299 results about "Load management" patented technology

The invention relates to the technical field of ship power systems, and discloses a ship comprehensive power system based on composite energy storage, comprising an energy generation module which comprises a diesel generator, a gas turbine and a renewable energy sub-module and is used for generating electric energy; the energy storage management module comprises a lithium battery, a super capacitor and a flywheel energy storage system and is used for storing and releasing electric energy; the energy distribution and scheduling module is used for controlling energy distribution between the energy generation module and the energy storage management module; the load management module is used for adjusting the load power requirement according to the load priority; and the health management and fault monitoring module is used for monitoring the operation states of the energy storage management module and the system in real time. Through cooperative work of multiple energy storage units and combination with optimization control of the energy distribution and scheduling module, characteristics of different energy storage devices are fully exerted. Therefore, loss in the energy conversion process is remarkably reduced, distribution and utilization of electric energy are optimized, and the overall energy utilization efficiency of the system is improved by about 15%-20%.

The invention discloses an intelligent load management and allocation method for an electric meter box, relates to the technical field of intelligent power grid management and energy optimization, and solves the problems that the adaptability of load management in an existing electric meter box is insufficient, the prediction accuracy is influenced by data prejudice, and the dynamic adjustment capability is limited. According to the method, diversified data are collected and analyzed through a real-time data interface and a gradient boosting tree technology, a power consumption prediction model is constructed in combination with a long-short term memory (LSTM) network, and meanwhile, prediction performance is optimized by adopting a data enhancement algorithm and an adaptive enhancement algorithm; fast response and dynamic adjustment are realized by using a sliding window prediction method based on Kalman filtering, a load distribution strategy is optimized through an adaptive depth deterministic strategy gradient algorithm, and collaborative distribution scheduling and management are performed on cross-regional electric meter boxes through a distributed collaborative scheduling system; according to the invention, the stability and efficiency of the power system are greatly improved, and the energy utilization efficiency and economic benefits are remarkably improved.

The invention relates to the technical field of power equipment load management, in particular to a station electric equipment steady-state feature extraction system based on load awareness, and the system comprises a load data processing module which obtains load data based on the operation state of power equipment, analyzes the interaction effect between each type of load data, extracts the correlation features between parameters, and outputs the correlation features between the parameters; and identifying periodic characteristics and abnormal fluctuation of the load data to obtain a standard load data set. According to the method, more accurate and comprehensive steady-state feature extraction of the power equipment can be realized through multi-dimensional parameter analysis and processing of the load data. Meanwhile, the load data interaction effect is calculated, the incidence relation between the parameters can be better recognized and analyzed, the data consistency is ensured, and the feature extraction accuracy is improved. In data quantile analysis, data subsets of different quantiles are constructed, parameter characteristics under the steady-state condition are extracted, and subtle differences of equipment under different steady-state conditions can be captured.

The invention discloses a water-cooling central air conditioner load management and control method based on artificial intelligence, and relates to the technical field of load management and control. Load prediction is carried out by combining a long and short-term memory time sequence model and a physical constraint neural network in a water chilling unit load prediction link and utilizing historical data; errors are corrected through an energy balance equation of the water chilling unit, and the prediction accuracy is improved; multi-objective optimization control is adopted, COP maximization of a water chilling unit, energy consumption minimization of a cooling tower fan and cooling water supply temperature stability serve as optimization objectives, environment temperature and humidity prediction is combined, and a cooling water supply temperature set point and a cooling tower fan start-stop strategy are dynamically calculated; based on a cooling water supply temperature set point and a fan start-stop strategy, a water chilling unit, a cooling tower fan and a water pump are dynamically adjusted, and the high-frequency operation phenomenon is reduced through fan start-stop optimization.

