# Real-Time Visualization Optimization Management Simulation of Big Data Stream on Industrial Heritage Cloud Platform

Authors: Mengya Gao
Journal: Complexity
 (2020)

---

## Abstract

Recently, the development and utilization of industrial heritage resources by using big data has gradually attracted attention. This paper proposes a real-time visualization optimization management simulation of an industrial heritage cloud platform, which realizes the high reliability and diversified storage and utilization of industrial big data by the cloud data distributed storage subsystem. The big data prediction model of the GRU neural network based on a spark distributed framework is constructed to realize the prediction of industrial genetic data. Finally, visualization technology can provide information supporting for industrial production by displaying effective information intuitively. The model’s effectiveness and reliability are verified by simulation.


---

## Body

## 1. Introduction

China’s industrial development level has reached a new stage due to the gradual improvement and rapid development of today’s social and economic system [1]. In order to better adapt to the industrial development environment of modern society, it is imperative to do a good job of industrial heritage information analysis, which is also an important research topic of modern industrial enterprise development [2, 3]. The protection and management of industrial heritage data is of great significance for enterprises to better analyse the industrial development history and the evolution process of industrial development [4]. Therefore, according to the existing big data information technology application, it is necessary to do a good job in the management and integration of industrial heritage resources, so as to provide effective industrial history development content for the development of modern industrial enterprises and make the modern industrial development conform to the development of the social system. The basic demands are of great significance to guide the industrial development [5]. Especially, in the era of big data, people’s workload of processing information is increasing [6]. Therefore, in order to effectively save the time of information processing and improve the efficiency of information processing, more and more fields begin to use visual methods to process data, and the main emphasis is on the comprehensive processing of some large-scale information [7]. The advent of the big data era is both an opportunity and a challenge for information processing. Therefore, relevant staff should strengthen the development of visualization technology so as to provide technical support for actual data processing [8].At present, many scholars at home and abroad have carried out certain research and exploration on the application of industrial big data at the level of technical tools. The literature [9] has sorted out the current situation of the use of big data in industrial enterprises in the world, elaborated and summarized the current use needs of industrial big data and the difficulties to be overcome, and pointed out the key direction of its application research in the future. The paper elaborates the importance of industrial big data for the development of intelligent manufacturing, as well as the current development status of industrial big data in this field, and puts forward the current lack of industrial data standards, data security problems, and future development suggestions. The literature [10] studies the application status of big data technology in industrial production workshop and summarizes the importance of big data technology in this field. The literature [11] analyses the current use of big data technology in industrial and mining enterprises from various aspects and analyses the challenges faced by big data in this field and the development direction in the future. In recent years, as a representative of the LSTM algorithm, the time series data prediction and analysis method based on the deep neural network model has accumulated certain research results. ARIMA (autoregressive integrated moving average model) time series model and other neural network algorithms are used to mine and analyse a kind of time series data [12]. However, the ARIMA method has strict requirements for data conditions and needs to meet the stability of the target data and cannot process the nonlinear information in the data to a certain extent. In the literature, the algorithm of combining CNN (volatile neural networks) and RNN (recurrent neural networks) is used to transform keystroke time data into keystroke vectors so as to complete the learning of personal keystroke vector sequence data [13, 14]. In the literature [15], an improved RNN model based on the time series decomposition backward propagation algorithm is proposed, and a RNN prediction model based on time series decomposition is established, which improves the accuracy of prediction [16]. Although the RNN algorithm has a certain memory ability for the input of the past time, it cannot solve the problem of gradient vanishing or gradient explosion [17]. The LSTM neural network algorithm is used to predict the bus time from the starting point to the target location [18]. A prediction model based on RNN for obtaining information is proposed, which can achieve high accuracy prediction for messages [19]. Although the LSTM algorithm overcomes the problems of RNN gradient vanishing and gradient explosion, its structure is too complex and the model parameters are too many, so the training time is doubled. Especially in the face of large-scale data set analysis and processing requirements, this method is difficult to meet the actual computing speed requirements [20, 21].Therefore, in order to solve the practical application problems of the above industrial big data technology and the performance problems of data mining algorithms, this paper constructs a small private cloud platform, which is built on the current mainstream Hadoop distributed computing platform. At the same time, to solve the algorithm performance problems of cloud platform analysis, it uses the GRU (gated recurrent unit) recurrent neural network combined with a spark distributed computing engine to realize the prediction and analysis of industrial time series big data, and the effective information is displayed by data visualization method. The simulation shows the effectiveness of the visualization platform.


