39 results about "Multivariable optimization" patented technology

Systems, methods, and devices that relate to routing requests to large language models (LLMs) are disclosed. In one example aspect, the system receives session-specific data elements in response to a request to generate an output using LLMs. The system determines a hierarchy of operational constraints including privacy protocols and performance requirements. Weights for a multi-variable optimization are dynamically updated using the session-specific data elements. The system executes the multi-variable optimization across candidate LLMs that satisfy privacy constraints and optimize performance constraints. Based on the optimization, at least one candidate LLM is selected and the request is routed to it. In response to performance feedback, the system automatically selects a different LLM to improve one constraint, resulting in degradation of another constraint.

A DoA estimation method and device based on steering vector matrix reconstruction, related to the field of array signal processing. The method includes: obtaining an array sampling covariance matrix according to an array received signal; setting a target variable, and limiting a feasible domain of the target variable by using two operators to determine a first constraint condition; characterizing an estimation error based on the target variable and the array sampling covariance matrix, and using the characterized estimation error as a second constraint condition; establishing an initial optimization model according to a preset norm based on partial sum of singular values and constraint conditions of the target variable; determining a multivariable optimization model according to the initial optimization model; and solving the multivariable optimization model to obtain an optimal result; analyzing the optimal result to obtain a DoA of the target incident signal.

The invention belongs to the technical field of vehicle fairing control, and particularly discloses a vehicle fairing control method and system fusing oil consumption and wind resistance, a terminal and a medium. Comprising the steps that the speed, fuel consumption parameters and wind pressure parameters of a vehicle in the running process are obtained, and fuel consumption change parameters and wind pressure distribution characteristic parameters are extracted; a multivariable optimization model with the minimum comprehensive energy consumption as the target is constructed; based on the model, a model prediction control algorithm is adopted to solve the optimal control quantity of the attitude of the fairing in a prediction time domain, and control instructions of the angle and the deflection direction are generated; when environmental disturbance or attitude response deviation is detected, dynamically adjusting a target function weighting coefficient or an input penalty factor to correct control output; and adjusting the posture of the fairing in real time according to the corrected control instruction. Through fusion modeling of fuel consumption parameters and wind pressure characteristic information, predictive control and self-adaptive adjustment of the attitude of the vehicle fairing are realized, and fuel economy and aerodynamic performance can be considered under multiple working conditions.

The invention relates to the technical field of rectification circuit control, in particular to a three-phase rectification circuit control method based on sliding-mode predictive control and a charging pile, and the method comprises the steps: obtaining a sliding-mode control law through employing a super-spiral sliding-mode control algorithm in outer-loop voltage regulation, and taking the sliding-mode control law as an active power reference value; in inner loop current control, voltage components and current components of three-phase alternating voltage in a two-phase rotating coordinate system are obtained through coordinate system transformation, and active power and reactive power at the future moment are obtained through a discretization prediction model; and performing rolling optimization by the cost function to obtain the optimal switching state at the current moment, updating the controller according to the optimal switching state, and performing prediction control on each switching device of the three-phase rectification circuit through the updated controller. According to the three-phase rectification circuit control method based on sliding mode predictive control, stable reference voltage can be provided, output is stable, the disturbance suppression capacity is high, and quick response and multivariable optimization control are achieved.

The invention discloses a water volume optimal configuration method and equipment based on topological correlation, and belongs to the technical field of water resource management. An existing water distribution scheme does not consider the coupling relation and the space-time correlation between water utilization units, and therefore an existing water allocation scheme cannot achieve optimal allocation of water utilization of all the units. According to the water volume optimization configuration method based on the topology correlation, a topology simulation module, a first water distribution calculation module and an optimization decision module are constructed, for an unconstrained scene, the coupling relation and the space-time correlation between water consumption units are captured, a distribution strategy is dynamically adjusted, and an optimization water distribution scheme is obtained; and for a constrained scene, a water distribution problem is converted into a multivariable optimization problem, and an optimized water distribution scheme is obtained, so that the coupling relationship and the space-time correlation between the water consumption units can be fully considered, the optimal distribution of water consumption of each unit can be realized, the waste of water resources is avoided, the contradiction between supply and demand is reduced, and the sustainable development of water supply economy can be realized.

