39994results about "Computer control" patented technology

The invention provides a drive control circuit with a fault detection function, and relates to the technical field of fault detection, and the drive control circuit comprises the steps: obtaining historical state parameters, carrying out the time-frequency domain analysis through wavelet transformation, and constructing a heterogeneous neural network for fault judgment; operating parameters are collected in real time, multi-domain feature extraction is carried out, and a fault type is determined based on a dynamic time warping algorithm and fuzzy logic reasoning; and constructing a time sequence prediction network of a multi-layer attention mechanism, outputting a multi-dimensional early warning index in combination with the integrated learning model, and generating hierarchical early warning information. According to the invention, the fault of the driving control circuit can be accurately detected and predicted.

The invention provides an intelligent maintenance decision-making method and system for a numerical control machine tool based on digital twinning, and relates to the technical field of equipment maintenance, and the method comprises the steps: obtaining the structural data and simulation precision requirements of the numerical control machine tool, determining the optimal grid size and distribution parameters of a finite element model, obtaining operation conditions, and adding the operation conditions to a multi-physics coupling modeling module, generating a machine tool digital twinborn model, fusing the machine tool digital twinborn model with the physical field, generating a machine tool operation degradation model, and optimizing to obtain a self-evolution digital twinborn model; obtaining machine tool operation parameters, performing preprocessing to obtain multi-modal perception data, extracting multi-view potential features, determining potential correlation features, and obtaining a machine tool health state evaluation result; the method comprises the following steps: generating a multi-level fault prediction result, determining a candidate maintenance scheme, adding the candidate maintenance scheme and the multi-level fault prediction result into a knowledge graph construction module, generating a semantic association network, and solving to obtain an optimal maintenance decision in combination with a multi-objective optimization algorithm.

The invention provides a numerical control machine tool wear automatic detection and compensation method based on artificial intelligence, and the method comprises the steps: collecting the cutting force data of a high-curvature region in real time through multi-sensor fusion, and obtaining the cutting force fluctuation characteristics; cutting temperature data of the high-curvature area are monitored and obtained, the cutting temperature change rate is extracted, whether the temperature exceeds a preset threshold value or not is judged, and if yes, an alarm mechanism is triggered, and cutting parameters are adjusted; predicting the tool wear rate in combination with the co-evolution relationship between wear and temperature, the online monitoring data and the processed time, and generating a wear prediction curve in a preset time period; and performing trend analysis and feature extraction on the wear prediction curve to obtain wear parameter changes of the cutter in a preset time, and if the prediction curve shows that the wear parameter changes at a certain time point in the future exceed a preset critical value, adjusting the cutting parameters and generating a target cutting parameter combination.

The invention relates to the technical field of numerical control machine tools, and discloses a five-axis high-precision numerical control machine tool control method, device and equipment and a storage medium, and the method comprises the steps: collecting temperature data and displacement data of a five-axis high-precision numerical control machine tool, and carrying out state space modeling to obtain a machine tool motion state model; stress data and thermal error influence factors of the feed shaft are calculated; feeding shaft stress data and the thermal error influence factors are input into the compensation model, real-time compensation parameters are calculated, compensation control is conducted on the machine tool feeding shaft, and a compensation control result is obtained; according to the compensation control result, an initial machining track optimization model is constructed, and optimized machining parameters are generated; performing segmented machining control based on the optimized machining parameters to obtain real-time machining data; the initial machining track optimization model is optimized based on the real-time machining data, a target machining track optimization model is obtained, and the dynamic response performance and the control precision of the five-axis high-precision numerical control machine tool are improved through the method.

The invention relates to the technical field of smart home, and discloses an automatic coordinated regulation and control system and method for smart home equipment, and the system integrates a plurality of functional modules such as equipment access, environment perception and user behavior analysis. The equipment access module supports access of multiple protocols and endows the equipment with a unique identifier; the environment sensing module collects environment parameters in real time and compares the environment parameters with thresholds. The user behavior analysis module generates a user scene preference model by means of an algorithm, the cooperation strategy generation module synthesizes multiple factors to formulate a regulation and control strategy, and the dynamic priority management module allocates weights according to equipment and scene conditions. And the equipment control module executes the instruction and feeds back the state, and the abnormity detection module monitors the equipment state and triggers a strategy regeneration mechanism when abnormity occurs. The data storage module securely stores various data, and the user interaction module efficiently analyzes instructions. According to the invention, many problems of an existing smart home system are solved, automatic coordinated regulation and control of equipment can be realized, and user interaction experience is optimized.