The invention discloses a power grid intelligent scheduling decision-making system and method based on multi-source heterogeneous data fusion, and relates to the technical field of power grid intelligent scheduling, the power grid intelligent scheduling decision-making method based on multi-source heterogeneous data fusion specifically comprises the following steps: 1, collecting multi-source heterogeneous data, constructing a meteorological feature vector set, and predicting power grid load change; the method comprises the following steps of: 1, outputting a load prediction value, 2, fusing the load prediction value and an equipment health coefficient to construct a double-risk collaborative analysis matrix, generating a risk entropy value and mapping the risk entropy value into a standard risk mode, and 3, executing generator output pre-adjustment, energy storage charging and discharging control and interruptible load management in a grading manner according to the standard risk mode. Micro-grid resources are coordinated through the block chain; according to the method, the demand side prediction risk and the equipment health risk are orthogonally decomposed into multiple state modes by constructing a two-dimensional risk collaborative decision architecture, and the one-sidedness of a single risk factor decision is avoided.

The invention relates to the technical field of intelligent power control, and discloses an intelligent power control system based on the Internet of Things, and the system comprises a monitoring terminal, a power execution device and a cloud platform control center which are in communication connection. The monitoring terminal collects current and voltage signals; the power execution equipment receives an instruction to switch the switch state; a load monitoring unit of the cloud platform control center determines a fault section and generates and maps a load characteristic curve, a prediction processing unit corrects a power supply path to generate an execution strategy, and a steady state regulation and control unit and a transient state regulation and control unit of the power control unit control execution operation under normal and abnormal working conditions respectively. The monitoring terminal comprises a data acquisition device and the like, and the power execution equipment comprises a circuit breaker and other modules. The system realizes intelligent monitoring, fault positioning and load management and protection of a power system, and improves reliability and efficiency.

The invention discloses a load management system based on a load model in the plastic product industry. The load management system comprises a load time sequence model building module which classifies and marks equipment according to operation behaviors of the equipment to obtain a load time sequence model; the data acquisition and fusion module acquires power, current, voltage and start-stop state data of equipment in real time through a monitoring point position, and generates a real-time load data set in combination with real-time production state data; the load prediction and adjustability evaluation module generates a load prediction curve by using a long short-term memory network, generates an adjustability score by adopting a support vector machine model, and determines an adjustable process list in combination with real-time production state data; and the scheduling execution module determines candidate adjustment equipment according to the load prediction curve, the adjustability score and the adjustable process list, constructs a multi-objective optimization function through a multi-objective optimization algorithm, and generates a load adjustment strategy by using a particle swarm optimization algorithm to execute scheduling on the equipment. According to the invention, the refinement level of load management is improved.

An adaptive IoT power plug system for intelligent load management and remote control, consisting of: a power plug housing configured to be inserted between a power outlet and an electrical appliance; an embedded sensor array comprising at least one voltage sensor, at least one current sensor, and at least one power sensor and configured to continuously monitor the electrical parameters of the connected device; a microcontroller unit (MCU) operatively coupled to the sensor array and configured to execute a load prioritization technique that dynamically determines power allocation based on real-time consumption data and user-defined priority levels for one or more connected loads; a semiconductor switching module electrically connected to the MCU and configured to selectively interrupt or modulate the power supply to the connected device based on commands from the load prioritization technology; a wireless communication module integrated into the MCU for bidirectional data exchange via at least one wireless protocol selected from Wi-Fi, Zigbee or Bluetooth Low Energy; a user interface that can be accessed remotely via a mobile or web application and enables the configuration of load priorities, real-time monitoring of power consumption, scheduling of device operation, and manual override of switching commands; and a power management subsystem including a power harvesting module configured to provide supplemental power to the system's electronic components, thereby reducing dependence on the main power source; The system autonomously adjusts the power distribution between multiple connected devices to load fluctuations and predefined user priorities in order to optimize energy efficiency and prevent overloading of the circuit.

