2174 results about "Energy based" patented technology

The invention provides a die-casting die heat balance distribution optimization method and system based on an energy model, and relates to the technical field of die-casting dies, and the method comprises the steps: collecting heat distribution data, and building a heat energy transfer mapping relation; extracting space-time correlation features by using a graph attention network; constructing a heat distribution dynamic evolution model; constructing a temperature field predictor based on a deep belief network; training a predictor by using a multi-agent coevolution algorithm; constructing a temperature field optimization strategy network; generating an optimal temperature field control sequence; establishing a regional adaptive control strategy; constructing a temperature field controller based on deep reinforcement learning; and a dynamic fuzzy immune optimization algorithm is adopted to adjust cooling control parameters, and intelligent optimization control over heat distribution of the die-casting die is achieved.

The invention relates to the technical field of audio noise reduction, and discloses a Bluetooth headset environment noise adaptive adjustment method and system, and the method comprises the steps: collecting multi-band sound data of headset environment noise through a preset acoustic sensor array, generating a noise spectrogram, extracting a noise masking threshold through time-frequency joint analysis, and collecting a headset audio signal; next, noise suppression is carried out on the audio signal by using a multi-order digital filter bank to obtain a primary noise reduction signal, the primary noise reduction signal is optimized into an optimized noise reduction signal based on a signal-to-noise ratio, and the frequency response matching degree of the optimized noise reduction signal and the original audio signal is calculated; then, performing phase correction and amplitude-frequency equalization processing according to the frequency response matching degree to obtain an audio equalization parameter; and finally, monitoring residual noise energy based on the parameter, generating a noise regulation and control instruction, driving adaptive sampling, and generating an adaptive noise reduction scheme. According to the invention, the audio noise reduction effect of the Bluetooth earphone can be improved.

The invention provides a self-adaptive power supply energy-saving configuration method and system based on energy gradient utilization, and relates to the technical field of power supply management, and the method comprises the steps: collecting the operation parameters of electric equipment, building a power utilization model, and carrying out the self-adaptive correction; constructing a multi-stage energy distribution matrix according to the corrected power utilization model, and performing energy efficiency grading on the power utilization equipment; performing spatial-temporal feature extraction by adopting a bidirectional attention mechanism, generating an electric equipment priority strategy in combination with a self-calibration network, and further calculating an energy distribution proportion of each level of electric equipment; and finally, generating a control signal according to the energy distribution proportion, adjusting the power supply power of each stage of electric equipment, distributing the exceeded power to the electric equipment with the secondary high energy conversion efficiency when the power value exceeds a target power threshold value, and outputting a self-adaptive energy balance control instruction according to the prediction control model to adjust the operation state of the electric equipment.

The invention provides a hydrogen energy storage equipment leakage fault early warning method and system based on deep learning, and the method comprises the steps: collecting the multi-source sensing data of hydrogen energy storage equipment in real time, constructing a multi-dimensional feature matrix, building the nonlinear mapping of pressure fluctuation and gas concentration through a bidirectional interaction network, and carrying out the early warning of the leakage fault of the hydrogen energy storage equipment. And performing spatial modeling on the temperature gradient and the vibration spectrum to generate a fusion feature tensor. And then, inputting the fused feature tensor into a spatio-temporal reasoning model, predicting equipment leakage and energy loss, and outputting a leakage probability cloud picture and an energy loss vector. And an entropy sudden change area is identified based on an energy loss vector, when the high-density leakage area and the entropy sudden change area are overlapped in a continuous detection period, a multi-stage early warning signal is triggered, a leakage source thermodynamic diagram and a pressure balance parameter set are generated, a self-adjusting system is activated to dynamically correct the valve opening degree, network weight is updated, and a closed-loop control link is formed. The early warning accuracy and the response speed of the leakage fault of the hydrogen energy storage equipment are improved.