The invention discloses a swivel bridge attitude monitoring system based on dynamic feedback control, and belongs to the technical field of bridge construction. Comprising the following modules: a multi-source sensor integration module for constructing an omnibearing attitude monitoring network and realizing high-precision real-time monitoring of key parts of a bridge; the edge calculation and data fusion module realizes real-time on-site processing and fusion of sensor data, effectively reduces data transmission delay and improves monitoring stability; the multi-physics field coupling modeling module is used for constructing a multi-physics field coupling model comprising structural mechanics, a temperature field, contact friction and fluid action, and realizing accurate prediction of the attitude change of the swivel bridge; the self-adaptive control strategy module is used for realizing high-precision self-adaptive control on the attitude of the swivel bridge; the grading early warning and cooperative control module automatically matches a corresponding control strategy and realizes risk grading response and intelligent cooperative control; and the digital twinning interaction verification module is used for constructing a high-precision digital twinning model of the swivel bridge.

The invention relates to the technical field of numerical control machining, in particular to a tool cutting path machining error compensation method based on online monitoring, which comprises the following steps: establishing a cutting path data model; constructing a multi-modal sensor assembly, and collecting error data in real time; establishing an error state equation to carry out error calculation; constructing a double-layer compensation mechanism; and executing the compensation instruction by adopting a real-time control system. Through real-time synchronous monitoring of the multi-mode sensor, a mixed error resolving framework and a double-layer intelligent compensation mechanism, the influence of dynamic error sources such as cutter abrasion and machine tool thermal deformation can be effectively overcome, the machining precision and the surface quality are remarkably improved, and the high-precision machining requirements in the fields of aerospace, precise instruments and the like are met.

The invention provides an intelligent fault diagnosis method for a numerical control machine tool, which is applied to a control system of the numerical control machine tool. The method comprises the steps of collecting multi-source sensor data in the operation process of the numerical control machine tool, wherein the multi-source sensor data comprises at least two of a vibration signal, a temperature signal, a current signal and a sound signal; performing preprocessing and feature extraction on the multi-source sensor data to obtain preprocessed data; based on the preprocessed data, constructing and training a machine tool dynamic model to obtain a target machine tool dynamic model; when abnormal operation of the numerical control machine tool is detected, fault state estimation is carried out based on the target machine tool dynamic model and a model prediction control method, and a fault estimation result is obtained; and based on the fault estimation result, constructing a fault evolution path by adopting a Monte Carlo tree search algorithm, and outputting a fault diagnosis result. The accuracy and efficiency of fault diagnosis of the numerical control machine tool can be improved.

The invention discloses a numerical control tool machining monitoring method. The method comprises the following steps that firstly, data collection and primary processing are conducted; 2, constructing a digital twinborn model; 3, adjusting a self-adaptive threshold value; 4, performing prediction maintenance and fault early warning; and 5, optimizing machining parameters in real time. Multi-dimensional data such as temperature, vibration, stress and cutting force are collected in real time through a multi-sensor system, high-frequency sampling and signal denoising technologies are combined, comprehensiveness and accuracy of monitoring data are ensured, and a foundation is laid for follow-up analysis; the virtual machining model is constructed through the digital twinning technology, real-time simulation and synchronization of the machining process are achieved, the current state of the tool can be comprehensively reflected, anomaly detection and pre-judgment are conducted in combination with historical data and real-time data, and the fault monitoring accuracy is improved.

The invention relates to the technical field of multi-axis linkage numerical control machining, and discloses a multi-axis linkage numerical control machining state monitoring method and system.The method comprises the steps that vibration, acoustic emission and cutting force signals in the multi-axis linkage numerical control machining process are collected and preprocessed, and first multi-source sensor data are obtained; performing feature extraction and compressed sensing to obtain a low-dimensional feature subset; performing fusion judgment to obtain an abnormal state detection result; training a deep learning model comprising a backbone network and a plurality of branch networks to obtain a state evaluation model; and inputting second multi-source sensor data collected in real time into the state evaluation model for state evaluation to obtain a processing state classification result and a parameter estimation value. According to the method, cooperative state evaluation and prediction simulation of multiple machine tools are realized.

