60results about How to "Loss minimization" patented technology

The invention provides a power supply energy analysis method and system for a medium-voltage power distribution network. The method comprises the following steps: collecting multi-modal data of the medium-voltage power distribution network; constructing a health state evaluation model, and predicting a health score and a fault risk of the transformer of the medium-voltage power distribution network according to the fusion features after dimension reduction; constructing an optimization target of the power electronic equipment of the medium-voltage distribution network according to the health score and the fault probability of the transformer; constructing a multi-modal load prediction model to predict the load change of the power electronic equipment in the future; constructing a multi-objective optimization function, and generating an optimal scheduling strategy; and executing the optimal scheduling strategy to actually control the medium-voltage power distribution network equipment. According to the method, the defects of a traditional method in the aspects of equipment monitoring, load prediction and scheduling optimization are overcome, and the overall management level and coping capacity of a power grid are improved by introducing a leading-edge artificial intelligence algorithm and a data fusion technology.

The invention provides a distributed energy regulation and control method and system based on 5G network and block chain collaboration, and the method comprises the steps: building an energy block chain-based IES power transaction architecture which comprises a block chain node; docking the regulation and control object with a block chain node in an IES power transaction architecture, wherein the block chain node is used for storing the smart contract subjected to security verification; constructing a distributed energy optimization scheduling model considering the 5G network and a mathematical model of a cooperative scheduling mechanism; and the intelligent contract generates a scheduling instruction according to the distributed energy optimization scheduling model and the mathematical model of the cooperative scheduling mechanism, and the scheduling instruction value is issued to the power generation and utilization unit after passing constraint condition check.

The invention discloses a five-phase permanent magnet motor multi-objective optimization method and a control system thereof, and belongs to the technical field of motor body design optimization and motor control. According to the multi-objective optimization method for the five-phase permanent magnet motor, the optimal optimization value of the motor parameters is obtained based on the genetic aggregation response surface and the multi-objective genetic algorithm, the design variables of the motor are comprehensively considered, and the optimization accuracy is improved; according to the control system of the five-phase permanent magnet fault-tolerant motor, iterative learning control serves as a current control technology and is used for recovering the performance of the five-phase permanent magnet motor under the fault condition, the advantages of iterative learning control and counter electromotive force control are combined, high-performance operation of the motor can be ensured under the fault condition, and the reliability of the five-phase permanent magnet fault-tolerant motor is improved. Ripple-free torque output is realized, and ohmic loss is minimized. The fault diagnosis module of the control system diagnoses short-circuit faults and open-circuit faults by using a time sequence neural network and a Kalman filter, and has real-time performance and strong adaptive ability.

The invention belongs to the technical field of power system protection and control, particularly relates to a short-circuit simulation test method and system for a subway direct-current power supply system, and aims to solve the technical problems of insufficient flexibility, difficulty in adapting to complex actual working conditions and lack of adaptivity in the prior art. The method comprises the following steps: presetting and adjusting various short-circuit fault parameters to generate a short-circuit working condition; determining a transient fluctuation separation result based on the short-circuit working condition; constructing a system topology model by utilizing a graph neural network, generating a long-term protection plan in combination with an optimized double-Q learning strategy, and generating an initial risk assessment result by adopting a neural network based on physical information and Monte Carlo simulation; constructing a hybrid risk assessment model in combination with a Bayesian network and fuzzy logic to obtain a target risk assessment result, and generating an emergency response plan based on the target risk assessment result by using a natural language generation technology and a planning algorithm; according to the invention, the precision and reliability of the short-circuit simulation test of the subway direct-current power supply system are improved.

The present disclosure relates to an electron beam output device for medical use, and the electron beam output device may comprise: an electron beam generation unit for generating an electron beam in the form of a pencil beam; an electron beam acceleration unit that receives the electron beam generated by the electron beam generation unit and accelerates the received electron beam; a conduit for allowing the electron beam received from the electron beam acceleration unit to pass through and be disposed opposite to a target site or enter an internal passage of the target site; and a first magnetic field generation unit that is provided in the conduit and generates a magnetic field that refracts the electron beam output from the conduit.

