11538 results about "Generating unit" patented technology

The invention relates to the technical field of state monitoring of wind generating sets, and discloses a fault early warning and service life prediction method and system for a wind generating set, and the method comprises the steps: obtaining first state data, second state data and image data of a target wind generating set, and forming multi-dimensional data; fusing the multi-dimensional data by using a multi-modal fusion model to obtain multi-modal data fusion features of the target wind generating set; and performing fault early warning and / or life prediction on the target wind generating set based on the multi-modal data fusion features. By integrating the multi-modal data, the problem that fault features are difficult to comprehensively capture by a single data source is solved, fault early warning and service life prediction are performed by utilizing the multi-modal data fusion features, the false report and missing report rate of faults is reduced, accurate quantitative prediction of the remaining service life of the wind generating set is realized in combination with the data driving model, and the prediction efficiency is improved. By improving the accuracy of fault early warning and life prediction, the wind generating set is effectively operated and maintained in advance.

The invention provides an intelligent detection method and system for the health state of a wind generating set in an intelligent wind field, and relates to the technical field of intelligent wind field multi-source monitoring. The method comprises the following steps: firstly, collecting multi-source operation data such as a transmission chain, structural parts and environment working conditions, and performing time reference unification; performing noise reduction, calibration, compensation and time window segmentation on the original data to form a preprocessed data set; extracting and aligning features in multiple domains to construct fusion feature representation; establishing a health baseline model based on historical normal samples and working condition variables to generate a self-adaptive alarm threshold value; inputting a health discrimination model to obtain an anomaly index, and generating an early warning event according to a trigger condition; and comprehensively fusing the features, the health base line and the judgment result to calculate a health index and output early warning information, thereby realizing accurate detection and risk early warning of the whole life cycle and the whole working condition.

The invention relates to the technical field of abnormal monitoring of water-turbine generator sets, in particular to an abnormal monitoring system and method for a water-turbine generator set, and the system comprises a sensor module, an edge computing node, a cloud analysis platform and a scheduling decision center. The edge computing node performs data preprocessing, the cloud analysis platform realizes accurate early warning by applying technologies such as multi-source data fusion, a dynamic early warning model library and adaptive threshold adjustment, the scheduling decision center optimizes scheduling decisions through a resource matching engine and an emergency plan generation module, and the industrial communication network guarantees reliable transmission of data. In addition, the system is also provided with a visual interaction interface and a state evaluation report generation module. According to the invention, comprehensive and accurate monitoring and intelligent early warning of the water-turbine generator set are realized, the fault processing efficiency is effectively improved, the probability of false alarm and missing alarm is reduced, resource allocation is optimized, the operation safety is enhanced, and a powerful guarantee is provided for stable operation of the water-turbine generator set.

The invention provides a hydropower station generator set excitation system fault prediction method and device, and relates to the technical field of intelligent power grids, and the method comprises the steps: carrying out the continuous sampling of a generalized state observation vector through a sliding time window, so as to form an operation parameter observation tensor; fusing the operation parameter observation tensor and the boundary constraint type operation parameters of the generator, and constructing a fault observation tensor fused by multi-source operation parameters; introducing a structural causal atlas in a modeling layer, establishing a causal path network embedded based on causal reasoning and topological time sequence for a fault observation tensor, and dividing system interaction faults encountered by excitation system prediction into a plurality of typical modes; and performing graph attention modeling on the evolution trend and the instability boundary of each key parameter in the fault causal chains of different typical modes contained in the causal path network, and dynamically identifying the path characteristics of the excitation system entering the fault critical state. According to the method, the interaction type fault of the excitation system of the hydropower station generator set can be predicted.

