction; and adaptively adjusting and optimizing operation through a threshold value to obtain a final control scheme for optimizing the tiny fluctuation of the equipment under the extreme environment condition. The operation stability and efficiency of renewable energy equipment of a photovoltaic power station in an extreme environment can be remarkably improved, and intelligent self-adaptive control is achieved.

The invention discloses an intelligent self-adaptive control bionic flapping wing flight experiment data acquisition system and method, and belongs to the technical field of bionic aircraft testing and intelligent control, and the system comprises a hardware system integration module, a wind tunnel environment simulation module, a three-degree-of-freedom motion control module, a flexible sensor array module and a reinforcement learning interaction module. According to the intelligent self-adaptive control bionic flapping wing flight experiment data acquisition system, the upper computer is used as a control center, and a multifunctional visual interface is integrated; the multi-fan wind tunnel is linked with the three-degree-of-freedom platform to reproduce an airflow-attitude coupling relationship and improve the suitability of the two; data-training-optimization integration is realized, and system compatibility is improved.

This invention discloses a decapping device for wafer cleaning rod processing, comprising: a support structure including a support platform with two parallel support rails on the support platform; a clamping structure including multiple first sliders slidably connected to the upper ends of the support rails, with two first sliders on the same axis fixedly connected to the same first mounting plate at their upper ends, and a clamping component for fixing the wafer cleaning rod provided at the upper end of the first mounting plate; and a decapping structure including multiple second sliders slidably connected to the upper ends of the support rails, with the second sliders correspondingly disposed outside the first sliders. This invention, through the deep integration of precision mechanical structure design and intelligent adaptive control system, not only achieves a leap from manual to automated wafer cleaning rod decapping operations, but also endows the device with the core capability to cope with complex working conditions and ensure processing quality through real-time sensing and feedback control.

The invention relates to the technical field, and specifically discloses an LED lamp intelligent adaptive control method and device, and the method comprises the following steps: obtaining real-time air humidity and real-time surface temperature; calculating a condensation risk coefficient of condensation of water vapor on the outer surface of the lampshade; if the condensation risk coefficient is larger than a preset risk threshold value, a brightness adjustment process is executed, and the brightness adjustment process comprises a pre-adjustment stage and an operation adjustment stage; in the pre-adjustment stage, the LED lamp is controlled to illuminate at the pre-drying brightness, and the pre-drying brightness is larger than the conventional operation brightness of the LED lamp; and in the operation adjustment stage, the light transmittance of light rays emitted by the LED lamp after passing through the outer surface of the lampshade is calculated, and the output brightness of the LED lamp is adjusted based on the light transmittance. According to the invention, the problems of optical interference and visual comfort damage when the humidity is too high for a long time in unventilated spaces such as underground garages and the like can be solved.

The application relates to the technical field of all-in-one machine control, in particular to a five-axis drive control all-in-one machine control method, system, equipment and medium. The application detects a power supply state through a power management module and performs filtering, each separated inverter module is initialized in parallel to obtain an enable signal; target position instructions are generated based on user input and dynamic constraint parameters, real-time position and speed instructions are calculated; a five-axis servo system is driven to execute movement and feedback adjustment; current fluctuation characteristics and position deviation change rates are monitored in real time, when a source abnormality is detected, only the inverter module corresponding to the abnormal axis is turned off; after replacing the abnormal module, the parameters are automatically loaded and the inter-axis synchronization compensation is optimized through adaptive configuration. The application reduces electromagnetic interference through distributed filtering, reduces maintenance cost through modular fault isolation, and realizes system reliability through intelligent adaptive control.

The invention relates to the technical field of environmental engineering, and discloses a sewage filtering method and system based on multi-crop collaboration.The sewage filtering method based on multi-crop collaboration.The sewage filtering method based on multi-crop collaborationcomprises the steps that an original sewage sample is obtained, a multi-factor comprehensive evaluation method is adopted for analysis and treatment, and a multi-crop collaborationpollutant removal mechanism model is established; a multi-level system structure is constructed, and a three-dimensional hydraulic modeling method is adopted for analysis and calculation; a multi-sensor fusion monitoring network is constructed, and a data fusion algorithm is adopted for processing and analysis; system parameters are optimized and adjusted by adopting an intelligent self-adaptive control strategy; regularly adding a microbial agent to carry out system biological enhancement and dynamic regulation and control optimization, and establishing a stable microbial ecological system; all functional units are integrated into a modular system, and performance verification is carried out based on a stable microbial ecological system to obtain purified water; the system has the advantages of being high in treatment efficiency, stable in operation, high in intelligent level and low in construction cost.

