通信数据包大小对PLC单位能耗的影响分析数据

本研究聚焦于分析通信数据包大小对PLC单位能耗的影响，揭示了通信数据包大小与PLC单位能耗之间的定量关系。企业可通过该数据分析不同通信数据包大小设置下PLC的能耗变化规律，从而优化控制策略和操作参数，提高能源利用效率并降低运行成本。该数据可为智能制造领域的科研人员、技术开发团队、设备维护工程师以及节能管理人员提供重要支持，助力他们围绕PLC能耗优化、性能提升及绿色制造等方向开展预测分析、机理研究、节能评估和技术改进工作。通过科学调整通信数据包大小，不仅可以实现节能减排目标，还能提升生产系统的整体效能，为智能工厂的可持续发展提供有力支撑。1.数据采集：记录不同通信数据包大小下的PLC单位能耗测试数据，具体包括测试编号、测试时间、通信数据包大小/KB、PLC单位能耗/W等字段。
2.数据预处理：(1)对采集的数据进行去噪处理，确保数据准确性。(2)把历史采集的数据（包含本次采集）进行聚合，形成数据集X，并针对数据集X中的PLC单位能耗字段，计算出其平均值。
3.计算线性回归斜率a和截距b：基于数据集X（以通信数据包大小为自变量、PLC单位能耗为因变量），运用SLOPE函数和INTERCEPT函数，基于最小二乘法原理确定斜率a和截距b。斜率a表示单位通信数据包大小变化对PLC单位能耗的影响程度，截距b表示基准通信数据包大小下PLC的单位能耗。
4.结果运用：（1）计算比例系数k：k=|a/PLC单位能耗平均值|×100%；（2）若k≥10%，则判定为“高影响”，若5%≤k＜10%，则判定为“中影响”，若k＜5%，则判定为“低影响”。

This study focuses on analyzing the impact of communication packet size on the unit energy consumption of PLCs, and reveals the quantitative relationship between communication packet size and PLC unit energy consumption. Enterprises can use this data to analyze the energy consumption variation rules of PLCs under different communication packet size settings, thereby optimizing control strategies and operating parameters, improving energy utilization efficiency and reducing operating costs. This dataset can provide important support for researchers, technology development teams, equipment maintenance engineers, and energy conservation management personnel in the field of intelligent manufacturing, enabling them to carry out predictive analysis, mechanism research, energy conservation evaluation, and technical improvement work focused on PLC energy consumption optimization, performance improvement, and green manufacturing. By scientifically adjusting the communication packet size, not only can the goals of energy conservation and emission reduction be achieved, but also the overall efficiency of the production system can be improved, providing strong support for the sustainable development of smart factories.
1. Data Collection: Record the test data of PLC unit energy consumption under different communication packet sizes, specifically including fields such as test number, test time, communication packet size/KB, and PLC unit energy consumption/W.
2. Data Preprocessing: (1) Denoise the collected data to ensure data accuracy. (2) Aggregate the historically collected data (including this collection) to form dataset X, and calculate the average value of the PLC unit energy consumption field in dataset X.
3. Calculation of Linear Regression Slope a and Intercept b: Based on dataset X (taking communication packet size as the independent variable and PLC unit energy consumption as the dependent variable), use the SLOPE and INTERCEPT functions to determine the slope a and intercept b based on the principle of least squares. The slope a represents the degree of influence of unit changes in communication packet size on PLC unit energy consumption, and the intercept b represents the PLC unit energy consumption under the baseline communication packet size.
4. Result Application: (1) Calculate the proportional coefficient k: k = |a / average PLC unit energy consumption| × 100%; (2) If k ≥ 10%, it is judged as "High Impact"; if 5% ≤ k < 10%, it is judged as "Medium Impact"; if k < 5%, it is judged as "Low Impact".

搜集汇总

背景与挑战

背景概述

该数据集分析了通信数据包大小对PLC单位能耗的影响，包含671条测试数据，每日更新，适用于优化PLC能耗和智能制造研究。