197 results about "Energy consumption minimization" patented technology

The invention relates to the technical field of air conditioner chilled water control, in particular to an air conditioner chilled water control method and system based on deep learning, and the method comprises the following steps: collecting states of an air conditioner water chilling unit, including water pump frequency and valve opening feedback, and pipe network water supply temperature, return water temperature, flow and differential pressure operation readings; according to the method, the state parameters of the water chilling unit are dynamically collected, the system operation state feature set is constructed, real-time fusion and standardization processing of multi-source heterogeneous data are achieved, the problem of time sequence dislocation caused by sensor sampling frequency differences is solved, the heat load change key input quantity is recognized based on the feature set, and the system operation state feature set is established. And the performance attenuation trend of the water pump is accurately pre-judged in combination with prediction calculation, and extra energy consumption caused by lagging adjustment is reduced. A layered optimization framework with total energy consumption minimization as a target is introduced, water supply temperature, pressure difference setting and unit operation combination are decoupled into independent optimization sub-problems, and invalid work of a water pump is reduced on the premise that the tail end heat requirement is met.

The invention relates to the technical field of industrial control, and discloses a dry-wet combined cooling tower operation state prediction and diagnosis system comprising a load prediction module used for obtaining production plan data and environment information, constructing a basic heat load prediction model, and outputting a total prediction heat load; the thermal modeling module is used for establishing a heat transfer model and an energy consumption model, the heat transfer model outputs cooling amounts in different operation modes, and the energy consumption model outputs total predicted power; the dry-wet decision module is used for constructing a multi-objective optimization function and solving the multi-objective optimization function to obtain a dry-wet switching strategy; and the control execution module is used for executing the dry-wet switching strategy. According to the method, the basic thermal load prediction model is established, so that the prediction precision of the thermal load is improved, and differentiated cooling strategies are provided for different production stages; on the basis of real-time load requirements and environmental conditions, a dry mode or a wet mode is intelligently selected for operation, and a multi-objective optimization function is constructed, so that the balance between cooling requirement meeting and system energy consumption minimization is realized.

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.

The invention relates to the technical field of swimming pool heat pump control, and provides an electric heating coupling control method and system based on a swimming pool heat pump. The method comprises the following steps: collecting multi-source monitoring data of a swimming pool in real time; calculating a comprehensive temperature difference value and a heat loss rate based on the multi-source monitoring data; the comprehensive temperature difference value and the heat loss rate of the swimming pool are input into a coupling heating model based on a space-time attention mechanism, and dynamic heat pump power control parameters are generated; inputting multi-source monitoring data into the water quality and temperature linkage control model, and solving an optimal heat pump cycle adjustment strategy by adopting a Nash equilibrium algorithm with maximization of a water quality standard-reaching rate and minimization of temperature control energy consumption as a game target; according to the optimal heat pump cycle adjusting strategy and the heat pump power control parameters, electric heating control is conducted on the swimming pool heat pump, so that the temperature control efficiency of the swimming pool is improved, the operation safety of the swimming pool is guaranteed, and energy consumption minimization can be achieved on the premise that it is guaranteed that the water quality reaches the standard.

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 provides a sewage treatment multi-objective optimization method and device and a storage medium, and relates to the field of sewage data analysis and processing.The method comprises the steps that firstly, multiple types of sewage data are collected in a time sequence mode at a specified sewage data station and preprocessed to form a data set, then a sewage prediction model is constructed based on a deep learning model, and a sewage prediction result is obtained; and inputting the data set for training, and predicting the energy consumption of the sewage treatment equipment and the effluent pollutant concentration. Then, a multi-objective optimization function is constructed, so that the total energy consumption is minimized, and the effluent pollutant concentration reaches the standard; and finally, solving the function by using a multi-objective particle swarm optimization algorithm to obtain an optimal solution set, and screening out an optimal operation parameter from the optimal solution set. According to the method, two important targets of energy consumption minimization and effluent pollutant concentration standard reaching can be considered at the same time, the relation between energy consumption and water quality can be balanced, the optimal operation parameters are screened out from the optimal solution set, and the overall performance of sewage treatment equipment is improved.

