49 results about "Energy simulation" patented technology

The invention provides a dynamic grid-based cavitation erosion and secondary cavitation erosion prediction method, and belongs to the technical field of fluid machinery and cavitation flow simulation. The prediction method comprises the following steps: carrying out parameter measurement on a deformed engineering material, selecting a computational domain of the deformed engineering material, and carrying out grid division on the computational domain; determining boundary conditions, gas-liquid phase physical property parameters and a mass transfer rate between liquid and gas caused by cavitation; calculating collapse energy, and performing node movement on the gridded computational domain to simulate damage caused by the collapse energy; according to the method, the fluid volume method is used for capturing a multi-phase interface, the cavitation model is used as the basis, the cavity collapse release impact energy is subjected to prediction calculation and converted into distribution of point-shaped cavitation pits with different depths and radiuses on the surface of the model, and the cavitation distribution is predicted according to the collapse energy. And the cavitation erosion collapse on the surface of the prediction model is more visual and accurate.

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 belongs to the technical field of energy management, and particularly relates to a regional energy management optimization method based on edge computing and multi-objective optimization, which comprises the following steps of: determining edge equipment with qualified processor performance based on processor performance parameters of the edge equipment, establishing an edge equipment site diagram based on geographical sites of the qualified edge equipment, applying the initial multi-objective optimization scheme to the regional energy simulation model, obtaining an energy efficiency evaluation value and an energy efficiency comprehensive difference value, comparing the source efficiency comprehensive difference value with an energy efficiency comprehensive difference standard value stored in a database, matching a pre-selected management optimization scheme, and applying the pre-selected management optimization scheme to the regional energy simulation model, and based on the optimal management optimization scheme, adjusting an optimization scheme decision according to a real-time evaluation result. According to the invention, the flexibility of overall regional energy management can be improved, and sustainable development of long-term regional energy management can be maintained.

The invention concerns a method (100) for optimizing an energy usage and / or a carbon footprint for an urban area. The method comprises the steps of receiving (101) map information that is related to the urban area and receiving (104) multiple sets of second building attributes from a building database for each building of the multiple buildings, wherein each set of second building attributes defines a number of building attributes that lead to an impact on an energy performance of a building. The received sets of the building attributes are assigned to the corresponding buildings of the multiple buildings that are located in the urban area and a dynamic first energy simulation is performed for the multiple buildings based on the first building attributes and a result of the first energy simulation is provided (107).

The invention discloses a knowledge distillation method and equipment for nuclear accident deduction and fault diagnosis of a reactor. The method comprises the following steps: acquiring fault data of a nuclear energy simulation device; building a multi-task network; dividing the acquired data set D into a training set, a verification set and a test set, and pre-training the multi-task network by using the training set; after pre-training is completed, parameters in the multi-task network are fixed, that is, a large-parameter pre-training model is frozen to serve as a teacher model; migrating the knowledge of the teacher model to the student model by adopting a method of multi-task proportion-by-proportion penetration knowledge distillation; and obtaining a physical quantity deduction result and a fault result of the target pipeline by utilizing student model reasoning. According to the method, the multi-task network based on the physical information neural network is established, fault diagnosis and parameter deduction in a plurality of reactor nuclear accidents can be processed at the same time, the correlation between different tasks is fully utilized, and the fault diagnosis precision and the deduction capacity for the development process of the nuclear accidents are improved.

The invention discloses a linear cutting path intelligent optimization method based on deep learning image recognition. The method comprises the following steps that a workpiece multi-view image is obtained and imported into an electronic drawing, the image is recognized, workpiece structure information is extracted, a unified coordinate model is established in combination with a datum point, and an element list is output; based on the element list and machine tool and material parameters, a machining graph is constructed with contour segments as nodes and feasible transition between the segments as edges; candidate cutting paths are generated on the machining drawing according to the machining constraint, and an initial cutting scheme is formed; inputting the processing graph into a quantum heuristic neural network, performing optimization selection on the initial cutting scheme, and outputting an optimal cutting scheme; and geometric and energy simulation is conducted on the optimal cutting scheme, the cutting seam crossing, residual area and material falling risks are detected, and local correction is executed when the constraint is not met. According to the invention, through combination of deep learning identification and quantum heuristic optimization, automatic generation and correction of the linear cutting path are realized.

