2806 results about "Energy management" patented technology

The invention discloses a hydrogen-lithium dual-power system structure optimization design and integrated control method applied to an unmanned aerial vehicle. The method comprises the following steps: constructing a hydrogen-lithium dual-power system of the unmanned aerial vehicle; setting an integrated control strategy according to the hydrogen-lithium dual-power system; the integrated control strategy comprises an energy management strategy and a life and safety management strategy; the energy management strategy comprises a rule-based strategy, a power following strategy and a self-adaptive optimization strategy; the service life and safety management strategy comprises a hydrogen fuel cell protection strategy, a lithium battery health management strategy, a thermal management strategy, a dual-power coupling safety management strategy and a redundancy and fault processing strategy. By optimizing the power system design and the energy management strategy, the mass of the unmanned aerial vehicle device is reduced, the cruising ability, the load capacity and the environmental adaptability of the unmanned aerial vehicle are improved, and optimization processing of the control strategy is achieved.

The invention proposes a smart park energy consumption management system and method based on artificial intelligence, and relates to the technical field of smart park energy consumption management, and the system comprises a data collection module which collects original time sequence energy consumption data and equipment state information; the data preprocessing module is used for performing data quality processing on the original time sequence energy consumption data to obtain preprocessed time sequence energy consumption data; the topology management module is used for constructing an energy consumption relation graph and generating node topology representation characteristics; the feature engineering module is used for obtaining a space-time-business joint feature tensor; the anomaly monitoring module is used for obtaining an anomaly score and outputting anomaly positioning information based on an interpretable analysis method; and the visual display module is used for generating an alarm report and performing visual display. According to the invention, accurate anomaly identification and intelligent positioning analysis of multi-level and multi-energy-type energy consumption data of the park can be realized, and the automation level and the operation and maintenance efficiency of park energy management are improved.

The invention discloses an intelligent power prediction method considering dynamic load change, and relates to the technical field of power grid load prediction, and the method comprises the steps: collecting original load data, carrying out the preprocessing, constructing a VMD constraint optimization model, carrying out the four-stage improvement of optimization parameters through employing an improved dung beetle optimization algorithm, so as to generate IMF components, reconstructing the IMF component by calculating a sample entropy to obtain a low-frequency component and a high-frequency component; establishing a Kalman filtering state space model based on the low-frequency component, and decomposing the low-frequency component into a residual component and a pseudo trend component through a Kalman filtering recursive algorithm; external features are obtained, the high-frequency component, the residual component and the pseudo trend component are aligned and spliced with the external features, multi-component collaborative prediction is carried out through a local-global interactive attention mechanism, and a final load prediction result is obtained; and generating a power demand visualization chart based on the final load prediction result. And reliable decision support is provided for power dispatching and energy management.

The invention discloses a two-phase cold plate liquid cooling ORC waste heat recovery and photovoltaic cooperative energy supply system and method, and relates to the technical field of energy management and waste heat utilization. The two-phase cold plate liquid cooling ORC waste heat recovery and photovoltaic cooperative energy supply system and method comprises the following steps that S1, thermal parameters and electric power states related to cooperative energy supply are collected, and a thermoelectric cooperative input data set is constructed and preprocessed; s2, the supply and demand difference of the high-temperature steam in the main condensation branch and the ORC branch is recognized; s3, coordinating a photovoltaic power supply structure and a cooperative energy supply sequence, and judging the matching degree of cooperative energy supply to a load; and S4, evaluating a cooling, heating and power cooperation and double-source coupling state in combination with a load matching condition and an output behavior. The problems that in an existing two-phase cold plate liquid cooling structure, high-temperature steam is not recycled, photovoltaic power generation does not participate in linkage energy supply, the power source is single, waste heat waste is serious, and energy collaboration is insufficient are solved.

The invention belongs to the technical field of office park electric load prediction, and relates to an office park electric load prediction and scheduling method for a novel electric power system. The method comprises the steps that S1, multi-source data fusion and panoramic view construction are carried out, historical load data of a park are collected, and park operation data, meteorological data and a special event calendar are obtained; s2, data preprocessing and feature directional extraction; s3, building and cooperating short-term, medium-term and long-term prediction models on the basis of multi-time scale prediction of model cooperation to form a hierarchical prediction system; s4, optimizing and evaluating a prediction result, and performing uncertainty evaluation on the prediction result in the step S3; and S5, generating and executing a hierarchical scheduling instruction based on hierarchical scheduling decision and execution of multi-objective optimization. According to the method, the problem of full-chain link splitting is solved, panoramic data view, collaborative prediction model, quantitative risk assessment and multi-target optimization scheduling are realized, prediction accuracy and scheduling flexibility are improved, and active participation in energy management of the office park is supported.

