12 results about "Multivariate optimization" patented technology

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 provides a multivariable collaborative optimization method for an energy-saving operation strategy of a public building heat pump machine room, which belongs to the technical field of energy optimization and comprises the following steps: taking a prediction result in a long short-term memory network as a prediction result of a dynamic simulation model of a public building heat pump system, establishing a dynamic simulation model of the public building heat pump system by utilizing a TRNSYS simulation platform, and defining the public building heat pump system as a tetrad of a Markov decision model; and a deterministic strategy gradient model of an Actor-Critic architecture is adopted to optimize a tetrad of the Markov decision model, and an optimal control strategy of the dynamic simulation model of the public building heat pump system is obtained. Under the condition that accurate control parameters cannot be obtained through complex multiple variables, the method can efficiently process the multivariable optimization problem in a high-dimensional action space, experience can be accumulated through continuous interaction with the environment, the quality of the strategy is continuously improved, and a globally optimal solution is achieved.

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.

The invention discloses a virtual resource dynamic scheduling method and system in a hybrid cloud environment, relates to the technical field of resource scheduling, and translates an abstract service demand protocol into a scheduling strategy object which can be understood and activated by a machine by capturing and analyzing the abstract service demand protocol. Meanwhile, multi-dimensional fusion is carried out on the resource state, the multi-cloud price model and the real-time service KPI flow in the underlying mixed cloud environment to form a globally unified system state view. On this basis, an adaptive mechanism based on a control loop is introduced, and continuous weight vector optimization and generation are carried out by taking an activation strategy analyzed by an intention as a guide and taking a multivariate optimization index as a decision factor capable of being dynamically deployed. And finally, the scheduler performs intelligent weighted scoring on the new task request by using the group of dynamic weights, and makes an optimal placement decision. Therefore, an intelligent decision closed loop from business intention definition to resource scheduling execution can be realized, so that the resource allocation efficiency and business agility of the hybrid cloud environment are improved.

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 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.

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.

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.

The invention provides an intelligent magnesium smelting equipment system suitable for a heat storage circulation combustion refining furnace. The intelligent magnesium smelting equipment system is characterized in that S2, a high-temperature-resistant material is adopted for manufacturing a feeding mechanism of the system, and cooling treatment is conducted during overtemperature; s3, the system is provided with an anti-corrosion discharging device, and discharging parameters are adjusted through a rate control algorithm; s4, the system is provided with a slag removal mechanism, and the slag removal track and strength are controlled through image recognition; s5, establishing a material flow management model by the system, and adjusting the feeding and discharging rhythm based on the material data; s6, the system integrates temperature field monitoring, and combustion parameters are optimized through a thermal image and fluid dynamic model; s7, the system constructs a state monitoring network, judges equipment abnormity through mode recognition and triggers protection; and S8, integrating the subsystems by the system, and adjusting operation parameters through a multivariable optimization algorithm.