A facility for predicting on site electric power requirements for maintaining a fully electric fleet of vehicles in a mining operation to ensure sustainable electric operations, comprising a server comprising communications module configured to receive data and to transmit load management instructions; plurality of electrical charging stations; plurality of electric vehicles configured to be recharged by a selected electrical charging station; wherein the server comprises processor coupled with non-volatile memory, the non-volatile memory storing processing instructions that, when executed by the processor, causes the processor to simulate a total electrical power demand of the mine site due to the plurality of cabled mine equipment and the plurality of electric vehicles for each electric vehicle and each charging station within the facility over a prescribed period thereby to simulate and determine an energy storage capacity requirement of the charging stations of the facility through the prescribed period; thereby to forecast the electrical load management needs of the facility through the prescribed period.

The invention discloses a load management operation method for comprehensive energy optimal configuration, and relates to the technical field of energy management, and the method comprises the following specific steps: microclimate partition division: carrying out the comprehensive field exploration of a target region, collecting the landform, building layout and vegetation coverage data, analyzing the influence of each factor on the microclimate formation, and carrying out the calculation of the microclimate; according to the method, a partition division basis is determined according to the difference between airflow and heat caused by topographic relief, the influence of a building on sunlight and ventilation and local microclimate characteristics of vegetation construction, and a linkage model comprehensively considering the relationship among a microclimate partition state, a building adjustment behavior and an energy load is established; the complex relation among microclimate parameter changes, building adjustment action changes and energy load changes is accurately reflected, meanwhile, according to state changes after building adjustment, the energy management system can dynamically optimize an energy configuration scheme, unnecessary energy transmission and conversion links are reduced, and the energy utilization rate is improved. And deep optimization configuration of comprehensive energy of the whole area from generation, transportation to use is realized.

The invention relates to the technical field of load management, in particular to a model and data driving combined user side load non-intrusive identification method. Comprising the following steps: extracting steady-state features and transient-state features of typical electric equipment; constructing a user side load non-intrusive identification architecture based on decision tree classification; the invention discloses a user side load non-intrusive identification algorithm based on multi-feature fusion. According to the design of the invention, through extraction of steady state features and transient state features of typical electric equipment, multi-dimensional feature quantity identification is carried out on load samples based on a decision tree classification criterion, and non-intrusive load identification is realized through feature quantity matching of similar loads; an identification criterion based on decision tree classification is established, and various types of electric equipment can be quickly decomposed from a user sample; the steady-state and transient-state multi-feature fusion of the electric equipment is applied to a load non-intrusive identification algorithm, so that the accuracy of the algorithm can be improved, and the calculation amount and time of the algorithm can be greatly reduced.

The invention belongs to the technical field of power equipment, and discloses a smart energy system based on a power electronic transformer and a monitoring method, and the method comprises the steps: installing a sensor; equipment operation data is collected in real time, and environment monitoring equipment collects environment data; the data processing module preprocesses the collected data; the abnormity monitoring module judges whether an abnormal condition exists or not; the fault detection module judges whether the system has a fault hidden danger or has a fault; the energy scheduling control module formulates an energy scheduling strategy; the electric energy quality collaborative optimization module ensures that the electric energy quality of the whole system meets the standard; and the load management unit manages different types of loads according to the hierarchical load control strategy. According to the invention, abnormal conditions and potential fault hidden dangers can be found; intelligent energy scheduling can be realized, energy utilization efficiency can be improved, electric energy quality can be cooperatively optimized, hierarchical load management can be realized, and key load power supply can be guaranteed; and a hierarchical load control strategy is adopted to carry out differential management on loads.

The invention relates to the technical field of automobile charging station load systems, in particular to a new energy automobile charging station dynamic load optimization integration system and method based on multi-source data collaboration and elastic compensation. According to the system, charging pile power, meteorological data and user behavior information are fused in real time through a multi-source time sequence data set construction module, an LSTM neural network optimized by an improved sparrow algorithm is adopted to predict the remaining power capacity of a charging station, and Bootstrap resampling is combined to generate a confidence interval to realize safety boundary control; the self-adaptive firefly algorithm dynamically adjusts the charging pile power, establishes a multi-dimensional priority queue including SOC emergency degree, compensation willingness and social value, and guides users to charge in a peak shifting manner; the system comprises a 5G communication gateway, an IGBT power control module and a three-level early warning mechanism, and millisecond-level flexible adjustment of charging power is realized. The utilization rate of the transformer is improved to 91%, the peak-valley load ratio is reduced to 1.7: 1, the user waiting time is shortened by 63.5%, and a dynamic load management system of power grid-station-user collaborative optimization is formed.

