Smart, interconnected products are transforming the industry. Construction is a paradigmatic example of digital transformation incorporating systems to improve safety and productivity. This researc...

The Viable Smart Product Model: Designing Products that Undergo Disruptive Transformations

The greenhouse system is based on Internet of things with sensor devices to detect temperature,relative humidity,light intensity,soil moisture,volume fraction of CO2 and other parameters.Datas collected by Zig Bee wireless sensor networks(WSNs) and then transmitted to remote monitoring center through wireless gateway which is based on STM32F103RBT6.Message warning can be carried out in greenhouse environment by analysis of received parameters,and PC or mobile phone terminal can get real-time state information of greenhouse and remote control irrigation device,natural ventilation,shading and other equipments of greenhouse realize intelligent management include automatic monitoring and automatic control.

Design of greenhouse system based on Internet of things

Data are playing an increasingly important role in the development of industry–education cooperation strategies in vocational education and training. The objective of this study was to promote the comprehensive progress of an industry–education cooperation system and improve the effect of the application of big data technology in this system. First, we designed of a big data technology application in an intelligent management platform system for industry–education cooperation. Second, we analyzed the synthetical design of the system. Finally, we optimized and designed a support vector machine (SVM) data mining (DM) algorithm model based on big data, and evaluated the model. The results revealed that the designed algorithm model provides outstanding advantages compared with similar algorithm models. In general, the highest average computation time of the designed SVM algorithm model is about 95 ms. The overall average calculation time linearly decreases around 200 iterations and tends to be stable, and the lowest overall average computation time is about 20 ms. In the DM process, the highest accuracy rate of the model is about 97%, and the lowest is about 92%. The DM accuracy rate is always stable as the number of iterations of the model continues to increase. The designed model slowly increases the occupancy rate of the system in the process of increasing computing time. At about 60 min, the system occupancy rate of the model tends to be stable, and the highest is maintained at about 23%. This study not only provides technical support for the optimization of DM algorithms with big data technology, but also contributes to the integrated development of industry–education cooperation systems.

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Design of Intelligent Management Platform for Industry–Education Cooperation of Vocational Education by Data Mining

Abstract Predictive maintenance (PdM) cannot only avoid economic losses caused by improper maintenance but also maximize the operation reliability of product. It has become the core of operation management. As an important issue in PdM, the time between failures (TBF) prediction can realize early detection and maintenance of products. The reliability information is the main basis for TBF prediction. Therefore, the main purpose of this paper is to establish an intelligent TBF prediction model for complex mechanical products. The reliability information conversion method is used to solve the problems of reliability information collection difficulty, high collection cost and small data samples in the process of TBF prediction based on reliability information for complex mechanical products. The product reliability information is fully mined and enriched to obtain more reliable and accurate TBF prediction results. Firstly, the Fisher algorithm is employed to convert the reliability information to expand the sample, and the compatibility test is also discussed. Secondly, BP neural network is used to realize the final prediction of TBF, and PSO algorithm is used to optimize the initial weight and threshold of BP neural network to avoid falling into local extreme value and improve the convergence speed. Thirdly, the mean-absolute-percentage-error and the Coefficient of determination are selected to evaluate the performance of the proposed model and method. Finally, a case study of TBF prediction for a remanufactured CNC milling machine tool (XK6032-01) is studied in this paper, and the results show that the feasibility and superiority of the proposed TBF prediction method. 

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A machine learning-based approach for product maintenance prediction with reliability information conversion

The concept of "Digital Transformation 2030", which defines the national goals and strategic objectives of the development of the Russian Federation for the period up to 2030, specifies specialized goals and objectives that are an important message for the introduction of intelligent information management technologies in the electric power industry. The main challenges for the transition to digital transformation are the increase in the rate of growth of tariffs for the end consumer, the increasing wear and tear of the network infrastructure, the presence of excessive network construction and the increase in requirements for the quality of energy consumption. The determining factor in the possibility of developing an effective energy policy is the forecasting of electricity consumption using artificial intelligence methods. One of the methods for implementing the above is the development of an artificial neural network (ANN) to obtain an early forecast of the amount of required (consumed) electricity. The obtained predictive values open up the possibility not only to build a competent energy policy by increasing the energy efficiency of an energy company, but also to carry out specialized energy-saving measures in order to optimize the organization’s budget. The solution to this problem is presented in the form of an artificial neural network (ANN) of the second generation. The main advantages of this ANN are its versatility, fast and accurate learning, as well as the absence of the need for a large amount of initial da-ta for a qualitative forecast. The ANN itself is based on the classical neuron and the error back-propagation method with their further modification. The coefficients of learning rate and sensitivity have been added to the error backpropagation method, and the coefficient of response to anomalies in the time series has been introduced into the neuron. This made it possible to significantly improve the learning rate of the artificial neural network and improve the accuracy of predictive results. The results presented by this study can be taken as a guideline for energy companies when making decisions within the framework of energy policy, including when carrying out energy saving measures, which will be especially useful in the current economic realities.

Intelligent Forecasting of Electricity Consumption in Managing Energy Enterprises in Order to Carry out Energy-Saving Measures

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The research on Intelligent Monitoring System

Prediction method of electricity stealing behavior based on multi-dimensional features and BP neural network

Research on monitoring technology of power stealing behavior in bitcoin mining based on analyzing electric energy data

Research on comprehensive diagnosis model of anti-stealing electricity based on big data technology

With the promotion and popularization of national distributed photovoltaic projects, more and more residential users install distributed photovoltaic, which increases the income for residential users. However, for power companies, it is difficult to find out whether there are problems in the operation of photovoltaic equipment and whether users increase capacity privately during photovoltaic operation. Starting with the low-voltage distributed photovoltaic acquisition scheme, this paper establishes a low-voltage photovoltaic private capacity increase diagnosis model, so as to actively identify abnormal power generation and ensure the stability and safety of power grid operation.

https://iopscience.iop.org/article/10.1088/1742-6596/2488/1/012041/pdf

Ying Shang

Papers

The research on Intelligent Monitoring System

Prediction method of electricity stealing behavior based on multi-dimensional features and BP neural network

Research on monitoring technology of power stealing behavior in bitcoin mining based on analyzing electric energy data

Research on comprehensive diagnosis model of anti-stealing electricity based on big data technology

Research on diagnosis model of low-voltage distributed photovoltaic users increasing power generation without authorization