An AI-based platform for enabling intelligent orchestration and management of power and energy is provided herein. The AI-based platform includes a digital twin system including a plurality of digital twins of energy operating assets, the plurality of digital twins of energy operating assets including at least one energy generation digital twin, energy storage digital twin, energy delivery digital twin, and / or energy consumption digital twin, and a set of energy simulation systems configured to generate a simulation of energy-related behavior of at least one of the plurality of digital twins of energy operating assets, and a machine-learning system configured to generate a predicted state of at least one of the energy operating assets. The simulation of energy-related behavior is based on historical patterns, current states, and the predicted state of at least one of the energy operating assets.

The invention discloses a simulation examination system for practical training of thermal power generating unit personnel, and relates to the related field of electric power energy simulation examination, and the system comprises a practical training scene determination module which is used for determining a practical training scene; the non-intervention operation data acquisition module is used for acquiring a non-intervention operation data set of the examiner; the intervention module is used for generating intervention signals; the intervention operation data acquisition module is used for performing operation process intervention to obtain a plurality of intervention operation data sets; the assessment scoring module is used for inputting the non-intervention data set and the multiple intervention operation data sets into an assessment scoring model to obtain non-intervention and intervention operation scoring results; and the assessment report generation module is used for carrying out weight calculation and generating an assessment report. The technical problems that an existing assessment system is single in assessment content, the strain capacity and the decision-making level cannot be comprehensively assessed, and detailed feedback and improvement suggestions cannot be provided are solved, and the technical effects that the strain capacity and the decision-making level are comprehensively assessed, and comprehensive and accurate feedback and improvement suggestions are provided are achieved.

The present invention relates to the field of distributed energy simulation, and in particular to a method and system for constructing an equivalent simulation model for a multi-type wind farm. The method comprises: collecting wind turbine information and constructing a multivariate scaled wind farm model, wherein the multivariate scaled wind farm model mainly includes a wind turbine dynamics model and a converter control model; constructing an embedded constant admittance equivalent model and collecting the control characteristics of an ideal switch, solving the control parameters of the embedded constant admittance equivalent model based on the control characteristics, and performing parameter optimization based on steady-state errors to replace the converter control model; performing model equivalence processing on several wind turbine categories according to the multivariate scaled wind farm model to obtain a multivariate wind turbine equivalent simulation model. The present invention effectively solves the problems in existing wind farm simulations, such as low simulation efficiency due to excessive converter calculations and inaccurate simulation results caused by the inability of the equivalent modeling method for multi-type wind farms to fully consider the dynamic characteristics and control strategies of different types of wind turbines.

The invention relates to the field of distributed energy simulation, in particular to a multi-model wind power plant equivalent simulation model construction method and system.The method comprises the steps that wind turbine generator information is collected, a multi-element large-scale wind power plant model is constructed, and the multi-element large-scale wind power plant model mainly comprises a wind turbine dynamical model and a converter control model; an embedded constant admittance equivalent model is constructed, control characteristics of an ideal switch are collected, control parameters of the embedded constant admittance equivalent model are solved according to the control characteristics, parameter selection is carried out according to steady-state errors, and a converter control model is replaced; and according to the multi-element large-scale wind power plant model, carrying out model equivalence processing on the wind turbine categories to obtain a multi-element wind turbine equivalence simulation model. Through the method, the problems that in existing wind power plant simulation, the simulation efficiency is low due to the fact that the calculated amount of a converter is too large, and the simulation result is inaccurate due to the fact that the equivalent modeling method of the multi-model wind power plant cannot fully consider the dynamic characteristics and control strategies of different models are effectively solved.