The invention discloses a thermal power plant multi-target economic dispatch optimization method and device, a medium and a product, and relates to the field of data processing. The method comprises the following steps: constructing an initial data set based on power grid operation parameters, generator set state parameters and environment monitoring data of a thermal power plant; determining characteristic parameters of the current operation scheduling demand according to the initial data set; based on the characteristic parameters, real-time target weight coefficients of the economic index, the environmental protection index and the scheduling flexibility index are determined through a preset dynamic weight distribution model; according to the real-time target weight coefficient, generating an initial candidate scheduling scheme set meeting the safety constraint of the generator set; performing main target directional optimization on the initial candidate scheduling scheme set to obtain a Pareto optimal solution set meeting a preset convergence condition; and generating a scheduling recommendation scheme based on the Pareto optimal solution set. According to the method, the problem of multi-target conflict caused by multi-target optimization imbalance in economic dispatching of the thermal power plant can be relieved.

The invention discloses a power equipment fault prediction method based on multi-modal data and related equipment, and belongs to the technical field of intelligent monitoring and fault prediction of power equipment, and the method comprises the steps: collecting and preprocessing the multi-modal operation data of the power equipment; extracting features of the preprocessed multi-modal operation data and performing global feature fusion to obtain multi-modal features; inputting the multi-modal features into a pre-trained fault prediction model to obtain a fault category and an occurrence probability of the power equipment; the fault prediction model is obtained by inputting the multi-modal features into a hybrid neural network for training. Multi-modal operation data of power equipment is collected, key features are extracted by using a deep learning model, multi-modal data fusion is performed, and a fault prediction model is constructed based on a hybrid neural network. Real-time fault early warning and maintenance suggestions are provided, the operation reliability of power equipment is improved, the unplanned shutdown risk is reduced, and the method is suitable for health management of transformer substations, high-voltage power transmission equipment and wind generating sets.

The invention relates to the technical field of steam turbine testing, in particular to a testing system for mismatch correction of a wide-load-interval thermal power generating unit speed regulation model, which comprises a thermodynamic system component, a speed regulation system device, a dynamic signal acquisition device and a control device, a dynamic signal processing module analyzes the relevance between main steam pressure sudden change and control valve opening oscillation in real time, a dynamic response database including a time sequence is constructed, and a control valve opening feed-forward compensation instruction and a combustor fuel distribution gradient adjustment signal are generated according to correction parameters. Power over-modulation wave crests caused by sudden load change are suppressed, and primary frequency modulation action delay is reduced; and the simulation verification module compares measured data with model output through an error feedback mechanism, dynamically adjusts high-pressure cylinder flow characteristic curve calibration parameters, optimizes a control strategy in combination with a network-related safety margin evaluation result, and realizes improvement of dynamic response precision in a full peak regulation interval and enhancement of power grid frequency stability.

The invention discloses a fire-fighting monitoring and generator set collaborative optimization multi-target constraint solving method, which comprises the following steps: carrying out real-time acquisition and structured processing on multi-source data, acquiring safety monitoring and equipment operation data through a sensor network, and storing the data in a distributed real-time database after preprocessing; carrying out dynamic thermodynamic field modeling and risk area division, discretizing a plant area as a grid unit, solving a heat conduction equation in combination with heat source modeling and boundary conditions, and dividing three levels of risk areas; building a multi-objective optimization model, defining three types of core constraints, building a normalized objective function, and realizing multi-objective decoupling through dynamic weight adjustment; according to an asymmetric load migration strategy, differential load adjustment is implemented according to an optimization result, an auxiliary system is linked, constraints are verified in real time, and closed-loop control is formed. According to the technology, the collaborative goals of safety risk controllability, system efficiency maintenance and equipment redundancy optimization are achieved, and the method is suitable for fire prevention and efficient operation in an energy production scene.

The invention provides a multi-region hierarchical PID parameter adaptive adjustment method and device, and the method comprises the steps: collecting the load deviation of a generator set in real time, and comparing the load deviation with a preset first threshold value and a preset second threshold value (the first threshold value is greater than the second threshold value); when the load deviation absolute value is larger than a first threshold value, entering an emergency adjusting area, when the load deviation absolute value is larger than a second threshold value and smaller than or equal to the first threshold value, entering a rapid adjusting area, and when the load deviation absolute value is smaller than or equal to the second threshold value, entering a high-precision adjusting area. And the proportional gain is dynamically adjusted according to the current adjusting area and the load deviation, and the output control pulse width is calculated by combining the adjusted proportional gain, the load deviation, the control period and the maximum output of the unit. In the high-precision adjusting area, the control period is adjusted according to the output control pulse width and the preset period growth coefficient, and precise control over the generator set is achieved. The adaptability, the stability and the adjusting precision of the control system can be improved, and the operation performance of the generator set is optimized.

