1413 results about "Energy reduction" patented technology

The invention discloses a central air conditioner energy-saving optimization system based on load prediction, and relates to the technical field of air conditioner energy saving.According to the central air conditioner energy-saving optimization system based on load prediction, a VMDTransformerGRU hybrid deep learning model is introduced, through multi-source data fusion, the precision of cold load prediction is remarkably improved, and the prediction model can recognize the load change trend in advance. According to the improvement, the dynamic response capacity of the system is effectively enhanced, pre-regulation and control can be carried out before load changes, energy waste is reduced, a water chilling unit, a cooling tower and a pump are modeled to be independent intelligent agents, multi-agent deep reinforcement learning (MADRL) is adopted, and all the intelligent agents can autonomously learn and optimize control strategies of the intelligent agents. The intelligent agents cooperate with one another, efficient operation of the whole system is achieved through autonomous decision, and operation conflicts between devices are avoided. In this way, dynamic coordination and joint optimization among all the devices are achieved, invalid energy consumption is reduced, and the overall energy-saving effect and operation stability of the system are remarkably improved.

The invention discloses an intelligent management platform suitable for a power plant, and relates to the technical field of power plant management, and the platform employs an intelligent scheduling system and a DDPG algorithm, and dynamically adjusts a power generation strategy according to the real-time equipment health score, residual life prediction, and the change of energy price and load demand. The power plant can quickly make adjustments when the load demand changes sharply, preferentially dispatch efficient and low-emission equipment, improve the overall energy efficiency and reduce energy waste, and through integration of a sensor network and edge computing, key operation parameters of the equipment are collected in real time, and a dynamic maintenance plan is generated based on health scores and residual life prediction of the equipment. The equipment about to break down is preferentially processed, the random forest model is used for health state evaluation and fault probability prediction, the health score of the equipment can accurately reflect the running state of the equipment, the maintenance time is dynamically adjusted in combination with the load condition of the equipment, and excessive maintenance or maintenance lag is avoided.

The invention relates to the technical field of smart power grids, and discloses a distributed renewable energy source and energy storage cooperative scheduling system of Internet of Things data, comprising a data acquisition and monitoring module used for acquiring the running states of distributed energy sources and energy storage units through the Internet of Things technology; the energy supply and demand prediction module is used for predicting energy supply and load demand according to historical data, real-time data and meteorological information and generating basic data of a scheduling strategy; the collaborative scheduling strategy module formulates a collaborative scheduling scheme of the renewable energy and the energy storage unit based on energy supply and demand prediction, and adjusts the charging and discharging time of the energy storage unit; the double-layer optimization decision module is responsible for long-period energy storage planning and short-period real-time scheduling optimization; and the real-time monitoring and feedback module dynamically adjusts a scheduling strategy based on feedback data. According to the invention, through real-time monitoring, supply and demand prediction and cooperative scheduling of renewable energy sources and energy storage equipment, the system can optimize distribution and use of energy to the greatest extent, and energy waste is reduced.

The invention relates to the technical field of food safety, in particular to a real-time monitoring method and system for a food storage environment, and the method comprises the following steps: obtaining temperature, humidity and illumination parameter state values based on the real-time monitoring data of the food storage environment, collecting the multi-parameter state, comparing the multi-parameter state with a storage standard, and determining the real-time monitoring data of the food storage environment; and adjusting the running speed of the fan and the starting duration of the refrigeration equipment according to the comparison difference value, and generating an environmental parameter dynamic adjustment result. According to the method, environmental parameters such as temperature, humidity and illumination are obtained and compared with standard storage conditions, operation of a draught fan and refrigeration equipment is dynamically adjusted in real time, the storage environmental conditions are optimized, it is ensured that food is kept in an ideal state, the environmental parameters are mapped to the food quality guarantee state through a neural network, parameter setting is optimized step by step, and food quality guarantee is achieved. The influence of environment quality on food is accurately predicted, optimal matching of storage conditions is ensured, optimal configuration is selected through cross combination scoring, equipment response is more sensitive, and energy waste is reduced.

