144 results about "Energy consumption minimization" patented technology

The invention discloses a gradient optimization control method for an aeration system of a sewage plant. The gradient optimization control method comprises the following steps: S1, acquiring key monitoring multi-source data of the aeration system of the sewage plant in real time through an online sensor; s2, setting a security threshold value of each monitoring data to form a security constraint condition; s3, taking unit nitrogen removal energy consumption minimization as an optimization target, and constructing a comprehensive loss function fusing an energy consumption item and a water quality deviation item; s4, iteratively updating the air volume of the aeration system by adopting a self-adaptive gradient algorithm; s5, the optimized and updated air volume is issued to the aeration equipment to be executed, and water quality data and energy consumption data after execution are collected in real time, a control effect is calculated, an award signal is generated, and the award signal serves as feedback to be input into an optimization module; and S6, dynamically adjusting key parameters including the learning rate and the gradient threshold according to a feedback result. According to the invention, the energy consumption can be effectively reduced and the control precision can be improved while ensuring that the effluent stably reaches the standard, and the stable operation of the aeration system of the sewage plant under a complex working condition is ensured.

A full-automatic packaging scheduling method based on a multi-objective optimization algorithm, which method relates to the technical field of intelligent production scheduling in packaging workshops. The method comprises: on the basis of work orders to be packaged of a production line, extracting production data of the production line, and initializing parameters; constructing a multi-objective optimization model of the packaging production line; to address the problems of premature convergence and susceptibility to local optima in an INSGA-II algorithm, using the INSGA-II algorithm to improve an initialization method, crossover and mutation strategies and crossover and mutation factors, and performing solving on the basis of objective functions such as minimizing the maximum makespan, minimizing the maximum energy consumption and minimizing the total machine load, so as to generate an optimized scheduling scheme; and starting packaging operations on the basis of the generated optimized scheduling scheme. The method can increase the unit-time production capacity of a production line, and effectively solve the problems in packaging production scheduling, thereby reducing the production costs of enterprises and improving the packaging production efficiency.

The invention discloses a machine room group control multi-objective optimization digital twin platform and an optimization method, and relates to the technical field of machine room group control. Constructing a multi-objective optimization model, wherein the multi-objective optimization model comprises a three-dimensional objective function and constraint conditions; the three-dimensional objective functions comprise an energy consumption minimization function, a temperature and humidity control precision maximization function and an equipment life loss minimization function. Compared with a traditional static twinborn model, the method has the advantages that the deviation is greatly reduced, and a precise virtual-real mapping basis is provided for optimization decision making. According to the whale optimization algorithm, the global search capability and the NSGA-IIPareto solution screening capability are fused, the convergence speed of the algorithm is improved through fuzzy membership degree fitness calculation and crowding degree screening, when deviation exceeds a threshold value, optimization can be shortened to 15 seconds, compared with an existing algorithm, the response speed is greatly improved, and the real-time control requirement under the dynamic load of a machine room is met.

The invention discloses an underwater gliding robot anchoring process energy consumption optimization method and system based on a subdomain grey box model. The energy consumption optimization method comprises the steps that a white box energy consumption mechanism model in the underwater gliding robot anchoring process is established; the whole anchoring energy consumption stage of the underwater gliding robot is divided into a plurality of sub-domains, and a Latin hypercube design method is adopted to simulate and generate a plurality of sub-domain samples meeting variable range constraints by using a hardware-in-the-loop simulation platform; selecting a Kriging model as a sub-domain agent model, fusing the white-box energy consumption mechanism model and constraint conditions of an oil bag volume and a movable mass position to form a sub-domain grey-box model, and performing segmented energy consumption fitting by using the sub-domain grey-box model; and with minimization of energy consumption fitted by the sub-domain grey box model as a target, performing iterative optimization on the sub-domain proxy model by adopting a dynamic guidance self-adjusting particle swarm algorithm, and outputting optimal planning parameters. The method realizes anchoring full-process low-energy-consumption control, and is suitable for marine resource exploration, hydrological monitoring and other tasks.

