779 results about "Energy cost" patented technology

The invention discloses a multi-level energy management system based on multi-dimensional data, and relates to the technical field of energy intelligent management, and the system comprises a multi-source data collection module which collects power utilization, environment and equipment state data in real time; the data fusion processing module is used for processing abnormal values through an algorithm and fusing multi-scale data features; the energy state evaluation module is used for realizing equipment state evaluation and early warning by using a fusion algorithm and a prediction model; the multi-level energy scheduling module adopts an optimization algorithm to balance the energy cost, the production efficiency and the carbon emission, and dynamically adjusts the strategy; the energy performance analysis module is used for developing an analysis tool and an evaluation model; and the decision support module is used for configuring an expert knowledge base and developing a fault diagnosis system and a knowledge graph. Through multi-dimensional data acquisition and multi-level management, the energy data error is greatly reduced, the comprehensive energy cost and carbon emission are remarkably reduced, the cost of participating in enterprise operation is reduced, and the accuracy, safety and sustainability of energy management are improved.

The embodiment of the invention provides an intelligent dynamic load regulation and control method and system for an intelligent power grid. Wherein power parameters of key nodes are collected in real time through time sequence analysis and a deep learning algorithm, future power demand fluctuation is predicted, and a power grid topological structure model is constructed to identify the key nodes and a potential influence range; a genetic algorithm, particle swarm optimization and other multi-objective optimization algorithms are adopted to generate specific operation instructions for different power utilization areas or devices, and optimization objectives comprise minimization of energy cost, maximization of renewable energy utilization rate and guarantee of power supply stability; converting the strategies into guidance suggestions, pushing the guidance suggestions to a terminal user, collecting feedback information, generating a preference model by applying a reinforcement learning algorithm, and selecting an optimal strategy for implementation based on user acceptance and feasibility evaluation; risks and abnormal conditions are detected through the Bayesian network, and the strategy is adjusted in time. According to the technical scheme provided by the embodiment of the invention, the efficient distribution of power resources and the energy utilization efficiency in the smart power grid are improved.

The invention discloses an acquisition and processing system and method for dry data of a transformer, and relates to the technical field of data processing. Comprising the following steps: step 1, multi-source data real-time acquisition and edge preprocessing; 2, constructing a drying end point prediction model; 3, constructing a composite objective function, and solving the composite objective function by adopting a multi-objective evolutionary algorithm; 4, performing real-time correction and feedback; according to the method, key parameters in the drying treatment process are monitored in real time, an accurate drying end point prediction model is automatically constructed, heating and air exhaust curves are dynamically optimized according to the real-time working condition and the production strategy, and traditional manual blind judgment and static presetting are replaced; and meanwhile, the operation energy consumption is remarkably reduced, the optimal balance of the energy consumption and the production efficiency is realized through a multi-objective evolutionary algorithm and an online Kalman filtering correction closed loop mechanism, the drying quality and the equipment safety are improved, the energy cost is greatly saved, and the dual requirements of modern intelligent manufacturing for flexibility, energy conservation and consumption reduction are met.

The invention discloses an airport energy balance management method, system and device and a medium, and relates to the field of energy management. The method comprises the following steps: constructing an energy topological graph according to multi-source data; predicting energy load demands of the airport in different time periods according to the energy topological graph, determining the supply amount of renewable energy in different time periods according to the predicted illumination intensity and wind power, and determining a time-sharing energy supply and demand balance probability according to the energy load demands and the supply amount; determining a preliminary energy management plan according to the cost parameter, the time-sharing energy supply and demand balance probability and the equipment operation constraint, wherein the preliminary energy management plan comprises an energy storage system charging and discharging time sequence, a standby generator starting and stopping plan and a power grid electricity purchasing proportion; and executing the preliminary energy management plan through the digital twin of the airport to obtain first energy execution data, and adjusting the preliminary energy management plan through the difference between the first energy execution data and the second energy execution data at the current moment. By implementing the technical scheme provided by the invention, the energy cost is reduced.