The invention discloses an intelligent heat supply pipe network optimization control method and system based on the Internet of Things, and relates to the technical field of heat supply pipe network optimization control, and the system comprises environmental parameter collection modules which are distributed in different areas covered by a heat supply pipe network and are used for collecting environmental data of each area in real time; and the Internet of Things control platform is used for receiving the environmental data from the environmental parameter acquisition module. According to the intelligent heat supply pipe network optimization control method and system based on the Internet of Things, flow and temperature data are monitored in real time, the valve opening degree is accurately controlled according to a PID control algorithm, a closed-loop control system is formed, heat supply demand changes are quickly responded, energy waste is reduced, heat supply quality is guaranteed, meanwhile, energy consumption is reduced, and the energy utilization efficiency is improved; environmental parameters and user heat using habit changes are monitored in real time, a heat supply strategy is adjusted in time, heat supply demand fluctuation is quickly responded, and it is ensured that heat supply always meets the actual demand of a user.

The invention discloses an intelligent control system for a compression heat zero-gas-consumption dryer, and relates to the technical field of compressed air drying. The intelligent control system comprises a data sensing module, an intelligent decision module and an execution module; the data sensing module acquires humidity, temperature, pressure and flow data of compressed air in real time, and can dynamically adjust acquisition frequency according to working conditions to provide a data basis for subsequent decision making; according to the invention, the over-integrated data sensing module monitors the humidity, temperature, pressure and flow of compressed air in real time, and dynamically adjusts the acquisition frequency according to working conditions, thereby providing an accurate data basis for intelligent decision making; the intelligent decision-making module adopts an advanced data processing technology and a VAE model to repair and supplement the data, so that the accuracy and integrity of the data are ensured; in this way, the system can accurately control the drying process, reduce energy waste and achieve the zero-gas-consumption drying effect, and therefore the operation cost is remarkably reduced.

The invention relates to the related technical field of refrigeration energy-saving control, in particular to an energy-saving control method and system for refrigeration equipment, and the method comprises the steps: achieving the state linkage of an air valve, a compressor and an air feeder through control logic based on a refrigeration equipment unit; after starting, an initial strategy is formulated according to refrigeration requirements to manage a plurality of microclimate spaces; in combination with video monitoring, personnel tracks are captured, behavior modes are analyzed, a local customized control strategy is generated, and a comprehensive refrigeration control strategy is formulated, so that the technical problem of unnecessary energy waste caused by incapability of accurately matching refrigeration requirements and equipment operation states due to lack of real-time sensing analysis on actual operation conditions is solved; the overall refrigeration demand balance is considered, the local area is considered, the refrigeration intensity is flexibly adjusted, accurate matching between the refrigeration demand and the equipment operation state is achieved, distribution of refrigeration resources is optimized, energy waste is effectively reduced, the overall operation efficiency is improved, and the technical effect of energy-saving, intelligent and efficient management of the refrigeration equipment is achieved.

The invention relates to the technical field of refrigeration control, in particular to a medical refrigerator compressor refrigeration temperature control system and method based on big data, and the system comprises a storage article recognition module, a heat capacity calculation module, a compressor load adjustment module, a refrigerant flow control module and a compressor performance correction module. According to the medical refrigerator, the types and characteristics of stored articles are precisely recognized, detailed parameters are extracted, the high-precision temperature sensor and the environment parameter set are utilized, and dynamic calculation is conducted on the refrigeration requirement, so that precise adjustment of the output power of the compressor is achieved, and the temperature stability and the medicine quality in the medical refrigerator are ensured; by refining the storage requirement of each medicine, optimizing the refrigerant supply amount and achieving a more accurate temperature control strategy, and by correcting the performance parameters of the compressor and improving the overall energy efficiency and reliability, energy waste can be more effectively reduced, optimization of storage conditions is ensured, and the overall efficiency and response speed of a refrigeration system are remarkably improved.

