159 results about "System model" patented technology

The invention relates to an unmanned aerial vehicle group-oriented sensing communication integrated network multi-resource joint scheduling method, and belongs to the technical field of wireless communication, and the method comprises the steps: building a system model of multiple UAV-ISAC tasks, and defining a joint optimization problem; extracting spatio-temporal features from the dynamic heterogeneous graph in which the unmanned aerial vehicle, the user and the sensing target are abstracted as nodes and the relationship is abstracted as edges; taking the features as input, and adopting a layered multi-agent reinforcement learning architecture to solve the joint optimization problem on line; in the architecture, resource allocation and trajectory planning actions are generated through cooperation of a central Actor and all unmanned aerial vehicle Actors, and system performance is evaluated by a central Critic; constructing a multi-target weighted reward function, stabilizing a training process by combining experience playback and a Mini-batch sampling mechanism, and updating network parameters in parallel; and obtaining an optimal resource allocation and unmanned aerial vehicle trajectory strategy through training. The sensing performance is improved, the communication quality is guaranteed, and the defects in the aspect of dynamic resource scheduling in the prior art are overcome.

The invention discloses an edge computing network task unloading and resource allocation method based on optimal service quality, which comprises the following steps of: constructing a system architecture comprising a cloud server, a plurality of edge servers and mobile equipment, and establishing a multi-dimensional system model covering task characteristics, service cache, communication transmission, computing resources, service cost and service quality; constructing an optimization problem aiming at maximizing the long-term service quality of all mobile equipment, and forming a mixed integer nonlinear programming model under the constraints of mobile equipment cost constraint, storage capacity limitation, bandwidth, computing resources and the like; a double-time-slot hierarchical decision-making mechanism is designed, and multi-dimensional joint optimization of service caching, task unloading and resource allocation is realized through a collaborative mechanism that short-term resource allocation is constrained through a long-term caching decision and short-term performance feedback optimizes long-term caching. According to the method, the overall quality of service (QoS) can be effectively improved and the task processing delay and energy consumption can be reduced under the condition that the cost of the mobile equipment and the system resource limitation are met.

The invention relates to the technical field of building safety, and discloses a safety transformation monitoring system for building construction and a method thereof. The system comprises a data acquisition and standardization module, a dynamic security entity relationship map construction module, an organization capability and cognitive load modeling module, a hybrid risk identification and conduction calculation engine, a self-adaptive security transformation strategy generation engine and a closed-loop self-calibration module. According to the method, a dynamic security entity relation graph is constructed and updated in real time, the cognitive load of management personnel is quantified, and the cognitive load is used as a dynamic adjustment factor of the conduction probability of risks on the graph; when a safety transformation strategy is generated, comprehensively evaluating a risk reduction effect, resource cost and additional cognitive load cost to output an optimal adaptive strategy; and finally, performing closed-loop self-calibration on the system model by utilizing execution feedback. According to the method, dynamic prospective risk prediction, self-adaptive strategy generation and continuous self-optimization are realized.

The invention relates to the technical field of automatic control, in particular to a switching positive system identification method and system and a storage medium, and aims to solve the problems that in the prior art, a model violates non-negative physical constraints, hyper-parameter estimation is prone to local optimum, calculation efficiency is low, and switching path identification precision is insufficient. The method comprises the following steps: firstly, determining non-negative priori of a system model and pulse response truncated Gaussian distribution through a preparation module; constructing a Bayesian network, and iteratively optimizing a switching path by adopting Gibbs sampling; and finally, based on regularization optimization and Gibbs sampling in combination with Monte Carlo approximation, accurately estimating the pulse response of the subsystem. According to the method, the non-negative characteristics are embedded into the whole identification process, global parameter optimization is achieved, high-dimensional integral calculation is simplified, identification precision and stability are improved, and the method is suitable for multi-scene engineering application.

The invention relates to the field of intelligent water affairs, and discloses a precise flow control system for intelligent water affairs, which comprises the steps of collecting local water flow change data in real time, extracting flow velocity disturbance characteristics and pipe network transient pressure response, and judging whether unexpected disturbance trigger exists or not; constructing a local disturbance label library based on disturbance trigger source features and a historical operation model, and identifying disturbance sources and flow direction tendentiousness by using fuzzy discrimination and variable weight filtering algorithms; according to the pipe section hydraulic characteristics corresponding to the mismatching behavior, the flow of the target pipe section is corrected through a high-resolution electric proportional valve set and a two-way feedback control unit; constructing a cross-section response chain through the intelligent edge control node, and generating a multivariable adjustment gradient according to the multi-period callback simulation data; flow state data and peripheral environment variables before and after the current round of regulation and control intervention are collected, and system model parameters are dynamically corrected through the integrated evolutionary prediction network. The stability of the intelligent water affair system is improved.