The invention discloses a reinforcement learning-based structure optimization method for a free-form surface meta-structure surface deflector, and relates to the technical field of optics. The method comprises the steps that a reinforcement learning framework is constructed, the optimization problem of the free form metasurface is reconstructed into an interaction system composed of an intelligent agent and an environment, electromagnetic simulation is conducted on the environment based on strict coupling wave analysis (RCWA), the effects of the actions are evaluated, and deflection efficiency obtained through simulation is converted into rewards; the intelligent agent follows a strategy, a state-action value function is constructed by selecting actions, and behaviors of the intelligent agent are guided to maximize long-term accumulated rewards; an experience playback buffer area is used for storing a state transition tuple, exploration and utilization are carried out through an algorithm, parameters of a parameterized network are copied regularly, the network is updated and exploration and utilization are balanced by minimizing Huber loss, and it is ensured that a path reaching an optimal solution can be found and learned; and the problem of optimization of the high-complexity one-dimensional free-form metasurface is solved.

The invention discloses an unsupervised industrial abnormal sound detection method and system based on time-frequency feature selective learning, and the method comprises the following steps: 1, extracting log-Mel spectrogram features from a training set sound signal, executing a spectrogram frame selection strategy, and obtaining a spectrogram frame of each sound sample; step 2, inputting the spectrogram frame in the step 1 into a frequency domain feature selection auto-encoder model for training, wherein a loss function is LAEFS; step 3, extracting log-Mel spectrogram features from the collected unknown sound samples, and selecting a plurality of frames from a log-Mel spectrogram according to a spectrogram frame selection strategy; step 4, inputting the spectrogram frame obtained in the step 3 into the trained frequency domain feature selection auto-encoder model in the step 2, and taking the reconstruction loss LRec as an abnormal score S; and step 5, comparing the abnormal score S obtained in the step 4 with a threshold value A theta, if the abnormal score S is greater than the threshold value A theta, indicating that the input sound signal is abnormal, otherwise, indicating that the sound signal is normal.

The invention provides a collaborative optimization method of a mixed water electrolysis hydrogen production system considering seasonal wind and light fluctuation. The method comprises the following steps of: analyzing a wind-solar power frequency spectrum by adopting a variational mode decomposition algorithm, and extracting three intrinsic mode function components of ultrahigh frequency, transition frequency band and ultralow frequency; establishing a multi-time-scale collaborative optimization framework comprising a seasonal scheduling layer, an intra-day optimization layer and a real-time control layer; the ultra-low frequency component is distributed to an alkaline electrolytic bath, and the ultra-high frequency component is distributed to a proton exchange membrane electrolytic bath; an electric-thermal-life multi-field coupling efficiency optimization model is constructed, and multi-objective optimization of efficiency maximization and life loss and operation cost minimization is achieved; and an equipment switching critical point calculation method based on an efficiency curve second derivative and a gradient protection strategy are adopted. The method can effectively improve the adaptability of the system to seasonal fluctuation, the operation economy and the service life of equipment.

The invention discloses a packaging structure of a microwave photon transceiving integrated device, which comprises a tube shell, the tube shell comprises a cavity, a driving circuit board is arranged in the cavity, the driving circuit board is respectively connected with a light receiving unit, a light emitting unit and a radio frequency microwave circuit, the light receiving unit is connected with the light emitting unit through the radio frequency microwave circuit, and the light receiving unit is connected with the light emitting unit through the radio frequency microwave circuit. The bottom of the cavity is connected with the fuzz button, the fuzz button is connected with the radio frequency microwave circuit, the light receiving unit and the light emitting unit are integrated in one cavity, receiving and emitting of microwave signals are integrated in one cavity, the integration level is improved, the fuzz button with the smaller size is used for replacing an SSMP radio frequency connector, and the cost is reduced. The size of the whole device is reduced, the size of the microwave photonic device is reduced, and the problems that receiving and transmitting of microwave signals are achieved based on discrete devices, the integration level is low, external interfaces all adopt SSMP radio frequency connectors or glass sintering terminals, and the size of the microwave photonic device is large are solved.