## 2. Structure and Design of Industrial Heritage Cloud Platform

The industrial heritage big data cloud platform constructed in this paper is an intelligent monitoring and analysis big data cloud platform integrating real-time monitoring and intelligent prediction analysis. Under the background of big industrial data acquisition and high efficiency [22], it is of great significance to solve the problems of traditional industrial data acquisition and high-efficiency data acquisition which has reference significance.


### 2.1. Architecture Design of Cloud Platform

According to the order of data analysis, the big data technology system is divided into three levels: data integration, data processing, and knowledge visualization, and the data processing process is shown in Figure1.Figure 1
Process chart of data mining and visualization on the cloud platform.The data processing process of the industrial big data cloud platform constructed in this paper can be roughly divided into data collection stage, data persistence stage, and data information processing to the final visual decision-making management. Firstly, in the data acquisition stage, the data from multiple data sources are collected into the industrial big data platform constructed in this paper, and the data are stored in different storage modes according to the characteristics and actual needs of different data. Then, in the data analysis stage, through the relevant data analysis tool components, the data are analysed and mined and further store the results. The analysis results of the previous step are finally visualized for the final decision management.On the basis of data processing process, combined with the overall requirements, the overall architecture of cloud platform is designed from the perspective of services required in each stage. The overall level service of the cloud platform is shown in Figure2. Its industrial genetic cloud platform includes data layer, data integration and storage layer, computing layer, data service layer, and application layer.Figure 2
Architecture of the industrial heritage cloud platform.In the service of industrial heritage cloud platform which is shown in Figure2, its work can be described as follows: the sensor equipment transmits the monitoring data to the data processing platform through HTTP, TCP, MQTT, and other transmission methods and stores it in MySQL after analysis, providing support for real-time query. The massive historical data use hive storage warehouse to provide large-scale data support for data mining and analysis of the system. In order to improve the query and analysis speed of the system, the query engine based on memory computing is used in statistical query and data mining analysis of historical data. In the application layer, the output of the big data platform layer is used for chart display. Build a web server platform to provide a visual interface for data display and analysis. The data processing of terminal equipment includes real-time monitoring, statistical query analysis, data mining analysis, and data visualization services.The cloud data distributed analysis subsystem constructed in this paper has the following advantages:(1)
The efficiency of the algorithm: the prediction algorithm based on the GRU threshold recurrent neural network has less parameters and better stability, which is very suitable for the prediction of industrial time series data.(2)
High availability: the GRU threshold recurrent neural network is constructed on the basis of a spark core engine. It makes full use of spark’s memory-based computing characteristics while parallel computing on multiple computers so that the algorithm still has high availability in the environment of large amount of data.(3)
High scalability: the bottom layer of the cloud data distributed analysis subsystem is based on a spark distributed computing framework. In other words, the data analysis subsystem can be integrated with other components of the Hadoop ecosystem, which ensures the high scalability of the cloud platform in future application development.(4)
Support of the cloud platform for other algorithms in the future: in terms of the data analysis algorithm, the cloud data distributed analysis subsystem itself is based on the design of a spark distributed computing platform, which makes it suitable for spark. At the same time, the subsystem supports the development and application of other types of data mining algorithms in the future


### 2.2. Structure Design of Industrial Heritage Cloud Platform Based on Hadoop

The structure of the industrial heritage cloud platform is shown in Figure3. The platform is composed of a master node and a group of task subnodes. In the platform, the master node is responsible for the task scheduling and platform management and each subnode is responsible for the task execution, and after the task is completed, the data processing results are fed back to the master node, and the main node presents the results to the user and completes the serialization operation.Figure 3
Architecture of the industrial heritage cloud platform based on Hadoop technology.Firstly, considering the high reliability and high scalability of the industrial big data cloud platform, the cloud data distributed analysis subsystem is also designed as an independent middleware to provide data analysis services for the cloud platform. Secondly, the cloud data distributed analysis subsystem provides model training and data prediction and analysis functions for the industrial big data cloud platform as a whole. Therefore, the data analysis subsystem provides two core functions: model training and cloud reserved model library. Finally, from the perspective of the overall performance of the industrial big data cloud platform, when facing the data analysis scenario with large amount of calculation and responsible calculation, the traditional single machine computing mode cannot meet the actual production requirements. Therefore, this part builds the data analysis subsystem on the spark platform and takes spark as the core computing engine. This design can make full use of the characteristics of spark parallel computing and memory-based computing advantages to improve the efficiency of data analysis.