The invention discloses a modular power supply efficiency intelligent management method, and aims to record the working state, power output and conversion efficiency of a power supply module in real time through a sensor and monitoring software, carry out system energy efficiency analysis regularly, and can significantly improve the overall performance of a power supply system. Firstly, according to the scheme, by optimizing power distribution and fault early warning, the energy efficiency can be effectively improved, energy waste is reduced, and the operation cost is reduced. Meanwhile, the system can dynamically adjust the load of each module, prolong the service life of equipment, reduce the equipment fault risk through preventive maintenance, and improve the stability and reliability of the system. In addition, an energy efficiency analysis report of the system provides a data-driven decision basis for a manager, helps to discover the potential of efficiency improvement, and guides the future optimization direction. And by reducing energy consumption, the power supply system can also reduce carbon emission, promote sustainable development of enterprises and improve environmental protection benefits.

The invention discloses a temperature and humidity control method for a marine energy storage container, and relates to the technical field of container temperature and humidity control, and the method comprises the following steps: monitoring the temperature and humidity in the container in real time, and obtaining the real-time environment parameters of the environment in the container through a sensor; through real-time monitoring, an intelligent algorithm and multi-level control, the system dynamically adjusts the temperature and humidity, and the accuracy and the response speed are improved. Different from traditional static setting, the system is combined with sensor data and a prediction model to cope with environment and load changes in advance, temperature and humidity are prevented from exceeding the standard, and equipment faults are reduced. The energy waste is reduced, the energy consumption distribution is optimized, the overall energy efficiency is improved, the cost is reduced, and the safe and stable operation of the equipment is ensured by a regionalized control and global optimization strategy.

The invention discloses a control method for a cooling circulation system of a multi-core processor, and relates to the technical field of computer hardware heat dissipation, and the control method comprises the following steps: the multi-core processor is responsible for executing a calculation task; the sensor module monitors the temperature of each core; the cooling system control module is used for receiving data from the temperature sensor and the load monitoring module, calculating the cooling requirement of each core according to the data and commanding a cooling unit to adjust; the cooling unit is used for adjusting cooling equipment to reduce the temperature of the processor according to an instruction of the control unit; and the debugging interface is used for monitoring the state of the system and displaying the temperature, the load and the cooling resource distribution condition of each core. The method has the beneficial effects that the rotating speed of the fan, the liquid cooling flow and the working state of the heat pipe system can be accurately controlled by combining the accurate analysis of the load change and the temperature data, the heat dissipation efficiency of the processor is improved, and the energy waste is reduced.

The invention discloses an intelligent factory data intelligent analysis and management system, and relates to the technical field of data analysis. Multi-source data are uniformly accessed through an industrial gateway, and a knowledge graph is constructed after cleaning and standardization; an edge node deploys a lightweight AI model to realize real-time analysis such as equipment anomaly detection; intelligent distribution of cloud side tasks is realized based on a decision model; optimizing production scheduling and quality control by using an algorithm; a zero-trust architecture is adopted to guarantee safety, and efficient energy management is realized in combination with reinforcement learning; all the modules work cooperatively, and the intelligent level of a factory is improved. The operation efficiency and quality of the intelligent factory are effectively improved. Efficient data fusion processing is realized, and equipment anomaly detection is more accurate; the order delivery period is shortened, and the product reject ratio is reduced; network security protection is enhanced, and energy waste is reduced; the decision response speed is accelerated, the decision accuracy is improved, cost reduction and efficiency improvement of enterprises are comprehensively assisted, and the competitiveness is enhanced.

The invention discloses an AI computing power equipment intelligent energy consumption management system and method based on big data, and relates to the technical field of data center energy consumption management, the method comprises the following steps: obtaining real-time operation data of AI computing power equipment, the real-time operation data comprising load characteristic parameters, energy consumption characteristic parameters, environment characteristic parameters and equipment state parameters; obtaining a load discrimination value according to the load characteristic parameter; obtaining an energy consumption discriminant value according to the energy consumption characteristic parameters; acquiring an environment discrimination value according to the environment characteristic parameters; acquiring a state discrimination value according to the equipment state parameter; fusing the load discriminant value, the energy consumption discriminant value, the environment discriminant value and the state discriminant value to generate a comprehensive energy efficiency index; judging whether the comprehensive energy efficiency index exceeds a preset dual-threshold decision interval or not; if the upper limit threshold value is exceeded, triggering graded consumption reduction response, including dynamic frequency modulation, task migration or equipment dormancy; and if so, determining that the current operation mode is in a high-energy-efficiency state and maintaining the current operation mode.

