236 results about "Energy consumption minimization" patented technology

The invention provides a BIM-based building energy consumption simulation analysis optimization method and system, and relates to the technical field of building energy consumption, and the method comprises the steps: building a building energy consumption digital twinborn model through integrating a building information model (BIM) and real-time operation data, such as indoor and outdoor temperature, equipment operation, electrical load and personnel density, collected by an Internet of Things sensor. An LSTM and GNN mixed deep learning algorithm is used to predict building energy consumption, and clustering analysis is used to identify an energy consumption abnormal mode and a peak period. And finally, based on a reinforcement learning algorithm, constructing an energy consumption optimization model taking total energy consumption minimization as a target and taking indoor thermal comfort and air quality as constraints, outputting equipment optimization operation parameters such as air conditioner temperature, fresh air volume, illumination brightness and the like, and realizing intelligent optimization control of building energy consumption, so that the building energy consumption is reduced, and the building operation efficiency is improved.

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 provides an intelligent building elevator traffic scheduling method and system for energy-saving optimization. The method comprises the steps that real-time data of building sensing equipment and an elevator monitoring system are collected; constructing a prediction model to obtain the flow thermal distribution of future floors; designing a scheduling strategy based on multi-target reinforcement learning, wherein the scheduling strategy comprises target floor distribution and a dynamic operation path of the elevator; and formulating an execution control strategy for energy consumption optimization to realize global energy consumption minimization, and generating an optimized control instruction. Through deep fusion of flow prediction, scheduling optimization and energy consumption management, the passenger experience is remarkably improved while the adaptability, flexibility and energy efficiency of the system are improved, and an intelligent and efficient elevator scheduling solution is provided for modern high-rise buildings.

The invention provides a task allocation and optimal scheduling method and system for a multi-robot collaborative industrial assembly line, and relates to the technical field of robots, comprising the steps of constructing a task allocation strategy network through deep reinforcement learning, encoding workpiece types and attribute information into a state vector input network, a multi-objective optimization function including total assembly time minimization, load balance degree maximization and energy consumption minimization is designed, a near-end strategy optimization algorithm is adopted to train network parameters, a robot task mapping matrix is output as a distribution scheme, and the network parameters are dynamically optimized and updated in an online learning mode. The task allocation efficiency of the multi-robot collaborative assembly production line can be remarkably improved, the dynamic optimization scheduling of the production line is realized, the system energy consumption is reduced, and the assembly efficiency is improved.

The invention relates to the technical field of wireless communication, and discloses an unmanned aerial vehicle mobile edge computing resource allocation method based on deep reinforcement learning, which constructs a mixed integer non-convex optimization problem, and the objective of the optimization problem is to collaboratively optimize the flight path of each unmanned aerial vehicle, a mobile user scheduling strategy and a task unloading proportion. Therefore, the time delay-energy consumption minimization is realized; and solving the optimization problem by adopting a multi-agent double-delay depth deterministic strategy gradient algorithm: converting the optimization problem into a Markov game model, and realizing the solution of the optimization problem through an experience playback pool and a double-evaluation network updating strategy. According to the method provided by the invention, the flight path and the unloading proportion of the unmanned aerial vehicle can be adaptively adjusted, so that calculation resource allocation for ground users is efficiently completed. Compared with a global unloading scheme, an average unloading scheme and other deep reinforcement learning methods, the method provided by the invention has excellent performance of lower energy consumption and shorter time delay while completing tasks.

The invention discloses a water-cooling central air conditioner load management and control method based on artificial intelligence, and relates to the technical field of load management and control. Load prediction is carried out by combining a long and short-term memory time sequence model and a physical constraint neural network in a water chilling unit load prediction link and utilizing historical data; errors are corrected through an energy balance equation of the water chilling unit, and the prediction accuracy is improved; multi-objective optimization control is adopted, COP maximization of a water chilling unit, energy consumption minimization of a cooling tower fan and cooling water supply temperature stability serve as optimization objectives, environment temperature and humidity prediction is combined, and a cooling water supply temperature set point and a cooling tower fan start-stop strategy are dynamically calculated; based on a cooling water supply temperature set point and a fan start-stop strategy, a water chilling unit, a cooling tower fan and a water pump are dynamically adjusted, and the high-frequency operation phenomenon is reduced through fan start-stop optimization.