The invention discloses a centralized unloading type power generation robot which comprises a mute box type diesel generator set and further comprises at least two sets of lifting supporting rod assemblies, and the lifting supporting rod assemblies are arranged on the two sides of the mute box type diesel generator set so as to lift the mute box type diesel generator set in the loading and unloading process. According to the centralized unloading type power generation robot, by arranging the lifting supporting rod assembly, the problem that a traditional generator set loading and unloading vehicle depends on hoisting equipment is solved, automatic lifting and unloading of the loading and unloading vehicle can be achieved in the environment without the hoisting equipment, the transportation efficiency is improved, the transportation cost is reduced, and it is ensured that the equipment can be rapidly transferred to the needed place to be put into use.

The invention relates to the technical field of fault detection, and discloses a diesel generating set fault detection method and system based on deep learning, and the method comprises the steps: obtaining first vibration signal data, and carrying out the time-frequency decomposition, and obtaining a dynamic change feature; de-noising processing is carried out on the dynamic change features to obtain a time-frequency feature sequence; extracting a peak energy distribution data set, and calculating each frequency band entropy value to obtain a frequency band entropy value sequence; classifying the frequency band entropy sequence, determining a random fluctuation reference mode, and separating to obtain an abnormal frequency component; calculating a spectral line spacing and amplitude ratio, obtaining a spectral line feature data set, classifying the spectral line feature data set, and determining a fault classification result; obtaining current second vibration signal data, performing similarity calculation on the current second vibration signal data and a pre-established normal mode library, and outputting a fault feature vector; and verifying the fault feature vector to obtain a final fault detection result. According to the method, closed-loop diagnosis from signal acquisition to fault classification can be realized, and the fault detection precision of the diesel generating set is improved.

The invention relates to the field of power systems, in particular to a multi-energy complementary power supply network cooperative control method and system. The method comprises the following steps: collecting dynamic characteristic data of wind power, photovoltaic power, energy storage and load through a sensor group, and generating a multi-energy state characteristic matrix with a timestamp; and matching the feature matrix with a dynamic response feature library, and outputting a dynamic feature parameter set. Based on the set, a multi-objective optimization model is constructed, second-level frequency deviation suppression, minute-level tie line power stabilization and hour-level economic dispatching are covered, and a cooperative control parameter set is generated. And generating a thermal power generating unit compensation amount, an energy storage charging and discharging sequence and an interruptible load priority list. After execution, power grid deviation data are collected, a dynamic deviation value is calculated, and when a threshold value is exceeded, feature library parameters are corrected and loop optimization is carried out. According to the invention, the problem of insufficient cooperative control of the multi-energy system in the prior art is solved, and the stability and economy of the power grid are improved.

A virtual synchronization method for an energy storage system and a radial-flow water turbine generator set, said method comprising: analyzing characteristics of a radial-flow water turbine generator set, and designing a suitable energy storage system so as to smooth the power fluctuation of the radial-flow water turbine generator set (S1); acquiring characteristic parameters of the radial-flow water turbine generator set, on the basis of the characteristic parameters, designing a virtual rotor model, and establishing a machinery dynamics equation (S2); on the basis of the virtual rotor model, designing a virtual synchronization controller, constructing a closed-loop control system, and implementing charging and discharging control for the energy storage system (S3); establishing a simulation model for coupling the energy storage system and the radial-flow water turbine generator set, and optimizing parameters of the controller (S4); and, on a test platform of an actual radial-flow water turbine generator set, implementing a proposed virtual synchronous control policy, and carrying out experiment verification and result analysis (S5).