The invention discloses an unmanned aerial vehicle-unmanned agricultural machine collaborative operation staged scheduling method under energy consumption constraint, belongs to the technical field of intelligent scheduling, and innovatively designs a three-stage dynamic scheduling mechanism based on operation urgency, and realizes stage adaptive switching through real-time energy consumption monitoring. And an unmanned aerial vehicle-agricultural machinery energy consumption prediction model considering flight conditions and operation parameters is established, and accurate prediction of the residual endurance time is realized by using an LSTM network. An improved NSGA-II multi-objective optimization algorithm is provided, and under a three-objective Pareto optimization framework of total energy consumption minimization, task completion rate maximization and time cost minimization, an energy consumption perception auction mechanism is combined to realize intelligent distribution of emergency tasks to high-electric-quantity equipment. A distributed real-time scheduling system based on edge computing is developed, an edge server is deployed to run a lightweight algorithm, and a real-time scheduling algorithm and a dynamic task conflict resolution mechanism are combined, so that the task redistribution delay is controlled within 500ms, and the conflict resolution success rate is increased to 92.3%.

The invention relates to the technical field of electric vehicle battery thermal management, in particular to an intelligent battery thermal management method and system based on environment temperature self-adaptive adjustment. A real-time response mechanism based on the dynamic temperature difference between the environment temperature and the battery temperature is provided, and through the four steps of temperature index data collection, thermal management battery protection, thermal management adaptive control and thermal management dynamic adjustment, when the environment battery temperature difference is larger than a threshold value, a continuous high-power mode is started, a temperature region can be rapidly converged, and the power consumption is reduced. The target temperature zone arrival time is effectively shortened; when the environment battery temperature difference is smaller than the threshold value, the intermittent low-power mode is switched, and energy consumption minimization can be achieved; the working temperature of the power battery can be quickly controlled near or within the most suitable working temperature, and the probability that the battery is reduced or even does not generate related side reactions is ensured, so that the cycle life of the battery is prolonged.

The invention relates to a power transmission line unmanned aerial vehicle airport optimal configuration method. The method comprises the steps of determining an inspection area and unmanned aerial vehicle inspection task requirements; constructing an unmanned aerial vehicle energy consumption model; based on the unmanned aerial vehicle energy consumption model, combining a neural network and a Kalman filtering algorithm to predict the energy consumption of the unmanned aerial vehicle arriving at the tower; an improved ant colony algorithm is adopted, and optimal energy consumption path planning of unmanned aerial vehicle inspection is carried out; based on the planned optimal energy consumption path, the tower distance and the energy consumption prediction of the unmanned aerial vehicle reaching the towers, determining the position and the number of optimal supply points with the goal that the remaining electric quantity of the unmanned aerial vehicle reaching the supply points is not lower than a safety threshold value; in the inspection process, according to real-time energy consumption prediction, a flight path and a supply strategy are dynamically adjusted, and it is ensured that the unmanned aerial vehicle arrives at the nearest supply point for supply before the remaining electric quantity is lower than a safety threshold value. The method can achieve the minimization of the total energy consumption of the inspection and the maximization of the inspection efficiency, and improves the cruising ability in the inspection of the unmanned aerial vehicle through the reasonable configuration of the supply point of the unmanned aerial vehicle airport.

The invention relates to the technical field of refrigeration energy efficiency monitoring of refrigeration houses, in particular to a dynamic monitoring system and method for refrigeration energy efficiency of a refrigeration house. A multi-objective optimization scheme analysis module in the system is used for analyzing the refrigeration energy efficiency of the refrigeration house based on the running state of each temperature zone of the current refrigeration house, the heat state of stored goods and an external electricity price signal; and by taking the minimization of the total operation energy consumption of the refrigeration house and the minimization of the temperature fluctuation of each temperature zone as optimization objectives, constructing a multi-objective optimization function, and generating at least one candidate energy-saving operation scheme in a future time window of a preset duration. According to the method, through multi-dimensional data perception, a digital twinborn model for specific characteristics of goods and refrigeration house scenes is established; a multi-objective optimization function with the lowest total energy consumption, the lowest temperature difference fluctuation of the temperature zone and the highest energy efficiency ratio of the system as the core is constructed; and a plurality of candidate energy-saving schemes are dynamically generated by using the function, and an optimal scheme is screened out through a normalized comprehensive evaluation system, so that clear energy efficiency data and optimization suggestions are provided for managers, and invalid energy consumption is avoided.

