122 results about "Intelligent adaptive control" patented technology

The invention discloses a pickling waste liquid recycling system and recycling method, and relates to the technical field of industrial wastewater treatment, the pickling waste liquid recycling system comprises: a treatment unit for respectively detecting the concentration of each heavy metal ion in pickling waste liquid, respectively adding corresponding chelating resin into the pickling waste liquid according to each heavy metal ion concentration, and collecting to obtain recycled acid liquid; the monitoring unit is used for collecting the pH value and the conductivity data of the recycled acid liquor in real time, inputting the pH value, the conductivity data and the heavy metal ion concentration data as monitoring data into the control unit, and establishing a monitoring database; and the control unit is used for calculating optimal control parameters in real time based on the monitoring data in the monitoring database, automatically adjusting the processing unit according to the optimal control parameters, and conveying the recycled acid liquor to the pickling process for recycling. Through multi-parameter cooperative monitoring and intelligent self-adaptive control, precise detection, dynamic optimization treatment and efficient recycling of the heavy metal ions in the pickling waste liquid are realized.

The invention relates to the technical field of lithium ore conveying, and discloses a lithium ore hopper energy-saving conveying system based on intelligent self-adaptive control. The system comprises a data acquisition module, a self-adaptive control module, an energy-saving optimization module and an execution adjustment module. The data acquisition module acquires multi-dimensional operation data of the lithium ore hopper through a distributed sensor network, and a hopper conveying state database is constructed after standardization processing; the adaptive control module performs dynamic feature extraction on the database, generates a real-time control parameter matrix, establishes an association rule graph of each parameter, and constructs an intelligent adjustment decision model based on the association rule graph; an energy-saving optimization module extracts a key energy consumption feature vector from the real-time control parameter matrix, calculates an energy consumption influence coefficient of each operation parameter, and screens out an optimal energy-saving control sequence in combination with an intelligent adjustment decision model; and the execution adjustment module drives an execution mechanism according to the optimal energy-saving control sequence, and self-adaptive adjustment of hopper conveying parameters is completed.

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 discloses a glass fiber particle assembly line-based magnetic suction quick-release vibration disc system and a detection method, and relates to the technical field of production and processing, the system comprises a data acquisition module for acquiring data of a vibration light-emitting chassis, a magnetic suction column, an assembly line and particles by means of a distributed sensor network, and transmitting the data to an intelligent self-adaptive control module after signal processing; the intelligent self-adaptive control module realizes intelligent dynamic regulation and control of the parameters of the magnetic suction quick-release vibration disc, optimizes the production process of the glass fiber particles, and automatically generates an accurate control instruction based on a data analysis result by integrating multi-source information and performing deep fusion and analysis, so that the production efficiency of the glass fiber particles is improved. Parameters such as the vibration frequency and amplitude of the vibration light-emitting chassis and the magnetic attraction strength of the magnetic attraction column are adjusted to meet the requirements in different production states, so that the system can quickly respond to changes in the production process, defective particles are reduced, and the production efficiency is improved.

The invention belongs to the technical field of circuit board metal smelting control, and discloses a circuit board metal smelting energy-saving parameter self-adaptive control system and method. The method comprises the following steps: carrying out real-time monitoring and fluctuation analysis on heating circuit voltage collected in unit time in the metal smelting process of the circuit board to obtain a voltage fluctuation level; processing the smelting state data acquired in unit time in the metal smelting process of the circuit board to obtain a smelting state characteristic data set, and identifying a current smelting stage; when the voltage fluctuation grade is greater than or equal to a preset voltage fluctuation grade threshold value, generating an energy-saving strategy control identifier in combination with the current smelting stage and the predicted smelting stage of the next unit time; dynamically adjusting energy-saving control parameters based on the voltage fluctuation grade, the smelting state characteristic data set and the energy-saving strategy control identifier; the intelligent self-adaptive control level of the circuit board metal smelting energy-saving parameters is enhanced, and energy-saving control is changed from passive execution to look-ahead response.

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 discloses a mountainous area sudden change wind field active control wind tunnel simulation method based on deep learning, and relates to the technical field of wind tunnel experiment research, and the method comprises the steps: obtaining a target wind field parameter in a mountainous area target region, constructing an MLP input vector according to the target wind field parameter in combination with wind tunnel real-time sensor data, a deep learning control model comprising an MLP forward mapping model and a Transform time sequence feedback model is established, the MLP forward mapping model generates a multi-fan array control signal according to the MLP input vector, a multi-fan array is controlled to generate a simulated wind field, the Transform time sequence feedback model generates a multi-fan array control correction signal according to a wind field difference value between the simulated wind field and a target wind field, and the multi-fan array control correction signal is controlled to generate a multi-fan array control correction signal according to a wind field difference value between the simulated wind field and the target wind field. The simulated wind field parameters are dynamically corrected in real time, and target wind field parameters are approached; according to the method, high-fidelity dynamic reproduction and intelligent adaptive control of the complex wind field in the mountainous area are realized through MLP feedforward generation and Transform feedback correction.