The invention belongs to the field of power system optimization scheduling, and particularly relates to a virtual power plant optimization scheduling method based on machine learning. Firstly, real-time data such as generating capacity and load demand of a distributed power supply, an energy storage device and a load management system in a virtual power plant are collected, cleaned and standardized; then, a comprehensive objective function is constructed, and multiple factors including economic benefits, environmental benefits and electricity market price fluctuation are covered; then, an improved NSGA-II algorithm is adopted for optimization, a dual selection mechanism is combined with local and global optimality to accelerate convergence, crowding degree measurement is improved to accurately evaluate individuals, and self-adaptive crossover and mutation operation is dynamically adjusted according to a population. Through the method, maximum balance of economic benefits and environmental benefits can be realized, equipment operation constraints are met, the operation efficiency and stability of the virtual power plant are improved, and an innovative scheme is provided for optimization of a power system.

The invention relates to the technical field of equipment monitoring, in particular to an air conditioning equipment state monitoring system and method, and the system comprises a load curve construction module, a compressor power scheduling module, a power fluctuation trend analysis module, a compressor fault prediction module and a refrigerant leakage identification module. According to the method, the load curve of the air conditioning equipment is monitored in real time, the key operation data including the cooling capacity requirement and the compressor exhaust temperature are integrated, load management is more accurate, then energy efficiency is optimized, the load change rate and historical data are compared, accurate prediction of the power requirement can be achieved, and the current power change rate and the historical power change rate are compared, so that the energy efficiency is improved. The power fluctuation trend can be effectively recognized, potential faults can be recognized in an early stage, the refrigerant leakage problem can be found in time by monitoring the pressure change rate and the pressure difference cumulative gradient, safety risks are avoided, the energy efficiency and reliability of an air conditioner system are improved, the service life of equipment is effectively prolonged, and the maintenance cost is reduced.

The invention discloses a hybrid energy storage inverter ATS control device, relates to the technical field of energy storage converter power supply conversion, and particularly relates to improvement of a novel photovoltaic energy storage inverter ATS control device. The power grid input end and the generator input end are connected through the automatic change-over switch controller and monitor the voltage state, the inverter 485 communication interface and the intelligent load output interface are both connected to the automatic change-over switch controller, and the power supply end of the detection unit is connected to a power supply of the automatic change-over switch controller. And the inverter AC interface is connected to the inverter connecting end of the automatic change-over switch controller. The power supply switching process is smooth, instantaneous large current impact is avoided, and the problem of power supply impact possibly occurring in an existing system is solved; real-time monitoring is realized through an RS485 communication interface, and the operation stability of the system is improved; intelligent load management is realized, and the energy utilization efficiency is improved; long-time power supply interruption of the energy storage system is avoided, the energy utilization efficiency is improved, the service life of equipment is prolonged, the anti-interference capability and the adaptive capability of the energy storage system are enhanced, and the requirements of complex application scenes are met.

The invention relates to the technical field of power equipment, in particular to an intelligent thermal management system for a cleaning robot, which comprises a state sensing unit, a risk assessment unit, a thermal state calculation unit, a data decision unit, an execution unit and a feedback correction unit, and is characterized in that the state sensing unit is used for acquiring internal temperature and humidity and key component temperature; the risk assessment unit calculates the characterization values of the dew formation trend and the overheating trend; the thermal state calculation unit quantifies the overall thermal state of the system; the data decision-making unit determines the actual adjustment amount of the heating and cooling power according to the parameters; the execution unit comprises a heating module, a cooling module and a load management module and correspondingly adjusts output power. And the feedback correction unit corrects the thermal state according to the operation deviation. According to the invention, through multi-parameter fusion decision and dynamic power adjustment, collaborative management of heat dissipation and condensation prevention is realized, and the working reliability of the cleaning robot in a severe environment is improved.