The invention discloses a multi-objective continuous optimization method for a chemical reaction kettle. The method comprises the following steps: S1, initializing a population and a trainable parameter set; s2, calculating an individual target value and a gradient, and obtaining a performance steepest improvement direction after standardization; s3, constructing a conjugate direction in combination with historical gradients, and identifying sensitive key dimensions of the target function; s4, fusing the gradient and the conjugate direction to generate a main direction candidate solution, and applying refined disturbance to the sensitive dimension to generate a structural disturbance candidate solution; s5, updating the Pareto optimal solution set, and if a termination condition is met, outputting a multivariable optimization parameter set such as temperature-pressure-flow of the reaction kettle for actual operation; otherwise, returning to S2; according to the method, through gradient correction, conjugate direction fusion, sensitive dimension directional disturbance and parameter adaptive adjustment, the efficiency, precision and stability of multivariable optimization of the reaction kettle are remarkably improved.

The invention discloses a big data analysis and automatic evaluation system in project cost consultation, and relates to the technical field of big data analysis and evaluation. By constructing a unified data acquisition framework and a dynamic updating mechanism, efficient integration and real-time interaction of multi-party heterogeneous data are realized, data transparency and reliability are remarkably improved, a solid data basis is provided for project cost management, the system quickly and accurately optimizes various variables by utilizing comprehensive analysis and multivariable optimization technologies, and the system has a good application prospect. The system adapts to the dynamic environment, enhances the anti-risk capability of the project, ensures that the project cost management meets the real-time requirement and realizes the globally optimal solution, and in addition, through resource allocation strategy generation, view updating and decision recording and a feedback loop mechanism, the system realizes the generation and continuous improvement of a resource allocation strategy oriented to global benefit balance. The resource allocation efficiency and the long-term competitiveness of the project are improved, and powerful support is provided for sustainable development of urban construction.

This invention relates to the field of optical communication, providing a method, system, device, product, and medium for optimizing the energy efficiency of multi-access point optical communication. The method includes: calculating the link channel gain to obtain the composite channel gain; obtaining the received energy at the receiver; establishing a nonlinear received energy expression based on the received energy; constructing a battery dynamics model using the nonlinear received energy expression to obtain device energy constraints; establishing a arrival Markov matrix and a service Markov matrix based on the number of participating communication devices and their obstruction states; calculating the arrival martingale and service martingale; calculating the queue length change based on the arrival martingale and service martingale; establishing a delay default probability inequality through the queue length change to obtain an upper bound on the delay default probability; establishing communication optimization constraints; constructing a multivariate optimization equation; solving the multivariate optimization equation based on the communication optimization constraints to obtain communication optimization parameters; and optimizing the optical communication using these communication optimization parameters.

The invention discloses a parameter optimization method, device and system for a wireless slip ring magnetic coupling mechanism, and belongs to the technical field of electromechanical equipment design, and the parameter optimization method comprises the steps: carrying out the optimization from the aspects of coil parameters, coil turns and magnetic core parameters; performing parameter scanning and optimization selection on the coil parameters in the 2D simulation model to obtain optimized coil parameters, and performing optimization selection on the number of turns of the coil and the magnetic core parameters in the 3D simulation model to obtain optimized number of turns of the coil and optimized magnetic core parameters; further, whether the safety condition that the main magnetic circuit of the magnetic core is unsaturated is met or not is judged, if not, optimization continues, and if yes, a parameter optimization result is output. According to the method, a multivariable optimization problem is equivalent to a multi-single-variable and double-variable optimization problem, and compared with other parameter optimization methods of a magnetic coupling mechanism in a wireless power transmission system, parameter optimization of a coil magnetic core composite system is comprehensively considered, so that the performance of the wireless slip ring magnetic coupling mechanism can be greatly improved.

The invention discloses an intelligent process control system for a butane separation tower, belongs to the field of petrochemical process control, and aims to solve the problems of high energy consumption and weak interference resistance caused by poor loop collaboration and dependence on univariate adjustment in the control of the existing butane separation tower. The system comprises a sensing layer, a control layer and an execution layer, the sensing layer collects full-process physical parameters and material component data, the control layer achieves data processing, trend prediction and multivariable optimization adjustment through data collection processing, multivariable prediction control (MPC) and an intelligent coordination module, and the execution layer executes a flow adjustment instruction. Through multi-variable cooperation, dynamic energy consumption optimization and feed-forward adjustment, the product quality stability and the system anti-interference capability are improved, the energy consumption is reduced, the manual intervention is reduced, and accurate, efficient and low-consumption control of the butane separation tower is realized.