## 2.1. Architecture Design of Cloud Platform

According to the order of data analysis, the big data technology system is divided into three levels: data integration, data processing, and knowledge visualization, and the data processing process is shown in Figure1.Figure 1
Process chart of data mining and visualization on the cloud platform.The data processing process of the industrial big data cloud platform constructed in this paper can be roughly divided into data collection stage, data persistence stage, and data information processing to the final visual decision-making management. Firstly, in the data acquisition stage, the data from multiple data sources are collected into the industrial big data platform constructed in this paper, and the data are stored in different storage modes according to the characteristics and actual needs of different data. Then, in the data analysis stage, through the relevant data analysis tool components, the data are analysed and mined and further store the results. The analysis results of the previous step are finally visualized for the final decision management.On the basis of data processing process, combined with the overall requirements, the overall architecture of cloud platform is designed from the perspective of services required in each stage. The overall level service of the cloud platform is shown in Figure2. Its industrial genetic cloud platform includes data layer, data integration and storage layer, computing layer, data service layer, and application layer.Figure 2
Architecture of the industrial heritage cloud platform.In the service of industrial heritage cloud platform which is shown in Figure2, its work can be described as follows: the sensor equipment transmits the monitoring data to the data processing platform through HTTP, TCP, MQTT, and other transmission methods and stores it in MySQL after analysis, providing support for real-time query. The massive historical data use hive storage warehouse to provide large-scale data support for data mining and analysis of the system. In order to improve the query and analysis speed of the system, the query engine based on memory computing is used in statistical query and data mining analysis of historical data. In the application layer, the output of the big data platform layer is used for chart display. Build a web server platform to provide a visual interface for data display and analysis. The data processing of terminal equipment includes real-time monitoring, statistical query analysis, data mining analysis, and data visualization services.The cloud data distributed analysis subsystem constructed in this paper has the following advantages:(1)
The efficiency of the algorithm: the prediction algorithm based on the GRU threshold recurrent neural network has less parameters and better stability, which is very suitable for the prediction of industrial time series data.(2)
High availability: the GRU threshold recurrent neural network is constructed on the basis of a spark core engine. It makes full use of spark’s memory-based computing characteristics while parallel computing on multiple computers so that the algorithm still has high availability in the environment of large amount of data.(3)
High scalability: the bottom layer of the cloud data distributed analysis subsystem is based on a spark distributed computing framework. In other words, the data analysis subsystem can be integrated with other components of the Hadoop ecosystem, which ensures the high scalability of the cloud platform in future application development.(4)
Support of the cloud platform for other algorithms in the future: in terms of the data analysis algorithm, the cloud data distributed analysis subsystem itself is based on the design of a spark distributed computing platform, which makes it suitable for spark. At the same time, the subsystem supports the development and application of other types of data mining algorithms in the future


## 2.2. Structure Design of Industrial Heritage Cloud Platform Based on Hadoop

The structure of the industrial heritage cloud platform is shown in Figure3. The platform is composed of a master node and a group of task subnodes. In the platform, the master node is responsible for the task scheduling and platform management and each subnode is responsible for the task execution, and after the task is completed, the data processing results are fed back to the master node, and the main node presents the results to the user and completes the serialization operation.Figure 3
Architecture of the industrial heritage cloud platform based on Hadoop technology.Firstly, considering the high reliability and high scalability of the industrial big data cloud platform, the cloud data distributed analysis subsystem is also designed as an independent middleware to provide data analysis services for the cloud platform. Secondly, the cloud data distributed analysis subsystem provides model training and data prediction and analysis functions for the industrial big data cloud platform as a whole. Therefore, the data analysis subsystem provides two core functions: model training and cloud reserved model library. Finally, from the perspective of the overall performance of the industrial big data cloud platform, when facing the data analysis scenario with large amount of calculation and responsible calculation, the traditional single machine computing mode cannot meet the actual production requirements. Therefore, this part builds the data analysis subsystem on the spark platform and takes spark as the core computing engine. This design can make full use of the characteristics of spark parallel computing and memory-based computing advantages to improve the efficiency of data analysis.