The invention provides an integrated photovoltaic energy storage management system and a control method thereof.The integrated photovoltaic energy storage management system comprises a photovoltaic power generation module, an energy storage module, an energy management module, a power grid interaction module, a load management module, a communication and control system and a user interface, and relates to the technical field of new energy. The state information of photovoltaic power generation, energy storage and load is monitored and analyzed in real time, the energy distribution and scheduling strategy is optimized, photovoltaic power generation is preferentially used, redundant electric energy is reasonably stored and utilized, the energy utilization efficiency is effectively improved, and compared with a traditional photovoltaic energy storage system, solar energy resources can be more fully utilized, and the energy utilization efficiency is improved. The loss of energy in the conversion and transmission process is reduced, and the degree of dependence on a power grid is reduced, so that the power utilization cost is reduced.

The invention provides a virtual power plant demand side response and load management method and system, and relates to the technical field of power system scheduling, and the method comprises the steps: obtaining a demand side response request and a historical response track of each distributed energy source; performing time-frequency domain decomposition on the historical response trajectory, extracting a transient component and a steady component, constructing a dynamic response model containing the current output power and the load deviation cumulant, and calculating load deviation distribution through Kalman filtering; a Markov decision process is constructed based on load deviation distribution, an optimal power distribution strategy is solved by adopting a value iteration algorithm, and a response execution scheme is generated; collecting a power change sequence after execution of each distributed energy source, converting the power change sequence into a load recovery demand vector, constructing a recovery capacity constraint cone based on the current available adjustment margin of the power grid, and solving an optimal projection point; and performing hierarchical division according to the optimal projection point to obtain a staged recovery sequence. According to the method, the accuracy of virtual power plant demand response and the coordination of load recovery are improved, and the power grid regulation pressure is reduced.

This invention discloses a power grid intelligent dispatch decision-making system and method based on multi-source heterogeneous data fusion, belonging to the field of power grid intelligent dispatch technology. The specific steps of the power grid intelligent dispatch decision-making method based on multi-source heterogeneous data fusion include: Step 1, collecting multi-source heterogeneous data, constructing a meteorological feature vector set, predicting power grid load changes, and outputting load forecast values; Step 2, fusing load forecast values ​​and equipment health coefficients to construct a dual-risk collaborative analysis matrix, generating risk entropy values ​​and mapping them to standard risk models; Step 3, according to the standard risk models, performing generator output pre-adjustment, energy storage charging and discharging control, and interruptible load management in a tiered manner, and coordinating microgrid resources through blockchain. This invention avoids the one-sidedness of single-risk factor decision-making by constructing a dual-dimensional risk collaborative decision-making architecture, orthogonally decomposing demand-side forecast risk and equipment health risk into multiple state modes.

The invention discloses a power grid load management method and system based on big data, and relates to the field of power grid management, and the method comprises the following operation steps: S1, multi-data collection and deep integration; s2, data analysis and intelligent modeling; s3, distributed energy virtual power plant cooperative operation; and S4, establishing a demand response incentive mechanism based on the block chain. According to the big data-based power grid load management method and system, social media public opinions and power utilization are associated and mined, the social media platform and the local life forum are dynamically monitored in real time, sudden change of power utilization load caused by urban activities and livelihood hotspots is quickly captured, a mobile energy storage vehicle can be scheduled in advance to guarantee power supply, and the power utilization efficiency is improved. According to the method and the system, the energy storage can be adjusted through an intelligent regulation and control plan, the electric power can be distributed according to the actual load condition of the power grid, and the electric power supply can be effectively ensured.