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

The invention relates to the technical field of robot control, and discloses a robot hybrid intelligent adaptive control method and system adopting reinforcement learning, and the method comprises the steps: collecting environment perception parameters, and constructing a dynamic environment model; setting and testing a robot execution mechanism dynamic model, and obtaining motion characteristics of the robot execution mechanism; integrating the dynamic model into the dynamic environment model; calculating a task demand parameter and a theoretical control load, and generating a control quantity in combination with motion characteristics; a driving unit action instruction is generated based on the control quantity and control is carried out; and evaluating the execution effect. The system comprises a dynamic environment model construction module, an execution mechanism dynamic model construction module, a model fusion module, a task parameter calculation module, an action instruction generation module, an action control module and an execution effect evaluation module. According to the invention, high-precision self-adaptive control of robot operation in a complex dynamic environment is realized, and environmental adaptability and task execution efficiency are improved.

The invention relates to the technical field of intelligent spraying, in particular to an intelligent spraying control method and system suitable for the end face of a large bridge. The method comprises the following steps: positioning the initial operation position of the bridge end surface spraying equipment based on machine vision and a multi-sensor calibration method; constructing a bridge end face spraying model, and obtaining an optimal spraying path based on a path generation algorithm; intelligent adaptive control is conducted on a spraying mechanism based on multi-sensor data fusion, and the position posture of an executing mechanism is adjusted through a dynamic angle compensation algorithm; a multi-degree-of-freedom stable operation control algorithm is used for controlling the speed and the action period of an executing mechanism; and a multi-mechanism joint control algorithm is used for controlling linkage control of the executing mechanism and the spray head. Aiming at the core problems of low manual operation efficiency, large quality fluctuation, heavy and limited traditional equipment, prominent potential safety hazards and the like in railway bridge end face waterproof construction, systematic technical breakthrough is realized through integration of electromechanical integration innovation and an intelligent algorithm.

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 discloses a display screen brightness control method for an intelligent storage cabinet. The method comprises the following steps: acquiring environment illumination data and time data acquired by a plurality of illumination sensors to generate an environment feature vector; constructing a brightness mapping model by using a neural evolutionary algorithm to obtain an initial brightness value; brightness optimization is carried out by adopting a constraint optimization method based on an Ising machine; and dynamically optimizing the brightness mapping model by using a bilateral structure evolution mode. Intelligent self-adaptive control of the brightness of the display screen of the intelligent storage cabinet can be realized, and the energy consumption is reduced while the visual experience of a user is improved.

The invention discloses a remote intelligent monitoring method for the health condition of an air conditioning system of a rail transit vehicle, and relates to the technical field of intelligent control, the rail transit air conditioning system is initialized, data are collected in real time, and the data are sent to edge computing equipment through wireless communication; after edge calculation, the preprocessed data are compared with preset parameters, and the current state of the air conditioner system is calculated; based on the state, a physical model is constructed for fault deduction, operation of the air conditioning system is simulated through thermal load prediction and pressure change analysis, and a fault deduction report is generated through comparison with real-time data; inputting the report into a machine learning algorithm for analysis, and judging a fault type and a fault source according to historical fault data and an air conditioning system working mode; and in combination with remote intelligent self-adaptive control, system parameters are optimized, and the state of the air conditioning system is updated. Real-time acquisition, modeling deduction and machine learning are fused, the fault diagnosis accuracy and response speed are improved, and the reliability and intelligence of the rail transit air conditioning system are enhanced.

The invention discloses a photovoltaic power generation intelligent adaptive control system for power grid flexibility demand response, and the system comprises a data collection module which is used for collecting power grid power characteristic data of n power utilization regions in unit time and time characteristic data of the next unit time; the first analysis module is used for inputting the nth power grid power characteristic data into a pre-trained power prediction model to obtain an active power interval of the nth power utilization area in the next unit time; the second analysis module is used for determining a reactive power support interval of the nth power utilization area in the next unit time according to the active power interval, the load type and the time characteristic data of the next unit time; the stability and smoothness of the power supply voltage of the nth power utilization area in the next unit time are guaranteed, so that the power supply reliability of a power grid system is guaranteed, and the risk of power supply interruption caused by violent voltage fluctuation is reduced.