## 3. Research on Big Data Mining Algorithm Based on GRU Network

### 3.1. Data Mining Model Based on GRU

In short-term industrial data forecasting, historical load series is the most important input. It contains rich information to forecast the future load demand and can represent the potential law of load demand. When the traditional machine learning method and single DNN are used to process historical load data, it is necessary to manually select relevant features from the original data, such as the load value of the previous hour and the load value of the same time of the previous day. The features selected according to the correlation will destroy the potential internal relations in the historical load series and affect the accuracy of the prediction results, and this feature selection method also increases the difficulty of prediction. The GRU neural network can effectively avoid this problem. Its internal gating cycle structure can automatically learn the relevant features from historical load data without manual extraction, which makes the prediction method more simple and easy to implement, and can also improve the prediction accuracy. In addition to the historical load series, short-term load forecasting is also affected by weather, holidays, dates, and other factors. The internal potential laws of these features are not obvious, so they are not suitable for the input of the GRU neural network. DNN can effectively deal with these external factors and learn the relationship between these characteristics and load demand so as to improve the prediction accuracy.The structure of the GRU prediction model is shown in Figure4. The historical data input matrix T1 in the model is the input of the GRU network, and the matrix TN is the output of the GRU network. The description of each layer in the input matrix model composed of other features is as follows.Figure 4
Architecture of the industrial heritage cloud platform based on Hadoop technology.The model is mainly divided into three layers. The first layer is data processing layer, which mainly normalizes the data and discretizes the normalized data into time series data; the second layer mainly extracts the features with small amount of data to reduce the prediction error; the third layer mainly uses the prediction model based on GRU unit to send the data and feature extraction data to this layer, where the prediction of data can be realized. GRU unit is the core part of this model, and the detailed information is designed as follows. (1) Preprocess the historical input data and select the input features. According to the characteristics of historical data, external factors with greater influence are selected as other features. The length of historical load data and external characteristics ism, which needs to be corresponding to each other. In addition, in order to facilitate training, it is necessary to normalize the real value data and code the symbolic data. (2) The GRU model is trained by the training sample set, and the network parameters are optimized and updated to get the prediction model. The input data samples at the time to be predicted are input into the trained model to obtain the load forecasting value at each time. The output of a neuron in GRU networks is calculated as follows:(1)zt=σWzxt+Uzht−1,rt=σWrxt+Urht−1,h˜t=tanhWxt+rt∘Uht+1,ot=ht=zt∘ht−1+1−zt∘h˜t,where ht−1 and ht are the output of hidden layer at time t−1 and t; zt, rt, and ot denote the output of update gate, reset gate, and neutron at time t; and Wz, Uz, Wr, Ur, W, and U represent the weight parameters that we learned during the training process.


### 3.2. Evaluation Index of Model

In the training process, the model parameters are optimized by using the Adam (adaptive motion estimation) [21] optimization algorithm. Adam is a first-order optimization algorithm which can replace the traditional stochastic gradient descent process. In the process of training, the weight and deviation of each neuron node in the network model are updated iteratively to reduce the output value of loss function to the optimal value. The loss function of the model uses the mean square error function:(2)minθ=ΩMAE=1M∑i=1MTeiiT|θ−TeiiT2,where TeiiT|θ and TeiiT represent the predicted engine torque and real engine torque at sample time iT, respectively, i is the length of the i−th time step, and M is the total number of the sample time that are used to train or validate. Ω is the solution space of θ.


### 3.3. Parallel Distributed GRU Prediction Model Based on Spark

In order to speed up the training of the model, the spark distributed parallel computing framework is used in the training of the model. The idea of “divide and conquer” is adopted. The training data are distributed to a specified number of cluster worker nodes through the spark parallel computing framework. Each worker node executes its own according to the logic of the actual task. In the model training task, the driver reduces and averages the weight parameters of each node by setting the average frequency of parameters and redistributes the weight results to each node until the predetermined training target of the model is reached. Figure5 shows the workflow of model parallelization training.Figure 5
Parallel distributed GRU prediction model based on spark.The detailed process is formulated as follows. Spark application communicates with spark cluster through Sparkccmtext in driver program. Spark program applies for the required resources from the cluster resource manager through Sparkccmtext. The cluster resource manager allocates the resources and creates an exciter on each running node. Sprkcontext distributes the program code and corresponding data trained by the model to the running node, and each work node allocates the current allocation the model training task is parallelized. Finally, the results of each node are collected to the driver program to realize the parallel training of the model.


