19 results about "Energy assessment" patented technology

The invention relates to the technical field of artificial intelligence, and provides a mountain wind energy resource dynamic evaluation method and device, equipment and a medium, which can perform attention fusion on multi-dimensional initial data, so that fusion features consider feature importance difference and comprehensiveness. A mountain wind energy evaluation data set with a wind energy power density label is constructed, and a basis is provided for supervised training of the model; the state space completely covers core variables influencing mountain wind energy evaluation, and the problem that a traditional model is insufficient in state space dimension and cannot represent a complex coupling relation is solved. The action space is in non-static output, so that the adaptive capacity of the model is improved; the reward function solves the technical problem that precision and efficiency cannot be considered at the same time; a dual experience playback mechanism improves the evaluation precision of the model on a complex wind field scene; the target network delay updating strategy enables the target network to maintain parameter stability in an iteration period, and the problems of training oscillation and slow convergence caused by real-time fluctuation of target return are solved.

An integrated home energy assessment apparatus, method, and computer program product are disclosed. The apparatus includes one or more processors and non-transitory computer readable storage media storing code. The code is executable by the processors to perform operations that include querying third-party databases for information pertaining to a home and receiving the information from the third-party databases. The operations include analyzing, via machine learning, the information to identify energy-related features of the home. Each of the features corresponds to a scoring category. The operations include determining a lead score corresponding to each feature and aggregating the lead scores within the scoring category to generate a category score. The operations include determining an overall score for the home based on the category score for each of the scoring categories.

The invention discloses a distributed roof photovoltaic entity potential analysis method based on a geographic grid, and relates to the technical field of photovoltaic energy assessment. The method comprises the following steps: obtaining built roof photovoltaic data and stock building roof data, and generating a photovoltaic entity basic table according to the built roof photovoltaic data and the stock building roof data; acquiring multi-source photovoltaic entity influence factor data of distributed roof photovoltaic, and generating a photovoltaic entity influence factor table based on the photovoltaic entity basic table and the multi-source photovoltaic entity influence factor data; and carrying out distributed roof photovoltaic entity potential analysis based on the photovoltaic entity influence factor table, and outputting an analysis result. By means of the method, the roof photovoltaic entity model based on the unified geographic grid can be established, the spatial feature relation is introduced into the roof photovoltaic potential evaluation technology, and therefore the roof photovoltaic potential evaluation calculation efficiency is improved.

The invention provides an AI energy management analysis decision-making method for a smart park, and relates to the technical field of energy assessment. The method obtains the power consumption of an air conditioner and corresponding environment data through a management platform, and judges the difference between the power consumption of the air conditioner under the same condition and the power consumption of the first year by taking the power consumption of the first year as a reference; the method comprises the steps of forming power consumption increase, reflecting the trend of air conditioner efficiency reduction, obtaining maintenance records of the air conditioner, and judging power consumption increase change conditions of the air conditioner before and after maintenance and power consumption increase change conditions of the air conditioner in the using process respectively, so that state scores are obtained to evaluate the energy consumption condition of the air conditioner. And the air conditioners exceeding the state score threshold value are alarmed to remind replacement, so that the air conditioner management evaluation method aiming at the use process and the maintenance condition is realized, and a basis is provided for energy management of the smart park.

A machine has a network interface circuit connected to a network with interconnectivity to a user machine and sensors. A processor is connected to the network interface circuit. A memory is connected to the processor. The memory stores instructions executed by the processor to prompt a user for video of a user home, prompt a user for survey data characterizing the user home, collect sensor data from the sensors, analyze the video of the user home, the survey data characterizing the user home, and the sensor data to produce a user home energy model with a three-dimensional (3D) model of the user home including labeled structural elements, construction materials, appliances, and energy-related features and a list of personalized recommendations for home improvement projects.