Embodiments of the present disclosure provide for generating improved, feasible, and optimized pathways for optimizing operation of an industrial plant or component thereof. Embodiments utilize a multi-variate optimization model that may utilize real-time data and any number of available dynamic and static configurations to optimize for multiple optimization parameters. The multi-variate optimization model outputs an optimization pathway, for example including any number of transformation action(s) representing decarbonization step(s), that enable configuration of the plant or processing unit(s) thereof in a manner that optimizes operations of the plant or processing unit(s). For example, the optimization pathway in some contexts is optimized to generate a pathway that reduces the impact of emissions generated by the plant or processing unit(s) in a feasible manner that is most cost efficient for a particular plant in a particular location in consideration with any number of other constraints or considerations.

The invention provides a global optimization design method of a cluster system under a network attack, which comprises the following steps: for adjustable parameters in a controller, defining a system overall cost function which comprehensively considers transient performance, steady-state performance and control cost of the system, and converting an optimal design problem into a multivariable optimization problem; by solving a multivariate optimization problem, a group of optimal solutions enabling the overall cost function of the system to be minimum are sought; the uniqueness of the optimal parameter is proved; and substituting the optimal value of the sum into control to obtain an optimal design. On the basis of keeping the steady-state performance of the cluster system, the control parameters of the system are globally optimized, the control parameters of the whole system can be coordinated, and the performance of the system is further improved.

The application discloses a few-parameter pull-frame control method, and establishes a multivariable optimization relation of a pull-frame tension F, a comprehensive resistance f of a hydraulic support and a pull-frame tension duration t1 according to a dynamics principle; and an optimal solution of the multivariable optimization relation is obtained by using a genetic algorithm. The application has the advantages that for the purpose of engineering application, the pull-frame process of the hydraulic support is simplified into two processes of pull-frame and free movement, the time required for the pull-frame tension is estimated by using the genetic algorithm before the pull-frame, so that the disadvantages of segmented pull-frame and real-time adjustment are avoided, the pull-frame control precision of the hydraulic support group is improved, the straightness of the hydraulic support group is ensured, the coal wall spalling, the hydraulic support being pressed to death, the roof falling and other accidents caused by the insufficient straightness of the hydraulic support group are fundamentally avoided, the low coal output efficiency problem is solved, and the production safety of the coal mine working face and the coal mine production benefit are ensured.

The application provides a stability enhancement system for aircraft lateral layered control, belongs to the technical field of automatic flight control, and relates to the technical field of automatic flight control.The system meets the design standard of a civil aviation IIIa control system.The system adopts a layered control architecture combining outer loop PID track tracking control and inner loop lateral heading model predictive control (MPC).The outer loop PID control is responsible for basic track tracking, and the inner loop MPC finely adjusts the heading angle and the track to ensure that the aircraft accurately aligns with the runway and stably flies.The method not only improves the control precision and response speed, but also significantly reduces the calculation burden, enhances the robustness and anti-interference ability of the system by combining the simplicity of the PID control and the multivariable optimization capability of the MPC.The system can effectively improve the stability of the aircraft lateral control under complex flight environment, and ensures the safety and reliability of the aircraft during the automatic landing task.

The invention discloses a permanent magnet motor magnetic pole layout and stator auxiliary slot collaborative optimization method for cooperatively inhibiting cogging torque and electromagnetic torque pulsation of a permanent magnet motor to ensure motor balance. Angular adjustment amounts of P pairs of opposite magnetic poles of the motor are set as P independent adjustment amounts, and constraint that the algebraic sum is zero is applied to the P adjustment amounts so as to maintain the magnetic field balance of the motor. Meanwhile, the width and depth of the stator auxiliary slot are also used as optimization variables, an NSGA-II multi-objective optimization algorithm is adopted, minimization of cogging torque and electromagnetic m-order harmonics are used as objectives, and Pareto optimal magnetic pole angular adjustment quantity combination and stator auxiliary slot parameters are obtained through iterative search under the condition that all constraint conditions are met. Compared with the prior art, by introducing relative magnetic pole synchronous adjustment, key global balance constraint and an advanced multi-objective optimization algorithm, the problems of complexity and balance of a traditional method during multivariable optimization are effectively solved, and an efficient technology is provided for high-performance and low-vibration design of the permanent magnet motor.