The invention relates to the technical field of unmanned aerial vehicles, in particular to an intelligent flight path planning and energy management system and method for a long-endurance fixed-wing unmanned aerial vehicle. Comprising an environment sensing unit; the flight path planning unit is used for planning a flight path meeting task requirements, safety requirements and energy constraints based on the flight environment information of the unmanned aerial vehicle acquired by the environment sensing unit and pre-stored performance parameters of the unmanned aerial vehicle; and the energy management unit realizes energy dynamic management and optimization according to the real-time energy state of the unmanned aerial vehicle, the flight task and the planning result of the flight path planning unit. The flight path planning unit can call working condition energy consumption data such as navigational speed and height output by the energy management unit in real time, and dynamically adjust the weight of the path to avoid high-energy-consumption flight segments; the energy management unit synchronously and intelligently adjusts a main / standby battery charging and discharging strategy to optimize energy distribution according to a task time sequence (such as waypoint priority and track curvature) of a track.

The invention discloses a covariance self-adaptive control method for a quadruped robot for spine joint adjustment. The method comprises the following steps that 1, a spine quadruped robot kinematics model with spine joints, a robot trunk dynamics model with the spine joints and a robot leg multi-rigid-body dynamics model are established; 2, based on the energy stability margin theorem and the spine joint coupling effect, a posture control method of the spine joint quadruped robot is provided by combining a covariance self-adaptive evolutionary strategy, and the angle, rigidity and damping coefficient of spine joints are estimated and adjusted according to the terrain gradient; 3, extracting the spatial relationship between the trunk mass center and the foot ends of the robot as an expected reference, updating the expected mass center position in real time, and constructing a model prediction control method to control the posture of the spinal joint quadruped robot; 4, the position of the foot falling point of the swing leg is designed in advance in combination with the additional freedom degree provided by the spine joint and MPC prediction state information. The motion stability of the robot in the complex terrain is improved.

The invention discloses a photovoltaic glass defect detection method and system based on image recognition, particularly relates to the technical field of visual inspection, and is used for solving the problem that defect recognition is interfered by optical artifacts caused by a film layer interference effect on the surface of coated glass. The method comprises the following steps: extracting interference characteristics through a reflection image to generate an optical artifact distribution diagram, positioning an interference region in a transmission image frequency domain space, constructing a process response template in combination with a sedimentary pulse frequency, and executing polarization coherence screening and time-varying deconvolution collaborative filtering to eliminate artifacts; based on energy ratio dynamic decision reinspection or defect classification process implementation, inherent optical noise and real physical defects of a material are effectively separated, and the accuracy and reliability of coated glass defect detection are remarkably improved.

A database system is operable to perform a power supply strategy selection function based on first energy utilization-based operation optimizer input data to generate first energy utilization-based power supply strategy data denoting, for each power supply type of a plurality of power supply types, a corresponding first proportion of power supply resources that be implemented as the each power supply type. A first set of power supply resources is selected to execute a first database operation based on the first energy utilization-based power supply strategy data. The first set of power supply resources are utilized to power a first set of computing devices to enable the first set of computing devices to participate in execution of the first database operation in accordance with the first energy utilization-based power supply strategy data.

The invention discloses a diastrophic stratum deformation analysis and calculation method for an active fault zone, and relates to the technical field of geological engineering calculation, and the method comprises the following steps: (1) constructing a three-field dynamic coupling model based on an energy density gradient, and unifying dimensions of physical parameters of a mechanical field, a seepage field and a temperature field as an energy density function; according to the diastrophic stratum deformation analysis and calculation method for the active fault zone, the problems of multi-physical field splitting and time scale solidification in a traditional method are solved by constructing an energy-mediated multi-field dynamic coupling model and a space-time decoupling algorithm. On the basis of an interaction path of energy density gradient tensor unified mechanics, seepage and temperature fields, instantaneous bidirectional feedback of three-field parameters is achieved, compared with a traditional model, the pore pressure prediction error is reduced, and the calculation efficiency is improved; and meanwhile, the joint simulation efficiency of the second-level seismic event and the ten-thousand-year tectonic motion is improved, the interface parameter continuity error is controlled, and the precision and stability of cross-scale modeling are improved.

The invention discloses a multi-mode pet health monitoring and motion artifact elimination method and system based on a millimeter wave radar, and relates to the technical field of pet health monitoring, and the method comprises the following steps: S001, building a unified time baseline and an energy fingerprint auditing surface, constructing an energy distribution model from a chest to a tail, and taking the model as a reference for artifact evolution, identifying a signal spectrum coupling trend in a time-frequency domain; and S002, based on the energy distribution model, performing causal playback on continuous time sequence signals acquired by the millimeter-wave radar, extracting a pseudo-motion energy nucleus caused by tail or limb movement, and calibrating a phase anchor point and a space observation area of a respiratory signal. According to the method, multi-source physiological data are fused, pure respiratory signals are extracted through energy distribution modeling, causal playback, phase anchor point calibration and three-dimensional resampling, risk assessment is achieved based on physiological credibility tensor, and the accuracy and anti-interference capacity of health monitoring are improved by combining phase conjugate traction and a space-time regulation strategy.