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 relates to the technical field of computing power networks, in particular to a computing power network-oriented time delay and energy consumption optimization method for constructing a task hypergraph, which comprises the following steps of: S1, establishing a directed acyclic graph according to a computing power task, and establishing a hypergraph model according to a dependency relationship of the directed acyclic graph; s2, calculating task time and task energy consumption according to the hypergraph model, and minimizing total task time and task energy consumption; and S3, obtaining an unloading decision model according to the hypergraph model and the MDP model, training the unloading decision model according to a near-end strategy optimization PPO algorithm, and finally obtaining a task unloading prediction network. According to the method, the hypergraph model is established through the dependency relationship of the DAG, the topological relationship among the tasks representing the computing power and the different attribute dependency relationship and the spatial dependency relationship among the different tasks, under the condition that multiple hyperedges are established, the multi-association attributes of different partitions are balanced by using hypergraph partitions, the reward function is designed through the hypergraph model, and the reward effect is improved. The efficient unloading optimization effect is achieved, and the final unloading energy consumption is reduced.

The invention discloses an ACU intelligent control system, and relates to the technical field of adaptive control, and the system comprises a prediction module which is used for deploying a multi-parameter sensing network, collecting environment data in real time, transmitting the environment data to a data processing unit, combining meteorological data and historical operation data, building a load prediction model based on deep learning, and achieving the precise prediction of a system load. The control module constructs a multi-objective optimization model including energy consumption minimization, comfort maximization, equipment service life maximization and renewable energy utilization rate maximization based on the predicted load sequence, and calculates an optimal control strategy through an improved dynamic programming algorithm to realize balance control; and the optimization module issues the optimized control strategy to each execution unit through the central controller, and dynamically updates the prediction model parameters and the control strategy to form a closed-loop optimization control system. The control precision and the energy utilization efficiency of the ACU system can be effectively improved, the environment change is captured in time, and the response speed under the abnormal working condition is improved.

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 smart city lighting system adaptive brightness adjustment method, medium and system, and belongs to the technical field of lighting brightness adjustment processing, and the method comprises the steps: obtaining the partition information of a smart city lighting system, building a lighting system matrix, and obtaining the partition information of the smart city lighting system; and the ambient illuminance, the people flow density, the traffic flow density and the weather condition data of the lighting units of all the partitions are collected in real time. Similarity analysis is carried out based on real-time data, and a multi-objective optimization model is established, including illumination demand satisfaction, energy consumption minimization and illumination uniformity. And performing optimization calculation on the partitions by using the optimization model to obtain an optimized partition scheme, and constructing a second multi-objective optimization model including energy consumption minimization, illumination uniformity maximization and partition number minimization. And establishing a brightness adaptive model comprising a fuzzy controller for the optimized minimum partition, and calculating the target brightness of the illumination unit in each partition according to the real-time data, thereby solving the problem that the brightness cannot be adaptively adjusted according to the environment in the prior art.

The invention relates to an energy consumption control method and device for a data center cooling system, equipment, a storage medium and a program product, and the method comprises the steps: obtaining the current operation state data of the data center cooling system, taking the minimization of the total energy consumption of the data center cooling system as a target, and predicting the energy consumption of the data center cooling system according to the operation state data and a target prediction model; a first flow parameter of a primary side water pump and a second flow parameter of a secondary side water pump in the data center cooling system are adjusted to obtain a first target flow of the primary side water pump and a second target flow of the secondary side water pump, and finally the current flow of the primary side water pump is adjusted to the first target flow. And the current flow of the secondary side water pump is adjusted to the second target flow. According to the method, the flow parameters of the primary-side water pump and the secondary-side water pump are adjusted in real time based on the target prediction model, so that the primary-side water pump and the secondary-side water pump operate in the optimal state and can flexibly cope with different operation environment changes, and then the accuracy of energy consumption control is improved.