The application relates to the technical field of intelligent sensors, and discloses a Bluetooth earphone automatic control method and system.The method comprises the following steps: acquiring external environment original sound signals and user motion trajectory data; performing sound signal frequency domain conversion and noise reduction to obtain a pure acoustic data set; extracting a frequency spectrum feature to construct a matrix, analyzing background volume, and dividing interference levels; smoothing motion data to extract behavior features, and matching to obtain a current user behavior state; triggering noise reduction mode switching if a standard is met, setting an environment control strategy; generating environment adaptation parameter adjustment volume to obtain a volume adjustment value; converting into a voltage current signal to form earphone output configuration; generating a control signal to be applied to hardware, optimizing output timing, and obtaining a scene-adapted driving signal.The method can realize intelligent adaptive control under complex environments and dynamic user states.

The application discloses a soft sealing frictionless ball valve intelligent adaptive control method, and relates to the technical field of valve control, which comprises the following steps: in step one, a perturbation is superimposed on the seat section to determine the instant of the contact seat by judging the residual point of the stiffness mutation and the non-contact stroke reference curve, and the position of the contact seat and the local stiffness are output; in step two, the pressure difference, temperature and seat ring material are collected, the minimum necessary compression load is calculated, and the upper limit of the allowable torque-angle curve is generated; in step three, the smooth closing trajectory is executed with the position of the contact seat as the starting point, the minimum necessary compression load is reached under the amplitude limitation, and the process is stopped, and the contact energy and sealing confirmation are recorded; in step four, the breathing type pressure maintaining is implemented in the maintaining stage, the seat ring health index is formed, and the material-temperature correction coefficient is written back. The method can reduce overload and underload, stabilize zero visible leakage, reduce energy consumption and prolong the service life of the soft seat, improve the predictability of operation and maintenance and the consistency of parameters, and support online diagnosis and maintenance decision.

The invention discloses a negative pressure monitoring device for pathogenic microorganism aerosol in livestock and poultry fields, and belongs to the technical field of pathogenic microorganism aerosol monitoring. The shell is installed on the support, and an air inlet and an air outlet are formed in the shell; the air inlet unit is mounted on the shell, corresponds to the air inlet and is used for sucking ambient air, the collecting and filtering unit is mounted in the shell, and the air inlet end of the collecting and filtering unit is communicated with the air inlet unit and is used for intercepting aerosol particles carrying microorganisms in air; the negative pressure unit is installed in the shell, the air inlet is connected with the air outlet end of the collecting and filtering unit, and the air outlet is correspondingly connected with the air outlet. The environment monitoring unit is integrated on the shell and is used for monitoring environment parameters in real time; the control unit is electrically connected with the environment monitoring unit and the negative pressure unit. And the power supply unit supplies power to each power utilization unit. Through combination of negative pressure sampling and intelligent adaptive control, efficient capture, remote monitoring and long-acting stable operation of pathogenic microorganism aerosol in livestock and poultry fields are realized.

The invention relates to the technical field of intelligent manufacturing of automobile parts, in particular to an automobile cross beam machining tool and a using method. The automobile cross beam machining tool comprises a three-dimensional dynamic positioning platform, an intelligent clamping module, a monitoring module, a digital twinning control module and a quick-change interface module. The positioning precision is obviously improved, and the reference drift problem is eliminated; through the design of the intelligent clamping module and the quick interface module, the clamping efficiency is greatly improved; the application of the modularized positioning block assembly enables the tool to quickly adapt to cross beams of various models; a multi-source monitoring network and a digital twinborn prediction algorithm are integrated, intelligent monitoring and early warning of the machining process are achieved, and the tool system achieves upgrading from traditional mechanical positioning to intelligent self-adaptive control through closed-loop control of sensing, decision making and execution. The defects of a traditional tool in the aspects of positioning precision, clamping efficiency, production flexibility, intelligent monitoring and the like are comprehensively overcome.