The invention provides an intelligent temperature control method for a corrugating machine, and belongs to the technical field of corrugated paper products.The intelligent temperature control method comprises the steps that a steam temperature sensor set and a linear speed detection device are installed on a corrugating machine production line, and a speed-steam temperature dynamic compensation mathematical model based on nonlinear regression analysis is established; a feedforward control system architecture is constructed to realize real-time calculation and transmission of a steam temperature compensation value, a double-layer game optimization model is established to coordinate steam energy consumption minimization and composite quality optimization targets, and a self-adaptive adjustment algorithm is started according to composite strength and appearance quality feedback signals to dynamically adjust model parameters. Steam temperature coordination control is implemented to optimize temperature distribution, a steam temperature control effect evaluation system is established for real-time evaluation, and the technical problem that in the prior art, steam temperature dynamic compensation control cannot be achieved according to the speed change of a production line in the production process of the corrugating machine is solved.

The invention relates to the technical field of energy consumption optimization, and provides a heater energy consumption optimization method and system based on gas-steam combined cycle. The method comprises the steps that gas turbine exhaust, waste heat boiler steam and heater operation parameters are collected in real time, and a dynamic energy consumption data set is constructed; training a load prediction model based on the historical load curve and the meteorological data, and outputting future load distribution; performing multi-working-condition energy consumption simulation according to the data set and the predicted load, and generating an energy consumption characteristic topological graph and a thermal efficiency fluctuation graph; and based on the atlas, optimizing heater control parameters by taking energy consumption minimization as a target, generating an optimal control set, and regulating and controlling the running state of the heater. The technical problems that in an existing gas-steam combined cycle system, heater energy consumption control is not accurate, and efficiency fluctuation is large are solved, and the technical effects that through combination of real-time data collection and a prediction model, heater energy efficiency optimization and control parameter dynamic adjustment are achieved, and therefore energy consumption is reduced to the maximum extent, and heat efficiency is improved are achieved.

The invention discloses a closed-loop control method for a hydraulic control butterfly valve of a PCCP (prestressed concrete cylinder pipe) long-distance high-pressure water conveying pipeline, and belongs to the technical field of long-distance high-pressure water conveying pipeline control. The problems that in the prior art, due to manual scheduling or single-point PID control, a long-distance PCCP water conveying system has response lag and lacks global cooperation, the water attack risk is difficult to effectively deal with, and the optimal overall energy consumption of the system cannot be considered are solved. According to the invention, by constructing a hierarchical control architecture in which a distributed measurement and control network is coordinated with a central supervision control and data acquisition system platform, a variable-parameter PID control algorithm is built in an equipment PLC, so that millisecond-level identification and active suppression of a sudden water attack working condition are realized; a hydraulic simulation model and a multi-objective optimization algorithm are deployed in a central supervision control and data acquisition system master station, and a whole-line optimal pressure set value is solved through iterative calculation and is issued to each PLC for execution, so that the system energy consumption minimization is realized on the premise of ensuring the pipeline safety.

The invention discloses a greenhouse climate multi-target intelligent control method fusing rule constraint and reinforcement learning, and relates to the technical field of agricultural environment control, and the method comprises the following steps: building a temperature and humidity dynamic model based on an energy conservation law, and coupling an external environment disturbance term to obtain a temperature and humidity dynamic model; establishing a mapping relation among the temperature, the relative humidity and actuator input data, generating multiple groups of interval training data based on the mapping relation, constructing an intelligent decision model, and screening out an optimal actuator state vector by taking the deviation degree of an environment target value and an interval range as an evaluation criterion; and setting a difference mediation threshold value of mutual exclusion operation for safety correction, adopting environment adjustment difference and energy consumption minimization as optimization objectives, designing a multi-objective reward function, and determining an optimal control instruction, thereby updating an intelligent decision-making model, realizing accurate and efficient greenhouse climate control, enabling the comprehensive control of temperature and humidity to be more accurate and reasonable, and improving the reliability of the greenhouse climate control. And the energy consumption is reduced.