The invention relates to the technical field of power dispatching, and discloses a power supply system dispatching method and a power supply system thereof, and the power supply system comprises a monitoring module, a dispatching module, a protection module, a data prediction module, an energy storage module, a remote control and communication module, an artificial intelligence and adaptive dispatching module, a power quality monitoring module and an intelligent load management module. The power quality monitoring module collects harmonic wave and voltage fluctuation data in real time, gives an alarm immediately when the harmonic wave and voltage fluctuation data exceed the standard, and pushes governance suggestions; and the scheduling module is matched to adjust the non-linear load operation time, so that quality problems are reduced from the source. Scene features are identified through deep learning, and an emergency strategy is automatically generated; and the scheduling module receives feedback in real time and corrects the strategy. The protection module accurately locates a fault point through multi-sensor cross validation and automatically cuts off a fault loop; the remote control module pushes fault information to the operation and maintenance terminal in real time, remote diagnosis is supported, and the on-site inspection frequency is reduced.

The invention relates to the technical field of distributed photovoltaic-energy storage system management and control in an intelligent power grid, in particular to an intelligent load management and control power distribution system and method for a distributed photovoltaic-energy storage system. And the medium-voltage power distribution network sends a control instruction to a main node in each low-voltage power distribution network based on the output power of each power distribution transformer, so that the main node and a plurality of non-main nodes in the low-voltage power distribution network carry out continuous iteration updating of consistency variables based on a consistency algorithm. And enabling the main nodes and the non-main nodes in the plurality of low-voltage power distribution networks to reach a system balance state, thereby realizing intelligent load management and control of the distributed photovoltaic-energy storage system. According to the invention, the consistency among the photovoltaic power generation fluctuation, the charging and discharging state of the energy storage system and the grid-connected demand of the power grid is realized, and the problems of voltage out-of-limit and overload of the distribution transformer are avoided by using the cooperative control among the plurality of distributed photovoltaic-energy storage systems.

The invention discloses an intelligent self-adaptive solar power supply system and an energy management method, and relates to the technical field of solar intelligent power supply. A multispectral dynamic capture module improves the spectrum utilization rate and the photoelectric conversion efficiency; the global environment sensing module accurately predicts shadow and weather; the multi-mode MPPT cooperative control module reduces the power loss under the shadow; the heterogeneous energy storage cooperative scheduling module optimizes energy storage distribution; the intelligent load interaction module realizes accurate load management; the edge intelligent decision center realizes high-efficiency energy decision and safety guarantee, and all the modules work cooperatively, so that intelligent adaptive management of the solar power supply system is realized. Through multi-module innovation and cooperation, the performance of a solar power supply system is remarkably improved, the power generation efficiency is improved, environmental changes are accurately coped, power loss is reduced, energy storage and load management are optimized, efficient energy decision is achieved, safe operation of the system is guaranteed, the operation and maintenance cost is reduced, and intelligent development of solar power supply is promoted.

A meter collar adapter including a housing, a plurality of contacts to electrically connect the meter collar adapter to a meter socket and to a utility meter, a load management device within the housing to manage electrical load to an interface circuit with external, co-located electric equipment, and an overcurrent protection device electrically connected in series with an interface circuit. The load management device includes the interface circuit to electrically connect to either the line side or load side of an electrical service via the utility meter an external, co-located electric equipment. An interface contact to electrically connect the co-located electric equipment to the interface circuit, an adjustable electrical switch electrically configured to control electric load, and an energy management unit to control, monitor, and / or report a position of the adjustable electrical switch to manage the electrical loads.

The invention provides a load management data processing method in a power grid construction supervision project, and the method comprises the steps: carrying out the matching analysis of power fluctuation feature data and a pre-established load feature model, determining a corresponding load type if the matching degree meets a preset condition, and obtaining a multi-dimensional power decomposition result; carrying out quantitative calculation on the harmonic content of each electrical load based on the multi-dimensional power decomposition result, judging whether the harmonic content exceeds a standard limit range or not, and obtaining a to-be-treated harmonic component set; according to correlation analysis of the to-be-treated harmonic component set and the reactive power component, if the reactive power component is dominant, determining a compensation processing priority, and generating a targeted reactive power compensation strategy scheme; and carrying out classification processing on the load characteristics by adopting the reactive compensation strategy scheme, and if a classification result shows a high harmonic pollution characteristic, judging that the load type is a non-linear load type, and obtaining a load characteristic identification result.

