11 results about "Power usage effectiveness" patented technology

This invention belongs to the field of power system control technology. It discloses a system and method for the linkage between sub-core load adaptation and main core power replenishment in a dual-core fused power supply. The system includes: calculating the main core's instantaneous power replenishment margin based on the current state of charge of the main core management unit and the real-time available output capacity of the main core converter; sending a low-voltage detection pulse sequence to the sub-cores and simultaneously acquiring the returned damping attenuation waveform and instantaneous leakage current value; determining a dynamic access threshold, comparing the current real-time load power with the dynamic access threshold, and generating a continuous load adaptation tag and a sub-core locking identifier; establishing a sub-core priority power replenishment sequence, and immediately tightening the access conditions of the sub-core priority power replenishment sequence when the instantaneous power replenishment margin is lower than a preset warning value; activating a fast power replenishment channel for sub-cores that meet the preset power replenishment triggering conditions, while simultaneously performing dynamic power backtracking on unlocked sub-cores until the main core's instantaneous power replenishment margin recovers to or above the safety threshold; significantly improving energy efficiency.

The present application relates to a kind of method and device for dynamically adjusting computer room environment energy consumption, belong to adjusting energy consumption technical field, the method includes: collecting computer room environment energy consumption data, set this monitoring energy consumption initial threshold;According to energy consumption data, early warning and generating visual report;If the actual energy consumption in visual report this time is higher than historical average baseline and prediction rise and fall probability trend value is consistent, energy use efficiency value, energy use efficiency prediction value and difference are obtained, according to the size of difference, optimize this monitoring energy consumption initial threshold;When computer room environment energy consumption data is abnormal, locate current submodule, generate data set and mark by correlation relationship mining;The similarity of data set is calculated, set next monitoring energy consumption initial threshold;Determine optimal threshold;Optimal configuration threshold is generated under the computer room environment energy consumption data of optimal configuration threshold is stored, compare the computer room environment energy consumption data generated under optimal threshold and the computer room environment energy consumption data generated under monitoring energy consumption initial threshold.

This invention discloses a server configured with a dual-carbon management mode, comprising: a governance module, a management module, a carbon emission accounting module, a security module, an energy-saving module, and a data analysis module. The governance module is used for dynamically analyzing and evaluating Power Usage Effectiveness (PUE). The management module is used for collecting energy and material flows, performing intelligent analysis, refined management, and system optimization. The carbon emission accounting module is used for performing carbon emission accounting and generating carbon asset management solutions based on the accounting results. The security module is used to improve the underlying IT / OT application infrastructure. The energy-saving module is used to acquire power consumption data of server nodes and generate corresponding energy-saving strategies based on the power consumption data. The data analysis module is used to provide intelligent decision support for the planning, deployment, and operation and maintenance of data center clusters. This addresses carbon emission compliance requirements, ensures improved lifecycles for data center equipment, and meets data center requirements such as fundamentality, continuity, reliability, stability, security, and energy efficiency.

The invention relates to the technical field of camera equipment control, in particular to a camera power consumption control method and system.According to the camera power consumption control method and system, importance evaluation is conducted on the power consumption influence of an image dynamic area through a graph neural network, so that power consumption of all modules in the image collection process is accurately adjusted, and accurate calculation of the scene change rate is achieved; the power consumption of the camera in different scenes is optimized, the change trend and the power consumption demand of future image acquisition are predicted through the long-short-term memory network, more accurate power consumption control and adaptive adjustment capability are provided for the camera equipment, the effects of prolonging the endurance of the equipment, improving the response efficiency and reducing redundant data processing are achieved, and the method is suitable for large-scale popularization and application. Through combination of future power consumption mode prediction and actual requirements of equipment, the camera can be intelligently switched between a high-performance mode and a low-power consumption mode, the energy utilization efficiency of the equipment is optimized, and higher flexibility and energy utilization efficiency are achieved.

The invention belongs to the technical field of power systems, and relates to an operation risk prediction analysis method under power distribution network fault and abnormal working conditions, which comprises a multi-dimensional data acquisition module used for collecting multi-aspect information of a power distribution network, and a dynamic fault diagnosis module used for constructing a three-dimensional fault feature space and performing intelligent diagnosis according to fault features. The operation state prediction module carries out prediction and life evaluation on the change of each part of the equipment according to the fault feature information and sends a starting instruction to the risk quantitative evaluation module, and the risk quantitative evaluation module is used for calculating the possibility of danger occurrence and evaluating the risk of each part of the equipment. The self-adaptive decision-making module is used for automatically formulating a response scheme according to fault assessment information, and the knowledge evolution module is connected with a communication network, and can perform case library construction and automatic learning experience. The system realizes full-closed-loop intelligence of perception, diagnosis, prediction, assessment, decision-making and evolution through modular design; the system can reduce the power failure time and improve the energy use efficiency.