The invention relates to the technical field of audio and video emergency transmission, and discloses an audio and video low-delay return method and system in an extreme environment. The method comprises the following steps: acquiring original multi-modal data of audio and video acquisition equipment in a target area, and analyzing a data state of the original multi-modal data; and meanwhile, available network transmission links are monitored, and the quality is evaluated. Self-adaptive coding parameters are generated in combination with the link quality and the data state, dynamic coding is executed on original data, and a coding stream suitable for redundant transmission is formed. And carrying out cooperative distribution transmission based on the network link set and the coded stream to generate return data. According to the generation process, a device and energy management instruction is formed and issued to the acquisition and relay device. According to the method, the network state and the content characteristics are optimized in a dynamic coding link in a collaborative manner, and the audio and video quality, the real-time performance and the system energy efficiency which are transmitted back in an extreme environment are improved through a feedback closed loop for transmitting a result to equipment management.

The invention provides a ship shaft power generation and lithium battery coordinated multi-working-condition energy optimization management system, which is applied to the technical field of ship power systems and energy management and comprises a data acquisition module, an energy optimization processing module and a power regulation and control module, wherein the energy optimization processing module is provided with a prediction unit, a working condition identification unit, a loss evaluation unit and a decision-making unit, the prediction unit is used for predicting a ship load demand in a preset period, the working condition identification unit is used for identifying a ship operation working condition, and the loss evaluation unit is used for determining a life loss factor by combining a prediction result and an energy storage unit state; the decision-making unit generates a regulation and control instruction based on the life loss factor and the state of the power generation unit to realize power distribution of the power generation unit and the energy storage unit; according to the system, multi-working-condition energy consumption requirements of the ship are met, meanwhile, comprehensive optimization of fuel consumption cost and energy storage unit service life loss cost is achieved, the energy utilization rate is increased, the service life of the energy storage unit is prolonged, and the full-life-cycle operation cost of the ship is reduced.

The invention provides a ship micro-grid energy management method based on hierarchical coordination of model predictive control and a chaos evolution optimization algorithm, and the method comprises the steps: constructing a hierarchical coordination control architecture which comprises an upper rolling optimization scheduling layer and a lower real-time stability control layer; the upper layer adopts a model prediction control framework, a chaos evolutionary optimization algorithm is built in to serve as a solver on the basis of load and new energy output prediction of a future time domain, and an optimal power reference instruction in the future short time domain is solved in a rolling manner by taking operation cost, energy storage aging cost and power grid stability penalty as a comprehensive target; and the lower layer executes power scheduling according to the reference instruction issued by the upper layer, absorbs and compensates high-frequency power fluctuation in real time by using a droop control quick response mechanism of the energy storage system, and maintains the stability of the power grid. According to the method, decoupling cooperation of global economic optimization and local dynamic stability of the ship microgrid can be realized, the fuel consumption is remarkably reduced, and the safety and reliability of the system under complex working conditions are improved.

The invention discloses an extended-range hybrid propulsion double-source dynamic coupling energy management method, which comprises the following steps: carrying out global physical modeling on a double-source power system and a flight scene, and establishing a double-source dynamic coupling model; designing a reinforcement learning physical constraint reward function, performing optimization training on each coefficient of the reward function by adopting a QMPSO algorithm, and outputting an optimized reward function coefficient; a qualified double-source dynamic coupling model is verified, and a power distribution coefficient is optimized; outputting the optimal power distribution coefficient of the battery and the range extender; the superiority of the dual-source power cooperative control strategy in the aspects of flight economy, operation stability and system life guarantee is verified through multi-dimensional comparative analysis of each performance index. Cooperative power distribution of the battery and the range extender is achieved through dynamic coupling modeling and reinforcement learning, the flight scene load requirement is met, the system energy efficiency is improved, and the service life of parts is prolonged.