The invention discloses a parallel operation control system for multiple fuel generator units, and relates to the technical field of parallel operation control, the system collects unit operation data in real time through a sensor, and an operation steady state data set is obtained after data preprocessing; calculating an operation steady-state function index ywt based on the operation steady-state data set, and performing parallel operation conformity evaluation on the operation steady-state function index ywt and a preset operation steady-state safety threshold Z; calculating a load distribution index fhf and the actual output power sjg of the unit when the parallel operation requirement is met; performing unit load evaluation according to the load execution rate zxl, and judging whether the load state is abnormal or not; and when the unit load is abnormal, the off-line decision module determines whether to execute off-line and parallel operation instructions by combining the total demand load to of the industrial area and the residual unit load capacity sf. According to the system, through multi-module collaborative optimization, the parallel operation stability and the load management efficiency are improved, and the reliability and the economical efficiency of the power generation system are ensured.

The invention discloses a feed processing load management method based on a staged strategy gradient algorithm, and relates to the technical field of industrial automation control, and the method comprises the steps: collecting a feed processing dynamic state set, and obtaining a standardized decision input vector through preprocessing; based on the standardized decision input vector, dynamically adjusting equipment parameters through a two-factor cooperative compensation algorithm, and obtaining an equipment control instruction; executing an equipment control instruction, and generating an efficiency feedback data packet by monitoring equipment energy consumption change and output material granularity in real time; a load management decision model is constructed, and optimization strategy parameters are obtained by combining the efficiency feedback data packet and adopting a staged strategy gradient algorithm; and updating the load management decision model based on the optimization strategy parameters, and generating a closed loop verification report through load management verification. According to the method, the load management decision model is constructed through a quantum annealing mechanism, the local optimal limitation of traditional reinforcement learning is broken through, and global optimization efficiency jump is realized.

The invention discloses a lunar dust protection system for a low-energy particle detector. The lunar dust protection system comprises a protective cover, a nitrogen blowing opening, a pressing device, an unfolding device, a power supply subsystem and a control subsystem, wherein the protective cover and the nitrogen blowing opening are integrally designed; the protective cover covers the head of the sensor and is consistent with the head of the sensor in shape; the nitrogen blowing port blows nitrogen into the sensor in a ground test stage to prevent pollution; the blocking cap is used for blocking before the satellite is launched to prevent dust pollution; when the system is in a protection state, the pressing device presses the protective cover and the head of the sensor, and the protective cover is released through thermal cutting according to an instruction of the control subsystem; the unfolding device is matched with the pressing device to achieve unfolding of the protective cover, and the protective cover is locked after being released to a designated position. The power supply subsystem supplies power to the pressing device according to an instruction of the control subsystem; the control subsystem periodically collects state monitoring parameters and transmits the state monitoring parameters to the load management unit, controls power-on and power-off of the pressing device according to a ground remote control instruction, and executes unfolding operation of the system.

The invention discloses a load management-oriented intention identification and electric quantity data analysis method and device, and belongs to the technical field of electric power system analysis. According to the method, feature extraction is performed on the input text of the load management personnel through the preset natural language processing method, intelligent analysis is performed on the load electric quantity data in linkage, modal component decomposition is performed on the first historical electric quantity data, short-time fluctuation and long-term tendency features of new energy can be accurately captured, and the new energy management efficiency is improved. The method comprises the following steps: carrying out prediction on electric quantity data, carrying out feature division in combination with the prediction load data to obtain first historical electric quantity feature data and second historical electric quantity feature data, carrying out time sequence feature analysis to obtain periodic feature data and abrupt change electric quantity data in an electric power system, and generating electric quantity probability distribution data through a preset large model. According to the method, the load fluctuation of the power system can be quantified, so that the more accurate electricity consumption valley time period and electricity consumption peak time period of the power system can be obtained, and support is provided for novel load management work of a power grid company.