1159 results about "Energy based" patented technology

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 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.

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.

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 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.

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 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 relates to the technical field of mine coal and rock dynamic disaster prevention and control, in particular to a micro-seismic-based deep coal and rock mass instability dynamic multi-parameter evaluation method. The method comprises the following steps: acquiring waveform data of each micro-seismic event of a deep coal and rock mass through a mine micro-seismic system; according to the waveform data, acquiring a micro-seismic characteristic index set of each micro-seismic event; according to the seismic source position and the micro-seismic characteristic index set of each micro-seismic event, acquiring an energy-based micro-seismic magnitude; according to the micro-seismic magnitude, obtaining a micro-seismic b value corresponding to a preset duration, and according to the micro-seismic characteristic index set, obtaining a micro-fracture type and change trend information of accumulated energy of each micro-seismic event; and according to the micro-seismic energy, the change trend information of the accumulated energy, the frequency, the ringing count, the micro-fracture type and the micro-seismic b value, the dynamic instability fracture risk information of the deep coal and rock mass is obtained. According to the invention, the determination accuracy and efficiency of the instability and fracture risk information can be improved.

The invention discloses a hybrid model driving-based space-time consistency regulation and control method for a central heating system, which comprises the following steps of: performing hydraulic equilibrium analysis on the central heating system, and performing hydraulic analysis on a space structure of a heating pipe network by using a graph theory method based on an energy conservation law, simulating the hydraulic working condition of the official website by using a basic loop analysis method; based on an energy conservation and mass conservation heat balance equation, a heat source and a heat user are indirectly connected through heat exchanger transition, a dynamic physical model of each subsystem is established based on a lumped heat capacity method, a refined model of the heat supply system is established, and the thermal dynamic characteristics of the system are researched and analyzed; based on a Pearson's correlation function, considering thermal inertia of equipment such as a pipe network of the central heating system and a building fence structure, and determining a regulation and control period and time of the regulation and control model; according to the method, by controlling the opening degree of the regulating valve of the heat exchange station, the suitability degree of the room temperature of a heat user is guaranteed.

The invention relates to the technical field of elevator energy consumption analysis, and discloses an elevator energy consumption analysis method and system.The elevator energy consumption analysis method comprises the steps that elevator operation data are obtained, anchor point events are detected based on the elevator operation data, time alignment processing is conducted on the elevator operation data, and a synchronous data sequence with a unified time axis is generated; based on the synchronous data sequence, elevator running states are recognized, regeneration running stages are marked, and time segments corresponding to the elevator running states are output; carrying out integral calculation on the active power of the line in a time segment, realizing bottom consumption stripping in combination with a standby power reference, and outputting segment energy consumption through positive and negative power decomposition; and in the regeneration operation stage, feedback energy and braking energy are calculated according to the elevator operation data, feedback efficiency is calculated, and an energy consumption analysis result is output. According to the method, a reliable basis is provided for energy efficiency evaluation, abnormal early warning and operation optimization of the elevator system.

The present invention relates to the technical field of intelligent out-of-distribution fault detection for construction machinery, and discloses an out-of-distribution fault detection method and system based on energy propagation and graph learning, and the method includes: acquiring vibration acceleration signals in typical fault states, carrying out similarity calculation to obtain an adjacency matrix composed of the maximum mutual information coefficients, and taking the adjacency matrix as input in a graph neural network; carrying out feature extraction on the adjacency matrix through adopting a GraphSage graph convolution method, and generating each node representation; calculating an energy score of each node, and distinguishing between in-distribution data and out-of-distribution data; and enhancing out-of-distribution data confidence estimation for each node, and carrying out out-of-distribution data identification and out-of-distribution data detection under different working conditions of a rolling bearing.

A distributed system and method for compressing and restoring data across edge computing devices and cloud infrastructure is disclosed. The system dynamically adjusts compression based on available computing resources, network conditions, and now energy constraints. Edge devices monitor power consumption and battery levels, optimizing compression parameters to extend battery life while maintaining data quality. A workload scheduler prioritizes tasks based on energy availability, offloading intensive processing to cloud infrastructure when necessary. The system utilizes an energy-aware coordination layer to balance workloads across multiple devices, ensuring efficient data flow and long-term operational stability. Homomorphic operations allow secure distributed processing on compressed data, while an adaptive neural upsampler enhances reconstructed outputs. By integrating energy optimization, the system improves performance and longevity of edge devices in power-limited environments.