The invention belongs to the technical field of air compressors, and provides an air compressor station intelligent group control energy-saving system based on multi-machine linkage, which comprises a data acquisition unit, a central control unit and an execution regulation and control unit, the data acquisition unit is used for acquiring operation state parameters and environment working condition parameters of all air compressors in an air compressor station and load demand parameters of a gas using end in real time. The central control unit integrates a load prediction module, an energy efficiency evaluation module, a group control scheduling module, an equipment health monitoring module and an energy efficiency optimization feedback module, realizes minimum energy consumption scheduling under cooperative operation of multiple air compressors through high-precision sensing, dynamic modeling and multivariable optimization, has equipment abnormity early warning and model self-correction capabilities, and is high in reliability and high in reliability. Therefore, the system energy consumption can be obviously reduced, and the gas supply stability and the intelligent level are improved.

The application discloses a water quantity optimal allocation method and equipment based on topological correlation and belongs to the technical field of water resource management. The existing water distribution scheme does not consider the coupling relationship between water using units and the space-time correlation, so the existing water quantity allocation scheme cannot realize the optimal distribution of water use of each unit. The water quantity optimal allocation method based on topological correlation constructs a topological simulation module, a first water distribution calculation module and an optimization decision module, captures the coupling relationship between water using units and the space-time correlation for an unconstrained scene, dynamically adjusts the distribution strategy and obtains an optimal water distribution scheme. For a constrained scene, the water distribution problem is converted into a multivariable optimization problem to obtain an optimal water distribution scheme. Therefore, the coupling relationship between water using units and the space-time correlation can be fully considered, the optimal distribution of water use of each unit can be realized, the waste of water resources is avoided, the contradiction between supply and demand is reduced, and the sustainable development of water supply economy can be realized.

The invention relates to the field of mold pressure regulation, in particular to a pressure regulation mechanism for a plastic mold, which comprises a pressure detection module and a temperature detection module, and the pressure detection module and the temperature detection module respectively transmit pressure signals and temperature signals monitored in real time to a data processing module. The data processing module receives the signals from the pressure and temperature detection module and then processes the signals through a pressure data analysis algorithm and a temperature trend prediction algorithm to generate processed pressure signals and temperature signals. The processed pressure signal and temperature signal are respectively transmitted to the pressure control module and the temperature control module, and the modules generate corresponding control instructions according to the received signals. The control modules send control instructions generated by the control modules to the executing mechanism so as to implement specific pressure and temperature adjusting operation. And the integrated control module integrates output from the pressure control module and the temperature control module, generates an optimized control instruction through a multivariable optimization control algorithm, and feeds back the optimized control instruction to the data processing module to form closed-loop feedback.

The invention discloses a design method of a built-in permanent magnet synchronous motor and a motor obtained by the method, and aims to solve the problems of magnetic field modeling precision and efficiency imbalance, multivariable optimization dimension redundancy and performance index imbalance of the existing motor. According to the method, a stator slotting equivalent air gap magnetic permeability model is constructed through a magnetic potential penetration method, and rapid and accurate calculation of a magnetic field is realized by coupling permanent magnet magnetomotive force harmonic expansion; analyzing a quantitative variable interaction effect in combination with variance, and compressing an experiment dimension from 32768 dimension to 32 dimension through an L32 mixed horizontal orthogonal array; and constructing a multi-performance evaluation system based on entropy weight-TOPSIS. After implementation, the magnetic field calculation efficiency is improved by 92%, and the fundamental wave error is lt; the method has the advantages of high power density and high stability, the calculation resource consumption is reduced by 37%, the motor torque ripple is reduced by 42.7%, the cogging torque is inhibited by 85%, the comprehensive efficiency is improved by 1.5%, and the requirements of high power density and high stability of scenes such as new energy automobile driving and the like can be met.

The application discloses a kind of based on the robust estimation method of highway traffic state of network-connected vehicle trajectory data, comprising: constructing virtual oblique grid;Calculate the representative value of traffic state in each oblique grid unit, construct traffic state observation matrix;Traffic state estimation is converted into low-rank matrix completion problem;Establish the robust traffic state estimation model based on low-rank and sparse matrix recovery;Introduce auxiliary variable to retain original state observation information, convert the robust traffic state estimation model into multivariable optimization problem, use alternating direction multiplier method to decompose multivariable optimization problem into multiple sub-variable optimization problems, and update multiple sub-variables iteratively.The application has high-precision, reliable estimation performance, provides more reliable support for fine active traffic management and control application, improves the robustness of traffic state estimation model in real application scenario, and has higher computational efficiency when facing large-scale road network scene.