This invention relates to the field of agricultural regulation, and particularly to a method for linking tillage implement condition identification with soil compaction remediation. The method includes: collecting real-time multi-source sensor data during tillage implement operation; obtaining a force-curvature coupling energy factor by performing force-curvature coupling energy assessment and relative difference normalization modulation on a zero-mean resistance data sequence; obtaining a viscous rheological asymmetry factor by performing energy asymmetry ratio assessment and logarithmic difference amplification modulation on a zero-mean resistance data sequence during loading and unloading processes; obtaining operating condition category labels by jointly correcting the basic Euclidean distance and performing clustering; and obtaining a linkage control command for tillage depth and hydraulic pressure by mapping the operating condition category labels to differentiated control strategies for tillage depth and hydraulic pressure. This addresses the problem that existing Euclidean distance clustering cannot distinguish between true high resistance due to compaction and false high resistance due to wet adhesion, leading to malfunctions in tillage depth control.

This invention relates to an energy management method, apparatus, and display screen for a display screen. The energy management method of this application is applied to a display control system. The method includes: analyzing and processing energy parameters acquired by an energy harvester to obtain an energy intensity curve representing intensity changes and stability; processing the energy intensity curve based on a preset energy assessment model to obtain collectable energy data and controlling the energy harvester to collect the energy; the display control system can process the power supply requirements of the display module and the energy conversion information fed back by the energy conversion memory to generate energy allocation instructions to control the energy conversion memory to rationally allocate the converted and stored energy. This method can convert renewable resources into electrical energy, achieving self-powering of the display screen, which is more energy-efficient and environmentally friendly, while also eliminating dependence on the power grid and expanding the application scenarios of the display screen.

Disclosed is a method and system to combine virtual reality techniques with a consumer's real-world space to identify ways to reduce energy consumption and reduce their attendant carbon footprint through an immersive energy assessment. An object is identified that is related to the consumption of energy in the consumer's real-world space, the system is able to present energy information that is specific to that object. This is in contrast to generic information such as an energy star rating. The specific energy information in one example is derived from data from many sources, including smart electric meters, smart appliances, smart home equipment, Internet of thing equipment, smart thermostats, smart speakers, and in-line watt meters.

The invention discloses a multi-scene gas power station frequency-voltage supporting capability quantitative evaluation method, system and device, and relates to the technical field of power system stability control and energy evaluation, and the method comprises the steps: collecting multi-source data of a plurality of scenes in real time and carrying out the preprocessing of the multi-source data in the grid-connected operation process of a gas power station; determining a scene feature vector and an index feature vector based on the preprocessed multi-source data; inputting the scene feature vector and the index feature vector into a power grid frequency-voltage supporting capability comprehensive evaluation model, and outputting an index score of each index in the index feature vector and a predicted value of a power grid frequency-voltage supporting capability comprehensive score of the current scene; and determining an evaluation grade of the power grid frequency-voltage supporting capability in the current scene based on a predicted value of the comprehensive score of the power grid frequency-voltage supporting capability in the current scene and the index score of each index in the index feature vector. According to the invention, the evaluation precision of the frequency-voltage supporting capability of the gas power station is improved.

The invention discloses a space sensing type modular wind resource sensing method suitable for an offshore wind plant. The method comprises the following steps: step 1, arranging a wind plant three-dimensional sensing system; 2, carrying out multi-source data acquisition and edge preprocessing; 3, carrying out spatial-temporal feature modeling and intelligent prediction; and 4, wind energy evaluation and system feedback optimization are carried out. The invention provides a multi-source layout strategy combining a tower drum, a jacket, a booster station and a buoy, and a three-dimensional sensing network covering the vertical wind speed gradient, boundary layer disturbance and the overall wind field dominant trend is formed by fully utilizing the structure positions of different elevations and regions in a wind power plant. Compared with a traditional single-point or linear layout mode, the scheme realizes higher spatial resolution and wind field state restoration capability on the premise of not increasing a large amount of hardware investment, and provides a solid data foundation for high-precision prediction.