The invention discloses a low-carbon building monitoring system and method based on digital twinning, and relates to the technical field of digital twinning, the system comprises a data acquisition unit, a computing power cooperation unit, a regulation and control execution unit and a user interaction unit, the data acquisition unit carries out regional targeted timing monitoring, builds a unified semantic standard and a dynamic knowledge graph, and sends the unified semantic standard and dynamic knowledge graph to the user interaction unit; the comprehensiveness and efficiency of data association analysis are improved; a three-level computing power collaboration system is constructed through the computing power collaboration unit, building monitoring information is integrated, complex simulation of a digital twin module is carried out, an energy transmission path and equipment fault risks are accurately tracked, prediction optimization and long-term regulation and control management are carried out, and the construction efficiency is improved. Therefore, a full-life-cycle integrated management framework in a macroscopic geographical environment, a mesoscopic energy link and microscopic equipment of a building cluster is built, cross-domain collaborative analysis is realized, the real-time requirement is met, multi-target optimization design for balancing energy cost, energy efficiency and comfort is realized, and carbon neutralization targets in the building industry are assisted to fall into the ground.

The invention discloses an energy storage equipment power balance dynamic load automatic regulation and control method and system, and the method comprises the steps: building a charging power optimization model based on an electricity price policy divided in peak and valley time periods, combining historical electricity demand data, and obtaining the optimal charging power in each time period; the method comprises the following steps: constructing a discharge power dynamic adjustment model through operation state parameters acquired by a real-time environment parameter acquisition device in combination with real-time electricity demand data, and acquiring optimal discharge power in each time period; parameters of the two models are optimized, and the load state based on the optimal charging and discharging power is balanced; based on the historical regulation and control data, the overload condition under the abnormal factors is predicted; and on the basis of a power imbalance condition, preferentially ensuring power supply of a key load according to a power distribution priority and executing energy storage equipment state self-checking. The method has the advantages that the charging power and the discharging power are optimized in combination with the electricity price and the real-time requirement, so that the load state is balanced, the energy cost is reduced, and the equipment overload risk is avoided.

The invention discloses a smart home energy management method based on deep countermeasure reverse reinforcement learning. The method comprises the following steps: (1) modeling a smart home energy cost minimization problem and designing an environment state and an action corresponding to a Markov decision process; (2) constructing n groups of random parameter historical data based on a group of random parameter historical data and a rolling generation method; (3) solving the minimization problem by using n groups of random parameter historical data and an optimization algorithm and obtaining n expert tracks; (4) training the reward network by the discriminator based on the expert trajectory and the trajectory generated by the generator agent; under the guidance of the reward network, training a generator agent by using an experience tuple and a near-end strategy optimization algorithm; (5) repeating the step (4) until an intelligent agent strategy with stable training performance is obtained; and (6) deploying the intelligent agent strategy obtained by training in an actual environment for operation. Compared with an existing method, the method can effectively reduce energy cost and improve user comfort.

The invention provides a heat storage heat pump system control method based on a physical information neural network, and belongs to the technical field of heat storage pump system intelligent control. Aiming at the problems that in the prior art, an algorithm is difficult to adapt to dynamic energy consumption requirements, engineering application of a model is difficult due to building space heterogeneity, high-order RC model prediction credibility is weak, engineering feasibility is poor and the like, a solution combining a physical information sequence to sequence neural network technology and a finite-state machine control strategy is provided. On the model level, a 2R2C resistance-capacitance RC model of building temperature change is established, and then a PI-Seq2seq prediction model is proposed based on the physical model. On the control flow optimization level, on the basis of an industrial and commercial time-of-use electricity price policy, an FSM control model is designed, a system state set is defined, parameters and a transfer function are input, and a control rule is constructed in combination with the working period of a building heat pump and the characteristics of a heat storage tank. And finally, energy consumption cost optimization and indoor temperature stabilization under the peak-valley electricity price are realized.

The invention discloses a smart home energy management method and system based on personalized federal reinforcement learning, and belongs to the field of smart home energy management, and the method comprises the following steps: modeling a plurality of heterogeneous smart home energy cost minimization problems, and designing an environment state, an action and a reward function corresponding to a Markov decision process; the edge end intelligent agents of the N heterogeneous smart home environments locally utilize a deep reinforcement learning algorithm optimization strategy and perform federated learning by means of the cloud center server to obtain a pre-training global model with stable training performance; performing post-training fine tuning on the edge end intelligent agent of each heterogeneous smart home environment to obtain N personalized energy management strategies suitable for the N heterogeneous smart home environments; and the personalized energy management strategy obtained through fine adjustment is deployed in an actual environment for operation, and the energy cost of the system can be effectively reduced on the premise that the thermal comfort of residents is fully guaranteed.