The invention discloses a numerical control machine tool operation control system and a double-station sawing and drilling integrated machine tool, and relates to the technical field of numerical control machine tool operation. A multi-axis linkage state, a gas cylinder stress state, vibration frequency, cutting force and deformation data are acquired in real time through a data acquisition module; the dynamic modeling module constructs a digital twinborn model by using finite element analysis and real-time sensing data, and accurately simulates stress dynamic change and deformation trend; the intelligent error compensation module dynamically evaluates the multi-axis linkage precision by analyzing the machining data, generates an error compensation instruction and optimizes the operation parameters of the machine tool; the closed-loop feedback control module adjusts the cutting speed, the feeding depth and the clamping force in real time through a fuzzy logic algorithm, a closed-loop adjusting mechanism is formed, the machining precision and stability are greatly improved, the machining error and the workpiece surface damage risk are reduced, and the machining quality and efficiency of complex workpieces are effectively improved.

One or more data streams may be used for controlling multi-system interaction. For example, a data stream may be received, and a surgical option associated with a surgical instrument may be selected based on the data stream. A control signal associated with the surgical instrument may be generated based on the selected surgical option. A surgical device may receive an external data stream from a source external to the surgical system. The device may derive, based at least on the external data stream, decision contextual information. The device may select a surgical option associated with a surgical instrument based on the decision context information. The device may generate a visual indication of the decision context information associated with selecting the surgical option. The device may generate a control signal associated with the surgical instrument based on the selected surgical option.

The invention relates to the technical field of machine tool intelligent monitoring, and discloses a numerical control machine tool intelligent monitoring method and system based on the Internet of Things and a storage medium. The method comprises the steps of collecting machine tool multi-source data and transmitting the data to an edge node; original data is processed to obtain a working condition feature set, the working condition feature set is input into a recursive convolution hybrid memory network to construct a common dependency relation graph, and state parameter prediction is realized through time-frequency domain joint analysis; converting the two-dimensional feature map into a two-dimensional feature map, and extracting interaction features through SE-ResNet; fusing the two types of features to execute state classification; and predicting the life and generating a diagnosis report based on the health state judgment result in combination with the process parameters and historical data. According to the method, effective fusion analysis of multi-source heterogeneous data of the numerical control machine tool is realized, and a self-adaptive state recognition and fault early warning mechanism is constructed, so that accurate monitoring of the operation state of the machine tool, intelligent diagnosis of early faults and accurate prediction of residual life are realized.

The invention relates to the technical field of numerical control machine tools, and discloses a self-adaptive error compensation method and system for a high-precision numerical control machine tool, and the method comprises the steps: carrying out the mathematical modeling of a multi-dimensional error of a five-axis numerical control machine tool, and obtaining a thermal deformation displacement vector, a friction force vector and a path deviation vector; inputting into a multi-dimensional expansion state observer for error observation to obtain thermal deformation, a friction force value and a path error value; performing trust domain parameter optimization to obtain a thermal deformation compensation gain matrix, a friction force pre-compensation coefficient matrix and a path error compensation coefficient matrix; performing three-stage series compensation calculation to obtain a compensation position instruction, a compensation speed instruction and a compensation acceleration instruction; and track reconstruction operation is carried out to generate a position instruction sequence, a speed instruction sequence and an acceleration instruction sequence of each axis, so that self-adaptive error compensation of the high-precision numerical control machine tool is realized, and the machining precision of the high-precision numerical control machine tool is improved.

The invention relates to the technical field of intelligent building energy-saving control, and discloses a green building equipment intelligent energy-saving control method and system based on artificial intelligence, and the method comprises the steps: collecting building environment data, and carrying out the preprocessing; and simulating the energy consumption of the building equipment by using the digital twin model, and training the baseline neural network model to obtain an initial control strategy. And adopting a genetic algorithm to optimize hyper-parameters of the baseline model, and generating a plurality of candidate energy-saving control strategies. And inputting the optimized strategy into the deep reinforcement learning model, carrying out strategy iteration, calculating the energy-saving efficiency and comfort score after each iteration, screening the optimal strategy through a reward mechanism, and improving the accuracy of energy consumption prediction of the building equipment. The hyper-parameter of the neural network is optimized by using the genetic algorithm, and the intelligent degree of the energy-saving control strategy is improved. On the basis of deep reinforcement learning, the intelligent agent can adaptively adjust an energy-saving strategy under different environmental conditions, and the user comfort is guaranteed while the optimal building energy consumption is ensured.