The invention provides a rolling pass parameter intelligent inversion and dynamic correction method based on machine learning, and the method comprises the steps: outputting a deformation resistance Kf value according to the obtained current rolling temperature and strain rate; based on the deformation resistance Kf value, predicted values of the total rolling force and the rolling moment are obtained; establishing a transient heat balance equation, and outputting a final rolling temperature predicted value; by taking the minimization of the pass number and production time and the minimization of the total energy consumption as targets, an optimal rolling schedule is generated and decided; and the corresponding model parameters are optimized in batches by regularly utilizing historical data, so that short-term self-adaption and long-term optimization control of the rolling process is realized. According to the method, various feasible rolling schemes are automatically generated by utilizing an optimization algorithm, so that an operator can select the optimal scheme according to actual conditions; by comparing a predicted value with an actual value in real time, current model parameters are quickly adjusted, and accumulated production data are regularly utilized to comprehensively optimize the model, so that long-term stable operation and accurate prediction of the system are ensured.

The invention relates to the technical field of intelligent control and optimization in the chemical production process, in particular to a silver nitrate crystal impurity content real-time prediction and process parameter multi-objective optimization system, which comprises a data acquisition layer, an analysis prediction layer, a multi-objective optimization layer and a dynamic control layer, the data acquisition layer obtains multi-source heterogeneous data; the analysis and prediction layer predicts impurity content and correlates process parameters by means of deep learning and a crystallization phase change kinetic model; the multi-objective optimization layer aims at minimizing impurities, maximizing crystal yield and minimizing energy consumption, and optimal parameters are solved by using an improved algorithm; the dynamic control layer carries out real-time regulation and control according to an optimization result, and abnormal processing and closed-loop feedback are set; the system integrates a process knowledge graph and supports case-based reasoning to realize rapid parameter recommendation. Accurate monitoring, intelligent optimization and dynamic control of the silver nitrate crystallization process are achieved, the impurity content is effectively reduced, the crystal yield and the energy consumption efficiency are cooperatively improved, and the system robustness is enhanced.

The invention discloses an energy-saving and emission-reducing production method and device for a blast furnace, a medium and electronic equipment.The method comprises the steps that furnace charge information of original furnace charge to be fed into the furnace is obtained, and the furnace charge information is used for indicating furnace charge components of the original furnace charge; target production operation meeting the production requirements of the blast furnace is predicted based on the furnace charge information through the trained prediction model, and the production requirements comprise the steps of controlling the energy consumption of the blast furnace to be minimized and controlling the carbon emission of the blast furnace to be minimized. The prediction model constructs a quantitative relation between energy consumption and production operation and a quantitative relation between carbon emission and production operation; and controlling the blast furnace to execute the target production operation. According to the technical scheme, energy conservation and emission reduction in the blast furnace production process can be achieved.

The invention discloses an intelligent management system and method for a vehicle-mounted air conditioner, and relates to the technical field of vehicle environment control systems.The system comprises a multi-mode environment sensing module, a passenger thermal comfort degree dynamic evaluation module, a thermal load prediction and energy efficiency optimization module, an intelligent actuator cooperative control module and a central processing and data fusion unit; the method comprises the following steps: acquiring environment and physiological data through a multi-source sensor; a deep neural network model is utilized to classify and recognize the thermal sensation state of the passenger, and a unified dynamic comfort index is generated; combining vehicle bus data and navigation information to predict a future cabin thermal load, and constructing a multi-target optimization problem with energy consumption minimization as a target; solving an optimization problem to generate an optimal control strategy combination; and an actuator is driven to cooperatively act through a closed-loop control algorithm. The method improves the recognition precision of the real thermal feeling of the passengers, reduces the energy waste, optimizes the comfort and energy efficiency in a collaborative manner, and reduces the average operation power consumption of the air conditioning system by more than 15%.

The invention discloses an intelligent water affair cooperative control method and system based on a space-time diagram neural network. The method comprises the following steps: firstly, constructing a static graph dynamic association logic diagram containing a pipe network physical topology; and then, inputting the multi-view image structure into a space-time diagram neural network, accurately predicting the liquid level and the flow of future key nodes, and particularly predicting the possible overflow risk. And finally, on the basis of the prediction result, constructing and solving a multi-objective optimization problem including multiple conflict objectives of minimizing pump station energy consumption, minimizing pipe network overflow quantity, balancing water inlet load of a sewage treatment plant and the like. Through the optimization, the system can generate a set of global cooperative control instructions for regulation and control facilities such as pumps and gates. Compared with the prior art, the method has the advantages that the prediction precision of the flood peak flow of the pipe network during rainfall can be remarkably improved, the mode conversion from passive first-aid repair to active regulation and storage is realized, urban waterlogging and sewage overflow events are effectively reduced, and stable operation of a downstream sewage treatment plant is guaranteed.