The invention discloses an intelligent optimization control method and system for a compressed air energy storage power station, and relates to the technical field of power grids, and the method comprises the steps: S1, building a system model corresponding to the compressed air energy storage power station, and collecting data; s2, training a reinforcement learning agent, and learning to obtain an optimal control strategy capable of adapting to the operating environment of the compressed air energy storage power station; s3, using the prediction model to predict the future operation state of the compressed air energy storage power station, and optimizing the control sequence; and S4, executing real-time control operation, adjusting a control strategy according to feedback data, and performing dynamic optimization control. According to the method, reinforcement learning and model prediction control are organically combined, a reinforcement learning agent is introduced, a control strategy is continuously learned and optimized, and adaptive adjustment is quickly made according to factors in multiple aspects such as the current generation power, the power load, the energy storage state and the environment in the face of real-time fluctuation of the new energy generation power.

The invention relates to the technical field of artificial intelligence and control, in particular to a non-linear multi-agent system fixed time consistency control method and device and a medium. According to the control method, a backstepping recursion method is used as a control design framework, a self-adaptive fixed time consistency controller based on an auxiliary compensation system is provided, and the problem of non-linear multi-self-body system consistency control under time-varying input delay is solved. By providing and changing a control input signal by a person, the motion trajectory of the multi-agent system can be modified as required. Time-varying input time delay and an unknown nonlinear function are considered in a system model, so that the system is more general. Wherein an unknown time-varying delay function is processed by constructing an auxiliary compensation system, and an unknown nonlinear function is processed by using radial basis function neural network approximation. According to a fixed time control correlation lemma, an actual fixed time adaptive consistency control method is provided to ensure that the consistency error of the multi-agent system converges to a neighborhood near an original point in fixed time.

The invention discloses a polar region island micro-grid optimal scheduling method considering frequency constraint, and relates to the technical field of island micro-grid optimal scheduling, and the micro-grid optimal scheduling method comprises the following steps: building a system model; constructing and solving a multi-time scale joint optimization architecture; embedding a frequency transient constraint as a minute layer decision constraint in a linearization or convex approximation form; and according to a solving result, issuing a hierarchical instruction to various distributed resources and implementing online monitoring and safety guarantee measures. According to the method, through explicit embedding and linear convex approximation of frequency transient constraint, the defect that only steady-state frequency is considered or an empirical threshold value is adopted in traditional scheduling is overcome, uncontrollable frequency transient caused by short-time high-power disturbance is avoided, minute-level frequency safety guarantee is achieved, the frequency peak value and the minimum point are controlled, and the scheduling efficiency is improved. And load removal or equipment protection actions caused by transient overrun are reduced, so that the power supply continuity and the system reliability are improved.

The invention discloses a dynamic anti-disturbance coordinated scheduling method for a hybrid energy micro-grid, and aims to solve the problems of poor stability, high cost, low consumption rate and the like of the hybrid energy micro-grid caused by disturbance such as renewable energy fluctuation and load abrupt change. The method comprises the following steps: firstly, constructing a system model containing multiple units, and collecting and preprocessing real-time operation data; establishing a multi-target scheduling objective function, and solving an initial scheduling instruction by adopting an improved predictive control algorithm; and finally, generating a coordinated scheduling strategy by combining the disturbance information dynamic correction instruction. Meanwhile, a security constraint and scheduling effect evaluation link is set, and mechanisms such as multi-time scale scheduling and layered response are adopted. The method can significantly improve the anti-disturbance capability and stability of the system, reduces the operation cost by 15%, improves the renewable energy consumption rate by more than 8%, is adaptive to multiple scenes, guarantees the safe operation, and is suitable for various hybrid energy microgrids.