The invention relates to the technical field of mine energy loss cost statistics, in particular to a cost statistics method based on mine energy loss balance, which comprises the following steps: deploying multiple types of sensors in mine equipment and areas to collect data, and carrying out anomaly detection, interpolation supplement and classified storage processing; constructing an energy consumption model by integrating equipment power, load and environmental factors, accounting electric power, fuel and total energy cost according to the energy consumption model, and analyzing the proportion; a genetic algorithm, a particle swarm algorithm and an LSTM model are used to optimize equipment parameters and an energy distribution path, and energy demand and cost are predicted; developing a visual interface to display energy consumption and cost data, generating a decision report, and pushing the decision report to a manager; comparing actual and predicted data regularly, collecting equipment, process and market information, and optimizing an energy loss and cost accounting model and algorithm; according to the method, fine energy management is realized, the utilization efficiency is improved, the cost is reduced, scientific decision support is provided for mine energy management, and the system adaptability and sustainability are enhanced.

The invention is suitable for the technical field of mobile edge computing, and provides a hybrid energy WPT-MEC system-based task unloading and resource allocation method, which comprises the following steps of: establishing a system model; an optimization problem with the goal of minimizing the long-term average power grid energy consumption is drawn up and converted into a Markov decision process; implementing an action space reduction strategy, and executing data normalization processing; constructing an enhanced depth deterministic policy gradient (EDDPG) algorithm; and generating and outputting an optimal strategy according to a real-time system state by using the trained network. According to the method, the wireless power transmission (WPT) technology, the green energy harvesting technology and the mobile edge computing technology are combined, so that the energy use duration and the computing capacity of the wireless equipment are improved. The online decision-making mechanism based on the EDDPG algorithm can dynamically optimize task unloading and resource allocation strategies, adapts to a dynamic environment, reduces energy cost and environmental pollution, and provides reliable technical support for large-scale deployment of the Internet of Things.

The invention discloses a multi-type power supply cooperative scheduling operation method and device, a medium and a product, and relates to the field of new energy. The method comprises the following steps: firstly, establishing a power supply side output fluctuation minimum objective function O1, a new energy abandoned energy cost minimum objective function O2 and constraint conditions thereof, wherein the constraint conditions comprise constraint conditions of wind power generation, photovoltaic power generation and an energy storage power station; adopting a multi-target grey wolf optimization algorithm to solve an optimal solution set of new energy and energy storage power station output as an optimal solution of a new energy optimization layer; establishing a minimum target function O3 of dispatching operation energy consumption of the thermal power generating unit and constraint conditions thereof by combining the coal fuel quantity of the thermal power generating unit and the start-stop operation energy consumption; solving an optimal solution set of the start-stop state, the output state and the operation energy consumption of the thermal power generating unit by adopting an improved quantum particle swarm algorithm to serve as an optimal solution of a thermal power generating unit optimization layer; cooperative scheduling operation of multiple types of power supplies is guided based on the optimal solutions of the new energy optimization layer and the thermal power generating unit optimization layer, and the stability, accuracy, flexibility and economical efficiency of cooperative scheduling can be improved.

The collection, communication, storage, monitoring, and analysis of environmental, vehicle, charger, and charging session data over a single EV or over a fleet of EVs is used to enhance or optimize existing services as well as deliver new services based on analysis of the collected data. One such service is reduction in total cost of travel. The cost minimization is achieved with near-real-time data, models populated using past collected data, and knowledge of energy costs and of chargers located in different geographic areas with potentially limited power for charging available at each charger site or station. A fleet management application used for cost minimization optimizes cost of travel for a single EV or for an entire fleet. Using individual EVs cost optimization as a goal, each EV may be optimized for potential local minimum cost of travel based on predictions using data collected from the fleet or from multiple fleets.

A microgrid has a microgrid controller that controls power delivery from two or more energy sources to one or more microgrid loads. A prediction is made as to when a main power grid may lose power. The microgrid controller operates in a prepare for islanding mode when a future loss of power is predicted, during which power received from the main power grid source, a solar power source and / or a wind power source are prioritized over energy costs and CO2 emissions to charge the battery power source to a prepare for islanding charge level. The microgrid controller operates in an islanding mode when the main power grid source loses power, during which power received from the solar power source, the wind power source, the battery power source and / or a diesel generator power source are prioritized to maximize a time that the microgrid can remain powered.