An AI-based platform for enabling intelligent orchestration and management of power and energy is provided herein. The AI-based platform includes a digital twin system including a plurality of digital twins of energy operating assets, the plurality of digital twins of energy operating assets including at least one energy generation digital twin, energy storage digital twin, energy delivery digital twin, and / or energy consumption digital twin, and a set of energy simulation systems configured to generate a simulation of energy-related behavior of at least one of the plurality of digital twins of energy operating assets, and a machine-learning system configured to generate a predicted state of at least one of the energy operating assets. The simulation of energy-related behavior is based on historical patterns, current states, and the predicted state of at least one of the energy operating assets.

The invention provides a digital simulation interface system of a face satellite attitude and orbit control subsystem, and relates to the technical field of computer simulation. Interfaces are divided into two classes according to attributes, the first class is simulation class interfaces, and the second class is simulation class interfaces; the interface is used for initialization parameter setting, simulation process control, simulation target data acquisition and data interaction of a simulation state, a running log, a model event, energy simulation data, thermal simulation data and environment simulation data of an attitude and orbit control subsystem model; and the second type is a business type interface, and the interface is used for data interaction with other subsystems or the outside in the function operation of the attitude and orbit control subsystem. Definitions of the two types of interfaces can cover all simulation activities of a satellite attitude and orbit control subsystem, meanwhile, the related internal simulation activities and external simulation activities can be distinguished, and interface testing of a satellite digital subsystem model can be systematically carried out with the fewest units.

The invention relates to an integrated energy simulation test method and device, a storage medium and equipment. The method comprises the following steps: constructing a power distribution topological structure of an integrated energy system model; constructing a control system in the integrated energy system model; and carrying out development testing on the integrated energy system according to the power distribution topological structure and the control system. According to the embodiment of the invention, the simulation test can be carried out by establishing the integrated energy system model, the current operation strategy of the integrated energy system is obtained according to the development test target and working condition of the integrated energy system, and the control instructions of the virtual system and the low-power system are output according to the operation strategy; monitoring and evaluating the power distribution states of the virtual system and the low-power system; and iteratively upgrading a comprehensive energy system model according to the monitored and evaluated data, and improving the development speed and precision of a comprehensive energy control algorithm. The method has universality, is suitable for modeling of different types of comprehensive energy sources, and has remarkable practical value.

The invention discloses a ship plane segmentation workshop fuzzy scheduling method, which comprises the following steps of: firstly, establishing a ship plane segmentation fuzzy scheduling model, adding uncertainty of processing time to the traditional scheduling model, and representing the processing time by using a triangular fuzzy number; secondly, improving the Harlisia eagle algorithm, introducing nonlinear decreasing escape energy and simulated annealing temperature parameters, solving the problems of premature convergence and falling into a local optimal solution of the algorithm, and improving the quality of an initial population by adopting an improved NEH algorithm and chaotic mapping; and finally verifying the validity of the algorithm through a reference function, and applying the optimization method to an engineering example. The method is helpful for improving the efficiency of a ship plane section processing workshop, and has important practical significance.

The application discloses a knowledge distillation method and device for reactor nuclear accident deduction and fault diagnosis, and comprises the following steps: obtaining nuclear energy simulation device fault data; building a multi-task network; dividing the obtained data set into a training set, a verification set and a test set, and pre-training the multi-task network by using the training set; fixing the parameters in the multi-task network after pre-training, that is, freezing the pre-training model with large parameters as a teacher model; adopting a multi-task proportional penetration knowledge distillation method to transfer the knowledge of the teacher model to a student model; and obtaining physical quantity deduction results and fault results of a target pipeline by reasoning by using the student model. D The application can simultaneously process multiple fault diagnoses and parameter deductions in a reactor nuclear accident by building a multi-task network based on a physical information neural network, fully utilizes the correlation between different tasks, and improves the accuracy of fault diagnosis and the deduction capability for the development process of a nuclear accident.