The invention belongs to the technical field of environmental protection and energy efficiency optimization, particularly relates to a seawater desulfurization energy efficiency optimization and early warning method, and aims at solving the problems that a traditional desulfurization system is high in energy consumption, and hidden faults are difficult to early warn. According to the method, a multi-physical-field coupling dynamic monitoring system is constructed, parameters such as pH, dissolved oxygen and flow velocity are collected in real time, a transient working condition self-adaptive energy efficiency evaluation model is established, the pump frequency, the air volume and alkaline agent adding are dynamically adjusted in combination with a model prediction control strategy, and energy consumption minimization on the premise that the desulfurization efficiency is larger than or equal to 95% is achieved. Wavelet packet-principal component fusion analysis is synchronously implemented, degradation characteristics such as filler pressure drop and aeration uniformity are extracted to construct a degradation index, a three-level early warning mechanism is triggered, and a maintenance decision is pushed. According to the method, the energy efficiency response speed and the control precision are remarkably improved, the unit energy consumption is reduced by 12%-18%, the alkaline agent consumption is reduced by 20%, hidden degradation can be recognized 72 hours in advance, and the early warning accuracy gt is achieved; the false alarm rate is 92%; the non-planned shutdown is reduced by 60%, and the annual energy efficiency stability standard deviation of the system is 1t; and 4%.

The invention provides an energy management method, electronic equipment and a vehicle, and belongs to the technical field of energy management of vehicles. By adopting the technical scheme provided by the embodiment of the invention, the target driving condition of the vehicle is determined on the basis of the obtained speed information including the average speed of the vehicle in the preset time period and the highest speed of the vehicle in the preset time period, and then discretization processing is performed on the related parameters corresponding to the equivalent consumption minimization strategy on the basis of the target information. On one hand, the current target driving working condition of the vehicle can be accurately identified by combining the average speed and the maximum speed, and on the other hand, the energy management problem in the vehicle is associated with the real-time driving working condition or other target information, so that the matching of the energy control strategy and the actual working characteristics of the power system of the vehicle is realized; according to the method, discretization processing can be accurately carried out on parameters in the ECMS using process, the fuel economy of the vehicle under different driving working conditions is improved, and the precision of energy management is improved.

The invention discloses an aerocar energy-saving resource adjusting method and system based on a large model, and the method comprises the steps: adding a depth separable convolution layer at the front end of a Transform encoder to extract local airflow features, carrying out the modeling of time sequence dependence through a GRU network, and replacing a part of the GRU network with a GLU convolution layer; optimizing hyper-parameters of the hybrid architecture model through an improved NRBO bat algorithm to obtain a target hybrid architecture model; inputting the data into the target hybrid architecture model to output pneumatic control parameters, optimizing the pneumatic control parameters by taking energy consumption minimization as a reward function based on a PPO reinforcement learning algorithm, and generating an energy-saving control instruction; and controlling a wing variable trailing edge flap angle, distributed propulsion motor power distribution and an energy recovery system of the hovercar according to the energy-saving control instruction. The energy consumption of the hovercar under different flight working conditions is reduced, and the resource adjusting efficiency of the hovercar is improved.

The invention relates to the technical field of intelligent lighting, in particular to an intelligent-driven sensing lighting dynamic energy-saving adjusting method, which comprises the following steps of: sensing and reconstructing time-space distribution of outdoor natural light by calling outdoor and indoor environment sensors; in combination with the occupancy state of the office station and the activity type of the user, establishing a lighting demand constraint; activating a linkage acquisition device, and generating a user comfort vector; light comfort perception, illumination demand constraint and a user comfort vector serve as input and are sent into a multi-target cooperative adjustment controller, and adjustment decision vector optimization is carried out with user comfort maintenance, energy consumption minimization and natural light fusion matching as optimization targets; and finally, parameters such as brightness and color temperature of the LED lighting system are dynamically adjusted according to an optimization result, and collaborative improvement of the light environment quality and the energy-saving effect is realized. According to the method, accurate response to natural light changes and self-adaptive adjustment of personalized requirements of users are achieved, and the intelligent level of office lighting and the energy utilization efficiency are remarkably improved.