The invention relates to the technical field of energy efficiency optimization, and discloses a lamp energy efficiency optimization method based on artificial intelligence, and the method comprises the steps: data collection: carrying out the all-around and multi-dimensional data collection of an environment where a lamp is located through a plurality of sensors. According to the lamp energy efficiency optimization method based on artificial intelligence, in order to make energy utilization efficient, the multi-dimensional environment and lamp operation data are obtained through the data acquisition step, optimization is carried out in combination with the adaptive learning algorithm, lamp energy efficiency control parameters can be adjusted in real time according to different environments, places, time periods and other situations, and the energy efficiency optimization efficiency is improved. The lamp can be kept in the optimal energy efficiency state in various complex and changeable environments, the energy utilization efficiency of the lamp is effectively improved, energy waste is reduced, in the scene-based energy efficiency optimization link, for different application scenes, differentiation strategies are implemented according to respective typical lighting requirements and energy-saving potentials, parameters such as lamp brightness are accurately adjusted, and the energy efficiency of the lamp is improved. And the energy utilization efficiency of the lamp in various scenes is improved.

The invention relates to the technical field of intelligent control, in particular to an intelligent control method and system for heat supply parameters of a thermal power plant, and the method comprises the following steps: capturing temperature sensor data of multiple key parts of the thermal power plant, combining operation time with external weather condition data, carrying out data integration, analyzing the current operation state, and obtaining heat balance basic data. According to the method, comprehensive analysis is carried out in combination with external weather conditions, dynamic adjustment of heat supply parameters is achieved, the reaction speed is increased, the fuel ratio and load are optimized, the energy utilization efficiency is improved, long-term weather prediction data are integrated, heat supply demand changes are predicted and adapted, energy waste is reduced, and the energy utilization efficiency is improved. Real-time load adjustment and fuel efficiency calculation enable the thermal power plant to accurately match the actual heat demand, the combustion efficiency is optimized, and by predicting maintenance points and potential faults, the equipment operation reliability is improved, the maintenance plan is updated in time, the production safety is ensured, the operation flexibility and economic benefits are improved, and the environmental pollution risk is reduced.

The invention relates to the technical field of mine environment control, and discloses an intelligent environment monitoring and control method in a mine laneway. The method comprises the steps that a real-time environment parameter set of a mine laneway is collected, the set contains gas concentration data, dust concentration data and temperature and humidity data, and real-time capture of laneway environment multi-dimensional parameters is achieved; then, based on the collected real-time environment parameter set, calculating an environment safety evaluation index so as to scientifically and objectively reflect the roadway environment safety condition; a dynamic control strategy is generated according to the environmental safety evaluation indexes, and the strategy comprises ventilation equipment regulation and control parameters and early warning level parameters and can be flexibly adjusted according to environmental changes; and finally, the ventilation equipment regulation and control parameters are sent to a mine ventilation system, and roadway environment regulation and control are executed. According to the method, environmental parameters can be comprehensively monitored, safety risks can be accurately evaluated, ventilation equipment can be flexibly regulated and controlled, the early warning function can be perfected, energy waste can be reduced, labor intensity can be reduced, and underground operation safety and mining efficiency can be guaranteed.

The invention relates to the technical field of building control, in particular to a BIM-based building energy consumption and carbon emission dual control system, which comprises a building element carbon emission analysis module, a building operation strategy optimization module, a building environment adjustment response module and a building performance continuous monitoring module. According to the invention, through real-time tracking and dynamic management, structural information of a building is deeply analyzed by using a BIM technology, design and material use are accurately evaluated, energy consumption and carbon emission prediction are optimized, an internal equipment operation strategy is dynamically adjusted, the energy use efficiency is improved, indoor environment parameters are adjusted, energy consumption is reduced, and through continuous monitoring and strategy updating, the energy utilization efficiency is improved. The optimal performance of the building is maintained in various environments, energy waste is reduced, the cost efficiency is optimized, and the refined control not only improves the operation flexibility of the building, reduces the maintenance cost, but also enhances the living and working environment quality, and supports the sustainable development and environmental protection goal of the building industry.

