12 results about "Multivariable optimization" patented technology

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.

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.

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 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 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 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 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.

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 a strip steel plate shape control method and device, a medium and a program product, and the method comprises the steps: determining an effective measurement region actually covered by the strip steel width according to the strip steel width and a plate shape roller measurement region; obtaining a tension value vector of the effective measurement area; determining a weight factor of each effective measurement area to obtain a weight factor vector; according to the weight factor vector, converting the tension value vector into a corresponding strip shape actual value curve, and according to the strip shape setting curve, determining a strip shape relative error vector; according to the plate shape relative error vector and a preset adjusting quantity over-limit value, a penalty function method is used for setting a target function of plate shape control multivariable optimization; and using a quasi-Newton method to iteratively calculate and obtain the optimal adjusting quantity of the next control period of the plate shape control, wherein the optimal adjusting quantity is the plate shape control adjusting quantity which enables the value of the target function to be smaller than a preset minimum value. By means of the technical scheme, the calculation precision of the optimization adjusting amount can be improved under the condition that the online calculation amount is not remarkably increased.

The invention relates to the technical field of four-cart emission reduction control, and discloses a four-cart emission reduction control method for dust removal linkage of smoke guide vehicles, which comprises the following steps of: constructing an intelligent smoke guide starting system based on a multivariable optimization algorithm, deeply fusing furnace number information, a U-shaped pipe combination rule and real-time wind resistance data, dynamically calculating optimal equipment combination and operation parameters, and calculating the dust removal linkage of the smoke guide vehicles. Meanwhile, closed-loop precise adjustment of the air volume is achieved in the whole coal charging process by combining a self-adaptive PID control algorithm, the nonlinear characteristic of the system is effectively overcome by introducing anti-saturation treatment and a dynamic gain adjustment mechanism, and then the purposes that on the premise that the optimal dust removal efficiency is guaranteed, the fan energy consumption is remarkably reduced, and the dust removal efficiency is improved are achieved. Synchronous optimization of the emission reduction effect and the operation economical efficiency is achieved, and the process control problem that in a traditional control mode, energy efficiency and the emission reduction effect are difficult to consider at the same time is effectively solved.