This invention discloses a multimodal fusion method, apparatus, equipment, and storage medium for assessing microseismic energy in mines. The method includes: preprocessing multimodal signals collected from the mine area to obtain initial waveform feature vectors and initial audio feature vectors; fusing the initial waveform feature vectors and initial audio feature vectors across modes based on the spatial location of each detector to output fused mode features; inputting the fused mode features of each detector into a graph convolutional network for aggregation to obtain target features of microseismic events; and predicting the source energy of microseismic events based on the target features. By acquiring multimodal signals and fusing them across modes, the limitations of single signal monitoring are overcome, the multimodal correlation features of microseismic events are fully explored, and the impact of interference factors on the assessment results is reduced. Combined with the spatial feature aggregation capability of the graph convolutional network, the complementary information of multi-source data is effectively integrated, significantly improving the accuracy and stability of source energy assessment.

This invention belongs to the field of water quality monitoring technology. It discloses a comprehensive monitoring and early warning method for water quality in a park's water supply network, comprising: identifying water quality collection points in the water supply network; collecting water quality data and corresponding water quality labels from these collection points in the same time series; performing energy assessment on the water quality data to obtain point energy; dividing the point energy into several point-level clusters; classifying these clusters for anomalies to obtain anomalous point clusters; removing anomalous data from the water quality data to obtain valid water quality data; correcting the valid water quality data for flocculation impact based on a pre-constructed bioflocculation impact assessment model to obtain corrected water quality data; constructing a water quality monitoring model based on the corrected water quality data and water quality labels; and achieving accurate monitoring of water quality in the water supply network through the water quality monitoring model. This significantly improves the accuracy and precision of water quality monitoring in the park's water supply network.

This invention discloses an optimization analysis method and system based on multi-dimensional energy data. The invention acquires regional energy type data and transmission equipment data, and determines multi-dimensional energy assessment indicators. A multi-dimensional optimization set is constructed based on the energy optimization set and the transmission equipment optimization set. A matter-element extension model is constructed based on the multi-dimensional optimization set and the multi-dimensional energy assessment indicators to determine the hierarchical correlation degree between each multi-dimensional energy assessment indicator and each multi-dimensional optimization pair in the multi-dimensional optimization set, thereby constructing a multi-dimensional correlation sequence. The correlation coefficient of each multi-dimensional energy assessment indicator is calculated using the grey relational coefficient based on the multi-dimensional correlation sequence. The optimal multi-dimensional energy optimization scheme is selected according to actual needs based on the correlation coefficients of the carbon dioxide emission sequence, energy conversion rate sequence, and energy transmission efficiency sequence. This invention avoids the one-sidedness of single-indicator evaluation and ensures the comprehensiveness of the optimization scheme.

This invention discloses a seamount ecosystem energy flow path assessment system based on coupled evaluation indicators, belonging to the field of marine ecological assessment and management technology. The system includes an assessment center, which is communicatively connected to a regional deployment module, a multi-source acquisition module, a coupled analysis module, an energy assessment module, a data management module, and a decision support module. The system achieves systematic and standardized collection of biological and environmental data in seamount areas through gridded zoning and directional equipment deployment in the regional deployment module, combined with stratified acquisition and standardized sample preprocessing in the multi-source acquisition module. The coupled analysis module constructs coupled indicators, analyzes key energy flow paths, and screens key environmental factors, providing quantitative assessment basis. The energy assessment module calculates biopump efficiency, assesses benthic food supply and spatial changes, and visualizes the results, enabling refined assessment and management of ecological energy flow in seamount areas of the western Pacific Ocean.