The invention relates to the technical field of immersion liquid cooling control, in particular to an immersion liquid cooling data control method and system combining photovoltaic and low-temperature waste heat power generation, and the method comprises the steps: transmitting low-temperature waste heat generated by the operation of a server to a liquid cooling circulation system, capturing solar radiation energy through a synchronous photovoltaic module, forming basic energy input, and transmitting the basic energy input to a server; generating a symbiotic energy substrate; the symbiotic energy substrate drives dual-path conversion, and photovoltaic electric energy is directly supplied to a cooling system pump set and a control unit to maintain liquid cooling circulation; afterheat pushes an expansion unit to generate power through organic working medium gasification, so that energy consumption of a cooling system is supplemented, and an energy flow self-driving system is formed; operation parameters of the energy flow self-driven system are input into the control core, and a three-state strategy is dynamically executed based on the load state and photovoltaic output. The system comprises a liquid cooling cabinet, a CDU control unit, a PLC, a solar panel, a photovoltaic cell, an illumination intensity sensor, a waste heat recovery device, a mode switcher and the like. According to the invention, system operation mode switching time delay is eliminated.

The invention provides a novel multi-source information and damage coupled coal mine rock burst prediction method. The method comprises the following steps that rock mass fracture signals, a three-dimensional stress field and acoustic emission parameters are collected in real time through a micro-seismic monitoring array, an optical fiber stress sensor and a rock sound probe; an LSTM-attention fusion module is adopted to carry out spatio-temporal feature extraction on the collected multi-source data, an attention weight coefficient is generated, and a weighted fusion feature vector is output; calculating the rock mass damage degree based on a statistical damage mechanics Weibull distribution theory; based on the law of conservation of energy and the damage mechanics theory, a dynamic energy threshold function is constructed, the mining time effect and the rock mass damage state are incorporated into a quantitative evaluation system, and a dynamic energy accumulation rate threshold function is established by introducing a time accumulation effect and a damage coupling term; when the real-time energy accumulation rate is larger than or equal to the energy accumulation rate threshold value and the time derivative of the energy accumulation rate is larger than the sudden change threshold value, third-level early warning is triggered. According to the invention, advanced accurate early warning of the rock burst can be realized.

The invention discloses a vibration signal denoising reconstruction method and device based on machine learning and a medium, and relates to the technical field of data processing. The method comprises the following steps: firstly, carrying out adaptive time-frequency decomposition on an original signal, and then identifying a noisy dominant component based on energy entropy and kurtosis; then, a generative adversarial network is constructed, denoising is carried out through joint optimization of adversarial loss and frequency domain perception loss, high-level semantic features of the frequency domain perception loss are extracted through a convolutional network pre-trained on bearing fault data, and it is ensured that high-frequency impact and modulation features related to a bearing structure and a defect type are reserved; and finally, reconstructing the de-noised component and other components, and outputting a high-quality signal. The method does not need a clean label, effectively inhibits complex noise, and improves the fault diagnosis reliability.

The invention discloses a mine blasting safety monitoring and early warning system, which comprises a multi-source sensing module, an edge computing node, a dynamic risk assessment module, an intelligent early warning decision module and an emergency linkage execution module, and is characterized in that the multi-source sensing module acquires multi-dimensional data such as blasting vibration, shock waves and toxic gas through various sensors; edge computing nodes filter noise and extract time-frequency features; the dynamic risk assessment module inverts blasting energy based on vibration and shock wave data, and predicts secondary disasters in combination with a geological model and gas concentration; the intelligent early warning decision module dynamically adjusts an early warning threshold by using a deep learning model and fusing multi-modal data; the emergency linkage execution module executes instructions such as power failure and evacuation according to devices such as early warning level, linkage ventilation and personnel positioning, overcomes the defects of traditional single monitoring and lack of correlation models, realizes full-process and all-directional monitoring of blasting operation, and effectively improves the safety of mine blasting operation and the efficiency of emergency management.

