4991 results about "Energy supply" patented technology

The invention relates to the technical field of energy scheduling, in particular to a multi-modal data fusion method and system based on energy scheduling and a storage medium. The method comprises the following steps: obtaining multi-modal data, and carrying out abnormal fluctuation feature extraction to obtain a space-time fusion abnormal feature labeling set; deducing a multi-objective optimization path according to the space-time fusion abnormal feature labeling set to obtain a dynamic scheduling decision map; performing edge node game equilibrium calculation according to the dynamic scheduling decision map to obtain a trusted scheduling verification chain; performing digital twinborn constraint optimization on the trusted scheduling verification chain to obtain a closed-loop scheduling digital twinborn body; compiling a dynamic scheduling instruction set based on the closed-loop scheduling digital twin to obtain an anti-disturbance energy scheduling strategy library; and obtaining real-time energy supply and demand data, and performing scheduling deviation tracing on the real-time energy supply and demand data according to the anti-disturbance energy scheduling strategy library to obtain an energy distribution decision. According to the invention, the efficiency and reliability of energy scheduling can be improved.

The invention discloses an energy storage energy management system for improving a demand protection algorithm, and the system comprises a data collection module which is used for obtaining energy storage system load data, distributed energy operation data, and energy storage system operation data; the correlation feature analysis module is used for acquiring a correlation feature set between the load and the energy supply; the control strategy generation module is used for adjusting a charging and discharging threshold value and formulating a charging and discharging control strategy; the control strategy execution module is used for formulating a charging and discharging control strategy and adjusting the working mode of the energy storage converter; the cooperative execution module is used for dynamically optimizing a power distribution strategy by dispatching distributed energy and power grid cooperative work; and the dynamic power adjustment strategy adjustment module is used for adjusting the charging and discharging protection threshold value and the power adjustment strategy through a feedback mechanism. Through an intelligent control and scheduling mechanism, the charging and discharging efficiency of the energy storage system is improved, the stability and flexibility of the power grid are enhanced, and effective guarantee is provided for safe operation of the power grid.

The invention discloses a virtual power plant collaborative optimization operation method based on distributed energy, and the method achieves the real-time monitoring of the operation state of a distributed energy resource through the precise modeling of the distributed energy resource, and predicts the change of the power load demand. A multi-objective optimization model and an advanced optimization algorithm, such as a genetic algorithm or a particle swarm algorithm, are utilized, so that the virtual power plant can dynamically adjust energy supply and load demands, and economic benefit maximization, energy efficiency optimization and carbon emission minimization are realized. The method also considers the energy efficiency and economic benefits of the virtual power plant, and improves the comprehensive benefits of the system through comprehensive analysis and optimization. Through effective interaction with the power grid, the virtual power plant can provide peak regulation, frequency modulation and standby services, a demand response mechanism is implemented, and operation of the power grid is further optimized. The method has the beneficial effects of improving the energy utilization efficiency, enhancing the stability of a power grid, optimizing economic benefits, promoting environmental protection, improving the flexibility and adaptability of the system and supporting policies and market mechanisms.

The invention discloses a park energy router multi-agent coordination control method based on a dynamic game model, and relates to the technical field of energy internet optimization control. The method comprises the following steps: according to real-time meteorological data and historical load fluctuation characteristics, identifying multi-energy supply and demand time sequence conflicts, constructing a multi-subject game framework, quantifying dynamic coupling strength of meteorological parameters and load fluctuation, and generating a dynamic constraint boundary; then, combining dynamic constraints and operation states, dynamically adjusting economic, reliability and low-carbon index weights, superposing a risk penalty term of a future supply and demand difference value, and constructing a coordination revenue function; when the photovoltaic output and the thermal load change rate exceed threshold values, an emergency game mode is triggered, Nash equilibrium is solved through a distributed optimization algorithm, and a coordination control instruction is output; and finally, collecting execution data, calculating a supply and demand deviation correction rate and an income balance degree, adjusting a coupling weight and a constraint boundary based on feedback, and realizing multi-subject benefit dynamic balance and stable and efficient operation of the park energy system.

