25 results about "Power usage effectiveness" patented technology

The invention relates to the technical field of logistics management, in particular to an intelligent supervision control system for operation and maintenance of a cold chain system, which comprises the steps of collecting path environment data to screen an operation and maintenance optimal section, then identifying an energy consumption over-limit risk in the section, and executing main-standby switching on equipment with temperature rise exceeding the standard, meanwhile, power supply sufficiency is judged according to load requirements so as to start a redundant power supply, finally, a priority scheduling object is determined according to resource and task urgency by integrating a power supply state and energy consumption data, and a cold chain operation and maintenance centralized supervision control set is generated. According to the method, path selection is optimized by monitoring path environment fluctuation in real time to filter unstable road sections, energy consumption data and load standards are compared to identify and early warn overload risks, equipment switching and power supply scheduling are automatically executed to avoid operation and maintenance interruption when equipment temperature rise is too high or power supply is insufficient, and finally each link is intelligently scheduled through linkage management and control. The system operates stably and efficiently, the energy use efficiency is improved, and the sudden failure influence is reduced.

Aspects and embodiments disclosed herein include a method of performing an adaptive calculation of power usage efficiency (PUE) of a data center. The method comprises receiving a first input defining quantities and types of equipment in the data center, determining an estimated PUE and a first measure of uncertainty in the estimated PUE from the first input using a range of possible values associated with parameters of one or more items of equipment in the data center, receiving a second input comprising at least one value of one or more parameters of one or more items of equipment in the data center, refining the estimated PUE and determining a second measure of uncertainty in the estimated PUE based on the second input, and presenting the refined PUE and the second measure of uncertainty in the refined PUE to a user.

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 embodiment of the invention provides a temperature control strategy determination method and system, computing equipment and a storage medium, and the temperature control strategy determination method comprises the steps: determining service equipment parameters of service equipment deployed in a target machine room, and temperature control equipment parameters of temperature control equipment; under the condition of determining that the target machine room meets a preset test condition according to the service equipment parameters and the temperature control equipment parameters, performing energy consumption test on the service equipment and the temperature control equipment according to a preset temperature interval to obtain an energy consumption test result corresponding to each temperature test node in the preset temperature interval; and determining a temperature control strategy corresponding to the target machine room according to the energy consumption test result corresponding to each temperature test node. The temperature of the target machine room is subsequently controlled according to the temperature control strategy, the energy consumption of the target machine room is further reduced, and the energy use efficiency of the target machine room is improved.

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.

The application discloses a construction method and system of an asset value model in a digital twin engine, characterized in that the method comprises the following steps: step 1, measuring the importance of business based on a business model of building energy consumption assets, and constructing a first asset value model according to the business importance; step 2, establishing a correlation between the building energy consumption assets and productivity, and constructing a second asset value model according to the productivity loss caused by the building energy consumption assets; and step 3, calculating the energy use efficiency of the building according to optimal decision variables, and predicting the degradation index of the assets through the energy use efficiency, the first asset value model and the second asset value model to generate a maintenance scheme of the assets. The application has a clever concept and excellent effect, and can realize accurate evaluation of asset value by establishing the correlation between the assets and different businesses and different efficiencies, so that the optimal and most accurate asset maintenance scheme is generated.

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.

The invention discloses an electric vehicle charging station load prediction and schedulable resource aggregation method and system based on a CNN-TSMIX model, and relates to the field of electric vehicle charging station energy management. Load data of a plurality of charging stations are collected, and data preprocessing is carried out; training a global model by using the preprocessed data; training a local model suitable for each charging station by finely adjusting global model parameters; predicting the power of each time point under each charging station and the number of various automobiles at each time point by using the established local model in a rolling updating mode; and establishing a consumer psychological model, evaluating the flexible resource adjustment potential of each charging station by using the obtained predicted value, and aggregating the flexible resource adjustment potential into a virtual power plant according to a rule. According to the invention, high-precision prediction of the load of the charging station is realized. Moreover, the energy use efficiency of the charging station is optimized, and a virtual power plant is formed to provide services for a power grid on the premise that the charging experience of an electric vehicle user is not affected.