The invention relates to a mechanical equipment fault data identification method based on artificial intelligence, and belongs to the technical field of data processing and artificial intelligence. The problems of insensitive signal processing, insufficient feature extraction, significant noise interference, low model training efficiency and the like in fault diagnosis in the prior art are solved. According to the method, a self-adaptive normalization method based on energy density is provided, the influence of non-stationary signals is effectively inhibited, and key features are reserved; through mixed energy entropy feature extraction and inter-band energy jump penalty terms, the sensitivity to a complex fault mode is significantly enhanced; constructing a fault feature enhancement strategy based on a Gaussian potential well, and reinforcing the response of a fault feature accumulation area; a weighted loss function and a gradient directional correction mechanism are adopted, so that the robustness and accuracy of the model are improved; and in combination with an entropy weighted learning rate and a covariance scaling strategy, adaptive training is realized, and the convergence speed and adaptability are improved. According to the method, the precision and efficiency of mechanical fault diagnosis are improved, and support is provided for industrial intelligent development.

The invention discloses an intelligent photovoltaic power generation system control method, and relates to the technical field of photovoltaic power generation management, and the method comprises the steps: collecting data in real time based on a sensor in a photovoltaic system, employing an edge computing technology, and transmitting important information to a cloud end; the future illumination change is predicted, so that the output of the inverter is adjusted in advance; based on an energy management system, the power demand and the generating capacity are monitored in real time, and interaction of photovoltaic power generation, energy storage and a power grid is coordinated; acquiring historical data, identifying a fault mode, and performing fault detection and prediction maintenance; based on the AR technology, the system state is displayed in real time. According to the method, environment data such as illumination, temperature and wind speed are collected in real time through a sensor, the edge computing technology is combined, the system state and the abnormal condition can be rapidly detected, the power generation efficiency is improved, illumination prediction based on the deep learning algorithm can dynamically adjust the working point of the inverter, the power generation capacity of the photovoltaic system is improved to the maximum extent, and the power generation efficiency is improved. And the method adapts to rapidly changing environmental conditions.

The invention discloses a small sample radar target identification method based on multi-feature fusion. According to the invention, the natural resonant frequency characteristics of radar echo signals are introduced into a small sample learning framework, and the advantages of certain stability and simple and rapid extraction under the condition of attitude and observation angle change are fully utilized; attention multi-feature weighted fusion based on energy guidance is carried out on natural resonant frequency features and time frequency features, scattering features are described from different feature mechanisms, and the completeness of the features under the small sample condition is improved; in addition, a loss function based on combination of feature similarity measurement and classification loss is also designed, so that the model can learn feature representation with higher distinction degree while the feature space discrimination is optimized, and the performance is improved in a feature fusion-classification network. According to the method, on the premise that the reasoning time and the calculation complexity are not remarkably increased, the recognition accuracy under the condition that the observation angle changes is effectively improved.

At least one example embodiment provides a system for controlling a heater in a non-combustible aerosol-generating device, the system comprising a memory storing computer-readable instructions and a controller configured to execute the computer-readable instructions to cause the non-combustible aerosol-generating device to apply a first power to the heater based on a first preheat temperature determine an estimated energy applied to the heater during application of the first power, and apply a second power to the heater based on the estimated energy, an energy threshold and a second preheat temperature, the second power being less than the first power.

Methods, systems, and devices for wireless communications are described. For instance, a first wireless device may transmit, to a second wireless device, a first signal for probing an energy state of the second wireless device. The first wireless device may monitor for a second, backscattered signal indicating the energy state of the second wireless device and may transmit, to the second wireless device, an indication of resources for harvesting energy based on monitoring for the second, backscattered signal. The first wireless device may transmit, to the second wireless device and over the resources, a third signal distinct from the first signal and associated with harvesting energy.