The invention discloses a fan blade state monitoring method based on multi-sensor fusion, relates to the technical field of wind power, and is suitable for wind energy prime mover equipment manufacturing and blade state monitoring technologies of onshore and offshore wind generating sets. The method comprises the following steps: acquiring operation data, a vibration signal, an acoustic signal and a pulse signal of a fan; the current working condition state of the fan is recognized, common-mode fault verification, local damage positioning and transient stress damage analysis are carried out on the vibration signals and the acoustic signals, and a fault analysis result and a first damage analysis result are obtained; performing phase-locked amplification analysis on the vibration signal and the acoustic signal through active excitation to obtain a second damage analysis result; and finally, a comprehensive state monitoring report of the fan blade is generated, so that the problems of difficulty in identification of weak damage and high false alarm rate of blades of land and offshore wind generating sets in wind energy prime mover equipment manufacturing under a non-stable working condition are solved, and the equipment operation and maintenance intelligent level in the wind energy prime mover equipment manufacturing industry is effectively improved.

The invention relates to the technical field of power system operation and control, and discloses a primary and secondary frequency modulation cooperative control system of a hybrid energy storage coupling wind generating set, which comprises a wind power generation unit, a hybrid energy storage unit and a central cooperative controller. A total power demand including inertia response and primary and secondary frequency modulation is synthesized, a primary and secondary frequency modulation cooperative distribution module generates a dynamic adjustment weight by using an S-type nonlinear function based on a frequency change rate and frequency deviation coupling relationship, and an energy relay compensation mechanism is introduced to fill a power gap in a switching process. And the SOC adaptive constraint and execution module applies direction selective boundary constraint to the state of charge, and controls hybrid energy storage to perform internal energy self-balancing in a frequency modulation dead zone. According to the invention, seamless connection of multi-time scale frequency modulation is realized, frequency secondary drop is effectively prevented, and the frequency support capability and the self-recovery capability of the system are improved.

The invention discloses a wind-solar complementary intelligent charging system and heat management optimization. The system comprises a wind power generation unit, a photovoltaic power generation unit, an energy storage unit, an intelligent control unit and a heat management unit, the wind power generation unit adopts an efficient wind generating set, is provided with an intelligent control system, and can automatically adjust a pitch angle and a yaw angle according to real-time wind speed and wind direction; the photovoltaic power generation unit selects a high-efficiency photovoltaic cell assembly and tracks the position of the sun in real time in combination with an intelligent light following system; and the energy storage unit adopts a high-performance energy storage battery pack and a battery management system. Through accurate prediction and analysis of local wind and light resources, dynamic configuration and cooperative power generation of the wind power generation unit and the photovoltaic power generation unit are realized. Compared with a traditional fixed configuration mode, local wind and light resources can be fully utilized, and the power generation efficiency is improved. Meanwhile, the multi-energy complementary cooperative power generation technology is adopted, organic combination of wind and light power generation and energy storage is achieved, and stable power supply of the system is ensured.

The invention discloses a multi-energy-storage thermal power generating unit optimal scheduling method, which belongs to the technical field of power system scheduling, and specifically comprises the following steps: acquiring operation data and power grid load data of a multi-energy-storage thermal power generating unit; constructing a load prediction model based on the collected data to obtain a load prediction result; taking the minimum comprehensive cost as a target, and combining a thermal power generating unit output constraint, an energy storage equipment charging and discharging constraint and a power grid security constraint to construct an optimal scheduling model; solving the optimal scheduling model by adopting an improved intelligent optimization algorithm to obtain an optimal scheduling scheme; performing real-time scheduling on the multi-energy-storage thermal power generating unit according to the optimal scheduling scheme; the method can improve the scheduling flexibility of the multi-energy-storage thermal power generating unit, effectively deals with the load fluctuation of the power grid, and is suitable for the optimal scheduling task of the multi-energy-storage thermal power generating unit in a complex power grid environment.