The application relates to the technical field of compression-shear testing machines, in particular to a dynamic compression-shear testing machine adaptive control method and system based on machine learning, which comprises the following steps: constructing a material parameter prediction model to predict the detection parameters and mechanical properties of the material; correcting the control parameters of the compression-shear machine through an adaptive control algorithm according to the real-time response data of the material in the testing process; collecting the surface image and internal ultrasonic signal of the material, extracting failure characteristic parameters such as crack area proportion, number of broken sections and maximum fracture length, and weighting and fusing to construct a critical failure criterion of the material; triggering safety protection when the material failure characteristic parameters reach a preset threshold value, automatically stopping the test and recording relevant data; and using the recorded data under the failure condition to incrementally learn and continuously optimize the material parameter prediction model. The method realizes intelligent adaptive control of the dynamic compression-shear testing process, and improves the testing efficiency, precision and safety.

The invention belongs to the technical field of adaptive control, and discloses an LED parameter adaptive control system and method based on a neural network. The method comprises the steps of collecting sleep quality data; performing sleep quality analysis based on the sleep quality data to obtain sleep quality feature data; acquiring space environment data; analyzing and processing the space environment based on the space environment data to obtain space environment feature data; collecting sober key data after the user is sober; performing feature extraction on the sober key data to obtain sober feature data; performing intelligent self-adaptive control on LED parameters based on the sleep quality characteristic data, the space environment characteristic data and the waking characteristic data; according to the scheme, intelligent self-adaptive regulation and control of the LED parameters are carried out according to different physiological states of the user, and particularly, a suitable illumination environment is provided in a just waking state, so that personalized self-adaptive illumination regulation is realized, and the comfort and adaptability of the user are improved.

The invention relates to a nondestructive testing technology of electrical equipment, and discloses an infrared detection device for cracks of an electrical tube, which comprises a support plate, a closed detection camera obscura, a rotary moving mechanism, an infrared imaging unit and an active infrared lighting unit, and is also provided with an infrared light source intensity real-time adjusting system, the system integrates an environment state evaluation module, a system state evaluation module, an adaptation evaluation module and a motion state evaluation module, and can sense multi-dimensional parameters such as environment light intensity, air suspended matter concentration, light source working temperature, optical window transmissivity, production line operation speed and pipeline rotation angular velocity in real time; and the infrared illumination intensity is dynamically adjusted by integrating the average gray scale of the image and the target feature contrast ratio. According to the invention, intelligent self-adaptive control of the infrared light source in a complex and changeable production environment is realized, the image quality stability and the accuracy and reliability of crack detection are effectively improved, and the method is suitable for full-automatic online nondestructive detection in large-scale production of power tubes.

A method for the self-development of a control agent for a prosthesis, orthosis or an artificial limb, said control agent being configured for its control by its control means, comprising the steps of: obtaining a motion model defining a mechanical relationship between elements of the prosthesis or the like; acquiring a training response model defining a predefined motion response of the prosthetic limb and the like; and determining the current motion state of the artificial limb and the like. And additionally comprising repeating the steps of: determining a candidate mechanical action based on a current motion state associated with an element of an artificial limb or the like; outputting a next candidate motion state based on the current motion state and the candidate mechanical action; outputting a next predefined motion state based on the current motion state; obtaining a reward value based on the next candidate motion state and the next predefined motion state; and setting the next candidate motion state as the current motion state.

The application discloses an unmanned platform intelligent adaptive control method and system, electronic equipment and a storage medium. The method generates a globally optimal reference path through a first target function; calculates target thrust and target rudder angle through a second target function, controls the unmanned platform to navigate through the target thrust and the target rudder angle, and generates an actual path; performs weighted summation on a current flow field speed, a current flow field vorticity and a current flow line curvature to obtain a current flow field utilization factor; judges whether the path needs to be re-planned and whether to switch to an energy efficiency optimization mode; if it is judged that the energy efficiency optimization mode needs to be switched to without re-planning the path, the target thrust and the target rudder angle are re-calculated; if it is judged that the energy efficiency optimization mode needs to be switched to and the path needs to be re-planned, or it is judged that the energy efficiency optimization mode does not need to be switched to but the path needs to be re-planned, the globally planned path is re-generated and the target thrust and the target rudder angle are re-calculated. The application can improve the path tracking accuracy and the energy utilization efficiency.