This invention belongs to the field of energy simulation technology and relates to an energy flow network simulation system and method for steel enterprises. It includes: a system configuration module for configuring parameters based on user-inputted information and forming a structured configuration data package; a front-end operation module for configuring a visual interactive interface based on the configuration data package; generating a Gantt chart based on pre-set production and maintenance plans; and drawing a dynamic energy flow diagram based on user-defined equipment and energy nodes; and a back-end service module for calculating energy prediction results based on the Gantt chart, dynamic energy flow diagram, and configuration data package using a pre-set production and consumption model, and performing scheduling optimization when energy prediction results are unbalanced to obtain an energy scheduling balance result. This invention achieves collaborative simulation of production and energy, more intuitively displays the energy balance process, provides a more comprehensive simulation platform for the steel industry, and facilitates systematic management by enterprises.

The invention belongs to the technical field of energy storage, and particularly relates to an energy storage capacity determination method and device, a computer readable storage medium and electronic equipment. The method comprises the following steps: determining photovoltaic power data, load power data and time-of-use electricity price data of a target area in each energy storage scheduling period; traversing various candidate energy storage capacities of the energy storage equipment in the target area, and performing energy storage charging and discharging power scheduling on the energy storage equipment in the target area according to the photovoltaic power data, the load power data and the time-of-use electricity price data to obtain energy storage charging power data and energy storage discharging power data of the energy storage equipment in each energy storage scheduling period; performing energy simulation on the target area according to the photovoltaic power data, the load power data, the energy storage charging power data and the energy storage discharging power data to obtain an energy simulation result of the target area; determining a financial index of the target area according to the energy simulation result; and determining the energy storage capacity of the energy storage equipment from the various candidate energy storage capacities according to the financial indexes.

The invention provides a distributed power supply series-parallel power distribution network fault identification model construction method and system, and the method comprises the steps: constructing a series-parallel simulation power distribution network based on preset power distribution network simulation information, and constructing a distributed energy simulation system based on preset distributed energy structure information; connecting the distributed energy simulation system to the series-parallel simulation power distribution network, performing single-phase earth fault simulation on the series-parallel simulation power distribution network by using the arc model, and collecting a fault transient current simulation signal of the series-parallel simulation power distribution network; performing feature extraction on the fault transient current simulation signal by combining a wavelet transform method to obtain a fault signal training feature, determining a fault recognition result based on the fault signal training feature, and further taking the fault signal training feature as an input and the fault recognition result corresponding to the fault signal training feature as an output to obtain a fault recognition result; and training the fault identification model to obtain a trained fault identification model, thereby realizing accurate identification of the fault of the hybrid power distribution network containing the distributed power supply through the fault identification model.

The invention relates to the technical field of artificial intelligence, and provides a building energy optimal transformation strategy generation method and device, equipment and a medium. According to the method, a plurality of machine learning models are constructed, and a training sample is constructed according to an urban building energy simulation result to perform integrated training on the plurality of machine learning models to obtain an agent model, so that the agent model can be directly utilized for prediction during subsequent building performance simulation, long-time energy consumption simulation is not required each time, and the processing efficiency is improved; and on the other hand, multi-target optimization processing is performed based on the proxy model and the parallel genetic algorithm plug-in to quickly generate the optimal transformation strategy of the target building, so that the quantization of the transformation result is realized, the method does not depend on empirical meaning, and the generated transformation strategy is more reasonable.

This invention relates to the field of unmanned aerial vehicle (UAV) application technology, specifically a method for energy consumption prediction and path planning in heavy-payload UAV hoisting operations. The method includes the following steps: loading the original flight data and mission target point data of the heavy-payload UAV; constructing a hybrid energy consumption model coupled with physical mechanisms and residual correction; introducing a heavy-load unbalanced weighted training mechanism to train the hybrid energy consumption model, assigning significantly higher weights to samples exceeding a preset heavy-payload threshold; using an improved K-Means clustering algorithm to divide the spatial domain of discrete operation points, introducing the hybrid energy consumption model as an evaluation function into the clustering iterative closed loop to achieve region clustering based on real operation point corrections; and calling the trained hybrid energy consumption model to perform energy simulation, performing path planning and search based on the limit efficiency of a safe power threshold. This invention effectively solves the safety hazard of inaccurate endurance estimation in heavy-payload operations and maximizes hoisting efficiency within a single takeoff and landing cycle.