The present application provides a system energy efficiency optimization method and apparatus based on a comprehensive energy demand, an electronic device, and a storage medium. The method comprises: in response to a received target comprehensive energy demand, on the basis of comprehensive energy demand parameters, operation parameters of a target system, and environmental loss parameters of the target system, constructing an energy efficiency optimization function of the target system (S210); on the basis of a balance relationship between the energy supply and the energy consumption and a device operation demand of transmitting energy, constructing constraint conditions for optimizing the energy efficiency of the target system (S220); and on the basis of the energy efficiency optimization function and the constraint conditions, using a target algorithm to process operation parameters of a plurality of devices by taking the operation parameters of the plurality of devices in the target system as population individuals to obtain device parameters of the target system meeting the target comprehensive energy demand (S230).

Disclosed in the present invention are a low-carbon operation method and system for an integrated electric-thermal energy system under a demand response. The method comprises: collecting data in a combined heat and power generation unit, establishing a low-carbon demand response model on a demand side, and constructing an integrated electric-thermal energy system model; on the basis of the integrated electric-thermal energy system model, establishing an optimal scheduling model that satisfies an operational constraint condition and aims to maximize the revenue of an energy supply side; establishing a carbon emission flow model to calculate a carbon emission flow, mapping the carbon emission of the energy supply side to a load side, and establishing the low-carbon demand response model on the demand side by maximizing the consumer surplus as an objective; and using a genetic algorithm to solve the optimal scheduling model that aims to maximize the revenue of the energy supply side, and the low-carbon demand response model on the demand side, obtaining a low-carbon optimization scheduling plan of the integrated electric-thermal energy system, and implementing scheduling of the integrated electric-thermal energy system. According to the present invention, the carbon emission of the integrated electric-thermal energy system is reduced, the cost of energy conservation and emission reduction is reduced, and the low carbon performance and the economical efficiency are both considered.

The invention provides a rebalance dynamic adjustment method applied to a multi-source system and a service system, and relates to the technical field of smart energy, and the method comprises the steps: collecting a real-time operation data set of the multi-source energy system, covering energy supply, load demand and system operation state features, carrying out the dynamic supply and demand deviation feature extraction, and carrying out the dynamic adjustment of the dynamic supply and demand deviation features; a feature set containing supply, demand fluctuation and system stability deviation is generated, then a pre-trained multi-source collaborative prediction model is called, and a multi-source collaborative prediction result containing energy supply and load demand trend prediction features is generated based on the feature set; and dynamic adjustment strategy matching is carried out according to a multi-source collaborative prediction result and real-time operation data, and a strategy set of energy output adjustment, energy storage charging and discharging, load priority distribution and the like is obtained. And finally, executing an adjustment strategy and obtaining feedback data, optimizing parameters of the multi-source collaborative prediction model based on the adjustment strategy, triggering closed-loop adjustment operation, and realizing rebalance dynamic adjustment of the multi-source system.

The present disclosure relates to the technical field of intelligent monitoring and management of power systems, and specifically relates to an intelligent collaborative power consumption regulation and control method, apparatus and system for source-grid-load-storage, an electronic device and a storage medium. Said system comprises an energy regulation and control center and energy regulation and control units provided in microgrids; the energy regulation and control units use a temporal attention mechanism-based LRCN dual-layer network combined model to predict power consumption amounts, so as to generate power consumption surpluses and shortages within a future preset time; and on the basis of the power consumption surpluses and shortages and latest current electricity prices of the microgrids, the energy regulation and control center uses a fusion multi-objective algorithm based on a Pareto front curve and a fuzzy algorithm to generate a microgrid collaborative power consumption regulation and control solution, and sends the regulation and control solution to the energy regulation and control units for execution, so as to ensure the balance of energy supply and demand of the microgrids. Therefore, the present disclosure achieves efficient, intelligent and refined energy management for microgrid clusters, reducing energy consumption and costs, and providing solid support for sustainable development of microgrids.