The invention discloses a dynamic proportion control system and method in a prebiotic production process, relates to the technical field of production process control, and continuously reflects actual states of a raw material component proportion Red, a fermentation tank temperature Tem, a pH value Phv, a dissolved oxygen content Oxy and a flora metabolism rate Met in a current batch based on a real-time production state set Sta. Linkage adjustment is carried out on production parameters through a multivariable optimization input index set Inp and a current optimal dynamic matching strategy Opt, so that parameter changes in the production process are not mutually-separated local repair any more, but overall cooperative control is carried out around the same target; in this way, the problems that in the prior art, a fixed proportion is difficult to adapt to raw material batch fluctuation, parameter changes in the fermentation process cannot be responded in time, and finally the product quality and the output efficiency are unstable can be directly solved. And meanwhile, a closed-loop control state set Clo is formed in combination with a feedback signal Fbk in the execution process to continue correction.

The application relates to the technical field of mechanical processing and intelligent manufacturing, and discloses a self-adaptive adjustment method and system for processing process parameters of cubic boron nitride blades. The method comprises the following steps: acquiring a blade edge image sequence and an attitude matrix, determining the spatial boundaries of different functional areas through regional segmentation labeling; constructing a target detection network model to output the boundary box coordinates and category information of the damage target; counting the damage distribution and judging the severity level; based on the damage level, adjusting the current processing parameters through a correlation data model, and then optimizing the parameter combination through a collaborative correction model and a multivariate optimization algorithm; processing and feeding back the workpiece quality by using the optimized parameters, updating the collaborative correction model, and obtaining the final process parameters. The application improves the processing quality, tool life and process self-adaptive ability of the cubic boron nitride blade.

An apparatus includes a plurality of high-side switching elements and a plurality of low-side switching elements arranged to form a reconfigurable power stage, and a controller configured to execute a multi-variable optimization routine to improve an efficiency of the apparatus, wherein the multi-variable optimization routine comprises adjusting a plurality of distinct parameters including a number of active switching elements, a switching frequency, and a turn-on gate voltage, and wherein an adjustment to one of the plurality of distinct parameters is based on a change in a monitored operational parameter indicative of efficiency resulting from a prior adjustment to a different parameter.

The invention provides an initialization method of an SLAM system for multi-sensor fusion, relates to three sensors of a 3D laser radar, a camera and an IMU, and aims to improve the accuracy and robustness of initialization of the SLAM system. The SLAM initialization process is divided into five modules, a multivariable optimization problem is converted into a multi-batch optimization problem, multivariable optimization is performed from easy to difficult, convergence difficulty is reduced, convergence speed is increased, and the position, attitude, speed and IMU offset of each key frame and deviation between the Z axis of a world coordinate system and the gravity direction are estimated progressively; converting a multi-variable multi-batch optimization problem into a joint optimization problem, taking a batch optimization result as a variable initial value, and improving the precision of a state estimation result through joint optimization; a gravity direction deviation optimization module is designed, and it is guaranteed that the deviation between a world coordinate system and the gravity direction can still be corrected under the condition that inertial parameters do not have good observability due to slow movement of the robot.

The invention discloses a privacy-protected multi-cell wireless distributed AI method and device, and the method comprises the steps: in a multi-base-station federated learning system, edge equipment participates in a federated learning process through being connected to base stations and communicating with the base stations, and the edge equipment adds noise in an uploaded model to apply a differential privacy strategy. The privacy leakage of all edge devices in the system can be minimized by jointly adjusting the connection distribution between the edge device base stations, the edge device transmitting power and the difference privacy noise added by the edge devices in the model. A multivariable optimization problem about edge device allocation, transmitting power and differential privacy noise standard deviation, which minimizes overall privacy leakage, is provided, and an iterative alternative optimization algorithm is used. And solving the edge device connection allocation optimization sub-problem, the transmitting power optimization sub-problem and the privacy noise optimization sub-problem in turn to obtain a suboptimal solution of the multivariate optimization problem.