The invention discloses a wind power-photovoltaic-photo-thermal cold power co-production system integrating heat pump energy storage and suitable for a large energy base, and belongs to the technical field of energy storage. A hot side outlet of a steam generator of the system is connected with an inlet of a second molten salt tank through a fourth molten salt pump, an outlet of the second molten salt tank is connected with an inlet of a first three-way valve through a second molten salt pump, and a first outlet of the first three-way valve is connected with an inlet of a heat collection tower. An outlet of the heat collecting tower is connected with a first inlet of a second three-way valve through a first molten salt pump; an outlet of the second three-way valve is connected with an inlet of a first molten salt tank; the compressor is connected with a hot-side inlet of the first heat exchanger, a hot-side outlet of the first heat exchanger is connected with an inlet of the turbine through a hot side of the second heat exchanger, and an outlet of the turbine is connected with an inlet of the compressor sequentially through a hot side of the third heat exchanger and a cold side of the second heat exchanger. According to the system, cold energy can be supplied to the outside through the heat pump energy storage system in the energy storage stage, cold and power cogeneration is achieved, and the comprehensive energy efficiency of the large energy base is improved.

The invention discloses an energy planning method for a low-carbon economic comprehensive energy system based on multi-agent deep reinforcement learning. The method comprises the following steps: constructing a comprehensive energy system structure including multiple energy forms such as wind power, photovoltaic, electricity-to-gas, combined heat and power generation, energy storage and the like; establishing a carbon emission intensity model of each subsystem based on an energy input and output relationship, and constructing a segmented carbon tax function to quantify the carbon cost; a double-layer two-stage Nash optimization model is designed, and the game relationship between collaboration between subsystems and external energy interaction is considered; the optimization problem is converted into a Markov decision process, and a state, an action and a reward function are defined; a multi-agent TD3 algorithm model is constructed and trained, and strategy stability and robustness are improved through a differential evolution mechanism; and deploying the trained strategy model in an actual system to realize self-adaptive energy planning and scheduling for the carbon economic target. The method has the characteristics of high adaptability, high carbon benefit and excellent intelligent cooperation capability.

The invention relates to the field of energy management, and discloses a distributed energy intelligent configuration method and device based on Internet of Things big data, and the method comprises the steps: collecting the real-time energy flow and connection state of each node, integrating the real-time energy flow and connection state into initial node embedding, constructing a graph model, calculating the mutual influence weight of the nodes, and adjusting the interaction frequency; based on the mutual influence weight, the interaction frequency and the graph model, performing feature extraction on each node state, generating a node state feature vector, identifying a potential fault starting point exceeding an abnormal fluctuation threshold, predicting a propagation trend, and generating a risk path and a priority; extracting a hub based on a risk path and a priority, estimating an influence range and simulating diffusion, calculating system stability, if the system stability is lower than a threshold value, generating a resource optimization scheme, fusing position marks to generate an adjustment verification vector, updating an abnormal fluctuation threshold value, and reevaluating a potential fault starting point to obtain an adjusted propagation path; according to the invention, accuracy and robustness of dynamic energy management can be realized.

A multi-virtual power plant collaborative optimization scheduling method considering energy sharing comprises the steps of constructing a master-slave game model, constructing an upper-layer game leader scheduling center model, constructing a lower-layer game follower multi-virtual power plant collaborative optimization scheduling model and a multi-virtual power plant income distribution model, and solving the models by adopting a particle swarm optimization algorithm and a Cplex solver. According to the invention, the scheduling center based on energy sharing and the master-slave game model of the multiple virtual power plants are provided, the scheduling center is used as a superior game leader, and the multiple virtual power plants are guided to perform optimal scheduling by dynamically adjusting the electricity price, so that energy sharing among the multiple virtual power plants is stimulated; effective interaction between the multiple virtual power plants and an upper-layer mechanism is achieved through electricity price adjustment; according to the method, the benefit relationship among the multiple virtual power plants is deeply studied, and the problem of benefit damage of part of main bodies in the collaborative operation process is solved based on the multi-virtual power plant collaborative operation benefit distribution model of the Shapley value method, so that the collaborative operation relationship of the multiple virtual power plants can be stably maintained.