The invention belongs to the technical field of intelligent machining path control, and discloses an intelligent numerical control machine tool automatic programming path optimization method based on workpiece modeling, which comprises the following steps: acquiring a CAD model, machine tool sensor data, tool wear data and historical machining logs, generating a workpiece characteristic parameter set, and fusing a three-level compensation mechanism to generate a dynamic error parameter set; then, dividing a preliminary risk level of the processing area, and performing secondary risk assessment to generate a comprehensive risk level; extracting a risk level conflict area, and determining a final risk level; constructing a static / dynamic cost matrix to obtain a path priority map; thirdly, generating an initial path, smoothing an optimized path trajectory, and performing multi-objective optimization to generate an optimized path planning table; cutting parameters are adjusted in real time, the path feasibility is verified, and a real-time control instruction set is generated; and finally, constructing a quality-process correlation model, generating a global strategy packet, forming closed-loop iteration, and completing system self-evolution.

The invention discloses an industrial equipment remote operation method based on distributed identification and control optimization. The method comprises the following steps: S1, constructing a hierarchical adaptive distributed computing architecture; s2, collecting multi-modal data of industrial equipment at an edge layer, and deploying an adaptive sparse convolutional neural network to carry out state detection and fault diagnosis; s3, deploying a distributed computing node cluster in a local layer, and performing local optimization on the control task through a distributed near-end strategy optimization algorithm; s4, processing data by utilizing cloud computing resources in a global layer, and generating a control strategy by adopting a multi-objective optimization algorithm; s5, carrying out data sharing and feedback among the hierarchies; s6, dynamically adjusting task distribution through a task migration mechanism; s7, introducing a hierarchical collaborative optimization mechanism, and optimizing a control strategy; and S8, performing data analysis by using a data-driven analysis technology to form closed-loop control. According to the invention, a distributed calculation and multi-objective optimization method is adopted, and efficient control of remote operation of industrial equipment is realized.

The invention provides a sewage treatment data real-time monitoring method and system based on the Internet of Things, and relates to the technical field of data processing.The method comprises the steps that a sensor network is arranged at key nodes in the sewage treatment process, and multiple kinds of original data including water quality parameters, flow, temperature, pressure and equipment operation states are collected in real time; transmitting the various original data to a cloud data center for preprocessing to generate processed data; according to the processed data, the water quality change trend is monitored in real time, pollution events or equipment faults are recognized, and real-time monitoring and analysis results are generated. Real-time monitoring and intelligent analysis of sewage treatment data are achieved through the Internet of Things, pollution events or equipment faults are effectively warned, a treatment scheme is automatically generated, emergency response is started in time, and the sewage treatment efficiency and safety are improved.

A method of constructing a digital twin physical field of a computer numerical control machine tool with double order reduction is provided. A physical field finite element simulation sample data set in a working state sampling space has an order preliminarily reduced to sparse grid node data via an improved K Nearest Neighbor (KNN) algorithm, so that the number of point sets is kept within the real-time rendering capability range of graphic rendering software. A digital twin physical field proxy model is trained using a radial basis function (RBF) to realize reconstruction of the physical field. The proxy model is iteratively optimized using a genetic algorithm to solve the problem of deviation between a proxy physical field model and a real physical model. Finally, the proxy model is deployed to a digital twin system, and the physical field distribution is solved in real time.

The invention discloses a precise numerical control machine tool operation state intelligent control method and system, and particularly relates to the technical field of numerical control machine tools. Multi-dimensional operation data, including working temperature, load, current, pressure, cutting force and tool wear data, of a machine tool are collected in real time, a Z-score algorithm is adopted for data preprocessing, abnormal values are extracted, a prediction model is constructed and trained based on a machine learning algorithm, abnormal data are analyzed in real time, and the abnormal values of the machine tool are analyzed in real time. And accurately identifying the operation state of the machine tool and evaluating the health condition. According to the health state evaluation result, an intelligent control strategy is automatically generated, and the feeding speed, the spindle rotating speed and the cutting depth of the machine tool are dynamically adjusted, so that the operation state of the machine tool is optimized, potential faults are found and dealt with in time, the machining quality is improved, the faults are reduced, the production efficiency and the equipment stability are improved, and the maintenance cost is reduced.