The invention discloses an energy storage system intelligent energy management control method and system, and relates to the technical field of energy storage system intelligent energy management control, and the method comprises the steps: obtaining an energy storage unit operation state parameter and an environment disturbance factor matrix through a multi-source data collection device, and carrying out the data preprocessing; and constructing a long-short-term memory neural network model, dynamically adjusting a prediction time window of the model and optimizing a target function weight coefficient based on the load predicted by the model, and determining a charging and discharging control instruction. And updating and optimizing the target function weight coefficient and the charging and discharging strategy library through a reinforcement learning algorithm to realize self-adaptive optimization adjustment of the charging and discharging strategy. According to the method, high-precision energy management and intelligent optimization control of the energy storage system in a complex environment are realized. The load prediction accuracy and the energy utilization rate of the system are improved, the aging rate of the battery is reduced, the service life of the battery is prolonged, the adjusting capacity of the energy storage system is improved, and the overall stability of the energy storage system in dynamic change is enhanced.

The invention provides an industrial air conditioner energy-saving optimization control method based on multi-source data fusion and related equipment, and is applied to the technical field of data processing. Industrial air conditioner energy-saving optimization is taken as a core, and multi-source data such as equipment parameters and environment variables and system-level reference data such as a fusion layer processing standard and an AI decision operation reference are acquired. And combining the multi-source data fusion parameters and the control strategy parameters of each level, matching data processing and instruction execution logic, and establishing a collaboration rule and generating an energy-saving constraint condition by means of hierarchical processing and multi-modal integration. And based on the data and constraint acquisition energy consumption, operation, fault early warning and other data, energy consumption prediction, supply and demand adjustment and other functional parameters are combined to generate energy-saving effect and operation state associated data. And finally, through visual analysis of an energy management platform and ISO50001 compliance auditing, an energy-saving optimization and system reliability evaluation result is output.

The invention discloses a fuel cell hybrid electric vehicle power distribution method based on heuristic action planning deep reinforcement learning. The method comprises the following steps: establishing a power distribution system model of a fuel cell hybrid electric vehicle equipped with a lithium battery and a super capacitor; a power layered structure is provided, a deep reinforcement learning model of the fuel cell hybrid electric vehicle is designed, a probability transfer matrix of required power is constructed, and model training is performed in a data driving mode; designing a reward function by adopting an equivalent consumption minimum strategy, and guiding optimization of an energy management strategy by fuel economy; the motion space of deep reinforcement learning is improved by adopting heuristic planning, and smooth output of the fuel cell is kept on the premise of reducing equivalent hydrogen consumption. According to the method, power distribution of the three-energy-source fuel cell hybrid electric vehicle can be achieved in a complex and random driving environment, hydrogen consumption is reduced, dynamic adaptability to different driving conditions is improved, and meanwhile the method has the potential of real-time application.

The invention discloses a fabricated building self-optimization energy-saving system and method based on DRL and BIM, and relates to the technical field of building energy management and intelligent control. The system comprises a BIM data acquisition module used for storing and providing BIM data of a prefabricated building; the energy simulation module is used for constructing a digital twin model based on BIM data, providing a simulation environment for the DRL control module in an offline training stage, and assisting verification and retraining in an online stage; the environment monitoring and execution module is used for acquiring operation parameters and energy consumption data in real time through a sensor, an actuator and an interface butted with a transportation system and executing a control instruction; and the DRL control module obtains an optimization control strategy through simulation interaction learning, generates an instruction according to real-time data in an online stage, and issues and executes the instruction. Energy consumption and carbon emission in the whole process of the fabricated building can be optimized, the energy utilization efficiency and comfort are improved, and the self-adaptability and long-term stability of the system are enhanced.

The invention discloses an urban rail transit energy management method based on multi-source fusion, and the method comprises the steps: employing a support vector machine algorithm to analyze a correlation mode between train intensive operation and passenger flow surge according to an obtained energy demand fluctuation index, and determining a potential energy consumption peak value position; the determined adjustment parameters are obtained, a power supply system control instruction is updated in combination with real-time train track information, and dynamic power supply load configuration is obtained; whether the obtained dynamic power supply load configuration is matched with the current passenger flow surge data or not is judged, if yes, a mode switching signal is sent to an equipment controller, and energy use feedback data after execution is obtained; according to the obtained energy use feedback data, evaluating the response accuracy of the system integration effect to demand fluctuation by adopting a gradient boosting decision tree algorithm, and determining further trajectory optimization suggestions; and updating a train operation scheduling model through the determined trajectory optimization suggestion to obtain an integrated multi-source information linkage mechanism.