The invention relates to the technical field of energy regulation and control, in particular to an inter-building energy regulation and control method based on the Internet of Things. The method comprises the following steps: predicting and judging whether the actual energy consumption of a building is abnormal or not; when the actual energy consumption is predicted and judged to be abnormal and the maximum energy consumption deviation value is positive, external climate data is associated to determine an execution mode of power dispatching; when the maximum energy consumption deviation value is negative, obtaining an identification type result of the current time period; and determining whether to execute power dispatching control based on the identification type result. By monitoring the energy consumption deviation value of each building in real time, the energy consumption deviation condition of the building in the to-be-monitored range is determined, energy consumption abnormity is found in time, power distribution is optimized, energy waste is reduced, external climate data and building types are associated to dynamically adjust a power dispatching strategy, the energy consumption requirement of the building is judged more accurately, and the energy consumption of the building is improved. Therefore, different environment and energy scheduling requirements can be met, power distribution can be rapidly adjusted, and the stability of energy supply can be ensured.

The invention discloses a seawater exchange type breakwater protection performance evaluation system and a disaster early warning system, and particularly relates to the technical field of oceanographic engineering.The breakwater is divided into a plurality of monitoring areas, seawater exchange parameters of different time periods in all the areas are obtained, the seawater exchange parameters are classified and subjected to weight assignment, and a disaster early warning result is obtained; the method comprises the following steps: calculating the weighted average to obtain the overall protection performance coefficient of the breakwater, dividing the protection performance into normal protection and abnormal protection according to the calculation result, and evaluating the energy reduction effect of the breakwater by monitoring the energy change of incident and transmitted waves in real time according to the overall protection performance coefficient and the energy reduction change condition. Performing disaster risk prediction and early warning by using fuzzy logic; comprehensive evaluation and timely early warning of the protection performance of the breakwater are realized, so that the protection effect of the breakwater and the safety of the coastal environment are improved.

The invention relates to the technical field of intelligent power grids, in particular to a virtual power plant multi-level collaborative optimization control method based on digital twinning. According to the invention, data of the power generation end, the power storage end, the power transmission end and the load end are collected and processed in real time, a digital twin model is established, and accurate modeling and real-time monitoring can be carried out on each device of the virtual power plant. Through real-time optimization scheduling and cooperative control, energy loss can be minimized, supply and demand can be balanced, the energy use efficiency is improved to the maximum extent, and the cost is reduced; according to the method, the genetic algorithm and the particle swarm optimization algorithm are combined, scheduling optimization is carried out based on a multi-level collaborative optimization model, a virtual power plant can rapidly respond according to changes of real-time environment and load requirements, fossil energy reduction and clean energy increase are preferentially considered in the power generation equipment scheduling process, and greenhouse gas emission reduction is facilitated.

The invention provides a coal mine fluidized mining dynamic disaster prevention and control method and system, and relates to the technical field of disaster prevention and control. The method comprises the following steps: establishing a risk map based on mining area multi-field information, setting a seismic source intensity upper limit, a path attenuation target and an energy absorption displacement target, implementing in-situ transformation under the constraint of a control index to form a distributed micro-fracture network, and calculating a residual dynamic load spectrum to weaken a seismic source; according to the residual dynamic load spectrum, constructing an artificial barrier and an energy absorption band with wave impedance abrupt change so as to realize propagation energy reduction; a flexible energy absorption protection system is configured according to the residual dynamic load spectrum and the propagation attenuation coefficient, micro-seismic, stress and displacement data are monitored in real time, and parameter self-adaptive correction is achieved through linkage treatment triggered by a threshold value. According to the method, a source-diameter-field integrated active prevention and control system is constructed, dynamic regulation and control and closed-loop management in the fluidized mining process are achieved, and the intrinsic safety and the intelligent level of deep mining are remarkably improved.