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 a server temperature monitoring and regulating method and electronic equipment, and relates to the technical field of servers. According to the scheme, the sensor group comprising a plurality of different types of temperature sensors is used for monitoring the temperature of each component in the server, so that the measurement error is obviously reduced; meanwhile, the working temperature interval of the component compared with the measured temperature value is not a fixed interval, but is dynamically adjusted according to the change of the load in the operation process of the server, so that the optimal heat dissipation effect is realized during temperature regulation and control; when it is confirmed that temperature regulation and control need to be carried out, the target cooling liquid flow is determined through a pre-constructed cooling liquid flow prediction model, the model is specifically constructed based on the average temperature of all parts of the server, the heat dissipation power and the server load, the optimal cooling liquid flow can be predicted to achieve energy consumption minimization of a pump set, heat dissipation energy consumption is effectively reduced, and the service life of the pump set is prolonged. And comprehensive optimization of the heat dissipation effect is realized.

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 intelligent manufacturing, and particularly discloses a space-time constraint modeling-based multi-AGV-mechanical arm collaborative carrying system, which comprises a space-time constraint modeling module, a task decomposition and priority evaluation module, a collaborative path planning module, a dynamic collision prediction and adjustment module and an energy consumption and efficiency optimization module, according to the method, the total energy consumption, the total duration and the congestion index are subjected to normalization weighting, the unified fitness function is constructed, and the genetic algorithm and the particle swarm optimization are adopted for joint optimization, so that energy consumption minimization and operation period minimization can be taken into consideration at the same time, and a high-congestion section is actively avoided on path selection. According to the multi-target fusion, side effects caused by single index optimization are avoided, the sustainable operation capacity of the system is improved, the actual time length fed back after task execution, energy consumption and collision near-loss information are improved, model parameters are updated through a reinforcement learning or online incremental learning module, and self-evolution of a strategy is achieved.

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 relates to the field of communication, in particular to a method for delivering cache content of a mobile edge computing network for energy consumption optimization. For a cache-based MEC cellular network, the method reasonably utilizes user movement information, and calculates corresponding path fading, channel gain and shadow fading by utilizing future position information of a user. Determining the minimum transmission power by using the calculated channel gain and path fading, then setting a time slot, carrying out content delivery decision on data packets requested by different users, and allocating wireless communication resources; and with minimization of the total emission energy consumption of the base station as a target, modeling a cache content delivery problem into a mixed integer programming problem to carry out optimization solution so as to obtain an energy consumption minimization decision for the base station to deliver all data packets.

The invention relates to a proportion optimization method for a cement stabilized macadam base of recycled aggregate. Taking an interface failure sensitivity coefficient IFSI as a quantitative index; the method comprises the following steps: performing microstructure reconstruction on recycled aggregate particles through SEM scanning, resistivity testing and CT scanning, extracting two indexes of defect density and surface adsorption strength, and constructing a degradation prediction model ITZ; the ITZ result is used as a basic parameter for adjusting the water cement ratio and the fine aggregate content, so that a mechanism from aggregate defects to interface failure is connected; establishing unit volume interface energy consumption minimization as an optimization target based on an IFSI value output by the interface failure model; the influence of the water cement ratio, the grading difference and the fine particle wrapping degree on the interface bonding energy consumption is analyzed, and a matching point under the specific recycled aggregate condition is found out through a multivariable function coupling model.

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 tunnel ventilation system and a control method thereof, and relates to the technical field of tunnel ventilation energy-saving control. According to the method, traffic data, environment data and equipment starting data in a tunnel are preprocessed and divided into data sets, a traffic prediction model based on LSTM and an environment prediction model based on full connection are constructed, and training is carried out through a root-mean-square error and an average absolute percentage error; and sequentially predicting traffic and environment data by using the model, and solving an optimal ventilation equipment combination through a sequential quadratic programming algorithm by taking energy consumption minimization as a target and environmental standard reaching as a constraint, and adjusting operation. The method has the advantages that advanced regulation and control are achieved through the two-stage prediction model, pollutants are prevented from exceeding the standard, and the air quality is guaranteed; dynamic training, multi-parameter optimization and a fault tolerance mechanism are combined, energy conservation and environment regulation and control are accurately balanced, meanwhile, the operation reliability and the intelligent level of the system are improved, and manual intervention is reduced.