The invention discloses a multi-node energy cooperative scheduling method for a high-speed rail line, and relates to the technical field of high-speed rail transportation, and the method comprises the following steps: real-time monitoring and data collection; constructing an energy consumption model and predicting a load; optimization design of an energy scheduling strategy; scheduling and real-time monitoring are implemented; aiming at the energy management and scheduling problems among multiple nodes in the high-speed rail line, the invention aims to optimize the overall energy use, improve the efficiency and reduce the energy waste and environmental pollution, and compared with the traditional energy scheduling method, the energy scheduling method has the advantages that by comprehensively considering the synergistic effect and time dynamic change among the multiple nodes, the energy scheduling efficiency is improved, and the energy scheduling efficiency is improved. Therefore, efficient and intelligent energy management can be realized under the support of information technology and intelligent analysis, closed-loop management from data collection to evaluation feedback is realized, intelligentization and automation are realized by means of computer technology, finally, the energy utilization efficiency is improved, carbon emission is reduced, and the operation stability and economy of a high-speed rail system are improved.

A computer-implemented method, system, and computer program product for estimating a carbon footprint of an incoming workload to be hosted on a cloud data center. Trained first and second machine learning models are used in combination to estimate the energy consumption for the incoming workload to be hosted on the cloud data center based on the active energy consumption and the idle energy consumption predicted by the trained first and second machine learning models. Upon estimating the energy consumption for the incoming workload, the carbon footprint for the incoming workload is estimated based on the estimated energy consumption for the incoming workload as well as the power usage effectiveness of the incoming workload and the carbon intensity of the incoming workload. In this manner, carbon emissions attributable to workloads to be deployed to a data center (e.g., cloud data center) prior to deployment may be estimated.

This application provides a system and method for improving energy efficiency. The method includes: periodically providing load data corresponding to the load status of multiple peripheral units to a neural network unit; calculating a power operation prediction score based on the load data; determining an operating mode based on the power operation prediction score provided by the neural network unit, and controlling the start-up and shutdown of the peripheral units; and adjusting a clock rate based on the operating mode.

The invention relates to the technical field of network communication, and specifically discloses an NFV-oriented switch virtualization resource scheduling system. The system comprises a resource state sensing module, a multi-target decision engine, a dynamic frequency adjustment module and a resource allocation execution module. According to the method, by constructing the multi-objective decision engine, the maximization of the total throughput of the system and the minimization of the total power consumption of the system are determined as parallel optimization objectives, and a traditional single-performance-oriented scheduling normal form is changed fundamentally. The engine generates a Pareto optimal resource allocation strategy by solving an energy efficiency and performance balance model, so that a system can intelligently balance performance and energy consumption in a high-concurrency service scene, blind soaring of processor frequency and invalid dissipation of energy are effectively avoided, and the system performance and energy consumption are improved. The energy use efficiency of the data center is obviously improved; and the operation cost is reduced.

The invention discloses a dispatching management system and method applied to a deep-sea organism monitoring buoy, and relates to the field of deep-sea organism monitoring, and the system comprises a data collection subsystem which collects the data of the deep-sea organism monitoring buoy; the data processing subsystem is used for determining the electric quantity state according to the electric quantity of the battery, screening underwater sound signals based on the sound signal state, screening water surface images based on the water surface object state and then uploading the electric quantity state and the water surface image to the intelligent scheduling subsystem; the meteorological data, the water quality data and the position data are analyzed and uploaded to the intelligent scheduling subsystem; and the intelligent scheduling subsystem selects one mode from a watching mode, a monitoring mode, an ending mode and a sleep mode as a current mode by combining a preset start-stop strategy, generates an instruction and respectively issues the instruction to the data acquisition subsystem and the data processing subsystem so as to control start-stop of data acquisition and start-stop of data reporting. The long-term stable operation capability and the energy use efficiency of the buoy are improved.

The present application relates to the technical field of logistics management, in particular to a cold chain system operation and maintenance intelligent supervision control system, the system comprises collecting path environment data to screen operation and maintenance optimal section, then identifying the energy consumption over-limit risk in the section, and executing master and standby switching for the equipment with temperature rise exceeding the standard, at the same time judging the power supply adequacy according to the load demand to start the redundant power supply, finally comprehensively determining the priority scheduling object according to the resource and task urgency based on the power supply state and energy consumption data, and generating the cold chain operation and maintenance centralized supervision control set. The present application optimizes the path selection by real-time monitoring the path environment fluctuation to filter the unstable section, compares the energy consumption data with the load standard to identify and warn the overload risk, automatically executes the equipment switching and power supply scheduling when the equipment temperature rise is too high or the power supply is insufficient to avoid the operation and maintenance interruption, finally intelligently schedules each link through linkage control to make the system stable and efficient operation, improve the energy use efficiency and reduce the impact of sudden failure.