The invention relates to an electric energy comprehensive management control method for high and low load working conditions, and belongs to the field of energy supply system control. The method comprises the following steps: acquiring current operation information of an energy supply system, including a working condition mode, a target power demand and a battery state of charge (SOC) value, and further judging a working condition type and calculating a total power demand value of the system; on the basis, a power distribution scheme of a power generation unit and a battery is judged according to an SOC value and a power demand: under a normal working condition, the balance of power generation and battery charging and discharging is realized by setting an SOC threshold interval, and under an emergency working condition, the maximum rate discharging of the battery is dynamically controlled or the charging current is limited to cope with load fluctuation. According to the system, the bus voltage and the SOC are maintained in a safe range through a closed-loop regulation mode, so that the stability and the response capability of the energy supply system are improved.

The invention discloses a distributed power supply storage and charging integrated system, and belongs to the technical field of power system management. The system comprises a clean energy access module, an energy storage control scheduling module, an intelligent charging control module, an energy management scheduling module, a communication network interconnection module and an equipment state monitoring module, the battery state is dynamically evaluated from multiple dimensions, and a matching strategy is called through a real-time environment sensing mechanism; the charging and discharging path and rate are more in line with the actual load demand and the energy supply and demand state of the system, the battery circulation loss is reduced, the energy utilization rate is maximized, the non-critical load reduction power of the user is dynamically guided, and the specific charging demand of the user in the future time period is dynamically predicted. Therefore, the system has the capability of allocating energy resources in advance, centralized coordination is carried out on the multiple charging piles, imbalance of the energy resources in spatial distribution is avoided, the scheduling flexibility of the whole system is improved, and the self-adaptive capability of the system to complex power consumption behaviors is enhanced.

The invention discloses a pipe network health real-time monitoring system and an evaluation method. According to the invention, through real-time health monitoring and dynamic deduction capability, the flexibility and safety of energy supply are significantly improved. The healthy twin module constructs a digital mirror image of the full life cycle of energy equipment, solar hydrogen production efficiency fluctuation, hydrogen storage tank pressure change and fuel cell attenuation trend can be accurately tracked, and risk points such as electrolytic cell abnormal hydrogen evolution and proton exchange membrane aging can be recognized in advance. The all-weather transparent monitoring enables the system to dynamically adjust the ratio of hydrogen production to power generation, the fuel cell is automatically switched to supply power in cloudy and rainy days, continuous energy supply is guaranteed, and the system can generate a coping plan in advance by simulating extreme scenes such as sudden drop of photovoltaic output and hydrogen leakage, so that the system is convenient to use. The energy interruption risk caused by sudden equipment failure is greatly reduced, meanwhile, the service life of key components is prolonged, the maintenance cost of a distributed energy network is reduced, and meanwhile the overall energy efficiency utilization rate is also improved.

The invention relates to the technical field of energy management and optimization, and discloses a resource allocation dynamic simulation optimization method based on energy condition constraints, which comprises the following steps: step 1, deploying a sensor network and a data acquisition system, monitoring an energy supply condition and a consumption mode in real time, and performing multi-dimensional collection on data; 2, the collected data are cleaned and analyzed, a dynamic energy condition report is generated, and the report comprises energy supply and demand prediction, energy price fluctuation and carbon emission intensity information; and step 3, constructing a resource allocation dynamic simulation model according to the collected data and reports. The resource allocation dynamic simulation optimization method based on the combination of real-time energy state recognition and self-adaptive weight adjustment is adopted, the technical effect of flexible scheduling according to dynamic changes of energy conditions is achieved, and it is ensured that the resource allocation process can be responded in time, and the system operation efficiency is optimized.

