Application of Data Mining in Effect Evaluation of Lean Management

Quantitative evaluation is an important part of enterprise diagnosis, which promotes the scientific and modern management of enterprises. At present, the existing enterprise management evaluation methods cannot complete the mining of enterprise index data, which leads to large error and low significance coefficient in enterprise management evaluation. Therefore, the application of data mining in enterprise lean management effect evaluation is put forward. The process and main functions of data mining are analyzed; data mining algorithm is used to establish the evaluation index system of lean management effect and calculate the index weight. Using the association rules method in data mining, according to the parameters of enterprise lean management level evaluation index and weight value, through the fuzzy set transformation idea, the fuzzy boundary of each index and factor is described by the membership degree, the fuzzy judgment matrix is constructed, and the final evaluation result is obtained by multilayer compound calculation. Experimental results show that this study has a high significance coefficient, and the proposed evaluation method of enterprise lean management effect has ideal accuracy and short time consumption. In practical application, the cumulative contribution rate is higher and has higher stability.

Application of Data Mining in the Evaluation of Enterprise Lean Management Effect

In order to improve the effect of enterprise lean management, this study proposes a lean data mining algorithm based on the characteristics of lean data in enterprise management. This study connects data mining and lean production to study the data of enterprise management operation, proposes an intelligent data processing model suitable for modern enterprise management, and constructs the model function module in combination with the enterprise operation management process. Moreover, this study constructs an evaluation system for the effect of enterprise lean management based on data mining. The system provides a human-computer interaction interface, and operators can use various functions and services provided by the system through a visual interface. Through experimental research, it can be known that the enterprise lean effect evaluation system based on data mining proposed in this study can play an important role in enterprise lean management.

A Management Model of Small- and Medium-Sized Enterprises Based on Deep Learning Algorithm

The traditional management method of small- and medium-sized enterprises (SMEs) has the problem of poor turnover prediction effect. Therefore, this paper proposes a management model of SMEs based on the deep learning algorithm. Firstly, the proposed system analyzes the characteristics of SMEs, and based on the deep learning algorithm, the gating structure of the management of SMEs is designed. The internal structure diagram of the long- and short-term neural network is given, and the circulating neural network model of the management of SMEs is constructed. Finally, the experimental indexes of the management model of SMEs are designed, and the comparative experiments are carried out. The experimental results show that the proposed method is more accurate in predicting the turnover of SMEs and has better management effect on them.

Mountain Rainfall Estimation and BIM Technology Site Safety Management Based on Internet of Things

Mountain rainfall estimation is a major source of information for determining the safety of a geographical (mountainous) area. It can be done easily by using a modeling and simulation application, BIM, which is a building information modeling tool. It helps in transforming the real-time scenarios into the construction and business models. Now, this whole process can be easily realized by the help of an evolving technology known as IoT (Internet of Things). Internet of Things is supposedly going to take over the world by the end of this decade. It will reshape the whole communication architecture. IoT is actually going to be a basis for D2D (Device to Device) communication. Here, the MTC (Machine Type Communications) are going to take place which have almost zero human involvement. Now, in order to overcome the problem that the traditional construction site safety management method is difficult to accurately estimate the rainfall, resulting in poor safety management effect, a mountain rainfall estimation and BIM technology site safety management methods based on Internet of things are proposed. Firstly, based on the Internet of Things data, the limit learning machine method is used to accurately estimate the mountain rainfall. Secondly, based on the rainfall estimation results and combined with BIM technology, the construction site safety and management model is constructed. In the end, experimental verification is carried out. The experimental results show that this method can precisely estimate the rainfall in mountainous areas, and the computational results of safety factor are basically consistent with the actual results, indicating that the safety management effect of this system is good. In this paper, I reveal the complications and drawbacks associated with the ongoing mechanisms used for mountain rainfall estimations and how to overcome them by using the new technology, i.e., Internet of Things.

Desalination Brine Management: Effect on Outfall Design

Recently proposed options for desalination brine management involve blending of brine with a lighter effluent or concentrating the brine prior to discharge, either of which can significantly alter the discharge concentrations of contaminants. We evaluate the effect of these brine management strategies on the design of submerged outfalls used to discharge brine. Optimization of outfall design is considered such that adequate mixing can be provided with minimum cost. Designs with submerged and surfacing plume are considered for outfalls located in shallow coastal regions with small currents (quiescent receiving water is assumed). Pre-dilution with treated wastewater is shown to reduce the outfall cost, whereas pre-dilution with seawater or pre-concentration are shown to result in higher costs than the discharge of brine alone. The effect of bottom slope is also explored and the results suggest that multiport diffusers are better suited than single jets at locations with a mild bottom slope.