A renewable power system with a twin-configurable architecture is described. The system includes a renewable energy source (RES), an energy storage system (ESS), and at least one controllable load (CL) (e.g., AI training / datacenter). The system can serve as a baseload or semi-baseload plant for CL(s) and / or as a peaker or semi-peaker plant for an electric grid, or vice-versa, and optionally in parallel, can also provide ancillary services to the electric grid and / or to the CL(s). In certain embodiments, e.g. solar PV RES(es), the system can have capacity factors of at least about 60% and up to 100%, higher asset utilization, better economics for the RES-ESS, improved system performance, and lower energy costs as compared with known systems without a CL(s). By making load a variable, and integral part of the system, sophisticated resource allocation strategies, including AI algorithms, can be developed not previously possible with known systems lacking a CL(s).

The invention relates to the technical field of water supply scheduling, in particular to an intelligent scheduling operation method for building water supply, which comprises the following steps of: collecting water flow, water pressure, energy price and water pump operation state, calculating water supply demand and energy cost at each time point, and obtaining real-time water supply demand data; and performing time sequence analysis on the real-time water supply demand data, and extracting trend and periodic characteristics of the water supply demand. According to the method, the water flow, the water pressure, the energy price and the running state of the water pump are collected, the water supply demand and the energy cost at each time point are obtained through calculation, water supply demand prediction is optimized, meanwhile, the periodicity and trend of water supply are determined through time sequence analysis, and it is ensured that decision is based on a solid scientific basis. Besides, the operation parameters of each water pump are optimized, the energy consumption, the water supply capacity, the operation cost and the equipment abrasion degree are comprehensively evaluated, and the energy efficiency is improved and the service life of equipment is prolonged by adjusting the operation parameters.

The invention relates to the technical field of high-performance computing resource management, and discloses a computing resource allocation method for a distributed supercomputing center. The method comprises the following steps: on the basis of obtaining real-time computing task and supercomputing center resource data and uniformly quantifying, integrally predicting resource requirements of future tasks; constructing a mixed integer linear programming model with the minimization of the total operation cost as a single target, wherein the total operation cost is the sum of the energy cost, the carbon emission cost, the data transmission cost and the SLA default penalty cost; solving the model by taking the time-varying electricity price, the green energy ratio, the resource capacity and the network parameters of each center as constraint conditions to generate an optimal resource allocation scheme; and then, by dynamically monitoring the resource state and the task progress, the model is triggered to resolve when the resource utilization rate is detected to be unbalanced or default risks, so that self-adaptive adjustment is realized. According to the invention, global collaborative resource allocation across super computing centers is realized, and operation economy, environmental sustainability and service reliability are considered.

The invention discloses an intelligent agent spraying decoking system for energy-saving decoking of a hearth, and relates to the technical field of decoking of industrial boilers, the intelligent agent spraying decoking system comprises a sensing module used for collecting coke scale characteristic data, and the data comprises the temperature, thickness, component distribution and adhesion strength of coke scale; the module is combined with a temperature sensor, a component analyzer and image detection equipment for cooperative work, and multi-dimensional scorch scale characteristic information is obtained in real time. According to the intelligent agent spraying decoking system for hearth energy saving and decoking, through cooperative use of the sensing module, the control module, the agent storage module, the agent spraying module and the feedback module, intelligent and accurate decoking is achieved, the diversity of coke scales can be effectively dealt with, the heat efficiency of a boiler is improved, the energy cost is reduced, and the safety problem caused by the coke scales can be prevented; the problems that a traditional decoking method is low in efficiency, poor in applicability and insufficient in adaptability to coke scale types are solved.

The invention provides a train operation adjusting method and device in a rain and snow mode. The method comprises the steps that a target running gear of a next whole-line train in the rain and snow mode is obtained; wherein the rain and snow mode comprises a plurality of driving gears, and different driving gears correspond to different train operation data; predicting an operation plan of the train according to the target driving gear based on a train operation adjustment model to obtain a target operation plan so as to guide the train to operate in a rain and snow mode; the train operation adjustment model is constructed by taking the arrival time of each train, the departure time of each train and the running gear of each train as state variables and taking the minimum passenger riding waiting time and the minimum train operation cost as objective functions; the train operation cost comprises train energy cost and train maintenance cost. According to the method, the driving efficiency and safety of the whole-line train in the rain and snow mode are improved.

A federated learning framework including household agents configured to continuously learn model parameters for managing charging periods and discharging periods of household batteries, and microgrid agents to maximize use of local energy based on a pricing policy, including accessing power from other microgrids when there is insufficient local energy to cover local demand. and selling surplus energy to the other microgrids when power generation by the microgrid surpasses the local demand. Each household machine learning agent is configured to control household energy demand from and supply to a microgrid which they are connected in order to minimize household energy cost while adapting to changes in the energy price that is determined based on the pricing policy of the microgrid agent that encourages reduction of carbon emission. A federated learning engine combines the model parameters from the household machine learning agents to update a global household machine learning agent.