The invention discloses a new energy base outgoing power carbon budget decomposition and carbon emission pre-evaluation method and system, and the method comprises the steps: generating a new energy output curve based on the installation data and typical curves of various types of new energy; based on the new energy processing curve and the minimum output curve of the coal power unit, considering the minimum energy storage demand, and obtaining a typical two-step curve of each month; correcting the typical two-step curve of each month based on the energy storage adjusting capability; determining a three-step curve based on the corrected typical two-step correction curve of each month; expanding based on a three-step curve to obtain a power transmission curve; correcting the power transmission curve to obtain a power transmission correction curve; and calculating a carbon emission time sequence decomposition and evaluation result based on the unit output in the power transmission correction curve. According to the method, the dual optimization objectives of maximization of the new energy electric quantity ratio and minimization of the energy storage adjustment demand can be achieved, and carbon emission time sequence simulation pre-evaluation work of a system energy base is carried out on the basis of the dual optimization objectives.

The invention discloses a control method for steam condensate waste heat recovery and waste water heating, a control device for steam condensate waste heat recovery and waste water heating, control equipment and a computer readable storage medium. The thermal load and the logarithmic average temperature difference are calculated based on the energy conservation principle, and the needed heat exchange area is calculated through a logarithmic average temperature difference method. A flow combination scheme of the plate heat exchanger is determined according to the heat exchange area and the flow parameters, a steam condensate side adopts a single flow, and a waste water side adopts asymmetric configuration of double flows or multiple flows so as to adapt to the flow difference of the two sides. In addition, a real-time monitoring mechanism is further established, when the total heat transfer coefficient is decreased by more than 10%, a cleaning program is automatically triggered, continuous and stable operation of the system is guaranteed, and efficient utilization of waste heat resources is achieved.

The invention relates to the technical field of geodetic survey and structural health monitoring, and discloses a GNSS-RTK coordinate domain error correction method, which comprises the following steps: acquiring GNSS-RTK dynamic observation data to form a mixed signal time sequence; executing an improved adaptive noise complete empirical mode decomposition algorithm on the sequence to obtain an intrinsic mode function component; identifying and eliminating high-frequency components representing Gaussian white noise based on an energy coefficient, and reconstructing residual components into a signal sequence after primary noise reduction; inputting the noise reduction sequence into a rapid independent component analysis model for blind source separation; and finally, carrying out sorting and phase and amplitude uncertainty correction on the separated independent components, and outputting a multi-path error model and a structure dynamic deformation signal. According to the method, the problem of blind source separation failure or low precision caused by strong noise covering source signal statistical characteristics is solved through a strategy of first noise reduction and then separation, and an effective physical signal can be accurately extracted from a strong noise background.

The invention relates to the technical field of industrial automation control and dynamic simulation, and discloses a centralized control console and 3D model linkage virtual-real simulation system, which comprises a control signal sampling interface, a load response modulation unit, a dynamic model evolution unit and a virtual-real closed-loop feedback unit, the load response modulation unit performs energy consumption weight accumulation on all the virtual controlled objects in transient action to generate a global time scaling factor so as to correct an inertia time constant of the system in real time; the dynamic model evolution unit introduces a hard limiting boundary by using an amplitude limiting link and calculates a process variable; the virtual-real closed-loop feedback unit extracts a control deviation signal and drives a physical instrument to generate a nonlinear damping action, and by establishing a global load modulation model based on energy constraint, a discrete control system spontaneously emerges a nonlinear hysteresis characteristic conforming to physical energy conservation under the low calculation power condition without complex fluid network solution.

Systems and methods described herein detect user equipment (UE)-based denial-of-service (DoS) or distributed denial-of-service (DDoS) attacks based on energy consumption levels. An energy consumption (EC) DDoS monitoring service, collects, from one or more network devices in a core network, energy consumption data and identifies, based on the energy consumption data, normal energy consumption levels of one or more UE devices or network functions. The EC DDoS monitoring service determines, based on the normal energy consumption levels, energy consumption thresholds indicative of a DoS or DDoS attack.

The invention provides a water depth pressure dynamic data characteristic value real-time calculation method and system, electronic equipment and a storage medium, and relates to the technical field of water pressure monitoring. The method comprises the steps that voltage signals reflecting water depth changes of multiple channels are collected, the relevance of the voltage signals is analyzed, and the main fluctuation frequency of water flow is determined; setting a merging proportion, and fusing the multi-channel signals into a target signal; separating the target signal into a smooth trend part and an abrupt change part, and setting an abrupt change judgment threshold based on the energy change; detecting and marking a mutation time interval in the mutation part by using the threshold, and extracting the maximum fluctuation amplitude and duration in the interval as a first feature value; meanwhile, the smooth trend part is used for predicting and correcting the long-term reference offset of the voltage collecting device, the maximum value, the minimum value and the fluctuation range are output on the basis of the corrected signal to serve as second feature values, real-time feature extraction of the dynamic water depth pressure can be achieved, meanwhile, a sudden change event is accurately captured, and a stable extreme value range is output after drift is corrected.