The invention relates to the technical field of comprehensive energy transformation, in particular to a digital twin-body-based comprehensive energy system transformation optimization method, which comprises the following steps of: constructing a digital twin body comprising a building thermal model and an energy equipment system model on the basis of static data, dynamic operation data and external environment data of a target area; global optimization with a preset objective function as a guide is carried out, an optimal system capacity configuration and architecture scheme is determined, and then local equipment type selection and control strategy optimization are carried out; issuing the optimized control strategy to a physical energy system for execution, and continuously collecting feedback data to calibrate the digital twin to form closed-loop optimization; a sensing-analysis-decision-execution-learning closed-loop system is constructed, an energy system of a physical world is deeply coupled with a digital model of a virtual world, global and local layered collaborative optimization is realized through a digital twin, and dynamic adaptability is improved through closed-loop operation.

The invention discloses a block system construction method suitable for various structural plane models, and relates to the field of three-dimensional rock block recognition, and the method comprises the steps: firstly classifying cutting planes and establishing a database based on the geometric data of an existing structural plane system model (DFN) and the data of a calculation region, calculating the intersection lines and intersection points between the cutting planes, and calculating the intersection lines and intersection points between the cutting planes; a vertex matrix of the block system is constructed, then vertexes are connected to form directed edges, closed surfaces are constructed based on the directed edges, and a polyhedron is constructed through surface combination. And finally, the conversion from the structural plane system to the block system is completed by outputting a complete geometric element matrix. Theoretical algorithms are adopted in the method, and only structural plane and calculation area data are used as input, so that the method is objective and simple to operate and easy to program. Compared with the prior art, the method breaks through the limitation of a traditional structural plane model, and can be suitable for various structural plane models including a disc structural plane model, a polygonal structural plane model and a general elliptical disc structural plane model. The calculation method disclosed by the invention can provide a preprocessing general normal form for rock mass stability analysis. A proper structural plane model can be selected according to engineering requirements, a rock block system closer to a real rock mass is further provided, and the adaptability and reliability of block system modeling under complex geological conditions are improved.

The invention discloses a hybrid multi-agent adaptive cooperative control method and device and a medium, and the method comprises the following steps: constructing a hybrid multi-agent system model for intelligent building temperature adjustment, defining a central energy management system as a leader, the central air conditioner host and the regional temperature controller are respectively defined as continuous time followers and discrete time followers; for a continuous time dynamic central air conditioner host, defining a consistent error, and designing a distributed cooperative control law based on the Lyapunov theory; a consistent error is defined for a discrete time dynamic regional temperature controller, and a distributed cooperative control law is designed based on an equivalent feedback principle. According to the invention, unknown dynamic and uncertainty in the system can be overcome, the robustness of the control system is enhanced, and the method has anti-interference capability.

The invention relates to an intelligent networking planning system based on multi-objective dynamic optimization, which comprises a data input module, a data preprocessing module, a system modeling module, a multi-objective optimization module and a scheme output module, and is characterized in that the data input module is used for receiving geographic information, environmental conditions, equipment models and performance parameters input by a user; the data preprocessing module is used for carrying out standardization processing on the input geographic information and equipment parameters; the system modeling module is used for establishing a terrain and terrain model; the multi-target optimization module is used for carrying out multi-target dynamic optimization and generating an optimal deployment scheme; and the scheme output module is used for generating an equipment deployment scheme report. According to the invention, complementary coverage between devices is realized, the system cost is reduced, and the deployment efficiency is improved. The system has good expansibility and adaptability, can be flexibly adjusted according to different application scenes and requirements, improves the overall performance and adaptability of a low-altitude security system, and meets diversified low-altitude security requirements.

The invention provides a ship cooling water system modeling and debugging method, which comprises the following steps of: performing parametric modeling on a cooling water system which is the most complicated pipeline system in a ship building process, and pre-judging the opening degrees of each branch and each valve of the system according to limited input conditions in combination with a numerical simulation calculation platform and an iterative optimization strategy; and important reference is provided for field debugging. Meanwhile, according to the method, serial and parallel valve position-flow control is conducted on a control branch step by step, the calculation time is reasonably saved, and a result meeting the flow deviation requirement can be obtained as soon as possible. Besides, when some ship systems are relatively complex in composition and a result meeting the flow deviation is difficult to obtain through full-system simulation, the same modeling debugging effect can be achieved in a system partition mode. According to the method, the field debugging work of the ship cooling water system can be more efficiently completed, and a theoretical reference is provided for operation and maintenance of the cooling water system when the ship sails under multiple working conditions after being put into operation.