The invention relates to the technical field of robot and artificial intelligence crossing, in particular to a robot cooperative autonomous decision-making energy management method and system based on an AI agent, and the system comprises an edge sensing module which is used for collecting original data including energy data, environment parameters and equipment working conditions in real time and carrying out the preprocessing, obtaining state data; the communication module is used for establishing operational coupling among the modules so as to realize data and strategy transmission; the AI agent module takes a deep reinforcement learning engine as a core, performs joint modeling on the state data, and outputs an energy decision strategy; and the robot collaboration module is used for receiving the energy decision strategy, converting the strategy into a task which can be executed by at least one robot, and scheduling the at least one robot to execute the task. By adopting the method, the problem that the prior art lacks a novel energy management scheme capable of breaking through the limitation of the traditional EMS can be solved, and the method has the characteristics of high adaptive decision-making capability, low time delay and high reliability.

The invention provides a wind, light and water storage double-layer robust optimization scheduling strategy construction method and system, and relates to the technical field of energy management, and the method comprises the steps: firstly obtaining multi-source data of a wind power plant, a photovoltaic power station and a cascade hydropower station; performing feature extraction and integrated prediction based on the multi-source data to generate a wind-solar power prediction result; combining hydrodynamic coupling modeling analysis to obtain a hydropower station power generation capacity and adjustable dynamic water volume sequence; the method comprises the following steps: constructing a multi-layer energy coupling graph and performing spectral domain structure optimization to form a power coupling network representing system space-time correlation and ecological sensitivity; and finally, adopting double-layer distributed robust optimization, synchronously optimizing the economic target and the ecological risk constraint, and generating a collaborative scheduling strategy. According to the method, the time sequence matching problem between the wind and light volatility and the hydroelectric time lag is effectively solved, the renewable energy consumption capability and the water resource utilization efficiency are improved, and the ecological operation risk is reduced.

The invention discloses an energy and low carbon cooperative regulation and control method and system based on reinforcement learning, and relates to the technical field of energy management and low carbon control, and the method comprises the steps: building an initial data set through real-time collection of energy consumption data, carbon emission data and key operation parameters in industrial production; preprocessing the data by adopting a multivariable correlation analysis and data standardization technology; constructing a dynamic optimization model based on a reinforcement learning algorithm, and adaptively adjusting operation parameters of production equipment by taking energy utilization efficiency maximization and carbon emission minimization as targets; and a closed-loop feedback mechanism is established, the regulation and control effect is monitored in real time, model parameters are optimized, and continuous efficient low-carbon operation of the system is ensured. The technical problem that energy efficiency and environmental protection are difficult to collaboratively optimize in a traditional industrial energy management method is solved, and the method is suitable for intelligent low-carbon production in high-energy-consumption industries such as chemical engineering and metallurgy.

The invention discloses an energy management method and device for a trailer, a storage medium and a program product, and the method comprises the steps: dividing any historical route through which the trailer travels, and obtaining sub-road sections of the historical route; determining the optimal motor power of each sub-road section according to the historical driving parameters corresponding to the sub-road sections of the historical route; according to the optimal motor power of each sub-road section and the fixed energy requirement of the trailer, different battery electric quantities corresponding to the optimal motor power in the historical route are calculated, and the corresponding relation between the optimal motor power and the battery electric quantities serves as an energy management strategy of the historical route; wherein when the driving route of the trailer is the historical route, the energy output of a battery in the trailer is controlled according to the energy management strategy corresponding to the historical route. According to the technical scheme, the technical effects that energy management is conducted on the battery of the trailer, and the energy of the battery is reasonably distributed are achieved.