Methods, systems, and devices for wireless communications are described. An energy transmitting user equipment (UE) may determine to perform a plurality of transmissions, each transmission of the plurality of transmissions comprising a data transmission, an energy transmission to charge an energy receiving device in response to a request, or both. The UE may select, based on an energy threshold and one or more parameters for prioritizing the transmissions, one or more transmissions of the plurality of transmissions to perform. For example, a network entity may transmit the one or more parameters to the energy transmitting UE for prioritizing the transmissions at the energy transmitting UE. The UE may transmit the data transmission, the energy transmission to charge the energy receiving device, or both corresponding to the selection.

Systems and methods are provided for modeling and for providing a graphical representation of tissue heating and electric field distributions for medical treatment devices that apply electrical treatment energy through one or a plurality of electrodes. In embodiments, methods comprise: providing one or more parameters of a treatment protocol for delivering one or more electrical pulses to tissue through a plurality of electrodes; modeling electric and heat distribution in the tissue based on the parameters; and displaying a graphical representation of the modeled electric and heat distribution. In another embodiment, a treatment planning module is adapted to generate an estimated target ablation zone based on a combination of one or more parameters for an irreversible electroporation protocol and one or more tissue-specific conductivity parameters.

A method of performing computation using a hybrid quantum-classical computing system comprising a classical computer and a quantum processor includes computing, by use of a classical computer, short-range inter-particle interaction energies and self-energies of a group of interacting particles, transforming the quantum processor from an initial state to a charge-position encoded state, applying Quantum Fourier transformation to the quantum processor, measuring an estimated amplitude of the Fourier transformed superposition state on the quantum processor, computing long-range inter-particle interaction energies based on the measured estimated amplitude of the Fourier transformed superposition state, and computing and outputting a sum of the short-range inter-particle interaction energies, the self-energies of the system, and the long-range inter-particle interaction energies as a total inter-particle interaction energies of the system.

The invention provides a battery thermal runaway early warning method and device. The method comprises the following steps: acquiring an original acoustic signal released by a target battery pack by using an acoustic sensor; working condition information of the target battery pack is obtained, dynamic noise reduction processing is performed on the original acoustic signal based on the working condition information, multi-source interference noise is suppressed, a de-noised optical signal is obtained, and acoustic characteristics of the de-noised optical signal in a preset frequency band are extracted; constructing a thermal runaway acoustic triggering index based on the energy threshold value, the combined characteristic change threshold value and the continuous triggering time, and generating an initial early warning signal according to the thermal runaway acoustic triggering index and the acoustic characteristics; state information of the target battery pack is obtained, multi-mode fusion analysis is carried out based on the state information and the initial early warning signal, and a thermal runaway verification signal is generated; and based on the thermal runaway verification signal and the initial early warning signal, performing thermal runaway grading early warning on the target battery pack. According to the invention, the accuracy of thermal runaway early warning can be improved.

The invention discloses a novel bionic arc-shaped reinforced re-entry honeycomb structure and a platform stress theoretical model, and arc-shaped supports are embedded into traditional re-entry unit cells by utilizing a bionic principle to trigger coupling deformation of multiple plastic hinges, so that the strength, rigidity, stability and energy absorption of the structure are remarkably improved. A platform stress theoretical model of the novel structure is provided based on an energy conservation principle, so that the cost of time, workload and the like of numerical simulation and experiments in a preliminary optimization design stage of the structure is remarkably reduced. Under the parameters of the invention, the platform stress and the specific energy absorption of the reentrant honeycomb structure are up to 10.4 times that of the traditional reentrant honeycomb structure, the rigidity and the stability of the reentrant honeycomb structure are improved to 11.7 times and 1.7 times, and the stable deformation mode and the crushing force efficiency of the reentrant honeycomb structure are superior to those of the traditional reentrant honeycomb structure. The composite material is suitable for scenes requiring high energy absorption and stable impact resistance, such as the protection fields of equipment such as pier collision resistance, canal collision resistance gates, automobile collision resistance, aircraft wing buffer layers and run-flat tires in civil engineering.