The invention relates to the field of distributed energy, in particular to a distributed energy intelligent allocation method based on Internet of Things big data in a smart city, which comprises three steps of data acquisition, processing and output. Firstly, photovoltaic, energy storage and charging pile real-time data are obtained through a multi-protocol analysis engine, and meteorological cloud charts, traffic thermodynamic diagrams and building energy consumption historical data are combined. And secondly, constructing a dynamic weight model by utilizing a space-time joint attention mechanism, generating a global wind and light output prediction network through federated learning, constructing an elastic scheduling strategy library by adopting a multi-target dynamic game algorithm, optimizing a scheduling model on a digital twin platform, and finally generating a real-time scheduling scheme through a two-stage increment optimization architecture. The method effectively improves the efficiency, economical efficiency, reliability and environmental protection property of an energy system, provides a powerful tool for urban managers, guarantees stable, economical and clean energy supply for residents, and promotes sustainable development of smart cities.

The invention relates to the technical field of digital twinning, in particular to a digital twinning service platform construction method based on cloud edge end collaboration. The method comprises the following steps: acquiring energy system basic data through an edge end device; carrying out preliminary state modeling on the edge end device by utilizing the energy system basic data, constructing a virtual energy system model, and transmitting the virtual energy system model to the cloud server; controlling the cloud server to receive the virtual energy system model uploaded by the edge end device, and performing digital twinborn model construction on the virtual energy system model to obtain an energy system digital twinborn model; and performing energy scheduling optimization according to the digital twin model of the energy system to obtain energy scheduling data of the energy system, and sending the energy scheduling data to an edge end device to perform energy system scheduling optimization operation. According to the invention, through multi-scene simulation and strategy correction, a power supply gap can be predicted and adjusted in advance, and stable operation of energy supply under various meteorological conditions and load fluctuation conditions is realized.

The invention discloses a gas generator and energy storage battery hybrid energy supply scheduling strategy, belongs to the technical field of storage and charging, and mainly solves the problems that in an energy supply mode of a gas generator in existing oil and gas drilling operation, the power generation efficiency of the gas generator is low, and the gas generator is prone to flameout when coping with an impact load. Measuring and acquiring data of voltage and current of oil and gas drilling operation energy consumption equipment, the starting number of gas generators and the residual electric quantity of an energy storage battery; 2, calculating the real-time total power, the power abrupt change rate and the load rate of the gas generator according to the measured data; 3, the number of gas generators needing to be started and the charging and discharging state of energy storage electricity are determined according to the calculation and analysis result; and 4, repeating the steps 1-3 until the drilling operation is finished. The scheduling strategy depends on an existing energy management system, the starting number of the gas generators and the charging and discharging states of the energy storage batteries are reasonably controlled by combining the total power fluctuation characteristics of oil and gas drilling operation, comprehensive management of hybrid energy supply of the gas generators and the energy storage batteries in the oil and gas drilling operation is achieved, the power generation efficiency of the gas generators is improved, and the energy consumption is reduced. Meanwhile, flameout of the gas generator is avoided, and operation safety is guaranteed.

The present application relates to a multi-objective scheduling method and apparatus for an urban power grid, a computer device, a storage medium, and a computer program product. The method comprises: connecting hydrogen energy vehicles to an original urban power grid operation system to obtain an updated urban power grid operation system; determining an electrical-hydrogen coupling unit from the updated urban power grid operation system; establishing a corresponding mathematical model for each subunit in the electrical-hydrogen coupling unit; determining a first objective function on the basis of operation cost parameters of respective modules of the updated urban power grid operation system, determining a second objective function on the basis of energy supply parameters of each subunit of the electrical-hydrogen coupling unit, and generating a multi-objective optimization and scheduling model on the basis of the first objective function and the second objective function; and solving the multi-objective optimization and scheduling model to obtain a target scheduling strategy.