The present application relates to the field of energy simulation, and discloses a method and device for predicting urban building energy consumption, electronic equipment and medium. The method comprises: reconstructing each component in the urban climate state of the previous time step by mapping to determine the building boundary state; determining the energy consumption state quantity of the building according to the building boundary state; updating the urban climate state of the current time step according to the energy consumption state quantity, thereby predicting the urban building energy consumption of each time step. The present application can accurately depict the evolution law of urban building energy consumption under future climate change scenarios.

The accuracy of photovoltaic simulation modeling is predicated upon the selection of a type of solar resource data appropriate to the form of simulation desired. Photovoltaic power simulation requires irradiance data. Photovoltaic energy simulation requires normalized irradiation data. Normalized irradiation is not always available, such as in photovoltaic plant installations where only point measurements of irradiance are sporadically collected or even entirely absent. Normalized irradiation can be estimated through several methodologies, including assuming that normalized irradiation simply equals irradiance, directly estimating normalized irradiation, applying linear interpolation to irradiance, applying linear interpolation to clearness index values, and empirically deriving irradiance weights. The normalized irradiation can then be used to forecast photovoltaic fleet energy production.

The invention discloses a power market transaction-oriented energy simulation system, and relates to the technical field of virtual power plant energy simulation. The system comprises a modeling topology abstraction module, a simulation equation construction module, a target function construction module, a cost minimum optimization module and a scheduling strategy determination module, the abstraction module is used for abstracting a power topological structure composed of an energy unit and a power market transaction center into a modeling topological structure composed of node objects, process objects and flow objects, and the construction module is used for constructing the modeling topological structure for each node object, each process object and each flow object. The method comprises the following steps: respectively constructing simulation equations used for representing corresponding constraint conditions and constructing an overall objective function by using an optimization module, and solving to obtain values of all variables by taking the minimum total cost as an optimization objective, so that the problem that a single market subject for managing various and multiple energy units participates in electricity market transaction can be solved, and the market transaction efficiency is improved. And the optimal energy scheduling strategy of each energy unit can be easily obtained.

This application relates to the field of wave energy simulation technology and discloses a simulation method and system for dual-mode frequency tuning of oscillating water columns in a double-slit caisson. The method includes: establishing a two-degree-of-freedom dynamic equation system based on the displacements of the water columns in the front and rear cavities, using the slit transmission coefficient as a parameter to obtain the coupling stiffness; substituting the coupling stiffness into the eigenvalue equation to decompose the oscillation modes of the two cavities to obtain the mode transformation matrix and mode amplitude ratio; performing convolution integration on the mode amplitude ratio and the wave power spectrum of the target sea area to obtain the first-order mode weight and second-order mode weight; using a preset target first-order mode weight as the outer convergence target and a preset target natural frequency as the inner convergence constraint to perform a double-layer nested iterative solution on the slit transmission coefficient and the rear cavity volume to obtain the optimal slit width and the optimal rear cavity volume. This application solves the problem of unobservable and undesignable energy contribution ratios of each mode in existing technologies, leading to spectral mismatch.

The invention relates to the technical field of energy system operation management, and particularly provides a digital twin energy simulation processing method, system and equipment based on data credibility and a storage medium, and the method comprises the steps: obtaining original energy data of similar equipment; calculating to obtain a credibility value corresponding to each piece of original energy data; performing credibility interpolation on the original energy data with the credibility value being 0 to obtain interpolation energy data, and recording the original energy data with the credibility value not being 0 and the interpolation energy data as effective operation data; based on the effective operation data and the credibility value, calculating a credibility weighting coefficient to obtain credibility weighted energy data; obtaining a station equivalent energy load based on the credibility weighted energy data and the credibility value; and updating the state variable of the pre-constructed energy digital twin model. By introducing a credibility weighting mechanism, the influence of abnormal data is reduced, and the stability and accuracy of energy simulation are improved.