The application relates to the field of mold pressure regulation, in particular to a pressure regulation mechanism for a plastic mold, which comprises a pressure detection module and a temperature detection module that respectively transmit real-time monitored pressure signals and temperature signals to a data processing module. After receiving the signals from the pressure and temperature detection modules, the data processing module processes the signals through a pressure data analysis algorithm and a temperature trend prediction algorithm respectively to generate processed pressure signals and temperature signals. The processed pressure signals and temperature signals are respectively transmitted to a pressure control module and a temperature control module, which generate corresponding control instructions according to the received signals. The control modules send the generated control instructions to an execution mechanism to implement specific pressure and temperature regulation operations. The integrated control module integrates the outputs from the pressure control module and the temperature control module, generates optimized control instructions through a multivariable optimization control algorithm, and feeds back to the data processing module to form a closed-loop feedback.

This invention discloses an intelligent control method and system for PEM electrolyzers, relating to the field of new energy technology. Addressing the problems of existing PEM electrolyzers under fluctuating operating conditions, such as difficulty in dynamically coordinating multiple physical fields, difficulty in sensing internal states, and the inability to balance operating efficiency and equipment lifespan, this invention proposes a multivariate collaborative optimization control based on the Electrolysis Process State Function (EPSF). By calculating the proton conduction efficiency function and the catalytic activity distribution optimization function in real time, the key internal states of the electrolyzer are accurately quantified. Furthermore, a multivariate optimization objective function integrating efficiency and degradation cost is constructed and solved to obtain optimal control parameters such as current density and pressure online. A multi-timescale rolling optimization architecture and a parameter online identification algorithm with a forgetting factor are further introduced to achieve synergy between rapid response, efficiency optimization, and lifespan management. This invention significantly improves the operating efficiency, stability, and durability of PEM electrolyzers under dynamic loads.

The invention discloses a thermal power generating unit cold end system operation optimization method based on machine learning. The method comprises the following steps: collecting and preprocessing unit multi-source operation data; calculating intermediate parameters and carrying out time alignment and feature fusion; establishing an operation boundary set and a control constraint set, and performing windowing and screening; four types of sub-models are trained, and an R-GCN network is adopted to carry out multi-relation feature aggregation and parameter uniformization processing; calling the cold end system unified prediction model, and outputting a cold end state quantity and a performance index; building a multivariable optimization problem based on the unified prediction model of the cold end system; solving a multivariable optimization problem by adopting a near-end strategy optimization method; and updating the unified prediction model of the cold end system on line based on real-time data, periodically retraining and deploying, and outputting an optimal control instruction. The machine learning and multi-model collaborative optimization method is adopted, intelligent operation control over the thermal power generating unit cold end system is achieved, and the method has the advantages of being high in prediction precision, low in energy consumption and high in self-adaption.

This invention discloses a method for optimizing the number of molecules released in a multi-input multi-output (MIMO) molecular communication system. The method includes: acquiring simulation data and establishing a time-varying channel impulse response function (TMR); minimizing the error between the predicted value of the TMR and the simulation data using a nonlinear fitting method to obtain a well-fitted TMR; deriving the average bit error rate (BER) of the MIMO system in the m-th time slot based on the fitted TMR, and establishing a multivariate optimization objective function for the current time slot; establishing and training a deep neural network; and inputting the initial distance vectors formed by each link into the trained deep neural network to obtain the optimal molecule release number vector for each sending nanomachine. This method can optimize the number of molecules released by the sending nanomachines in less time and with lower computational complexity, significantly reducing the average BER.

The invention provides an intelligent raw coal bunker separation system which comprises a plurality of independent storage bins, a plurality of lower insertion plate doors, a dynamic mixing device, a data acquisition device and an intelligent control device, and the intelligent control device controls the distribution of a temperature field according to the distribution of the temperature field, the smoke concentration, the output load of a boiler and current environmental protection requirement information. Generating a current demand matrix of the boiler; matching the coal quality data of each storage bin with the current demand matrix to obtain a plurality of target coal types meeting boiler demands; and based on the current demand matrix, solving a multivariable optimization equation to obtain an optimal feeding ratio of the plurality of target coal types so as to control the lower plugboard gates corresponding to the plurality of target coal types based on the optimal feeding ratio. Therefore, fuel storage flexibility can be improved, boiler combustion efficiency can be improved, pollutant emission can be reduced, and coal consumption cost can be optimized.