The invention provides a filling control method and system based on online parameter identification. According to the method, the synchronous error data of each servo shaft in the multi-shaft servo system is acquired, the error time sequence matrix is constructed by the synchronous error data according to the time sequence, and the displacement feedback data of each servo shaft in the multi-shaft servo system is acquired in real time through the high-precision rotary encoder; a dynamic compensation signal is generated according to the deviation between the displacement feedback data and a preset track, online estimation is carried out on mechanical vibration disturbance of the multi-axis servo system based on the error time sequence matrix, a disturbance parameter estimation value is obtained, the dynamic compensation signal and the disturbance parameter estimation value are fused, and a servo synchronous compensation amount is generated; according to the technical scheme, through fusion of real-time parameter identification and dynamic compensation, the filling precision and efficiency are remarkably improved, meanwhile, the manual debugging complexity is reduced, and a reliable solution is provided for high-stability production under multiple working conditions.

The invention discloses an automatic control method and system for secondary granulation of a high-voltage zinc oxide resistor disc, relates to the technical field of intelligent manufacturing of power equipment, and solves the problems of out-of-control particle morphology caused by dynamic coupling parameter identification lag and control instability caused by multi-physical field parameter coupling in an existing method. According to the invention, a dynamic physical property parameter matrix is generated in real time based on multi-band dielectric relaxation spectrum analysis and terahertz wave tomography; predicting a fluidized phase change threshold value and an energy gathering area through multi-physics field coupling modeling; a time sequence attention deep reinforcement learning algorithm is adopted to generate a multi-field cooperative adjustment instruction; positioning a parameter conflict source and triggering decoupling compensation by combining a high-frequency vibration and acoustic emission combined monitoring module; performing closed-loop correction on the control network weight based on the laser spectrum data and a partial least squares regression model; the real-time performance of fluidization parameter identification, the stability of multi-field coupling control and the recovery efficiency of abnormal working conditions are remarkably improved, and meanwhile the batch consistency of the electrical performance of the resistor discs is guaranteed.

The invention relates to the technical field of industrial automatic monitoring, in particular to a weftless tape machine intelligent monitoring system and method based on real-time data acquisition. The system comprises a data acquisition module used for acquiring real-time data of a multi-heterogeneous sensor; the data processing module is used for preprocessing the real-time data; the intelligent analysis module covers a feature extraction unit, an anomaly detection unit, a fault diagnosis unit, a service life prediction unit and a data fusion unit and can extract data features, detect anomaly, diagnose faults, predict the service life of a part and fuse multi-unit output to make a decision; and the early warning response module triggers corresponding levels of alarms according to the analysis result and generates a diagnosis report. According to the invention, comprehensive real-time monitoring, early fault early warning, accurate fault diagnosis, residual life prediction, multi-source information fusion and the like of the operation state of the weftless tape machine are realized, the production efficiency and the product quality are effectively improved, and intelligent management of production of the weftless tape machine is assisted.

The invention relates to the technical field of the Internet of Things, and discloses a machine room equipment environment monitoring method and system based on the Internet of Things, and the method comprises the steps: constructing an edge calculation hardware system of the Internet of Things to obtain an integrated terminal which integrates a communication unit, an acquisition unit and a control unit; performing multi-channel parallel acquisition and edge calculation analysis on equipment and environment parameters in the machine room to obtain a standard data tuple and an analysis data packet; executing local hierarchical storage and logic control to obtain a control instruction and storage data; executing three-layer network communication interaction on the storage data to obtain a remotely accessible monitoring view; according to the method, intelligent alarm and system self-maintenance are achieved, graded and classified alarm information and a system state report are obtained, the data transmission amount is greatly reduced, meanwhile, the system response delay is reduced, the control decision accuracy is improved, and the misoperation risk is reduced.

The invention provides an intelligent factory monitoring method and system based on multi-sensor fusion, and the method comprises the steps: firstly obtaining real-time monitoring data streams of multiple types of sensors in an intelligent factory, including operation state data collected by an equipment state sensor and scene state data collected by an environment state sensor; performing basic synchronization processing on the real-time monitoring data stream to obtain a standardized monitoring data stream, calling a pre-trained multi-sensor association analysis model to perform cross-source feature fusion processing on the standardized monitoring data stream to generate a fusion feature sequence, and performing abnormal mode detection processing based on the fusion feature sequence to generate an abnormal detection result; and finally, generating a monitoring intervention instruction containing equipment positioning information according to an anomaly detection result, and sending the monitoring intervention instruction to a factory control system to trigger a state adjustment operation, thereby effectively improving the monitoring precision and anomaly processing efficiency of the intelligent factory.