The invention relates to the technical field of energy management, in particular to a target-oriented energy optimization management system, which comprises a real-time data integration module, an energy configuration dynamic optimization module, a carbon emission deviation correction module and a carbon emission optimal control module. According to the invention, through integration of latest energy usage data and carbon emission records, dynamic optimization of energy configuration is realized, more accurate prediction and adjustment of energy distribution are allowed, carbon footprint is minimized, prediction deviation is corrected by adopting a real-time feedback mechanism, data accuracy is enhanced, and overall energy management efficiency is improved. Through real-time monitoring and dynamic optimization of control parameters, energy use can be continuously adjusted, the energy consumption proportion can be optimized, the energy cost can be reduced, the operation flexibility and the response speed can be remarkably improved, more efficient energy use and lower environmental influence can be realized, and environmental protection and resource saving can be more effectively supported.

The present disclosure discloses a system (106) and a method (400) for energy management in a network (108). The method may include initialization by defining input and output parameters (baseline energy cost, optimized energy cost). The method may include performing real-time data collection, systematically gathering real-time metrics and calculating the baseline energy cost. The method may include conducting AI / ML-based prediction for traffic and load forecasting and coverage demand analysis, using historical and real-time data to predict future patterns, and allowing for resource optimization and proactive adjustments. The method may include dynamically adjusting network parameters and calculating and optimizing real-time energy cost (EC), continuously monitoring the network's current EC based on dynamically adjusted parameters. Compare optimized EC with baseline EC, adjusting parameters as needed.

The utility model discloses a raw material blending tank for resin processing, and relates to the technical field of resin processing. The raw material blending tank for resin processing comprises a blending mechanism, a mixing mechanism and a base, a blending barrel is arranged at the top end of the blending mechanism, and the bottom of the blending barrel is connected with a grinding cavity in a sealed mode through a feeding port. In order to overcome the defects that raw materials are not ground and feeding is too concentrated when a blending device is used, raw material particles are ground through a grinding roller, so that the raw material particles are refined, the surface area of the raw material particles is increased, uniform mixing is facilitated, blockage caused during feeding can be avoided through a bidirectional auger paddle, and the production efficiency is improved. Raw materials can be guided into the stirring cavity in batches through the rotary table and the partition plate, so that the stirring uniformity is improved, the stirring time is shortened, the working efficiency is improved, and the time and the energy cost are saved.

The invention relates to a powder metallurgy high-performance copper-based alloy friction plate for a torque limiter of a wind power coupler and a preparation method of the friction plate. The friction plate is composed of, by mass, 9%-14% of carbon, 1%-3% of tin, 2%-8% of friction components, 12%-18% of iron, 0%-3% of nickel and the balance copper. The preparation method comprises the steps of burdening, mixing, forming, sintering and machining. The prepared high-performance copper-based alloy friction plate is particularly suitable for a torque limiter of a wind power generation coupler. The alloy disclosed by the invention is reasonable in component design, simple and controllable in preparation process, easy to realize batch, large-scale and automatic production, high in production efficiency, energy-saving and moderate in cost; the obtained product is excellent in performance, and can greatly improve the torque control precision, save the maintenance cost and improve the operation safety and reliability of wind power equipment when being applied to a wind power coupling torque limiter.

The invention discloses an optimal scheduling method, system and device based on active support of a micro-grid, and a medium. The method comprises the following steps: sampling to obtain a plurality of target source load prediction error scenes, scene probabilities and net load scenes; calculating a power grid side active support power demand based on the power grid side frequency variation; constructing a first evaluation equation of the active support power demand of the micro-grid based on the scene probability, the active support power demand of the power grid side and the inertia support form of the micro-grid; taking the plurality of net load scenes as constraint conditions, and constructing a microgrid optimization scheduling model based on a scheduling cost equation and a risk cost equation constructed according to the loss cost, the cyclic energy cost and the first evaluation equation in the microgrid scheduling process; and obtaining a micro-grid active support scheduling scheme based on the micro-grid optimization scheduling model. The method provided by the invention fully considers and utilizes the active support frequency of the micro-grid, remarkably enhances the frequency response capability of the power distribution network, and improves the operation efficiency and stability of the power distribution network.