The invention relates to the technical field of household water purification, and provides a control method and device, softening water purification equipment, electronic equipment, a medium and a product, the softening water purification equipment comprises a capacitive deionization system, and the capacitive deionization system is used for removing ions and salt in water. The control method of the softening water purification equipment comprises the following steps: acquiring historical operation data, a current state and user preference data of the softening water purification equipment; inputting the historical operation data, the current state and the user preference data into a prediction model to obtain a prediction result output by the prediction model; the prediction result comprises water demand and water quality change; determining working parameters of the capacitive deionization system according to the water demand, the water quality change and the current state; and controlling the capacitive deionization system according to the working parameters so as to output purified water meeting the water demand. Through the mode, the intelligent self-adaptive control characteristic of the softening water purification equipment is enhanced, and accurate water quality control, predictive maintenance and efficient, energy-saving and personalized water quality treatment are realized.

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 provides a display screen energy consumption optimization system based on adaptive control. The system improves the energy efficiency ratio of a display screen and the visual experience of a user. Firstly, ambient light data, user viewpoint data and display content data are captured through a sensing module, and an ambient light gradient map, a user watching weight matrix and a content feature tensor are generated; then, the hybrid control module carries out spatial alignment on the multi-modal data through a region mapping unit and a dual-channel control unit, establishes a light environment model of a region of interest, and generates a self-adaptive control strategy; and finally, the execution module implements two-stage energy consumption optimization based on the control strategy, and constructs a bidirectional feedback loop to feed back real-time energy consumption data and a user visual comfort index to the hybrid control module for strategy iterative optimization. According to the invention, through multi-mode sensing fusion, intelligent adaptive control and a closed-loop feedback mechanism, the energy consumption optimization effect and the system adaptability of the display screen are remarkably improved.

The invention discloses a BLDCM control method based on an intelligent fractional order controller, and the method comprises the steps: building a transfer function of the intelligent fractional order controller, and enabling the transfer function to be used for controlling a BLDCM; parameters in a transfer function of the intelligent fractional order controller are obtained, and a training set is established; determining a parameter set in a transfer function of the intelligent fractional order controller based on the training set; determining a transfer function of the intelligent fractional order controller based on the parameter set; and controlling the BLDCM based on the transfer function and the control parameter of the intelligent fractional order controller. The problems that in current BLDCM control, a common PID control method and other methods are poor in self-adaptability, insufficient in control precision, low in control efficiency and the like are solved. Based on parameter screening in the transfer function of the intelligent fractional order controller, it is guaranteed that the intelligent adaptive controller has higher control precision and better adaptivity, and the control efficiency can be improved under the condition that the requirements are met.

The invention discloses an intelligent self-adaptive control method for a diesel generating set, which aims at the problems of slow load fluctuation response, poor system stability and the like of the traditional diesel generating set, and comprises the following steps: triggering a quick response mechanism by monitoring a load power change rate in real time, and dividing a load into low, medium and high load areas by adopting a piecewise linearization power distribution strategy; matching an optimal power combination in a power distribution table generated offline by combining an energy storage SOC state; the winding current is decoupled and controlled through rotor flux linkage directional coordinate transformation, the electromagnetic torque is rapidly adjusted through a PI controller, and cooperative power output is achieved in cooperation with space vector pulse width modulation. The preset rule limits that the energy storage power does not exceed 80% rated value and the SOC is maintained at 20%-80%, and the power distribution table is stored in the digital signal processor for real-time execution. According to the method, the response time can be shortened, the controller load is reduced, and the system stability and the fuel economy under complex working conditions are improved.

The invention provides a self-adaptive anti-interference circuit for an industrial customized keyboard and a control method, power supply noise, surge and transient drop in an industrial environment are eliminated through a power supply anti-interference module, multi-protocol compatible communication is realized through a data anti-interference module, a double-insurance mechanism of a data path is formed, and the self-adaptive anti-interference circuit is applied to the industrial customized keyboard. A feedback link is formed through the data acquisition module and the adjustment control module, and real-time data support is provided for dynamic adjustment; besides, through intelligent self-adaptive control, a closed-loop control engine is formed, a magnetic bead array is activated to filter high-frequency interference, and a USB protocol is switched to ensure communication quality and power-off protection of an ESD event. And the anti-interference capability, the communication reliability and the electrostatic protection capability of the keyboard in an industrial scene are improved.