The present invention relates to a method for recovering metals from waste batteries using a bioleaching process in which a magnetic field is applied. According to the method of the invention, by applying a magnetic field to the bioleaching process, the metal recovery rate can be increased, and valuable metals can be leached. Furthermore, the present invention is environmentally friendly and has a simple and practical process compared to a dry melting process or an acid leaching process mainly used for metal recovery. Accordingly, the metal recovery method of the present invention can recover metals from wastes in the waste resource field, such as waste batteries and waste catalysts, and convert the recovered metals into resources.

This invention discloses a wafer release method for use in a wafer processing system and a wafer processing method. The wafer release method of this invention includes: a polarity reversal step of reversing the polarity of a voltage applied to a chuck; a first transfer step of raising a lifting pin housed in the chuck at a first transfer speed to transfer a wafer supported on the chuck to a first predetermined position; and a second transfer step of raising the lifting pin at a second transfer speed different from the first transfer speed to transfer the wafer positioned at the first predetermined position to a second predetermined position higher than the first predetermined position.

A secondary battery sealing device according to one example of the present invention comprises: a pair of sealing blocks on which a battery cell is placed and which are provided so as to press a bonding target region of a pouch of the placed battery cell and a lead film; and a pair of lead blocks on which leads of the battery cells exposed to the outside of the pouch are seated, and which are provided to cover the seated leads, in which at least one of the pair of lead blocks is provided with a recessed portion for accommodating the leads.

The application discloses a cold region pig house winter stratified energy-saving ventilation method and relates to the technical field of livestock breeding. In order to solve the ventilation problem of the prior art cold region pig house, the method comprises the following steps: according to the pig house space and pig growth stage, a ventilation system is laid out; real-time monitoring of pig house internal environment parameters is taken as a basis; a CFD simulation is used to optimize the air flow path, and then the operation parameters of the ventilation system are regulated; and according to the heat loss in the pig house, the operation of the ventilation system is assisted and optimized. The application provides a dynamic adjustable air supply mechanism based on the breathing height of pigs, through pig house top layer heat recovery and pig house internal air flow stratified interval ventilation, the heat energy loss is effectively reduced, more than 80% of fresh air is accurately delivered to the pig breathing area, invalid ventilation is avoided, the energy saving rate is more than 50%, the energy consumption is reduced, the air quality in the breathing area reaches more than 99% of the standard rate, and the growth environment and health level of the pigs are improved.

The present invention provides a low thrust fluctuation modular primary winding segmented permanent magnet linear motor, including a secondary component and a primary component, at least one pair of secondary components are provided, and a permanent magnet with alternating polarity is pasted on the secondary core; the primary component adopts a multi-module segmented structure, characterized in that: each primary component is provided with matching sub-laps and mother-laps at both ends, two adjacent primary components are assembled and combined by sub-laps and mother-laps, and the width of the end coils at the matching positions of the sub-laps and mother-laps in the primary component is less than or equal to the width of the coils at the middle position. The primary component of the present invention adopts a splicing design of the primary component sub-laps and mother-laps, which can reduce the influence of the gap caused by the processing and assembly of the primary winding, and reduce the thrust fluctuation caused by the gap compared to the traditional motor, thereby achieving a significant reduction in the speed fluctuation of the mover and an improvement in the position control accuracy.