The invention relates to the technical field of generator monitoring, in particular to an online state monitoring method and system for a water-turbine generator set, and the method comprises the steps: collecting data, and carrying out the preprocessing of the data through a lightweight TinyML reasoning model; dimension reduction processing is carried out on the preprocessed data through PCA, and dynamic normalization is carried out on the data after dimension reduction; performing convolution feature extraction on the normalized data through a feature extraction module, and extracting attention enhancement features from the convolution features through a sparse attention mechanism; calculating a posterior probability based on Bayesian network topology through a Bayesian network feature fusion module; a fault probability vector and an integrated feature vector are obtained through combination of a multi-modal fusion model and a posterior probability; and through the fault probability vector, predicting residual life and current working condition characteristics, and outputting an early warning level, a fault type and predicted fault time. According to the scheme, the diagnosis efficiency and reliability are improved through multi-modal fusion and Bayesian reasoning.

The invention relates to the technical field of wind power prediction, and particularly provides a generation power prediction method and system for a wind generating set, and the method comprises the steps: firstly obtaining a continuous operation data sequence of equipment state parameters and environment parameters containing timestamp marks, and then carrying out the time sequence feature analysis, generating an equipment state feature sequence and an environmental condition feature sequence, then performing correlation modeling on the two features through feature collaborative analysis operation to obtain a coupling feature sequence reflecting multi-factor collaborative influence, and inputting the coupling feature sequence into a pre-trained power prediction model to obtain a power prediction result; an initial power prediction sequence of a target time period is generated through time context learning and power value mapping operation, finally, an error calibration model is constructed based on historical data, the initial power prediction sequence is dynamically adjusted, and a calibration power prediction sequence is generated and output to a power dispatching system for power generation plan arrangement. And the accuracy of generating power prediction of the wind generating set is effectively improved.

The present invention relates to a skin care device including a lightweight handpiece. The skin care device comprises: a handpiece for skin treatment which comes into contact with the skin and stimulates the skin; a control body which is connected to the handpiece for skin treatment through a connection cable body and controls the operation of the handpiece for skin treatment; a driving force generating unit which is mounted inside the control body and generates a driving force; a handpiece operating body which is provided in the handpiece for skin treatment and operates by receiving the driving force from the driving force generating unit; and a driving force transmission unit for transmitting the driving force of the driving force generating unit to the handpiece operating body through the inside of the connection cable body. Since the driving force generating unit for operating the handpiece operating body is mounted on the control body, the weight of the handpiece for skin treatment is reduced, thereby greatly improving the convenience of a practitioner and the satisfaction of treatment during treatment, and simplifying the structure of the handpiece for skin treatment.

The invention discloses a semi-submersible wave energy-wind energy integrated power generation platform and a damping regulation and control method of a damping regulation and control system of the semi-submersible wave energy-wind energy integrated power generation platform. The platform comprises a semi-submersible wind driven generator platform, a wind driven generator set, a wave energy power generation device, an energy storage system, a sea area detection system and the damping regulation and control system. The wave energy power generation devices are divided into two groups on the inner side and the outer side of the platform, each wave energy power generation device comprises a platform inner side wave energy power generation device and a platform outer side wave energy power generation device, and each wave energy power generation device comprises a swing arm floater, a universal hinge, a connecting arm and a damping adjusting module. The semi-submersible wind power generation device and the wave power generation device are combined, and offshore renewable resources are comprehensively utilized; the six-buoy design is adopted, buoyancy is dispersed, stability and safety are improved, and meanwhile space is provided for a wave power generation device; the floaters are symmetrically arranged in a hexagonal shape and carry a damping regulation and control system, and the whole structure is more easily stabilized by adjusting the damping of the floaters in all directions.