The invention provides a solenoid valve flow intelligent self-adaptive control method and system for a composite working condition, and relates to the technical field of industrial automation control, and the method comprises the steps: firstly collecting mutual feedback type working condition flow data comprising working condition influence data, flow feedback data and dynamic mutual feedback trace data under the composite working condition; a working condition flow mutual feedback evolution model is constructed based on mutual feedback type working condition flow data, and dynamic correlation evolution of working conditions and flow is achieved; then, according to a model real-time evolution result, generating an evolutionary flow adaptation strategy containing a dynamically adjusted flow adjustment path and a parameter combination; iteratively optimizing an evolutionary flow adaptation strategy through the dynamic mutual feedback trace data to generate an intelligent adaptive control signal; and finally, transmitting an intelligent self-adaptive control signal to an execution mechanism, and reversely inputting data to push the model to be continuously updated. The method can effectively cope with composite working conditions, and the precision and adaptability of electromagnetic valve flow control are improved.

The invention discloses a dry quenching circulating gas concentration control method, which comprises the following steps of: introducing a novel intelligent PID (Proportion Integration Differentiation) self-adaptive control technology into a circulating gas concentration control system; the influence of novel intelligent PID control parameters on the suppression / aggravation effect of key data such as the opening change value of an air introduction valve, the pressure change value of a pre-storage chamber and the circulating gas H2 concentration response time is obtained, and an intelligent control system is developed by taking a constructed mathematical model of a dry quenching circulating gas H2 concentration control system based on intelligent PID as a core algorithm. According to the control method based on the system, automatic control over the H2 concentration is achieved, the H2 concentration is controlled within the range close to the expected value, therefore, the risk of equipment explosion is avoided, meanwhile, coke burning loss is reduced, CO2 emission is reduced, normal operation can be achieved even under the condition that the H2 concentration continuously rises due to leakage of a boiler, and the system is safe and reliable. And therefore, the control targets of safely increasing the yield, saving energy and reducing emission are achieved.

The invention discloses a plateau area LED integrated lighting system based on low-light photovoltaic driving, and relates to the technical field of intelligent lighting. The plateau area LED integrated lighting system based on low-light photovoltaic driving comprises a data acquisition and preprocessing module for preprocessing environment energy data and target sensing data; the intelligent environment perception and energy-saving scheduling module is used for performing suppression type harmonic product evaluation on the environment energy data, and performing adaptive adjustment of lighting output, energy distribution and charging management; the target type distinguishing and business shunting module is used for dynamically identifying the target type and pushing and judging whether to enter the intelligent lighting range and brightness regulation and control module in real time; and the intelligent illumination range and brightness regulation and control module is used for dynamically optimizing illumination space distribution, brightness output and a continuous strategy. The problem that an LED lighting system in a plateau area cannot realize intelligent self-adaptive regulation and control and efficient energy management under complex environment and variable load conditions is solved.

The application provides a sewage aeration intelligent adaptive control method and system, which is applied to the intelligent control field of activated sludge method sewage treatment; the method comprises the following steps: collecting water quality parameters of influent water through a sensor array and identifying abnormal fluctuations by using a support vector machine, combining a neural network to compare historical patterns to calculate the probability of sudden increase of pollutant concentration, calculating a response delay index based on the influent flow and biochemical kinetics parameters when the threshold is exceeded, and generating an oxygen injection sequence to regulate the aeration equipment, and simultaneously performing closed-loop feedback adjustment according to real-time dissolved oxygen data; through the above scheme, the application can predict pollution impact in advance and compensate for system lag, realize accurate matching of oxygen supply and load in time sequence, effectively improve the water quality stability of effluent and reduce energy consumption.

The invention provides a photovoltaic power generation-oriented combined transformer intelligent adaptation control method and system. The method comprises the following steps of: obtaining an operation state vector of a transformer system; the running state vectors at each moment form a running state vector sequence according to a time sequence. And performing trend extraction according to the running state vector sequence to obtain trend feature representation. And performing penalty calculation on the trend characteristic representation according to power slope constraint and temperature inertia to obtain a trend index set. And calculating according to a preset mapping function and the trend index set to obtain the contribution degree weight of each control channel. And performing strategy generation according to a preset control strategy optimization function and the contribution degree weight to obtain a control activation combination, and controlling the transformer system based on the control activation combination. Wherein the control activation combination is used for indicating whether the corresponding control channel is activated or not. Dynamic feed-forward adjustment of a transformer system is achieved, and stability is improved.