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 heating cables, in particular to a heating cable energy consumption analysis system which comprises an energy consumption data acquisition module, an abnormal energy consumption identification module, an entropy analysis module and a cable energy efficiency optimization module. Through real-time and multi-point data acquisition, the system can carefully monitor cable energy consumption, provide accurate energy consumption statistical overview and carefully analyze the total energy consumption in different time periods, energy consumption abnormity can be effectively identified and analyzed, and an abnormally high or low energy consumption value can be quickly and accurately positioned. And distribution of abnormal points is deeply known through monitoring data of the insulating layer, so that finding of a potential cable energy consumption problem, calculation of an information entropy value and continuous monitoring of a data change trend are facilitated, a system is helped to deeply analyze uncertainty and disorder degree of cable data, an energy efficiency loss area is accurately identified, a basis is provided for energy efficiency management, and the energy efficiency management efficiency is improved. The system is allowed to better evaluate and optimize the energy efficiency of the heating cable, so that the energy utilization efficiency is improved, and the energy consumption cost is reduced.

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.

The invention relates to the technical field of energy-saving cabinets, in particular to a more energy-saving energy-saving cabinet with a protection function, which comprises a cabinet body assembly, and the cabinet body assembly is composed of a mounting cabinet, a top plate component and two lifting lug components; a frequency conversion assembly, a microprocessor assembly, a power-on and power-off device, a sensor array device and an Internet of Things communication module are installed in the installation cabinet. The installation cabinet is provided with a display and interaction module. According to the application, accurate management and optimization of power or other energy consumption are realized through the Internet of Things technology, the energy use efficiency is improved, and meanwhile, the equipment safety is guaranteed; according to the energy-saving device, the energy use condition of the device can be monitored in real time during operation, the working state of the device is automatically adjusted according to a preset energy-saving strategy, effective energy saving is achieved, automatic disconnection of a circuit protection component can be achieved in time in an emergency, automatic connection can be achieved during safety, and a circuit and the device can be fully protected.

The invention discloses a multi-source sensor data acquisition method and device of an intelligent energy-saving system, and relates to the field of sensor Internet of Things. The data acquisition method comprises the following steps: S1, monitoring and acquiring environmental parameters and energy consumption parameters of the energy-saving system through multiple types of sensors; s2, transmitting the collected data to a local edge calculation module, and preprocessing the data; s5, identifying different source data according to the annotations of different types of sensor data, and associating and combining the multi-source data through the characteristic quantity; and S6, according to an association result of the data, constructing an overall situation of the energy-saving system, and forming a global view. According to the invention, the constructed high-precision sensor network has excellent real-time data acquisition capability, fine monitoring, dynamic analysis and on-demand regulation and control of the building energy system are realized, the energy use efficiency is obviously improved, and the energy management system has stronger load prediction, fault early warning and self-optimization capabilities.

The application discloses a kind of based on multi-layer clustering strategy's adjustable resource capacity evaluation method and system of distribution transformer area, and relates to the technical field of power demand side management.The method comprises: using equivalent thermal parameter model to model the adjustable resource of distribution area, and implementing multi-layer clustering, multiple control is carried out to the adjustable resource cluster formed after multi-layer clustering, whether the three-phase load of distribution transformer area is balanced is judged, if balanced, three-phase load balance control scheme is executed, if unbalanced, three-phase load imbalance control scheme is executed;According to the result of control scheme, user comfort, user willingness and user controllability are combined to build adjustable resource capacity evaluation model, and the adjustable capacity of adjustable resource is evaluated.The method and system of the application not only improve the regulation ability and stability of power grid, but also meet the balance between user demand and power grid efficiency, maximize energy use efficiency, and help reduce energy consumption.