The invention relates to a method for diagnosing a situation in a process automation system (1). A field device (2) is installed in the system (1) and data about the system (1) are provided. Based on the data and on the basis of a prototype associated with the field device (2), a system model is generated using artificial intelligence. On the basis of a measured value generated by the field device (2), a statement about a situation in the system (1) is generated using the system model.

The invention relates to a system development method and device, computer equipment, a computer readable storage medium and a computer program product. Obtaining a system model constructed based on a first language; the first language is a semi-formalized modeling language; analyzing the system model to obtain an abstract instantiated syntax tree corresponding to the system model, and performing system analysis at least including system resource budget analysis based on the abstract instantiated syntax tree to obtain an analysis result; under the condition that the analysis result meets a preset condition, generating a target code of a second language matched with the system model based on the abstract instantiation syntax tree; the second language is a programming language. By adopting the method, the model development efficiency and quality can be improved.

The invention discloses a Modelica model automatic generation Agent development method based on SysPhS, and belongs to the field of system engineering and AI based on models. Aiming at each core component in the electric vehicle system, extracting each component, a corresponding port and a connection relation through an analysis interface by utilizing a SysMLv2 system model; secondly, aiming at the analyzed component S, retrieving each component model matched with the information of the component S in a Modelica model library through an RAG algorithm; according to the RAG semantic similarity threshold value, sorting is carried out from high to low; selecting the component model with the highest sequence, mapping the component model with the highest sequence to a port consistent with the SysMLv2 system model in connection relation through a SysPhS standard specification, and forming a Modelica system model by using all the component models with the highest sequence and the mapped connection ports; finally, inputting the Modelica system model into a model checking Agent for lexical, syntactic and semantic checking, and if an error prompt exists in a checking result, automatically correcting the Modelica system model and re-checking the Modelica system model; and otherwise, outputting the final Modelica system model. According to the invention, the accuracy, consistency and efficiency of model generation are improved.

The present invention relates to a method and system for generating a system model in which a plurality of energy objects are operated in an integrated manner on the basis of sector coupling, and for visualizing energy flow on the system model, thereby enabling a user to intuitively understand and manage the flow of energy.

The invention belongs to the technical field of training cost optimization, and discloses an unmanned aerial vehicle cluster-oriented federated learning training cost optimization method, which comprises the following steps of: jointly optimizing a local convergence threshold value, a local iteration frequency, computing resource allocation, bandwidth allocation and transmitting power allocation; the training cost (defined as a weighted sum of training energy consumption and training time) is minimized. The framework comprises a dichotomy-based joint optimization algorithm, each sub-optimization problem is solved alternately, and balance between energy and time is ensured. The core elements of the framework are as follows: in a system modeling stage, the training cost is defined as the weighted sum in the whole federal learning process, and a problem is expressed as a non-convex mixed integer programming problem.

The invention relates to the technical field of crossing of computer software testing and artificial intelligence, in particular to a method for intelligent arrangement and dynamic execution of an automatic testing scene of a complex system driven by a large model, which comprises the following steps of: 1, acquiring multi-source data; step 2, data preprocessing; step 3, constructing a system model; 4, training a large model and arranging a scene; and 5, performing test execution and dynamic adjustment. According to the method, a dual-mode data fusion layer is designed to realize unified representation of heterogeneous data, an end-to-end intelligent arrangement framework is constructed to get rid of manual dependence, a reinforcement learning dynamic adjustment mechanism is proposed to shorten abnormal response time, and a multi-target joint optimization strategy is established to balance coverage rate, efficiency and resource consumption. According to the method, the test arrangement efficiency can be improved by 3-5 orders of magnitude, the exception handling response time is shortened to the minute level, the test coverage rate is improved to 90% or above, meanwhile, the resource utilization rate is optimized by 20%-30%, and the test quality and delivery efficiency of a complex system are effectively guaranteed.

This invention relates to a variable-structure interactive multi-model estimation method based on rolling time-domain estimation, belonging to the field of vehicle control technology. The purpose of this invention is to design a novel classification method using residual information, thereby reducing the dependence on model probability accuracy. The steps of this invention are: determining the system model set; in each iteration cycle, for all models in the model set, given a fixed time domain, if there is only one model with a certain time domain length, it is directly output as the interaction value; the interactive multi-model estimation from model set to model set based on rolling time-domain estimation is defined as the model set composed of all models adjacent to the main model. This invention reduces the dependence on model probability accuracy, better ensures the accuracy of model classification, has higher estimation precision, and solves the problem of non-interaction caused by different start times of the corresponding time domains of each model.