The invention relates to an industrial and commercial park shared storage and charging system intelligent scheduling method based on reinforcement learning, and the method comprises the steps: carrying out the modeling of various loads in an industrial and commercial park, building a model of an optical storage and charging system, analyzing the charging and discharging characteristics of the optical storage and charging system, and calculating the degradation cost of a battery. By designing a reasonable reward function, the reinforcement learning algorithm is guided to make optimal charging and discharging decisions at different moments so as to maximize the overall benefits of the industrial and commercial park. And the reinforcement learning agent continuously selects actions according to the current state, executes a corresponding charging and discharging strategy, and optimizes the strategy by feeding back a reward signal. Through multiple iterations, the optimal scheduling mode is found step by step, the balance between power supply and demand is ensured, energy waste is reduced, and the service life of a battery is prolonged. According to the invention, the use efficiency of energy in industrial and commercial parks is improved, the energy cost can be greatly reduced, the double improvement of economy and sustainability is realized, and the safety and stability of a 750KV and above power transmission system can be improved through seamless connection with a power grid through dynamic load adjustment.

The invention discloses a photovoltaic power generation and rod pumped well group off-peak power utilization coupling control method, and relates to the technical field of oilfield development and new energy utilization. The method comprises the following steps of: acquiring data such as photovoltaic power, irradiance, temperature, wind speed, an indicator diagram of a pumping unit, electrical parameters, a working fluid level and stroke frequency, and constructing a multi-modal data set comprising external meteorological characteristics, a shaft operation time sequence and a well group space relationship; a time sequence model combining reversible normalization and cross-variable attention is adopted to predict photovoltaic output, and a multi-mode liquid supply state recognition model is utilized to obtain the liquid supply sufficiency level of each well. On the basis, a peak shifting power consumption optimization model which takes minimum energy consumption and light abandoning quantity as targets and meets the requirements of yield, power grid capacity, start-stop times and liquid supply constraint is constructed, reinforcement learning is introduced to generate well group start-stop and stroke frequency strategies, and dynamic matching of photovoltaic output and pumping unit load is realized according to real-time data rolling updating, so that the optimal power consumption is realized. The photovoltaic utilization rate is improved; and the energy cost is reduced.

The invention relates to a photovoltaic low-carbon park smart energy management system and method based on digital twinning. Accurate mapping and simulation analysis of a park energy system are realized by constructing a digital twinborn model, dynamic optimization scheduling of energy equipment is realized by adopting a multi-agent cooperative control algorithm, an energy optimization strategy is formulated by taking low carbon as a target, the health state of the equipment is predicted by utilizing machine learning, and an intelligent operation and maintenance plan is generated. And decision support is provided through energy big data analysis. The system integrates multi-source data, and efficient utilization and low-carbon operation of park energy are realized through the steps of digital twin modeling, intelligent agent collaboration, low-carbon scheduling, equipment prediction, big data analysis and the like. Practical application shows that the system can improve the photovoltaic efficiency by 8%, reduce the energy cost by 18%, reduce the carbon emission intensity by 30%, and significantly improve the park energy management level and the low-carbon degree.

The present disclosure discloses a system (106) and a method (400) for energy cost (EC) optimization based on one or more beamforming actions in a network (108) The method may include initialization by defining input and output parameters (baseline energy cost, optimized energy cost). The method may include performing real-time data collection, systematically gathering real-time metrics and calculating the baseline energy cost. The method may include conducting AI / ML-based prediction for traffic and load forecasting and coverage demand analysis, using historical and real-time data to predict future patterns, and allowing for resource optimization and proactive adjustments. The method may include dynamically adjusting network parameters and calculating and optimizing real-time energy cost (EC), continuously monitoring the network's current EC based on dynamically adjusted parameters. Compare optimized EC with baseline EC, adjusting parameters as needed.

The invention discloses a side plate type heat exchanger performance testing system and method, and relates to the technical field of heat exchanger testing, and the system comprises a computer which is used for constructing a digital twinborn model, generating multiple groups of dynamic working condition data, inputting the multiple groups of dynamic working condition data into the digital twinborn model to obtain dynamic performance data, screening according to the sensitivity coefficient of each piece of dynamic performance data to obtain sensitive working condition data; the test equipment executes the sensitive working condition data and collects actual measurement state data of the side plate type heat exchanger; and the computer judges whether the performance of the side plate heat exchanger meets the design requirement according to the actually measured state data. Physical modeling is carried out on the heat exchanger in a digital twinning mode, heat exchange simulation is carried out on the heat exchanger, invalid test working conditions are deleted from a parameter set, only sensitive working conditions are reserved, and time, manpower and energy cost needed by testing is greatly saved.