The invention discloses a multi-axis notched part fatigue life estimation method based on an energy criterion, and the method comprises the steps: firstly, giving a calculation method of equivalent strain energy density, and representing an energy distribution condition on a critical surface through a hexa-degree polynomial; determining a critical distance by using the relative energy gradient, giving a weight function based on the energy gradient, and calculating strain energy density on a critical surface; secondly, giving a fatigue life prediction model of the multi-axis notched part by considering the influence of energy gradient, size effect and non-proportional additional strengthening effect; the prediction result of the model provided by the invention is within 2 times of the dispersion zone, and is superior to the prediction result of other models.

The invention discloses an optical storage and charging cooperative off-network control method based on an energy router, and relates to the technical field of optical storage and charging cooperative control, and the method comprises the steps: collecting energy router grid-connected point off-network control data and environmental parameters in real time, carrying out the preprocessing of the collected data, and storing the processed data into a time sequence database; constructing a multi-level prediction model to predict load, power generation and power grid condition data, and dynamically updating system operation parameters according to a prediction result; according to the off-network control data, performing intelligent off-network pre-judgment to switch a system operation mode, and performing off-network operation optimization control; and continuously monitoring the state of the power grid, recovering grid-connected operation after the bus voltage is stable, and carrying out optical storage and charging collaborative off-grid control based on the energy router. According to the invention, long-time off-grid operation of the DC micro-grid is realized, and the power supply reliability and the equipment safety of off-grid operation are improved.

A database system is operable to generate an energy utilization-based operation scheduling function based on power usage policy data for the database system. A storage-formatted data generation operation is determined for execution, and energy utilization-based operation scheduling input data is determined for the storage-formatted data generation operation. The storage-formatted data generation operation energy utilization-based operation scheduling function is performed upon the energy utilization-based operation scheduling input data to generate energy utilization-based operation scheduling data for the storage-formatted data generation operation. A plurality of storage-formatted data structures are generated from a plurality of records for storage based on executing the storage-formatted data generation operation in accordance with the energy utilization-based operation scheduling data.

The invention provides a reusable rocket landing stage starting height prediction method, a guidance system and a processor, and aims to dynamically and accurately determine the ignition time of an engine by constructing a real-time online starting height prediction model based on an energy conservation principle and combining multiple safety thresholds (including an allowable starting height upper limit and a standby starting height lower limit) so as to predict the starting height of the rocket in a landing stage. And the safety and fuel optimality in the whole flight envelope are ensured, so that the reliability, accuracy and economical efficiency of the reusable rocket landing are remarkably improved. The problems that in the prior art, engine starting height prediction precision and robustness are insufficient, online adaptation to uncertainty of a complex flight environment cannot be achieved, and safety margin consideration is insufficient are solved, and potential safety risks are thoroughly eliminated.

The invention relates to a self-adaptive interaction method based on multi-modal emotion calculation and a robot. The system realizes dynamic management of emotional values of different interaction objects by constructing a time sequence and a real time axis, and improves the memory weight in combination with key events. The system comprises a random event generation unit which simulates an environment event and gives an emotional influence; the user emotion judging unit is used for calculating emotion energy based on real-time input and historical interaction; the user impression dynamic adjusting unit is used for realizing impression weight attenuation and updating; the system emotional energy calculation unit fuses the virtual environment and the real time information to generate a system emotional state; the emotion response strategy unit is used for dynamically generating personalized emotion output according to the system emotion, the user emotion and the user impression; the aggressiveness detection unit prevents aggressiveness content from being output, and interaction safety is guaranteed. The system improves personalization, dynamic adaptation and safety of emotional response, and is suitable for scenes of intelligent accompanying, emotional counseling, customer service and the like.

A database system is operable to determine power usage policy data and generate operation requirement data based on the power usage policy data. A database operation is determined for execution, and an energy utilization-based operation limitation enforcement function is performed upon energy utilization-based operation restriction enforcement input data to generate energy utilization-based operation restriction enforcement data for the database operation based on applying the operation requirement data. The database operation is executed when the energy utilization-based operation restriction enforcement data indicates execution of the operation is allowed based on execution of the database operation adhering to the operation requirement data. Execution of database operation is foregone when the energy utilization-based operation restriction enforcement data indicates execution of the operation is disallowed based on execution of the database operation not adhering to the operation requirement data.