The invention discloses an intelligent prediction and scheduling management system based on hazardous waste incineration waste heat recovery power generation, relates to the technical field of hazardous waste incineration waste heat recovery power generation, and aims to monitor key parameters such as temperature, pressure, flow and flue gas components in the hazardous waste incineration process in real time, and determine the hazardous waste incineration waste heat recovery power generation through data processing and feature extraction. And the hazardous waste incineration heat value, the waste heat boiler steam production amount and the power generation amount are accurately predicted. Meanwhile, the system combines power grid load change and price fluctuation, realizes intelligent regulation and control of equipment operation and maximization of waste heat recovery efficiency and generating capacity through an optimization scheduling algorithm, can improve energy recovery efficiency to the maximum extent, reduces energy waste, improves economic benefits and environmental sustainability of the system, and has wide application prospects. The power generation benefit can be improved in the field of hazardous waste incineration, and reference can be provided for other industrial waste heat recovery power generation systems.

The invention discloses a logistics path scheduling method and system of a logistics vehicle, and relates to the technical field of path scheduling, carbon emission and energy consumption of a path are accurately calculated and screened through a high-precision map system and a graph search algorithm, and environmental friendliness is directly used as a key constraint condition of path selection; according to the method, the carbon emission and the oil consumption are definitely introduced as path evaluation indexes, and an environment-friendly coefficient is set, so that the selected path can reduce the carbon emission to the maximum extent; secondly, a multi-dimensional comprehensive cost evaluation model including load balance, road condition adaptability, vehicle long-term cost and the like is established, and energy consumption of logistics vehicles is effectively reduced by accurately calculating energy consumption loss under different paths; and the energy use efficiency can be scientifically quantified and optimized, so that the energy waste is reduced from the source, and the purposes of energy conservation and emission reduction are achieved.

The invention discloses an energy scheduling method, device and equipment based on a virtual power plant, and a storage medium. The method comprises the following steps: obtaining corrected predicted power of distributed renewable energy sources of the virtual power plant; a distribution optimization model of the virtual power plant is constructed, the distribution optimization model comprises a target function and constraint conditions, the target function takes response cost, energy storage cost, regulation and control cost and carbon transaction cost of the virtual power plant as targets, and the constraint conditions are charge and discharge characteristics of a distributed power supply; in the actual operation process, the corrected and predicted power is input into the distribution optimization model, solving optimization is carried out through a path optimization algorithm in an uncertain environment, and a scheduling strategy is obtained; and performing energy scheduling on the power distribution network accessed to the virtual power plant according to the scheduling strategy, thereby realizing cooperative and clustered operation of various power resources in the virtual power plant, improving the operation efficiency of the power distribution network, reducing energy waste, and improving the reliability of overall energy supply.

The invention provides a blast furnace coal injection control method and equipment and a readable storage medium, and relates to the technical field of blast furnace ironmaking. According to the method, the influence of the pulverized coal property change of the injected pulverized coal on the coal injection operation can be deeply considered, and the updated conversion model of each coal injection regulation and control device can represent the comprehensive influence of the pulverized coal property change on the parameter conversion relationship between the device control quantity and the coal injection rate through adaptive updating of the model parameters; and meanwhile, a coal injection regulation and control device combination matched with the real-time coal injection rate difference distribution condition is selected to execute device working state self-adaptive regulation and control operation based on the updated conversion model, so that through organic combination of a multi-model decoupling control mode and a coal injection rate indirect regulation mode, coal injection rate regulation and control fluctuation is effectively reduced, and the coal injection rate regulation and control efficiency is improved. And it is ensured that the coal injection rate is stably and uniformly maintained at a set rate value, so that the blast furnace steady-state coal injection effect is achieved, and the blast furnace ironmaking production operation meets the requirements of blast furnace production improvement, energy consumption reduction, pollution reduction and the like.