The invention relates to the technical field of energy assessment, in particular to a property energy loss assessment method and system based on deep learning, and the method comprises the steps: obtaining multi-source energy data and environment association data of a target property, and determining a basic energy consumption sequence in the multi-source energy data and a plurality of to-be-analyzed energy monitoring sub-regions; and according to the multi-source energy data, determining a time sequence correlation degree between each energy monitoring sub-region and the basic energy consumption sequence, and according to the time sequence correlation degree and the equipment operation characteristics of each energy monitoring sub-region, determining an energy consumption characteristic vector of each energy monitoring sub-region. According to the invention, through the analysis of the multi-source energy data, the energy consumption characteristics of different sub-regions can be evaluated in detail, so that the energy consumption loss evaluation is more accurate, a property manager can be helped to determine which regions have energy waste or loss problems, and through the calculation and analysis of the environmental sensitivity coefficient, the energy loss can be evaluated under different environmental conditions.

The application provides a kind of solid oxide electrolytic cell evaluation system and method, it is related to the technical field of energy evaluation, the system includes: data acquisition module, gas inlet device, power supply device, heating device, gas outlet device and control module, control module adjusts gas inlet device, power supply device and heating device according to real-time working condition parameter and target working condition parameter, and calculates monomer consistency score according to each performance parameter under different target working condition parameters, and evaluates the performance grade of the solid oxide electrolytic cell stack to be measured according to monomer consistency score, realizes the electrolysis experiment of solid oxide electrolytic cell under different working conditions, completes the comprehensive test and evaluation of the performance of solid oxide electrolytic cell.

This invention relates to the field of intelligent monitoring technology for power systems, and discloses a control device and method for a power grid edge monitoring terminal combined with active energy sensing. The device includes: a micro-meteorological sensing module for real-time acquisition of meteorological parameters of the environment where the monitoring terminal is located; an energy prediction module for predicting expected energy gains within a future time window using a time-series prediction model; an energy assessment module for calculating an energy availability score; a mode decision module for dynamically switching the operating mode of the monitoring terminal based on a comparison of the energy availability score with a preset threshold; a defect detection module for real-time defect detection of acquired images or video streams; and a verification decision module for determining whether to trigger a second power consumption mode for defect verification when the defect detection module detects a suspected defect in a first power consumption mode. This invention can significantly reduce the false negative rate of dynamic defects without increasing hardware costs or battery capacity.

The invention relates to the technical field of burn treatment AI energy evaluation, and discloses a burn patient energy evaluation system based on an AI technology. The system comprises an image analysis unit, a stage marking unit, an energy baseline generation unit, a multi-modal fusion unit and a convolutional neural network unit. An infrared thermal image of a burn area is registered with a standard anatomical partition, a tissue metabolism activeness score of the partition is calculated, and a dynamic energy baseline is generated in combination with a time label of a disease course record. And further fusing the continuously monitored resting metabolism rate and blood biochemical indexes, and generating a partition energy time sequence curve after calibration. And splicing the time sequence curve and the thermal imaging feature map, inputting the spliced time sequence curve and the thermal imaging feature map into a three-dimensional convolutional neural network for spatio-temporal feature extraction, and predicting the energy demand of each anatomical partition in the future. According to the method, regional, dynamic and accurate evaluation and prediction of the energy metabolism state of the burn patient are realized, and a basis is provided for individualized nutrition support.

The invention relates to the technical field of artificial intelligence, and discloses an enterprise environment management system energy efficiency standard-reaching authentication method based on an intelligent algorithm, which comprises the following steps: acquiring an energy evaluation architecture comprising management dimensions, energy flow items and operation data, then analyzing text records in the operation data through a semantic analysis algorithm, and obtaining an energy evaluation result; the method comprises the following steps of: obtaining a mature hierarchical description of practical characteristics, then dynamically fusing measurement data in operation data and description factors in the mature hierarchical description to generate fused measurement data, then inputting the fused measurement data into an energy evaluation architecture, analyzing by utilizing a collaborative learning algorithm to obtain an energy efficiency collaborative index, and finally, calculating the energy efficiency collaborative index according to the energy efficiency collaborative index. And determining an energy efficiency standard reaching state based on the index, and generating a corresponding energy efficiency authentication result according to the energy flow item. According to the invention, the accuracy of the enterprise environment management system during energy efficiency authentication can be improved.