A semiconductor diode drying device disclosed by the present invention comprises a box body, a placing assembly is arranged in the box body, the placing assembly comprises fixing frames, a plurality of fixing plates, liquid leakage holes, a warm air blower, a liquid discharge plate, an air outlet pipe and a box door, the fixing frames are installed at the two ends of the interior of the box body, the number of the fixing plates is multiple, and the liquid leakage holes are communicated with the fixing frames. The fixing plate is slidably connected with the fixing frame, the fixing plate is detachably installed in the box body, the liquid leakage holes are evenly formed in the fixing plate, the warm air blower is installed at one end of the box body, the liquid drainage plate is obliquely arranged at the bottom of the box body, the air outlet pipe is arranged at the top of the box body, and the box door is rotatably connected to one side of the box body. An exhaust assembly is installed on the air outlet pipe, the exhaust assembly comprises an exhaust pipe, a motor and a threaded rod, and the exhaust pipe is connected to one end of the air outlet pipe, so that the technical problem that the quality of the semiconductor diode is difficult to guarantee in the drying process of the semiconductor diode in the prior art is solved.

The application discloses a kind of hybrid transmission clutch control parameter calibration method, device and vehicle, it is related to automobile power transmission system technical field.Therein, the method includes: based on the first clutch control parameter calibration table, determine the second clutch control parameter calibration table of hybrid transmission in the first working mode;Based on the second clutch control parameter calibration table, determine the third clutch control parameter calibration table of hybrid transmission in the first working mode;Pressure is controlled to clutch based on the third clutch control parameter calibration table.The application solves the technical problems of additional energy loss and efficiency decline caused by improper clutch pressure control in related technologies.

The invention discloses a whole vehicle logistics transportation intelligent stowage system, and relates to the technical field of logistics transportation, and the system comprises a data collection module which is used for collecting the feature information of goods and vehicles. Real-time information of vehicles and goods is stored through the arranged storage module, in the transportation process, firstly, goods information and vehicle information are sequentially input through the data acquisition module, the goods information and the vehicle information are monitored in real time through the goods monitoring module, and the goods and the vehicles meeting the requirement standard are input into the storage module; after the information of the vehicles and the cargos enters the stowage module, determining a vehicle combination according to a set rule, sorting the vehicles in the combination according to the preset rule, and storing the sorted vehicles in the stowage module in the stowage module; and the maximization of the transportation efficiency and the minimization of the cost are ensured.

The invention relates to the technical field of intelligent detection of mold damage in the manufacturing industry, in particular to a mold damage identification method based on improved self-adaptive deep embedding clustering, which comprises the following steps of: firstly, acquiring a mold vibration signal or a surface defect image, and performing data enhancement preprocessing of scaling, normalization, random rotation and brightness adjustment; mapping to a low-dimensional embedding space through a deep neural network encoder containing a channel attention mechanism (SENet), optimizing an encoder and clustering center joint target, minimizing KL divergence and reconstruction loss to generate an embedding vector, and determining an optimal parameter through model evaluation; according to the method, the MNIST data set clustering accuracy reaches 99%, the NEU-DET data set defect detection rate reaches 94.2%, the precision reduction amplitude under noise interference is only 4.2%-6.3%, only 2.1 M parameters are needed, the parameter accuracy ratio is optimal, the mold damage recognition precision and robustness are effectively improved, and the method is suitable for complex industrial scenes.

This invention relates to a high-efficiency LLC half-bridge resonant switch controller circuit, comprising a current detection circuit, an oscillation clock generation circuit, a current loss tracking detection circuit, a comprehensive control logic circuit, an input buffer circuit, a feedback voltage detection circuit, a programmable dead time generation circuit, a level shifting circuit, a delay compensation circuit, an over-temperature protection circuit (OTP), an over-current protection circuit (OCP), and a high / low-side output drive circuit. This invention employs dead-time adaptive adjustment technology to improve the efficiency of the LLC half-bridge resonant controller. On one hand, it is controlled by the reference clock (OSC) to set different dead times; on the other hand, it can also respond to external changes in soft-switching losses by adjusting the dead time, thereby minimizing switching losses. This invention also employs load condition mode switching technology, dividing the operating mode into heavy-load and light-load modes based on the state of the voltage signal FB output by the LLC half-bridge resonant converter power system, thereby improving efficiency under light load conditions.