The present application belongs to the field of network type energy storage cabinet, especially relates to a kind of intelligent power consumption optimization method and system for network type energy storage, the method is by real-time acquisition operating state data, external dispatching instruction and system operation mode, constructs dynamic hypergraph model to integrally represent the coupling relationship between power scheduling task, heat load demand and heat management energy consumption;Further, with the minimum system total operating energy consumption as the goal, based on the model collaborative optimization solution, generate the joint operation strategy of power scheduling and heat management control in future period;Finally, execute the strategy on digital twin energy storage cabinet, realize the closed-loop joint control of power conversion and heat management subsystem, thereby solve the energy efficiency bottleneck problem caused by the split optimization of electric heating system, improve overall operation economy and safety.

The application belongs to the technical field of intelligent control of injection molding, and particularly discloses an injection mold temperature control system based on a PID closed-loop algorithm. The system comprises a multi-partition temperature control execution unit, a temperature sensing network, a central collaborative optimization controller, a thermodynamic state evaluation module and a dynamic set value generation unit. The central collaborative optimization controller models each temperature control partition as a non-cooperative game participant, solves a Nash equilibrium solution that takes into account product quality stability and minimum energy consumption under the constraint of thermodynamic entropy increase, and updates the PID target value of each partition in real time by the dynamic set value generation unit. The application suppresses the heat fighting phenomenon between adjacent partitions, reduces the invalid start and stop of the heating and cooling device, reduces energy consumption, and improves the temperature control robustness and the stability of the molding quality.

The invention relates to the technical field of seabed foundation treatment, and discloses a seabed sand layer softening construction method which comprises the following steps: integrating a multi-physical field sensing system, a sound wave resonance module and a torsion pulse module on double-wheel stirring equipment; during construction, a real-time state vector representing stratum interaction is constructed through a sensing system; continuously applying a high-frequency sound wave and a functional composite fluid, and judging a stubborn barrier layer triggering condition based on the state vector so as to conditionally apply a torsion pulse; and meanwhile, minimizing the total energy consumption of the system as a target function, and performing closed-loop regulation and control on sound wave and fluid parameters. According to the method, through real-time sensing, energy collaboration and closed-loop optimization, accurate self-adaptive control over the construction process is achieved, and the total energy consumption of construction is remarkably reduced while the stubborn barrier layer is effectively treated.

The invention discloses a cooperative control strategy for ice slurry energy storage to assist peak regulation and frequency modulation of a power grid, and belongs to the field of power system regulation and control, energy storage technology and intelligent power grids. The method comprises the following steps: setting corresponding agents for the ice slurry energy storage system and the power grid regulation and control module; initial parameters are set, and the power grid load and the user cold demand are predicted; taking three conditions of minimizing the total energy consumption of the system, minimizing the peak deviation and meeting the cold demand of a user as optimization objectives, and combining energy conservation, cold storage constraint and cold demand constraint to construct an optimization model; asynchronous iteration is adopted to realize cooperative control of parameter optimization, peak regulation, peak deviation suppression and cold storage state self-recovery of the intelligent agent; and through convergence judgment and real-time iteration, continuous peak regulation of the power grid, peak load optimization and user cold demand guarantee are realized. The method provided by the invention can solve the problems that the peak-valley load difference of the power grid is enlarged, the peak load pressure is prominent, and the ice slurry energy storage, power grid regulation and control and user cold demand collaboration is insufficient under the high permeability of new energy.

The invention relates to the technical field of water treatment equipment multi-dimensional management, and discloses a water treatment equipment multi-dimensional management system based on an intelligent water affair cloud platform, and the system comprises the steps: calculating a dynamic alpha factor in real time through on-site off-gas detection data and a clear water reference mass transfer coefficient, and carrying out the real-time calculation of a dynamic alpha factor based on the monotonous relation between a bubble stagnation cap and mass transfer resistance; and performing inversion to obtain continuous parameters for quantifying the gas-liquid interface state. And further calculating the real-time sensitivity of the parameter to the air volume of the air blower, constructing an air volume and mass transfer nonlinear model, and solving the target air volume with the minimum energy consumption under the condition of meeting the technological oxygen demand constraint condition. And finally, the target air volume serves as a control instruction to be issued, meanwhile, interface recovery time is output according to the time change rate of the gas-liquid interface parameters, and dynamic energy saving and interface state collaborative management of the water treatment aeration system is achieved.