The invention discloses an interference-assisted resource optimization method for an asynchronous federated learning system, and the method comprises the steps: constructing a dynamic model, about time delay and energy consumption, of the asynchronous federated learning system in a first arrival first arrival mode; a two-step algorithm which is deployed on a base station carrying an edge server and combines a depth deterministic strategy gradient and a Transformer network is utilized to make a resource allocation decision comprising an uplink and downlink terminal equipment scheduling strategy and CPU frequency, sending power and interference power of terminal equipment aiming at state information collected in a current environment during each global model aggregation; and the eavesdropping attack is resisted by enabling the downlink data transmission rate to be greater than a given secrecy rate by using an interference-assisted technology. Aiming at a first arrival first arrival mode, namely an asynchronous federated learning process that a local model of a first arrival server is selected to participate in aggregation, dynamic modeling about time delay and energy consumption in an actual wireless communication scene is analyzed, and a two-step resource allocation strategy and an interference assistance technology based on DDPG are adopted; and the time delay and the energy consumption required by asynchronous federated learning are minimized on the basis of ensuring the security of a physical layer.

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 power grid voltage control task unloading and computing resource allocation method and device and a medium, and the method comprises the steps: setting a power grid voltage node partition model according to the number of nodes and the load condition, and solving the calculation time delay of a voltage control task of each partition according to the number of nodes and the calculation complexity of different partitions; calculating the time delay based on each partition and then solving the global task calculation time delay; after the global calculation time delay is solved, the energy consumption in the task calculation process is solved; after the energy consumption in the task calculation process is solved, modeling is carried out to solve time delay and energy consumption minimization; and finally, determining an objective function and constraint conditions based on time delay and energy consumption minimization, and solving by using a deep reinforcement learning algorithm to obtain a power grid voltage control task unloading and computing resource allocation strategy. According to the method, a power grid partitioning strategy is optimized, and calculation resources are reasonably allocated, so that the calculation time delay and the calculation energy consumption cost of a voltage control task are minimized, and the self-adaptability and the robustness of a power grid regulation and control system are improved.

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 provides an intelligent fire-fighting parallelization service function chain deployment method based on energy perception and time delay guarantee. The method comprises the steps that a traditional service function chain is converted into a parallelization service function chain; under the condition that the end-to-end delay requirement is ensured to be met, establishing a target function which aims at minimizing the energy consumption of deploying the parallel service function chain, and establishing an integer linear programming model according to the target function; and solving the integer linear programming model by adopting a heuristic algorithm considering edge node computing resources, link bandwidth resources, a node starting state and a link starting state to obtain an optimal deployment scheme. According to the method, the intelligent fire-fighting service request is abstracted into the service function chain, the service request processing problem is converted into the service function chain deployment problem, and the deployment strategy and the routing strategy of the parallel SFC are dynamically adjusted under the condition that the low-delay requirement is met, so that the average total energy consumption of SFC deployment is minimized, the SFC request acceptance rate is remarkably improved, and the service performance of the service function chain is improved. The method is suitable for intelligent fire-fighting services with high requirements on time delay sensitivity and reliability.

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 discloses an edge data transmission optimization method based on energy consumption minimization and load balancing, which belongs to the technical field of edge computing, is realized through a system consisting of a central control server, an edge node, a transmission link and an SDN (Software Defined Network) controller, and comprises the following steps: S1, network topology modeling and data collection; s2, link selection optimization is carried out; s3, topological optimization and dynamic adjustment are carried out; s4, executing and controlling data transmission; s5, performing adaptive dynamic adjustment; and S6, the steps S1 to S5 are executed in a circulating mode. According to the method, a multi-target reinforcement learning algorithm is adopted, and joint optimization of energy consumption minimization and load balancing is achieved in edge data transmission; transmission links are dynamically adjusted and redundant nodes and links are closed, so that the overall energy consumption of the system is reduced; through link classification and load balancing optimization, the data transmission efficiency and the network stability are improved; and a heuristic factor and a Chebyshev scaling function are introduced, so that the convergence speed of the algorithm is accelerated.

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.