The invention discloses a ship power system optimization scheduling method and system based on a genetic algorithm, and relates to the technical field of ship power control, and the method comprises the steps: obtaining real-time operation parameters of a ship power system, building an initial population of the genetic algorithm based on the operation parameters, and carrying out the optimization scheduling of the ship power system based on an energy consumption characteristic index and an emission characteristic index. And calculating the comprehensive fitness value of each individual, performing selection operation, interlace operation and mutation operation on the initial population to generate a new population, repeatedly performing population iterative optimization until a preset condition is met, outputting a navigational speed adjustment parameter combination, a generator set start-stop decision sequence and a power distribution scheme, and generating a ship power system control instruction set. According to the ship power system optimization scheduling method and system based on the genetic algorithm provided by the invention, the operation efficiency of the ship power system is improved, the energy consumption is reduced, and the carbon emission is reduced.

The invention discloses a sound production device, a sound production module and electronic equipment, and relates to the technical field of electroacoustic transduction, and the sound production device comprises a first sound production monomer and a second sound production monomer which are arranged side by side along a first direction, the vibrating plates of the first sound production monomer and the second sound production monomer comprise first plate parts and second plate parts which are located on different horizontal planes in the second direction, the first plate parts are connected with the top of the voice coil and the vibrating diaphragm respectively, and the outer peripheries of the second plate parts abut against the inner peripheral wall of the voice coil; the central magnetic parts of the first sound production single body and the second sound production single body comprise a first central magnet and a second central magnet which are positioned on two opposite sides of the second plate part; the first central magnet and the second central magnet are magnetized along a second direction and are opposite in magnetizing direction; the sound production device further comprises a first magnet yoke and a second magnet yoke which are oppositely arranged in the second direction, and the outer edges of the first magnet yoke and the second magnet yoke are connected in a welded mode. According to the sound production device, the BL value is improved, and distortion is reduced.

The invention discloses a dynamic monitoring method and system for settlement and inclination of a tower drum of a wind generating set, and relates to the technical field of safety monitoring of wind power generation infrastructure, the method comprises the following steps: synchronously collecting inclination data and positioning data of each monitoring point, and carrying out space-time alignment to obtain a fusion data set; decoupling the dynamic elastic response of the tower drum and the steady-state deformation of the foundation from the fused data through frequency domain analysis, and further calculating the overall inclination rate, the settlement amount and the non-uniform settlement rate of the tower drum; performing safety assessment based on the calculated parameters and a preset safety threshold, and determining a basic safety state and a risk mode; and finally, generating graded early warning information and decision information including risk positioning and trend prediction according to an evaluation result. Through the above mode, the method achieves the precise separation and monitoring of the dynamic deformation characteristics of the tower drum, improves the accuracy of safety evaluation and the timeliness of early warning, and provides an effective data support for the operation and maintenance decision of a wind generating set.

The invention provides a consumption reduction method and device for a wind and light energy storage complementary thermal power plant system, and the method comprises the steps: obtaining the wind speed, illumination intensity, energy storage charge state, auxiliary power load curve, power grid dynamic electricity price signal and thermal power generating unit auxiliary machine operation parameters of the thermal power plant system, and taking the parameters as a data set; based on a weather prediction model, a load prediction model and an electricity price fluctuation model, generating a wind power generation output prediction value, a photovoltaic power generation output prediction value, a load demand prediction value and an electricity price interval prediction value in a future preset time period according to the data set, and taking the prediction values as prediction results; by taking minimization of plant power cost and maximization of renewable energy consumption as targets, generating an energy storage charging and discharging instruction, a wind power generation and photovoltaic power generation grid-connected priority sequence and a thermal power auxiliary engine regulation and control strategy according to the data set and the prediction result to serve as control instructions; control instructions are executed through control equipment in the wind-solar energy storage complementary thermal power plant system, and energy conservation and consumption reduction are achieved by integrating wind energy, solar energy and an energy storage system.