This invention discloses a method and system for coordinated management of oil and gas pipeline transportation at a joint station, relating to the field of oil and gas field gathering and transportation technology. The system consists of several functional modules, including: a status identification module, which collects multi-dimensional data from joint station equipment via edge computing nodes, identifies real-time equipment efficiency curves online, and generates real-time equipment status sequences; a pipeline network coupling construction module, which constructs a nonlinear hydrothermal coupling mechanism model of the pipeline network based on the real-time equipment status sequences through a cloud-based digital twin platform, and performs online self-correction to obtain a global pipeline network simulation model of real-time field conditions; and a two-layer hierarchical optimization algorithm, which, based on the global pipeline network simulation model and combined with historical inflow data, outputs the optimal pressure and temperature setpoints for each joint station in the future period; wherein the two-layer hierarchical optimization algorithm includes an upper-layer global energy consumption minimization optimization layer and a lower-layer local predictive control tracking layer.

The application discloses an underwater glider robot anchoring process energy consumption optimization method and system based on a sub-domain gray box model, and the energy consumption optimization method comprises the following steps: establishing a white box energy consumption mechanism model in the underwater glider robot anchoring process; dividing the whole anchoring energy consumption stage of the underwater glider robot into multiple sub-domains, and using a Latin hypercube design method to simulate and generate multiple sub-domain samples meeting variable range constraints by using a hardware-in-the-loop simulation platform; selecting a Kriging model as a sub-domain surrogate model, fusing the white box energy consumption mechanism model and constraint conditions of an oil tank volume and a movable mass position, forming a sub-domain gray box model, and performing segmented energy consumption fitting by using the sub-domain gray box model; taking energy consumption minimization fitted by the sub-domain gray box model as a target, iteratively optimizing the sub-domain surrogate model by using a dynamic guidance self-adjusting particle swarm optimization algorithm, and outputting optimal planning parameters. The application realizes low-energy consumption control of the whole anchoring process, and is suitable for tasks such as ocean resource exploration and hydrological monitoring.

The invention provides a global energy optimal control method and system for an electric vertical take-off and landing aircraft, and relates to the technical field of aircraft control. The method comprises the following steps: firstly, carrying out dynamic modeling on a transition stage, simplifying an equation, and then analyzing the lowest energy consumption performance of a conversion acceleration stage and a re-conversion deceleration stage; introducing an energy consumption formula, establishing a cubic tilt scheduling formula, solving a coefficient, and simplifying a strategy into optimization of a key parameter k; finally, a nonlinear optimization analysis method is adopted, the function relation between the power of each flight stage and related variables is defined, energy consumption minimization and constraint conditions are formalized into equations, global energy consumption minimization is achieved, the cruising ability of the electric vertical take-off and landing aircraft is improved, and the electric vertical take-off and landing aircraft has a wide application prospect. The problems of low energy management efficiency, lack of a global energy optimization view angle and control strategy simplification in the prior art are effectively solved, and the differentiated energy requirements of different flight stages are met.

The application discloses a kind of underground cavern group construction ventilation network energy consumption minimization method, it is related to ventilation network optimization and energy consumption control technical field, this method includes: constructing dynamic ventilation network perception and reconstruction model during construction, and the topological structure is dynamically updated by multi-source sensor and construction progress log;Based on Hardy Cross method, the theoretical air flow distribution satisfying physical conservation is solved;Fusion theoretical air flow and real-time data, construct Hardy Cross-GB hybrid prediction model, train gradient boosting regression to correct prediction bias, output air flow and energy consumption prediction value;Based on the model, the system total power minimization objective function is constructed, the frequency conversion strategy of fan is optimized under the constraint of air demand and the control instruction is output.The application realizes dynamic ventilation network accurate simulation and real-time energy efficiency optimization, significantly reduces the energy consumption of ventilation system, guarantees construction safety and control stability.