The invention provides a method for preventing ransomware in an industrial scene. The method comprises the following steps: installing a set of special drive program and matched application program on an industrial computer needing to be protected; a drive program performs Hook file opening operation on a system kernel layer, and intercepts all file opening requests; and performing real-time judgment on the file opening request, and if the target file is an important file and the execution program is an illegal program, refusing the request. The invention provides an innovative ransomware virus protection method aiming at industrial scene characteristics, and provides a more comprehensive, efficient and real-time network security guarantee for industrial enterprises through accurate file operation interception and program legality judgment.

The invention discloses a large-scale load transient response system and method for a power grid, and relates to the technical field of power systems, and the system comprises a data collection module, a central processing unit, a load adjustment module, a user interface module, a safety protection module, and an output voltage and frequency adjustment module. The data acquisition module is used for acquiring operation data of a power grid in real time and transmitting the data to the central processing unit through a high-speed communication network; the large-scale load transient response system can effectively improve the transient response capability of the power grid, ensures the stable operation of the power grid under the large-scale load condition, and can balance the load of the power grid, reduce the energy waste and improve the power utilization efficiency through real-time monitoring and rapid adjustment under the condition of not influencing the normal power utilization of a user. And meanwhile, an output voltage and frequency adjustment module is introduced, so that the loss in the energy conversion process is minimized, and the operation efficiency and the environmental protection performance of the power grid are further improved.

The invention discloses a hyperspectral image small sample classification method and system combined with contrast learning, and the method comprises the steps: obtaining hyperspectral image data, carrying out the dimension reduction processing of the hyperspectral image data, and obtaining target hyperspectral image data; pixel field block division is carried out on the target hyperspectral image data to obtain to-be-input image data, and the to-be-input image data comprises each neighborhood pixel block of a target hyperspectral image; and inputting the image data to be input into a pre-constructed CL-HSI network model, wherein the CL-HSI network model outputs a classification result corresponding to the target hyperspectral image data. The data is preprocessed through a PCA algorithm, pixel neighborhood division and data enhancement. And then, constructing a classification network by using self-supervised learning and comparative learning so as to realize hyperspectral image small sample classification.

The invention relates to the technical field of emergency power supply, and provides a power supply emergency power supply system, which comprises a power failure condition judgment module, a region grading power supply module, a distribution region judgment module, a voltage distribution module and an online supply module, and is characterized in that the power failure condition judgment module is used for detecting power failure reasons of a power supply to provide a reasonable emergency power supply scheme; the area grading power supply module is used for grading power supply areas and preliminarily excluding areas without power supply, the distribution area judgment module is used for secondarily excluding areas without power supply, consumption of a generator is reduced, power supply time is prolonged, and the voltage distribution module is electrically connected with a power-off simulation module. Power transmission simulation of each floor power supply can be performed in advance to adapt to the bearing voltage of the power supply after aging, so that proper voltage and current can be quickly and stably transmitted to the power supply without temporary detection and calculation in the emergency power supply process, and the power supply stability is improved.

The optical system disclosed in the embodiments may include a first lens to a fifth lens arranged continuously along the optical axis in a direction from the object side to the image side, wherein each of the first lens to the fifth lens includes an object-side surface and an image-side surface, the effective aperture size of the image-side surface of the first lens is larger than the effective aperture size of the object-side surface of the first lens, and the thickness of the first lens is smaller than the thickness of the second lens.