The invention discloses a near-zero energy consumption building energy supply method and system based on multi-objective optimization, and relates to the technical field of building energy efficiency and renewable energy application, and the method comprises the steps: collecting energy consumption data in a building, and constructing a multi-objective evaluation system according to the energy consumption data; performing weight optimization on the multi-target evaluation system to obtain an optimal weight coefficient; combining the optimal weight coefficient with a solar photovoltaic system and a ground source heat pump system, and establishing a renewable energy supply strategy model; and outputting an energy supply scheduling instruction of the building in each time period based on the renewable energy energy supply strategy model, and adjusting operation parameters of a solar photovoltaic system and a ground source heat pump system. According to the invention, by adjusting the operation mode of the equipment in real time, the balance between the energy efficiency and the comfort level is ensured, the common problem of energy efficiency waste or insufficient comfort level in the traditional method is avoided, and by means of intelligent scheduling and optimization, the energy consumption of the building can be effectively reduced, and the sustainability of energy utilization of the building is improved.

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 provides an industrial carbon footprint dynamic prediction method fusing multi-source data, which comprises the following steps: step 1, collecting multi-source data, core data including electricity consumption, converted standard coal consumption, key industrial process product yield and carbon emission, and three types of extended data including new energy consumption data, process upgrading data and external data; 2, performing dynamic modeling according to the multi-source data obtained in the step 1, including dynamic carbon emission factor calculation and a process-environment coupling equation; 3, an industrial carbon-energy-economy multi-objective optimization model of dynamic coupling new energy consumption is constructed, minimization of carbon emission and operation cost is taken as an objective function, and constraint conditions comprise production requirements, energy supply and policy compliance; and 4, performing optimization solution on the target function of the multi-target optimization model by adopting an NSGA-II algorithm, and outputting a carbon emission-cost tradeoff curve.

The invention provides a low-power-consumption self-repairing and energy self-supplying mine dust monitoring sensor network system, which comprises a plurality of sensor nodes and a central monitoring platform, and is characterized in that data of all the sensor nodes are transmitted to the central monitoring platform in real time; the sensor network synchronizes environmental parameters, and performs data analysis, processing and visual display on the central monitoring platform; a plurality of sensors are integrated in each sensor node, and the sensors are used for monitoring different environmental parameters in a mine environment; the types of the sensors comprise a dust concentration sensor and a temperature and humidity sensor, and the environmental parameters comprise dust concentration, temperature and humidity; the problems of long-time operation, insufficient energy supply, node failure, unstable data transmission and the like of a dust monitoring system in a complex mine environment are solved. The system integrates a multi-source environment energy collection module, and the technologies of mechanical vibration, airflow kinetic energy, thermoelectric power generation and the like are adopted, so that self-power supply of sensor nodes is realized, and the requirement of frequently replacing batteries or wiring is avoided.

The invention discloses an air conditioner supply and demand collaborative optimization scheduling method, which relates to the technical field of air conditioner supply and demand scheduling, and comprises a sensing prediction module, an acquisition comparison module, a deviation correction driving module, a control adjustment module, a cold source control module, a strategy adjustment module and an anomaly detection module. When the abnormality detection module detects that the air conditioner terminal breaks down, the control adjusting module enhances the power of the peripheral equipment to fill the cooling capacity gap, if the water chilling unit breaks down, the cold source control module starts the standby unit to switch the cooling capacity output path, and when the cooling capacity is insufficient, the control adjusting module is in linkage to reduce the air volume of the low-priority area to preferentially guarantee cooling of the core area; meanwhile, a strategy adjusting module automatically matches a predefined strategy chain according to the fault type, for example, global average temperature control is switched when temperature control fails, a load balancing strategy is triggered to be distributed to a standby unit when equipment is overloaded, and the strategy is gradually rolled back to the optimal strategy based on historical data after the fault is repaired; and strategy conflicts and equipment fluctuations are avoided through progressive improvement of cold energy supply.