The invention discloses a gravimeter temperature control system modeling control method, and relates to the technical field of precise instrument temperature control, and the method comprises the steps: 1, obtaining the real-time running speed of a carrier, the wind speed of an external environment, and the current temperature of a core part of a gravimeter; step 2, feed-forward compensation power is calculated based on the real-time driving speed and the external environment wind speed according to preset dynamic thermal disturbance model parameters; 3, based on the difference value between the current temperature and the preset target temperature, feedback control power is calculated through a preset feedback control algorithm; 4, superposing the feed-forward compensation power and the feedback control power to generate total control power, and outputting the total control power to control the heating actuator. Predictive disturbance compensation is achieved, temperature overshoot is effectively restrained, the thermal stabilization time is remarkably shortened, the operation efficiency is improved, the data quality is improved, the measurement reliability is enhanced, and the method is suitable for large-scale popularization and application. Practicality is high, compatibility is good, and upgrading and reconstruction are easy.

A flexible thin-wall crossed roller bearing multi-target parameter optimization method relates to the technical field of bearing design, and comprises the following steps: 1, establishing a parameterized system model containing a shaft system, a thin-wall crossed roller bearing and a box body assembly relation, the bearing model being determined by geometric and assembly parameters; 2, carrying out finite element modeling on the inner / outer ring of the bearing, and carrying out polycondensation on the degree of freedom of an interface to obtain an equivalent stiffness matrix; 3, inputting working conditions and material parameters, and setting a design variable feasible region and constraints; 4, coupling a mapping matrix to solve load distribution, calculating the maximum contact stress and the life damage rate, and constructing a dual-objective optimization model; 5, performing test design to obtain response data, and performing multi-target algorithm iterative optimization to obtain a Pareto optimal solution set; and 6, screening the solution set, and outputting a structure parameter combination meeting the requirement. The method can solve the problems that in the prior art, it is difficult to consider calculation efficiency and ferrule flexibility effect precision, and it is difficult to conduct rapid iterative optimization on roller load distribution and life / contact stress indexes.

The invention discloses a flexible building system vibration control method with output constraints and complex faults, and belongs to the field of flexible building system vibration control, and the method comprises the steps: obtaining a flexible building system model, and carrying out the dynamic analysis of the flexible building system model, and obtaining a dynamic analysis data result of the flexible building system; establishing a flexible building system dynamic model through the Hamiltonian principle according to the flexible building system dynamic analysis data result; sequentially carrying out finite dimension processing on the flexible building system dynamic model through a hypothesis modal method and processing a fault part by adopting a median theorem to obtain a simplified flexible building system dynamic model; and executing an event trigger control strategy of a relative threshold strategy on the simplified flexible building system dynamic model to obtain a controller of the flexible building system and an updating law of the controller, and applying the controller of the flexible building system to vibration control of the flexible building system. According to the flexible building system vibration control method with output constraints and complex faults, a fault-tolerant controller based on event triggering is designed for the vibration suppression problem of a nonlinear infinite-dimensional high-rise building. Compared with traditional time triggering, the control method is established on the basis of event triggering based on a relative threshold strategy, the defect of waste of transmission resources is effectively overcome, and sesame behaviors can be avoided.

The invention discloses a relative positioning method and system. The method comprises the following steps: acquiring respective inertial navigation positions, speeds, measurement distances, radial speeds and angles of double carriers; constructing a relative positioning model between the double carriers; substituting the obtained data into a system modeling module, and obtaining a relative positioning precision estimation value between the double carriers based on nonlinear optimization; and correcting the relative positioning error between the double carriers to obtain the relative positioning position between the double carriers. According to the method, the problems of unreliable satellite navigation positioning and large accumulative error of an inertial navigation system are solved, the relative positioning between the double carriers based on nonlinear optimization is realized, and the relative positioning precision between the double carriers in a complex environment is effectively improved.