The present invention discloses a control system for implementing a hybrid energy storage system (ESS) optimization strategy. The hybrid ESS optimization strategy can be implemented in a machine including a power system and a power controller, the power system including multiple power sources, and the power controller including one or more processors. The power controller can receive information related to a set of brake fuel consumption (BSFC) maps associated with the multiple power sources, determine performance indicators using cost functions associated with the multiple power sources, and generate commands to operate the power system based on a power distribution that minimizes energy costs, so as to operate the power system based on information related to the set of BSFC maps, the performance indicators, and the load associated with the power system.

The invention discloses a low-voltage cabinet distributed control method and system based on multi-source data fusion, and relates to the technical field of distributed control. The method comprises the following steps: extracting topological structures of an equipment layer, a loop layer and a system layer from a distributed low-voltage cabinet set to obtain the topological structure of the equipment layer, the topological structure of the loop layer and the topological structure of the system layer; obtaining an equipment physical interface parameter set; obtaining a feeder loop state feature set; performing adjacency state analysis on the equipment physical interface parameter set to obtain an adjacency state matrix set; and taking the feeder loop state feature set and the adjacent state matrix set as real-time low-voltage cabinet working conditions, matching the real-time low-voltage cabinet working conditions with a pre-constructed working condition distributed control library, determining a target distributed control scheme, and performing distributed control on a distributed low-voltage cabinet set. The technical problems that in the prior art, low-voltage cabinet distributed control is poor in flexibility and poor in working condition adaptability are solved, and the technical effect of achieving intelligent self-adaptive control based on the real-time working condition is achieved.

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 present invention provides an intelligent adaptive exhaust cleaning system for explosion-proof diesel engines for coal mines, which belongs to the technical field of exhaust cleaning of explosion-proof diesel engines, including a radiator, a lifting water pump, a turbocharger, an explosion-proof constant-flow pipe, an exhaust cleaning device, an intelligent adaptive control box and an explosion-proof motor; the water outlet of the radiator is connected to the water inlet of the lifting water pump; the water outlet of the lifting water pump is connected to the water channel inlet of the turbocharger; the water channel outlet of the turbocharger is connected to the water channel inlet of the explosion-proof constant-flow pipe, the air inlet is connected to the exhaust port of the combustion chamber of the explosion-proof diesel engine, and the exhaust port is connected to the air inlet of the explosion-proof constant-flow pipe; the intelligent adaptive control box supplies power to the heating network in the exhaust cleaning device; the explosion-proof motor supplies power to the intelligent adaptive control box. The system improves the situation that the exhaust emission temperature of the explosion-proof diesel engine is low and the DOC chemical reaction cannot be carried out.

The invention relates to the technical field of fermentation process control, and discloses a coffee fermentation tank intelligent interlayer system based on multi-mode temperature control, which comprises a three-layer composite structure composed of an outer spiral flow channel heat exchange layer, a middle nanometer phase change energy storage buffer layer and an inner solid heat conduction balance layer, the multi-physical field fusion sensing module is laid on the wall surface of the inner layer and can synchronously decouple a two-dimensional temperature field and a strain field; and the hierarchical predictive control module adopts a model predictive control algorithm. During working, the sensing module acquires a temperature field of a tank wall in real time and analyzes a fermentation dynamic state from a strain field; based on the multi-dimensional information, the control module cooperatively regulates and controls the three-layer composite structure through an upper-layer optimization decision and a lower-layer real-time execution architecture. Through multi-mode cooperative control and multi-physical field fusion perception, rapid, accurate, uniform and intelligent adaptive control of the fermentation temperature is realized, and the quality and consistency of coffee fermentation are significantly improved.

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 invention belongs to the technical field of gateway adaptive control, and discloses a CAN bus gateway intelligent adaptive control method. The method comprises the following steps: extracting infotainment domain feature data from message metadata of an infotainment domain CAN subnet, generating a network intrusion diagnosis score, and mapping the score into a network intrusion threat level; based on the driving scene and the network intrusion threat level, generating a reasonable instruction set and an unreasonable instruction set through an instruction setting model; if the new control instruction is matched reasonably, releasing the new control instruction, if the new control instruction is matched unreasonably, intercepting the new control instruction and counting abnormal counts, if the new control instruction is matched unreasonably, generating a comprehensive rationality score based on the network intrusion threat level, the driving scene, the information entertainment domain feature data and the new control instruction, and releasing or intercepting the new control instruction and counting abnormal counts according to the comprehensive rationality score. Generating an exception instruction when the exception count reaches a preset maximum exception frequency; according to the method, the control authority can be intelligently adjusted in different driving contexts, and forged control frames or abnormal instructions are accurately blocked.