The invention discloses a rescue material transportation following robot based on oil-electric hybrid power and a control method, and aims to solve the problems of endurance, carrying and energy complementation of a pure electric rescue material following robot under the conditions of long-time heavy load, complex terrain and inconvenience in charging. The method comprises the following steps: acquiring multi-source state, motion and environment data, and predicting a power demand; dynamically selecting a pure electric charging mode, a range extending charging mode, a parallel charging mode or a parking charging mode; and generating parallel power and a motion instruction to drive the robot to follow operation. The system comprises a hybrid power unit, a following positioning module, an environment sensing module, a main controller, an energy management unit, a motion control unit and a driving execution unit. The problems of endurance and energy complementation of the pure electric robot are effectively solved, the operation continuity, the environment adaptability, the carrying efficiency and the man-machine cooperation safety are remarkably improved, and the system energy efficiency is optimized.

The invention discloses a fuel cell vehicle control method integrating fault diagnosis and energy management. According to the method, vehicle key operation parameters are collected in real time, a whole vehicle operation state vector is constructed, and fuel cell health diagnosis and preliminary power action generation are carried out at the same time based on the state vector. The diagnosis module obtains a health factor and a severity index through residual calculation, particle filtering and generalized likelihood ratio test, and the action generation module outputs an intelligent agent power distribution vector. And according to the severity index, the dynamic reconstruction power, the SOC and the power change rate constraint, optimal power distribution is obtained through optimization solution, and meanwhile, a high-severity state is intervened in combination with a hierarchical fault-tolerant strategy. The method also comprises a demand power prediction and online learning module which is used for improving the robustness and the adaptive capability of the optimization strategy. According to the invention, by tightly combining the health state of the fuel cell with energy management optimization, high-energy-efficiency, long-service-life and reliable dynamic control of the fuel cell system is realized.

The present disclosure discloses a system (106) and a method (400) for energy management in a network (108). The method may include initialization by defining input and output parameters (baseline energy cost, optimized energy cost). The method may include performing real-time data collection, systematically gathering real-time metrics and calculating the baseline energy cost. The method may include conducting AI / ML-based prediction for traffic and load forecasting and coverage demand analysis, using historical and real-time data to predict future patterns, and allowing for resource optimization and proactive adjustments. The method may include dynamically adjusting network parameters and calculating and optimizing real-time energy cost (EC), continuously monitoring the network's current EC based on dynamically adjusted parameters. Compare optimized EC with baseline EC, adjusting parameters as needed.

The invention relates to the field of vehicle control, and particularly discloses a tractor hybrid power cooperative control method, system and device and a medium, and the method comprises the steps: collecting state parameters of a whole vehicle, an agricultural implement and a power system in real time, employing a fusion rule and a real-time optimized energy management strategy to analyze an operation mode, and calculating an initial power distribution target; dynamic coordination is carried out by taking optimal system efficiency and battery state maintenance as targets, smoothness verification and closed-loop feedback correction are carried out on an output instruction, and a final control instruction is generated, issued and executed; the system correspondingly comprises a perception analysis module, a decision making module, a coordination verification module, an execution module and a learning evolution module. By constructing full-link closed-loop control, self-adaptive dynamic optimization and reliable cooperation of the power system are achieved, the online learning ability is achieved, the problems that in a traditional method, control is extensive, response is delayed, and self-adaptability is lacked are effectively solved, and the operation economical efficiency, smoothness and long-term adaptability of the tractor are improved.

The invention provides a vehicle energy management method and system and a vehicle, and belongs to the technical field of vehicles, and the method comprises the steps: transmitting vehicle identification information to a cloud after the vehicle is powered on, enabling the cloud to obtain the historical driving data of the vehicle based on the vehicle identification information to obtain driving route data, and training a preset first energy management model, target model parameters are obtained, and then a target energy management model is obtained; and determining a target driving route from the plurality of historical driving routes through the application core based on the route data of the plurality of historical driving routes and the current working condition data of the vehicle, and obtaining control parameters for controlling the vehicle to drive on the target driving route based on the target energy management model and the feature data corresponding to the target driving route, and the control parameters are sent to the execution core so that the execution assembly can control the vehicle based on the control parameters. Through the method provided by the invention, the inherent defects of a traditional fixed strategy and a single deployment architecture can be overcome.