The invention discloses a multi-dimensional evaluation method, system and device for an electricity-carbon coupling system and a storage medium, and the method comprises the steps: collecting multi-source data of an electric power region, carrying out the preprocessing of the multi-source data, and constructing an electric power and external influence combined data set; the method comprises the following steps: extracting weather, day type and load combinations in each time period in a power and external influence joint data set, and clustering combinations with consistent load change directions, marking a lifting interval and frequency, screening typical modes, and forming a load trend segmentation identification set; acquiring multi-energy output data, matching the multi-energy output data with a load change direction, identifying an abnormal time period based on energy proportion fluctuation, and classifying according to dominant energy to generate an energy dominant type power-carbon offset interval set; and matching the load rising and falling interval in the load trend segmentation identification set with a dominant path in the energy-dominant electrical-carbon offset interval set, optimizing path parameters and generating an electrical-carbon coupling transfer structure table. The energy use efficiency and the carbon emission control precision can be improved.

The present application relates to the field of image processing, and it relates to an energy use efficiency evaluation method in IBMS based on image recognition, which solves the problem that the traditional static energy efficiency evaluation is disconnected with the actual dynamic human consumption. The method comprises the following steps: synchronously collecting floor energy consumption power and image data; calculating camera area stability weight (first weight) based on image change pixel fluctuation; extracting personnel distribution through frame difference and connected domain clustering, and generating density weight (second weight) according to the reciprocal of the average distance between geometric centers of classes; verifying and excluding noise by using the continuity of the positions of classes in front and back frames, and determining the flow weight (third weight) by the effective personnel pixel ratio; fusing the three weights and calculating dynamic energy consumption parameters in real time by using real-time energy consumption; and judging the energy efficiency rationality by the variance fluctuation of the parameters. The present application realizes the accurate evaluation of the dynamic correlation between human and energy.

This invention relates to the technical field of data center energy management, and discloses a cloud computing-based energy-saving control method and system for data centers. It utilizes a multi-dimensional sensor network to collect real-time equipment operation data, establishes a power consumption prediction model and an energy allocation optimization model, extracts key features using principal component analysis, constructs a multivariate regression model to determine the energy efficiency coefficient, and employs a long short-term memory neural network to predict future power demand. A genetic algorithm is used to optimize the energy allocation scheme, achieving precise power control and dynamic balance. This invention also establishes an adaptive energy management decision-making mechanism, analyzing energy consumption trends through a sliding window, initiating emergency adjustment procedures, and using a Kalman filter algorithm to correct state estimates and dynamically adjust prediction model parameters. This method enables intelligent management of data center energy utilization, effectively reducing overall energy costs, improving energy efficiency, and providing technical support for energy conservation and emission reduction in large data centers.

The present application relates to the field of artificial intelligence technology, specifically to a building carbon nerve center adaptive AI control system, the system comprises a carbon perception analysis module, an optimization strategy calculation module, a structure energy efficiency mapping module, a space-time matching screening module and a regulation and control fusion module.In the present application, by perceiving the building environment and equipment state, the energy consumption density and carbon emission path data are extracted, the energy flow and carbon flow continuity are analyzed, the high energy consumption area and low efficiency area are accurately calibrated, the optimization is provided with quantitative basis, the area recognition accuracy is improved, the energy efficiency difference significant section is demarcated, through space-time joint screening, the relevance of carbon emission index and environmental parameters is strengthened, the stable performance area is identified, the adaptability and sustainability of the optimization path are improved, the building as a whole is promoted to develop in the direction of energy saving, low carbon and high efficiency, the continuous optimization in the dynamic environment is realized, the energy use efficiency and carbon emission control response rate are improved, and the intelligentialization and foresight of building operation and maintenance are enhanced.

The invention discloses an expressway load side intelligent control method and system and a medium, mainly relates to the technical field of load side intelligent control, and is used for solving the problems that most of the existing technical schemes are concentrated on data acquisition and monitoring, an efficient intelligent scheduling and pre-response mechanism is lacked, and expressway load fluctuation is difficult to deal with. Comprising the following steps: performing short-term prediction by using analysis data to obtain a predicted value of non-schedulable data in a preset control time period; obtaining constraint conditions of the target function and the schedulable data; wherein the objective function comprises calculation formulas of power production cost, carbon emission and energy use efficiency, and the calculation formulas comprise analysis data; and inputting the predicted value into an objective function, and performing multi-objective optimization scheduling on the objective function by using a genetic algorithm and a particle swarm optimization algorithm to obtain a specific regulation value of the schedulable data.