The invention discloses a network card adapter plate and a server system, and relates to the technical field of computers, the network card adapter plate comprises a power supply connector, a plurality of node connectors, a power supply and time sequence signal processing module and a plurality of network card connectors, the power supply connector is connected to a power supply board, the plurality of node connectors are respectively connected with different server nodes, and the power supply and time sequence signal processing module is connected with the network card connectors. The plurality of network card connectors are respectively configured to be connected with different interfaces of a network card; the power supply and time sequence signal processing module comprises a power supply enabling control unit, an electronic fuse and a main power supply time sequence signal generating unit; the power supply enabling control unit enables the electronic fuse to supply power to the network card when the hot plug-in condition is met and no hot pull-out operation exists; the power supply normal signal output end of the electronic fuse outputs an auxiliary power supply time sequence signal to the network card; and the main power supply time sequence signal generation unit outputs a main power supply time sequence signal to the network card when receiving the network card power supply normal signal and having no hot pull-out operation. According to the invention, hot plugging of the network card is realized in a multi-node sharing environment.

The invention relates to the technical field of wind power generation, in particular to a wind turbine generator health state diagnosis system and method. The system comprises a monitoring module used for acquiring multi-source data by using a preset sensor group; the calculation module is used for receiving the multi-source data and performing feature extraction operation on the multi-source data to obtain multi-source feature information; and the analysis module is used for fusing the multi-source feature information and evaluating the health state of the generator set based on a fusion result. Therefore, through the generator set health state diagnosis system, multi-sensor fusion, edge calculation and cloud intelligent analysis are integrated, the problem that the prior art is lack of system-level health state evaluation capability and intelligent diagnosis capability is solved, and real-time monitoring and early fault early warning of the health state of the whole wind turbine generator system are realized.

The invention discloses a ship power system optimization control method and system based on a simulated annealing algorithm, and relates to the technical field of ship power control, and the method comprises the steps: obtaining operation parameters and historical energy consumption data in real time, and constructing a multi-objective optimization function of dynamic weight distribution; generating an initial temperature parameter and a solution set in combination with the navigation state and the environment data; a neighborhood search strategy disturbance solution set is improved, a new solution is evaluated by using a dynamic acceptance probability function, temperature parameters are adaptively adjusted for iterative optimization, and an optimal control parameter combination is output; and an adjustment instruction set is generated after multi-dimensional efficiency verification, and a propulsion device, a generator set and an energy storage module are cooperatively controlled, so that global energy consumption optimization is realized. According to the method, through data driving and intelligent algorithm fusion, energy efficiency and environmental adaptability are improved, and stable operation under complex working conditions is guaranteed. According to the ship power system optimization control method and system based on the simulated annealing algorithm, the energy efficiency level and the environmental adaptability of the ship power system are improved.

The present invention relates to a treatment apparatus using RF energy, a method for controlling same, and a treatment method using same. More particularly, the present invention provides a treatment apparatus using RF energy, a method for controlling same, and a treatment method using same, the treatment apparatus comprising: a handpiece having an electrode configured to contact a skin surface and to transfer RF energy; an RF energy-generating unit for generating RF energy to be transferred to the electrode; a cooling unit provided to cool the electrode; and a control unit for controlling the operations of the RF energy-generating unit and the cooling unit and controlling the cooling unit so as to preliminarily cool the electrode before RF energy is applied to the electrode.

The invention discloses a slope type gravity energy storage system based on a multi-vehicle circulating chain and an energy storage and power generation method. The system comprises an upper storage yard, a lower storage yard and a mountain slope located between the upper storage yard and the lower storage yard, an annular conveying track is arranged on the surface of the mountain slope, the upper end of the annular conveying track extends to the upper storage yard, and the lower end of the annular conveying track extends to the lower storage yard; symmetrically-arranged conveying belts are arranged on the two sides of the annular conveying track located on the upper storage yard and the lower storage yard respectively, and a lifting platform is connected to the outer side of each conveying belt; a crane is arranged near the conveying belt; an annular conveying chain is arranged between the double annular conveying track rails, and a moving trolley used for loading heavy objects is connected to the annular conveying chain. The annular conveying chain is further connected with a motor generator set. The multiple moving trolleys are connected in series to circularly transport the heavy objects, the moving trolleys do not need to change the running state, the heavy objects can be transported between the mountain bottom and the mountain top, and the transport efficiency is improved.