The invention relates to a digital twinning-based bogie numerical control machining error compensation method and system, and the method comprises the steps: constructing an initial digital twinning model comprising material characteristics and geometric constraints through fusing the historical machining data of a bogie and the structural characteristics of a workpiece; according to the real-time cutting force and temperature data dynamic correction model, time-varying stiffness parameters are updated, and a deformation prediction result is generated; identifying an error exceeding area based on the precision requirement of the bearing seat assembly surface; and the feed rate compensation amount is calculated according to the thin-wall area deformation gradient, and a pre-compensation parameter scheme used for adjusting the cutting path is generated. The method overcomes the industrial problems that a traditional compensation technology cannot dynamically suppress instantaneous deformation, multi-source error coupling is difficult to decouple, and parameter adjustment depends on trial cutting verification, and the machining precision and efficiency of the bogie are remarkably improved.

The invention discloses a smart home energy management optimization method in an Internet of Things environment. According to the method, the Internet of Things technology is utilized, intelligent identification and classified management are carried out on equipment, multiple energy management strategies are preset, and a user can conveniently and autonomously adjust; energy data is monitored in real time through a sensor and an intelligent electric meter, an energy consumption rule is mined by using a big data analysis technology, and a demand trend is predicted; constructing an intelligent energy dispatching center, and formulating a dynamic energy distribution scheme by using an optimization algorithm; a mobile phone application and an intelligent control panel are developed, and a user is supported to check data, control equipment and carry out personalized customization; and meanwhile, equipment firmware upgrading and remote management are realized. According to the invention, energy consumption can be reduced, cost can be controlled, equipment collaboration and energy distribution efficiency can be improved, user experience can be enhanced, system maintenance and upgrading are facilitated, and intelligent household energy management is more efficient, intelligent and flexible so as to adapt to diversified household requirements.

The invention relates to the technical field of large language models, and discloses a voice data interaction feedback control processing method based on a large language model. The method comprises the following steps: acquiring an industrial voice instruction through an AMR main controller, and obtaining a standardized vector through industrial lexicon matching and intention classification; inputting an industrial large language model for reasoning processing, and generating an AMR execution scheme; carrying out distributed coordination and task allocation on the AMR cluster to form a control instruction sequence; and the motion controller executes monitoring, processes exceptions and outputs a feedback strategy. Accurate classification and semantic understanding of complex industrial instructions are achieved, the processing capacity of professional knowledge in the industrial field is improved, and meanwhile high-precision real-time state monitoring is achieved.

The invention discloses a concrete mixing plant automatic control system based on intelligence, and belongs to the technical field of automatic control. Comprising a multi-modal sensing data acquisition module, an intelligent batching optimization module, a digital twin simulation module, a self-adaptive energy consumption management module, a fault self-diagnosis and predictive maintenance module, a dynamic quality tracing module and a multi-target collaborative scheduling module. Real-time synchronization of sensor data and a virtual model is realized in combination with an edge computing technology, dynamic and visual technical support is provided for full-flow simulation of the concrete mixing plant, and complex working conditions in production are reflected more truly; the system predicts a potential problem through a machine learning algorithm, triggers an early warning signal based on a multi-dimensional threshold rule, and generates a preventive maintenance plan in advance; the digital twin platform supports AR and VR interaction interfaces, so that an operator can visually observe the operation states of a virtual model and actual equipment.

The invention provides a thermal power generating unit performance optimization control system and method, and the system comprises a data collection module which collects various real-time data in the operation process of a thermal power generating unit; the load prediction module is used for establishing a load prediction model and predicting a load change trend by comprehensively considering a historical load condition, a load change rate, a load change acceleration and an external interference factor; the optimization control module is used for dynamically adjusting fuel, an air door and air quantity according to a control strategy in combination with the load prediction result and the unit operation state; the actions of the regulating valve, the air door actuator and the water supply regulating valve are correspondingly controlled; the monitoring feedback module is used for realizing closed-loop control and self-correction; according to the method, a load prediction model based on the combination of time sequence analysis and a physical mechanism and a control strategy comprehensively considering multi-parameter collaborative optimization are utilized, so that quick response to load change, balance consideration of steam quality and combustion efficiency, self-adaptive stable operation under complex working conditions and improvement of overall performance are achieved.