The invention relates to the technical field of energy data management and control, and discloses a smart park energy data dynamic management and control system. The energy data acquisition module of the system acquires and preprocesses the operation data flow in the park in real time. The energy feature analysis module performs multi-dimensional feature extraction on the data stream, and identifies feature vectors such as peak load and steady-state operation. The event trigger engine generates an energy management event signal based on conditions such as feature vector and threshold comparison, timing pattern matching and the like. The parameter node identification module analyzes an event signal time sequence and positions key nodes such as equipment start and stop and load sudden change. The offset calculation module calculates the offset of the energy parameter relative to the historical reference for the key node. The similar parameter derivation module derives a migratable parameter set containing general control parameters and scene adaptive parameters according to the offset and a historical database. And the dynamic regulation and control module performs real-time adjustment such as power distribution and equipment scheduling on park energy distribution by using the set.

The invention relates to the technical field of energy management, and provides a park light storage and charging energy distribution control method and system, and the method comprises the steps: transmitting an instruction to an energy storage system in an emergency, enabling the energy storage system to release an electric energy pulse to a micro-grid at the maximum available power within a preset time, and enabling the micro-grid to be in the maximum available power; providing time buffer for the power reduction process of the load units in the park; after the power reduction instruction is executed, identifying response characteristics of each load unit in the park to the power reduction instruction, and grading each load unit according to the response characteristics to obtain grading information; during the time buffering period, according to the grading information of the load units, a grading decreasing load reduction plan is formulated and executed, and a reduction instruction is issued in a peak shifting manner, so that the overall load is smoothly reduced; and continuously monitoring the power quality of the micro-grid, and finely adjusting the output of the energy storage system according to the power quality of the micro-grid. The method has the effect of improving the stability and reliability of light storage and charging in the park.

The invention provides a wind power prediction method based on improved Diffusion-Q. According to the method, a diffusion model and a Q-learning reinforcement learning mechanism are deeply fused through a data preprocessing module, a diffusion model forward process, a Q-learning module, a diffusion model reverse process, an output prediction module and a feedback and optimization module; a generative prediction system with a dynamic strategy optimization capability is constructed, a future wind power sequence is generated through probability distribution of modeling time sequence data in a forward diffusion process and a reverse diffusion process, online dynamic adjustment of diffusion process parameters is realized, and dynamic prediction of the wind power sequence is realized by defining a state space, an action space and a reward function. The model can automatically select the optimal sampling path according to current input and historical performance, prediction precision and robustness are remarkably improved, and the method can be widely applied to wind power plant short-term power prediction, power dispatching system and intelligent power grid energy management and has good engineering application prospects.

The invention belongs to the technical field of energy management, and particularly relates to a light storage direct current flexible load self-evolution cooperative scheduling method, system and equipment, and the method comprises the steps: constructing a parameterized energy utility curve, quantifying the comprehensive utility of flexible load response in energy efficiency, comfort and equipment loss, and calculating the unit power marginal utility as the flexibility; establishing a multi-target collaborative scheduling model considering the time-varying carbon intensity, the electricity price and the utility curve, and solving by adopting a model predictive control and reinforcement learning mixed strategy; static and dynamic data are fused to construct a knowledge graph, and flexibility is predicted and cross-scene migration is realized through a sequence diagram neural network; and cooperatively optimizing a knowledge graph prediction result and a scheduling instruction through a Lagrangian relaxation method to form a self-evolution closed-loop control system. According to the method, flexible load refined modeling, carbon perception economic optimization scheduling and system adaptive learning are realized.

The invention relates to the field of intelligent power grid and energy management, and discloses a park distribution box peak-valley scheduling monitoring method, system and device and a storage medium, and the method comprises the following steps: collecting and uploading the power data of a park distribution box in real time; on the basis of historical power data and meteorological information, predicting future power demands and identifying wave crests and wave troughs by using a deep learning model; generating a load scheduling strategy by adopting a reinforcement learning algorithm in combination with the prediction result and the real-time data; optimizing a scheduling strategy by using a particle swarm optimization algorithm, and outputting an optimal load distribution scheme; adjusting the load of the distribution box according to an optimization result, and balancing power resources; and when abnormity is detected, starting an emergency response mechanism to guarantee key load power supply. Through deep learning, reinforcement learning and a particle swarm optimization algorithm, accurate prediction and scheduling optimization of the park power load are realized, power resources are effectively balanced, the load response capability is improved, and key load power supply under abnormal conditions is guaranteed.