The invention relates to the field of distributed energy simulation, in particular to a county distributed energy coordination simulation system and method based on multiple agents, and the system comprises multiple layers of agent units which are distributed in a tree shape, and the number of each layer of agent unit is at least one; wherein each agent unit comprises a node agent and a plurality of simulators connected with the node agent, and the node agent is used for interactively establishing an operation scene of the county distributed energy system, controlling the plurality of simulators to operate and perform information interaction, and controlling the next-level agent unit connected with the node agent to operate and perform information interaction. The simulator is used for simulating equipment and environment in a county distributed energy system. According to the invention, based on the multi-agent cooperation framework, the distributed scenes of the distributed energy system are respectively simulated through a plurality of agent units, and large-scale and multi-scene coordination simulation can be realized.

The invention relates to the technical field of data classification, and discloses an energy big data classification and grading method, system and device and a medium, and the method comprises the steps: obtaining various types of energy simulation data; preprocessing the energy simulation data, and performing data set division and label reconstruction to obtain a first data set; inputting the data to an SVM model and a random forest model for training to obtain an optimized SVM model and an optimized random forest model; and inputting the preprocessed second data set into the optimized SVM model and the random forest model, carrying out collaborative reasoning and grading, and outputting a final grading result. Through deep cooperation of the lightweight SVM and the random forest, a classification strategy is dynamically adjusted by using confidence, low-confidence scene misjudgment is reduced, small sample precision and large-scale processing efficiency are balanced, accurate optimization of classification and classification is realized, generalization ability and robustness of the model are enhanced, and the method is suitable for scenes such as a smart power grid and sensitive load guarantee.

The invention relates to a mountain fire fault positioning method and device for a power transmission line in a mountainous area, computer equipment, a computer readable storage medium and a computer program product, which can be applied to the technical field of electric power. The method comprises the following steps: collecting simulation transient waveform information of a simulation fault point through a fault transient waveform detection device; generating a time-frequency transient energy simulation matrix of the simulation fault point; acquiring actual transient waveform information of a to-be-positioned fault point through a fault transient waveform detection device; generating a time-frequency transient energy actual matrix of the fault point to be positioned; performing matching processing on the time-frequency transient energy actual matrix and the time-frequency transient energy simulation matrix of the simulation fault point; selecting two target fault points from the simulated fault points, and determining a line section between the two target fault points as a fault line section of the power transmission line; and determining position information of an actual fault point corresponding to the to-be-positioned fault point in the fault line section. By adopting the method, the fault positioning efficiency can be improved.

The application discloses a Q-switched laser resonator single-pass loss simulation inversion calculation method and system, and belongs to the technical field of laser simulation. The method comprises the following steps: initializing basic parameters related to a resonator and a laser medium; calculating a laser threshold energy storage according to the basic parameters; calculating the energy storage of a laser crystal under different pump currents according to the laser threshold energy storage; calculating the output energy simulation value under each pump current according to the energy storage of the laser crystal; comparing the output energy simulation value with the actual measured value, and obtaining the final simulation result of the resonator single-pass loss according to the comparison result. The application does not need a large number of experimental tests, has low simulation error, is suitable for multiple pump working conditions, is applicable to the loss solving and performance optimization of various Q-switched solid-state lasers, and can significantly improve the laser design efficiency and simulation precision.

1. The name of the product of this design: Graphical user interface for urban rail energy simulation evaluation of electronic equipment. 2. Purpose of the product of this design: an electronic device. 3. The design highlights of this appearance design product are: to display the graphical user interface content displayed on the screen panel. 4. The picture or photo that best illustrates the key points of the design: main view. 5.Purpose of the graphical user interface: This product is a human-computer interaction interface for urban rail energy simulation and evaluation. 6. Human-computer interaction mode of the graphical user interface: 1) The main view is the main interface; 2) Check the "Energy Efficiency Evaluation" selection box on the left side of the main interface to enter the interface change status diagram.