This invention discloses an energy-saving compressed air drying and purification device and control method, belonging to the field of compressed air treatment technology. The device includes an inlet pipeline, an outlet pipeline, a gas-liquid separation unit, a dual adsorption drying unit, a regeneration heating unit, a purified output unit, a detection unit, and an intelligent control unit. The dual adsorption drying unit employs two tanks operating alternately. The regeneration heating unit is equipped with separate heating control valves and cold blowing control valves, and is configured with a small-diameter regeneration gas consumption proportional adjustment valve to achieve precise control of regeneration gas consumption. The control method includes adsorption drying, dew point detection switching, backup tank regeneration, and seasonal adaptive control for spring / summer / autumn / winter seasons, adjusting the switching cycle and heating and cold blowing times in real time based on the dew point. This invention reduces regeneration gas consumption to 2%–3%, and the output air dew point meets ultra-dry requirements. It combines advantages such as good purification effect, low energy consumption, stable operation, and intelligent adaptive control, making it suitable for deep drying and purification scenarios of industrial compressed air.

This invention discloses an adaptive control method and system for a stirring motor based on load feedback, belonging to the technical field of intelligent control. The method includes the following steps: calculating load responsiveness, temperature rise rate, regulation stability, and electrical stability; training a machine learning model; and regulating the current load torque based on expected indicators and the actual output of the machine learning model. This invention predicts load torque changes through a machine learning model, achieving active feedforward compensation and effectively suppressing speed fluctuations. By integrating four-dimensional features—load responsiveness, temperature rise rate, and regulation stability—it achieves synergistic optimization of dynamic performance, thermal safety, control quality, and electrical stability. This solves the problems of time-varying loads, complex disturbances, and multi-objective conflicts in stirring motors, achieving efficient, stable, and intelligent adaptive control.

This invention discloses an adaptive gradient four-phase separation system and method based on multi-field synergy. The system includes a feed pre-regulation unit, a gradient separation main unit, an intelligent adaptive control unit, and a self-cleaning anti-clogging execution unit. The system employs a three-layer architecture of "separation-regulation-anti-clogging." The gradient separation main unit utilizes a spiral channel and variable cross-section design to create a flow velocity gradient field of 3-5 times, synergistically utilizing centrifugal force and gravity to significantly improve separation efficiency. The intelligent adaptive control unit monitors the operating conditions in real time through a multi-parameter sensor array and dynamically and adaptively adjusts the inlet flow velocity, internal pressure, and outlet opening of each phase of the gradient separation main unit to maintain an optimal stable swirling field, ensuring stable operation and efficient separation under varying feed conditions. The self-cleaning anti-clogging execution unit effectively breaks up and washes away deposited sand particles, significantly extending the continuous operation cycle and service life of the equipment and reducing maintenance requirements.

The application discloses a metal material high-precision numerical control machining system and method based on intelligent adaptive control, relates to the technical field of metal material processing, and collects historical metal material cutting process data, constructs a metal chip diffusion model, obtains metal chip diffusion conditions, evaluates the aging degree of a moving part, simultaneously evaluates the adhesion and wear effect of metal chip diffusion on the moving part, evaluates the damage risk of metal chip on the moving part based on the aging degree of the moving part and the adhesion and wear effect of metal chip diffusion on the moving part, dynamically adjusts the negative pressure parameter of a recovery air bellow based on the damage risk evaluation result of metal chip on the moving part and the chip diffusion condition, predicts the chip diffusion condition under different cutting conditions, combines the damage of diffused chips on moving parts with different aging degrees, obtains accurate recovery risk adjustment parameters, and realizes accurate control of negative pressure adjustment.

The invention belongs to the technical field of computer technology and industrial automation control, and particularly relates to an intelligent control method and system for a corrosion-resistant mass flow sensor, and the method comprises the steps: obtaining an original electric signal of the sensor, a fluid corrosion grade and a temperature parameter in real time through a data collection interface; performing multi-level software processing including wavelet transform adaptive filtering, baseline drift compensation and feature vector extraction on the original signal to generate standardized flow data; the standardized data, the corrosion scene parameters and the historical performance data are input into a self-adaptive control engine, a flow control instruction is dynamically generated, and a sensor health state evaluation report is output; and executing the control instruction to drive an execution mechanism, and implementing a predictive maintenance strategy based on the health report. According to the invention, the long-term measurement precision and control stability of the sensor in a corrosive environment are effectively improved, and intelligent self-adaptive control and predictive maintenance of flow are realized.