The invention belongs to the field of power distribution networks, and particularly relates to a power distribution network optical storage system coordinated planning method based on a double-layer particle swarm algorithm, which comprises the following steps: S1, parameter setting: inputting a power distribution network structure, load parameters, constraint conditions, sizes of upper and lower layers of populations, the number of internal loop iterations of particles and the maximum number of iterations, initializing a power flow to obtain an initial branch power flow and a node voltage parameter; and S2, initializing and updating an upper-layer particle swarm: by taking the partition as a unit, initializing the position and the speed of the particle swarm, and putting particles meeting constraint conditions into a target function to obtain a global optimal value, a global optimal fitness, a local optimal value and a local optimal fitness. The planning capacity of the DPG and the ESS in each partition is uniformly distributed as far as possible through upper-layer planning; and the lower layer minimizes the investment planning cost on the premise of ensuring the minimum network loss, and ensures the stability and reliability of the system at the same time.

The invention relates to the technical field of intelligent power grids and industrial automation control, in particular to a DC power supply coordination control system based on industrial big data. The system comprises a multi-dimensional heterogeneous data sensing module, a feature mapping module, a multi-target collaborative decision-making module and a self-adaptive execution module. The system constructs a load characteristic and electric energy quality correlation model based on the operation state and the power consumption demand, outputs dynamic electrical characteristics and sensitivity characteristics, and solves a power distribution instruction by taking power grid quality, load balance and efficiency as cooperative targets; according to the method, a perception decision-making framework crossing an electrical domain and a physical domain is constructed, hidden physical layer states such as impedance drift are mined through deep learning, the problem that a load physical state is ignored and production quality is disjointed in traditional control is solved, and the industrial yield and energy efficiency are improved while the quality of a power grid is guaranteed.

The present invention discloses an active control method, system, device and storage medium of an intelligent distribution cabinet cluster, and relates to the technical field of electric power dispatching. When the electric power data and energy storage information of a first area where any load area exists meet the condition of overpower supply, an energy storage optimization model is constructed based on load layout information, power supply information, regional electric power data and energy storage information; the energy storage optimization model is solved to obtain an energy storage control scheme; when the electric power data and power supply information of a second area where any load area exists meet the condition of insufficient power supply, a scheduling optimization model is constructed based on load layout information, power supply information, regional electric power data and energy storage information; the scheduling optimization model is solved to obtain a load control scheme; the role of cross-regional distribution cabinet clusters and energy storage equipment is brought into play to a greater extent, and the utilization efficiency of electric power resources is improved.

The invention discloses a converter with staggered parallel integrated Buck-Boost LLC circuit topology, belonging to the field of DC power electronic conversion, wherein the primary circuit includes two-stage staggered parallel Buck-Boost circuits and LLC resonant circuits; each stage of Buck-Boost circuit includes four switch tubes and choke inductors, the first two switch tubes constitute Buck bridge arms, the latter two switch tubes constitute Boost bridge arms, and the choke inductors are connected between the midpoints of the Buck bridge arms and the midpoints of the Boost bridge arms; in each stage of Buck-Boost circuit, the phase shift angle between the Boost bridge arms and the Buck bridge arms is the minimum phase shift angle when each switch tube is soft-switched, and under the minimum phase shift angle, the converter loss is a single-valued function related to the inductance value of the choke inductor, and the inductance value of the choke inductor is the inductance value when the converter loss is the smallest. Two-stage phase shift 180-degree staggered parallel connection solves the problem of secondary current asymmetry, realizes secondary synchronous rectification, and designs the phase shift angle and choke inductance values ​​to realize soft switching and minimum loss at the same time.

A thermoelectric module comprises: a unit thermoelectric material, which comprises an N-type thermoelectric material and a P-type thermoelectric material and is arranged on one surface of a first substrate; first electrodes, each of which is electrically connected to one end of a corresponding one of the N-type thermoelectric materials or one end of a corresponding one of the P-type thermoelectric materials; second electrodes, each of which is arranged to be separated from the other end of the corresponding one of the N-type thermoelectric materials and the other end of the corresponding one of the P-type thermoelectric materials by a predetermined gap; and a second substrate, which supports the second electrodes, wherein when pressure is applied to the second substrate, each of the second electrodes is electrically connected to the second end of the corresponding one of the N-type thermoelectric materials and the second end of the corresponding one of the P-type thermoelectric materials.