The invention relates to new energy, and provides a battery management method, an energy management system and an energy storage system. The method comprises the steps that the energy management system and the battery management system form communication line connection; and the energy management system simulates the original vehicle battery pack to communicate and interact with the battery management system in a running environment of an actual vehicle, so as to obtain battery running parameters of the original vehicle battery pack from the battery management system in real time, and charge and discharge the energy storage unit through the energy storage converter unit based on an energy scheduling strategy. Therefore, the operation environment is simulated through the energy management system, the redundant original vehicle battery pack can be utilized to construct the energy storage system, the maintenance cost problem of the redundant original vehicle battery pack is solved, and new energy consumption and power supply guarantee are effectively supported.

The invention discloses a park carbon emission accounting method considering an integrated energy system and uncertain factors, and the method comprises the steps: 1, determining a park carbon emission accounting boundary, and determining a main carbon emission source in a park; 2, based on an energy flow theory and a carbon flow analysis method, establishing a multi-layer comprehensive model of park carbon emission accounting considering the comprehensive energy system, wherein the multi-layer comprehensive model comprises park energy supply carbon emission, park energy consumption carbon emission and park non-energy carbon emission; 3, uncertain factors including energy carbon emission factor uncertainty, electric power carbon emission factor uncertainty and corrected carbon emission factors of the park carbon emission accounting are considered, and the accuracy of the park carbon emission accounting is improved. According to the park carbon emission accounting method, carbon emission source classification is carried out from the angle of energy flow, uncertain factors in the carbon emission accounting process are researched, and the carbon emission accounting accuracy is improved.

The invention discloses a drilling construction intelligent decision support and risk early warning method, device and equipment, and the method comprises the steps: collecting drilling sound wave response data, carrying out harmonic analysis to recognize a reservoir resonance node, and constructing a reservoir energy conduction network; a fluctuation propagation path is tracked based on an energy conduction network, and a construction risk area is identified through stress wave spectrum features; porosity and permeability data are extracted for phase difference analysis, a seepage-construction mismatch factor is generated, and a construction optimization window is actively generated; constructing a geological potential energy gradient field to determine an overflow influence range, and performing tensor coupling on multi-dimensional risk factors to generate a three-dimensional construction decision matrix; an optimal construction scheme is generated through energy minimization and risk-efficiency dynamic balance processing; construction parameter response changes are monitored in real time, abnormal parameter combination symptoms are recognized, a grading risk early warning instruction is generated, and intelligent decision support and risk early warning of drilling construction are achieved.

The invention discloses a noise signal recognition method based on deep learning. The method comprises the steps of original signal collection, signal preprocessing, noise recognition feature extraction, noise signal recognition model establishment and noise signal intelligent recognition. The invention relates to the technical field of digital signal data processing, in particular to a noise signal identification method based on deep learning, and innovatively provides a self-adaptive signal mode decomposition method based on a combined judgment mechanism of energy threshold value judgment and energy range value judgment. Decoupling and reservation of different frequency bandwidth information in an original signal can be effectively completed, and the recognition accuracy of non-stationary communication noise is improved; a frequency band filter bank constructed based on a Gaussian function is introduced, so that the accuracy of communication signal identification is improved; a method for constructing a noise recognition model by fusing graph neural network structure modeling and an original type reference vector matching mechanism is innovatively proposed, and the recognition robustness of the model to non-stationary communication interference signals is improved.

The invention provides a power grid system control method and device, computer equipment and a storage medium in the field of power grid system control, and the method comprises the steps: firstly, determining a dynamic inertia coefficient through integrating node frequency deviation, an energy storage charge state and voltage deviation, calculating inertia compensation power through employing the frequency deviation, precisely capturing the dynamic characteristics of a system, and carrying out the calculation of inertia compensation power; and constructing a target function based on the equivalent impedance matrix and the power deviation, solving a target control sequence under constraint conditions, realizing system power optimization distribution, and further adjusting node power output from multiple aspects by determining composite inertia, synthesizing inertia power, regulating and controlling a power vector and an inertia matrix. And finally, based on energy function construction and partial derivative calculation, generating a control instruction of each node. The steps can effectively deal with system power disturbance, rapidly compensate insufficient inertia, optimize power distribution, maintain system frequency stability, improve safety, reliability and stability of operation of a power system, and enhance adaptability of the system to complex working conditions.