The invention relates to the technical field of topographic survey, in particular to an overwater and underwater topographic survey method based on cooperation of an unmanned ship and an unmanned aerial vehicle. The method comprises the following steps: acquiring platform parameter data of an unmanned ship and an unmanned aerial vehicle; according to the platform parameter data, isomerization grouping based on the residual electric quantity, the communication signal strength, the cruising ability and the measurement precision is carried out, a collaborative formation scheme is determined according to a preset water area measurement task sequence, and collaborative parameter data is obtained; acquiring environment data of a measurement area; and performing regional division modeling based on water area topographic features and environmental complexity according to the environmental data of the measurement region, and applying the collaborative parameter data to a regional model to obtain a topological model of the measurement region. The unmanned ship is used as a mobile charging platform, real-time electric quantity monitoring and docking charging mechanisms are combined, the endurance guarantee of the unmanned aerial vehicle is realized through energy supply strategy optimization, and the continuity and stability of tasks are ensured.

The invention relates to the technical field of smart home, and discloses a smart home energy supply and demand collaborative optimization system based on reinforcement learning. The system comprises an energy data acquisition module, a reinforcement learning decision module, a supply and demand collaborative optimization module, a real-time regulation and control execution module and a system performance monitoring module. The energy data acquisition module is used for acquiring various energy and environment data and processing exceptions; the reinforcement learning decision-making module constructs a state and action space and generates a regulation and control strategy in combination with a reward function; the supply and demand collaborative optimization module performs multi-objective optimization on energy distribution; the real-time regulation and control execution module regulates and controls the equipment according to the instruction priority; and the system performance monitoring module tracks the state and performs feedback optimization. In addition, an abnormal mode recognition module and a multi-agent cooperation module are further arranged and used for recognizing energy consumption abnormity and achieving equipment cooperation respectively. The system can effectively optimize household energy supply and demand, reduce cost, and improve energy utilization rate and user satisfaction.

Various electric energy supply vehicles and electric energy supply vehicle control system; a to-be-replaced electric vehicle and an electric vehicle battery replacement control system; a robot, a robot driving system, a carrying robot and a carrying robot control system; a plurality of programmable controllers, a plurality of wireless programmable controllers, a plurality of charging and replacing cabinets, a charging and replacing cabinet control system, a plurality of programmable controllers, a plurality of wireless programmable controllers, a plurality of to-be-replaced electric vehicles with different structures, a constructed electric vehicle finding mode, a plurality of electric vehicles to be subjected to battery replacement, a construction battery swapping station link is omitted, one electric energy supply vehicle can provide replenishment for a plurality of electric vehicles, an electric energy supply vehicle is put according to the amount of electric vehicles, invalid investment is avoided, and a vehicle owner does not need to center the battery again.

The invention discloses a plateau multi-energy complementary agricultural energy management system and a plateau multi-energy complementary agricultural energy management method, and belongs to the technical field of new energy. Wind, light and terrestrial heat multi-energy complementation is cooperated with a battery energy storage system and a phase change heat storage system, and a central intelligent management system is used for carrying out priority scheduling and real-time monitoring and prediction on a load; efficient energy supply of irrigation, processing and greenhouse heating is achieved, the renewable energy utilization rate and the load satisfaction degree are remarkably improved, the operation cost and environmental pollution are reduced, safe and efficient operation of the system is ensured, and good economic and environment-friendly effects are achieved.

The invention provides a flexible preparation system and method for sustainable aviation fuel, and the system comprises a green power supply module which is used for outputting fluctuating green electric energy; the process execution device is connected with the green power supply module and comprises five process modules, namely an air carbon capture module, a co-electrolysis module, a Fischer-Tropsch synthesis module, a hydrogen production module and a hydrogenation refining module; the data acquisition module is used for acquiring the temperature, the pressure, the liquid level, the raw material input flow and / or the product output flow of each process module in the process execution device; and the decision control module is used for predicting the future power supply condition of the green electricity based on the historical weather data of the green electricity location, adjusting the distribution of the power in each process module according to the future power supply condition, and adjusting the temperature, pressure and / or raw material input flow in each process module. According to the invention, the cost, the reliability, the operation and maintenance performance, the product quality and the green power utilization rate can be substantially improved at the same time, and the flexible and stable operation of the system under the green power condition is realized.