The invention discloses a mechanical arm trajectory planning method and device, electronic equipment and a storage medium. The method comprises the steps that the target pose of the tail end of the mechanical arm is obtained, inverse kinematics solving is conducted according to the target pose of the tail end of the mechanical arm, and a candidate solution set of the joint angle of the mechanical arm is obtained; the joint displacement and the joint energy consumption serve as coupling variables, the candidate solution set of the mechanical arm joint angle is screened, and a candidate solution, with the minimum joint energy consumption, of the mechanical arm joint angle in the candidate solution set is obtained; and trajectory planning is conducted on the basis of the candidate solution of the mechanical arm joint angle with the minimum joint energy consumption in the candidate solution set, and the target motion trajectory of the mechanical arm is obtained. According to the technical scheme, the joint displacement and the joint energy consumption serve as the coupling variables to conduct multi-candidate-solution screening, the candidate solution, with the minimum energy consumption, of the joint angle of the mechanical arm is obtained, joint displacement and energy consumption minimization is achieved synchronously, and therefore the problem that the operation efficiency and energy consumption of the mechanical arm are unbalanced is solved.

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 relates to the technical field of radar and communication, and particularly discloses an integrated base station calculation unloading method based on IRS assistance, which comprises the following steps of: unloading received data to a server for centralized processing by means of an intelligent reflection surface (IRS) technology in a base station near-end high-performance edge server environment, and constructing a radar detection and communication uplink joint receiving system; and then, under receiver power constraint, frequency band energy constraint and IRS constant modulus constraint, with minimization of total energy consumption of the system as a radar point target, constructing a calculation unloading problem model so as to perform synchronous optimization on receiving vector design. And finally, decomposing the calculation unloading problem model, and respectively solving a radar phase matrix and a communication receiving vector IRS phase matrix. According to the method, the overall performance of each functional module is improved, the adaptive capacity and the anti-interference capacity of the system in an actual complex environment are enhanced, and the global optimal balance of the communication rate, the sensing precision, the time delay and the energy efficiency is realized.

The invention discloses a photovoltaic cleaning robot operation method and a photovoltaic cleaning robot, and the method comprises the steps: collecting the surface pollution image data and environment perception data of a photovoltaic module in real time, and constructing a time-space correlation pollution state sequence; inputting the pollution state sequence into the edge AI prediction model, and outputting a pollution trend prediction value; constructing a dynamic pollution state transition matrix based on the pollution trend predicted value, and constructing a Markov decision process model based on the dynamic pollution state transition matrix; solving the Markov decision process model, and generating a dynamic path planning sequence considering clean energy consumption and power generation income balance; the cleaning robot is controlled to execute the dynamic path planning sequence, and pollution removal effect feedback data is obtained in real time in the operation process; and updating the parameters of the edge AI prediction model and the probability distribution of the state transition matrix on line according to the feedback data. According to the invention, the cleaning efficiency maximization, the energy consumption minimization and the long-term power generation benefit optimization can be realized.

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 provides an air conditioning system energy efficiency optimization method based on load prediction, and the method comprises the steps: collecting air conditioning load related data, building a load prediction model which employs a support vector regression model, and training the support vector regression model through the air conditioning load related data; constructing a model function by adopting a mixed kernel function of a linear kernel and a Gaussian kernel in the support vector regression model, and obtaining an air conditioner load prediction result according to the load prediction model; an energy efficiency optimization model is constructed, the energy efficiency optimization model adopts a mixed integer linear programming model, the mixed integer linear programming model takes the total energy consumption minimization of the air conditioning system as a target function, energy consumption of various devices in the air conditioning system is considered, and an energy consumption function is established; the energy consumption function represents the energy consumption under the given control variable and the air conditioner load prediction result. The operation state and parameters of the air conditioning system are adjusted in real time according to the prediction load and the result of the optimization model, and energy-saving operation of the system is achieved.

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.