The invention provides an energy charging power distribution method and system, and the method comprises the following steps: carrying out the priority evaluation and distribution of energy charging equipment, and obtaining the priority of the equipment; monitoring the power module to obtain the state and energy efficiency information of the power module; based on the device priority and the state and energy efficiency information of the power module, carrying out weight allocation on the power module to obtain a weight score of the power module; and distributing the output power based on the weight score of the power module. According to the technical scheme, it is ensured that energy is distributed according to needs, and energy waste is reduced; and service life shortening caused by excessive use of individual equipment is avoided.

The invention discloses an energy distribution management method based on regional resources, and relates to the technical field of distributed energy management, and the method comprises the steps: obtaining a two-dimensional map of a region, building a rectangular plane coordinate system, carrying out the zooming processing of the two-dimensional map, and obtaining a rectangular plane coordinate system; preliminarily dividing the region into a plurality of cells with the same size according to a preset area, calculating the area of the scaled map, and dividing the region into a plurality of macroscopic sub-regions according to a macro standard; dividing each macro sub-region into a plurality of micro-regions according to a micro standard, and setting a unique identification code for marking; setting an initial power supply end according to the macroscopic sub-region and the microscopic region; through macroscopic and microscopic regional division, fine management of regional resources is realized, the reasonability and efficiency of energy distribution are improved, and through accurate regional division and power supply end setting, the effects of accurately matching requirements, improving the overall energy utilization efficiency and reducing energy loss are realized.

The invention provides a distributed photovoltaic energy storage method and system based on a deep learning model, and the system comprises a data collection and preprocessing module, a deep learning model construction module, a model training and optimization module, an energy storage control strategy module, a cooperative work and optimization module, and a system monitoring and feedback module. A deep learning model can accurately predict photovoltaic power generation capacity and load requirements, realizes optimized charge and discharge control of energy storage equipment, maximally utilizes photovoltaic energy and reduces energy waste, and meanwhile, the deep learning model can quickly learn and adapt to a new operation scene and a data mode, so that the energy storage efficiency is improved. The system can cope with various complex and changeable environments and load conditions, and through accurate prediction and an intelligent control strategy, the fluctuation of photovoltaic power generation is effectively stabilized, the stable power supply of the micro-grid under different working conditions is ensured, and the fluctuation of voltage and frequency is reduced.

The invention belongs to the technical field of intelligent disaster prevention, and particularly relates to a digital twinborn intelligent operation, maintenance and evaluation system for energy dissipation and shock absorption parts. The system comprises an intelligent sensing module, a fidelity reduced-order model and a virtual-real interaction intelligent decision-making module, and a solution is provided for intelligent operation and maintenance of energy dissipation and shock absorption parts by integrating and adopting a digital twin technology, an internet of things technology, an edge computing technology and other technologies. According to the method, a digital twinborn technology is adopted to establish a fidelity virtual twinborn body of the energy dissipation and shock absorption part, the overall performance state of the part is perspective through a virtual sensor means, and a new technology is provided for whole-life-cycle operation and maintenance of the part. By means of the technology, the performance state of the core component of the engineering structure can be mastered in real time, the core component can be kept in a full-time online transparent state in the whole life cycle, and the anti-disaster toughness capacity of the engineering structure is improved on the whole.

The utility model relates to the technical field of energy dissipation and shock absorption of civil engineering structures, in particular to a replaceable belleville spring connecting device with a self-resetting energy dissipation function, which comprises a plurality of complex belleville spring connecting devices used in a frame swing wall structure, the device comprises a plurality of belleville springs, and gaskets are arranged among the belleville springs; second outer baffles are installed on the left side and the right side of the device, the first baffles and the second baffles on the upper side and the lower side are connected through high-strength bolts, a thrust bottom plate and a hexagonal locking nut are arranged on the right sides of the second baffles, and a single-lug plate is arranged on the left sides of the second baffles. The connecting device has the advantages that the resetting and energy consumption principles of the connecting device are basically the same as those of a common steel strand and a damper, the mounting mode is simpler, the manufacturing cost is lower, and the universality is better; and on the other hand, the connecting device has better replaceability, the damaged belleville spring, the damaged gasket and the damaged rubber pad are detached and replaced, the production cost is reduced, earthquake energy can be dissipated, and resetting of the structure after an earthquake is achieved.