The embodiment of the utility model provides clothes processing equipment, which comprises a box body, a clothes processing drum and a wireless energy supply assembly, the box body comprises a rear plate, the clothes processing drum is rotatably arranged in the box body, the clothes processing drum comprises a drum body and a rear cover, and the rear cover is arranged at the rear end of the drum body; the wireless energy supply assembly comprises a transmitting module and a receiving module, and the receiving module is used for receiving an electromagnetic signal transmitted by the transmitting module and generating an induced current; the transmitting module is arranged on the rear plate, and at least one part of the receiving module is arranged on the rear cover. According to the clothes processing equipment provided by the embodiment of the invention, the transmitting module and the receiving module can transmit electric energy without contact, and electric energy transmission between the relatively rotating clothes processing drum and the relatively static rear plate is realized. The receiving module supplies power to the electrical elements, so that the electricity demand of the electrical elements is met, and the clothes state can be sensed in a close range.

The invention discloses a power supply method and a power supply system for on-line monitoring equipment of a lightning arrester, and the method specifically comprises the steps: activating a multi-source energy module according to a preset priority order based on the operation state and environment parameters of the lightning arrester, and supplying power to the on-line monitoring equipment; based on the output characteristics of the multi-source energy module and the residual electric quantity of the on-line monitoring equipment, a constant voltage-constant current hybrid power supply strategy in a conventional mode or an anti-interference power supply strategy in an abnormal mode is executed on the on-line monitoring equipment; and processing the historical leakage current, environmental temperature and humidity and vibration spectrum characteristics of the lightning arrester acquired by the online monitoring equipment through the LSTM prediction model, predicting the energy supply trend of the online monitoring equipment in the preset future time, and dynamically adjusting the working parameters of the online monitoring equipment according to the energy supply trend. According to the invention, stable power supply of the lightning arrester on-line monitoring equipment in a complex environment is realized, and the power supply reliability and the equipment availability are improved.

The invention discloses a self-adaptive intelligent device system for water ecological restoration and water quality improvement, and particularly relates to the field of water ecological restoration, the self-adaptive intelligent device system comprises a monitoring module, a control module, a restoration module, an energy supply module and a remote communication module, and the modules are connected through signals; the monitoring module is used for monitoring the temperature, the pH value, the dissolved oxygen, the turbidity and the nutritive salt content of the water body in real time as well as the water level change, the water flow speed and the meteorological conditions; the control module analyzes and processes the monitoring data, automatically generates a control instruction according to a preset target and rule, and adjusts the operation parameters and the working state of the repair module; the restoration module is used for restoring the water body; the energy supply module is used for providing stable and reliable energy supply for the whole device system. The change trend of the water quality parameters can be predicted in advance, so that the system can take measures in advance, manual intervention is reduced, and the automation degree and the operation efficiency of the system are improved.

The invention discloses an intelligent lighting street lamp system and a control method thereof, and the method comprises the steps: obtaining an LED drive current and a dual-energy output parameter, and generating a PWM drive characteristic through frequency decomposition; performing pre-dimming compensation on the LED based on the human eye brightness sensitivity to obtain a luminous flux control feature and an energy prediction feature; obtaining LED junction temperature change data and carrying out thermal characteristic analysis to obtain LED driving compensation characteristics; dividing an illumination control time window and dynamically adjusting an LED driving current waveform to obtain a time sequence driving characteristic; and fluctuation analysis is carried out according to the real-time luminous flux data, and linkage adjustment is carried out on PWM driving characteristics, luminous flux control characteristics, LED driving compensation characteristics and time sequence driving characteristics. According to the invention, the rapid and accurate adjustment of the LED driving current can be realized, the influence of the dual-energy output fluctuation on the illumination quality is effectively inhibited, and the accurate matching problem of the energy supply and the illumination demand in a key time window is solved.