The embodiment of the invention provides a 5G-Advanced sensing communication collaborative resource scheduling method and system for a smart substation, and belongs to the technical field of power system operation and maintenance. The method comprises the steps that a 5G-Advanced cellular-free unmanned aerial vehicle communication and sensing integrated network architecture is constructed, the network architecture comprises a passive sensing layer, an air-ground cooperative access layer and a centralized processing layer, and deep integration of sensing, energy supply and communication functions is achieved; establishing a system model based on the network architecture, wherein the system model comprises a network topology model, a communication and inductance integrated transmission model and a constraint optimization problem model; a cross-CPU dynamic resource scheduling algorithm based on MADDPG is designed, and joint optimization of user association, power distribution and load balancing is achieved through a centralized training-distributed execution mechanism; based on a scheduling algorithm and a system model, substation global real-time sensing, data transmission and operation and maintenance performance collaborative guarantee are achieved. The sensing fusion and coverage capability is greatly improved, the dynamic scene adaptability is better, and the sensing-operation and maintenance collaboration value is higher.

The invention provides a cable temperature prediction method and device. The method comprises the following steps: constructing a data set of each cable layer included in a cable by combining a predefined radius range of each cable layer; constructing a system model constructed by a temperature prediction model, a heat conduction equation of the cable and a loss function; the temperature prediction model predicts and outputs temperature prediction data based on the data set, and the heat conduction equation is configured based on a trainable cable material parameter group; the cable iteratively updates parameters of the system model based on the data set according to a loss function under the constraint of a boundary constraint condition, and performs gradient penalty correction when monitoring that parameters in the cable material parameter group do not conform to corresponding operation thresholds until a convergence condition is reached, so as to obtain a trained system model; and predicting the predicted temperature of the coordinates of the target point on the cable during the operation time of the target cable based on the system model, and if the predicted temperature reaches the evaluation index, determining system model parameters. The precision of the temperature prediction data is ensured, and the credibility of the temperature prediction data is also ensured.

The invention discloses a shield tunneling whole process visualization test model, which belongs to the technical field of shield simulation tests and comprises a model box body, a shield simulation system, a propulsion control system, an acquisition system and an intelligent electric control system. The model box body is used for loading transparent soil, three surfaces are provided with transparent toughened glass, and the top is provided with a sliding cover plate; the shield simulation system comprises a pipe joint, a shield shell, a cutterhead and a sealing bin, and has the functions of reduction cutting, deslagging and grouting; the propelling system provides fine propelling speed and stable driving force; the acquisition system synchronously acquires the internal displacement of the soil body and the soil pressure data of the sealing bin; and the intelligent electric control system realizes parameter setting, real-time monitoring and cooperative control. The model solves the problems that an existing model is incomplete in observation, low in control precision and poor in scene adaptability, multiple scenes such as tunneling excavation and excavation face instability can be simulated, and a precise test carrier is provided for shield construction mechanism research and risk prevention and control.

The application discloses a component selection method, device and equipment based on Modelica, and the method comprises the following steps: obtaining a component principle model based on multiple system design schemes of a user, generating component model codes of multiple component models based on batch design of the component principle model; determining an arrangement mode included in the system design scheme, generating a system model template according to the arrangement mode; generating system model codes of multiple system models according to the system model template; calling the system model codes to perform simulation, obtaining simulation results of the multiple system models; determining key performance indicator variables of the multiple system models, drawing key performance indicator variable curves of the simulation results according to the key performance indicator variables; and determining an optimal system design scheme according to the key performance indicator variable curves. The application solves the problem that a Modelica-based modeling and simulation software currently lacks rapid verification of multiple system design schemes based on Modelica.

The application discloses a kind of gas boiler main steam temperature self-disturbance decoupling control methods, comprising: signal detection device measures various signal data involved in the present application, then data is processed;Using the data after processing, the system model of double-input double-output is established, and the system model is identified;The system model after identification is decoupled, and the system is decoupled into two single-input single-output systems;For system, design linear self-disturbance controller to realize boiler main steam temperature stable control;Firefly algorithm is used to optimize linear self-disturbance controller parameter, and the control quality of main steam temperature control system is improved, the present application is aimed at the problems, such as nonlinearity, multiple disturbance and the like existing in the process of gas power generation boiler main steam temperature control, adopts the control strategy that linear self-disturbance control and decoupling control are combined, and the mutual coupling action between furnace gas flow and low-temperature superheater, desuperheating water flow and high-temperature superheater outlet temperature is decoupled and eliminated using Gauss elimination method.