## 3.1. Data Mining Model Based on GRU

In short-term industrial data forecasting, historical load series is the most important input. It contains rich information to forecast the future load demand and can represent the potential law of load demand. When the traditional machine learning method and single DNN are used to process historical load data, it is necessary to manually select relevant features from the original data, such as the load value of the previous hour and the load value of the same time of the previous day. The features selected according to the correlation will destroy the potential internal relations in the historical load series and affect the accuracy of the prediction results, and this feature selection method also increases the difficulty of prediction. The GRU neural network can effectively avoid this problem. Its internal gating cycle structure can automatically learn the relevant features from historical load data without manual extraction, which makes the prediction method more simple and easy to implement, and can also improve the prediction accuracy. In addition to the historical load series, short-term load forecasting is also affected by weather, holidays, dates, and other factors. The internal potential laws of these features are not obvious, so they are not suitable for the input of the GRU neural network. DNN can effectively deal with these external factors and learn the relationship between these characteristics and load demand so as to improve the prediction accuracy.The structure of the GRU prediction model is shown in Figure4. The historical data input matrix T1 in the model is the input of the GRU network, and the matrix TN is the output of the GRU network. The description of each layer in the input matrix model composed of other features is as follows.Figure 4
Architecture of the industrial heritage cloud platform based on Hadoop technology.The model is mainly divided into three layers. The first layer is data processing layer, which mainly normalizes the data and discretizes the normalized data into time series data; the second layer mainly extracts the features with small amount of data to reduce the prediction error; the third layer mainly uses the prediction model based on GRU unit to send the data and feature extraction data to this layer, where the prediction of data can be realized. GRU unit is the core part of this model, and the detailed information is designed as follows. (1) Preprocess the historical input data and select the input features. According to the characteristics of historical data, external factors with greater influence are selected as other features. The length of historical load data and external characteristics ism, which needs to be corresponding to each other. In addition, in order to facilitate training, it is necessary to normalize the real value data and code the symbolic data. (2) The GRU model is trained by the training sample set, and the network parameters are optimized and updated to get the prediction model. The input data samples at the time to be predicted are input into the trained model to obtain the load forecasting value at each time. The output of a neuron in GRU networks is calculated as follows:(1)zt=σWzxt+Uzht−1,rt=σWrxt+Urht−1,h˜t=tanhWxt+rt∘Uht+1,ot=ht=zt∘ht−1+1−zt∘h˜t,where ht−1 and ht are the output of hidden layer at time t−1 and t; zt, rt, and ot denote the output of update gate, reset gate, and neutron at time t; and Wz, Uz, Wr, Ur, W, and U represent the weight parameters that we learned during the training process.


## 3.2. Evaluation Index of Model

In the training process, the model parameters are optimized by using the Adam (adaptive motion estimation) [21] optimization algorithm. Adam is a first-order optimization algorithm which can replace the traditional stochastic gradient descent process. In the process of training, the weight and deviation of each neuron node in the network model are updated iteratively to reduce the output value of loss function to the optimal value. The loss function of the model uses the mean square error function:(2)minθ=ΩMAE=1M∑i=1MTeiiT|θ−TeiiT2,where TeiiT|θ and TeiiT represent the predicted engine torque and real engine torque at sample time iT, respectively, i is the length of the i−th time step, and M is the total number of the sample time that are used to train or validate. Ω is the solution space of θ.


## 3.3. Parallel Distributed GRU Prediction Model Based on Spark

In order to speed up the training of the model, the spark distributed parallel computing framework is used in the training of the model. The idea of “divide and conquer” is adopted. The training data are distributed to a specified number of cluster worker nodes through the spark parallel computing framework. Each worker node executes its own according to the logic of the actual task. In the model training task, the driver reduces and averages the weight parameters of each node by setting the average frequency of parameters and redistributes the weight results to each node until the predetermined training target of the model is reached. Figure5 shows the workflow of model parallelization training.Figure 5
Parallel distributed GRU prediction model based on spark.The detailed process is formulated as follows. Spark application communicates with spark cluster through Sparkccmtext in driver program. Spark program applies for the required resources from the cluster resource manager through Sparkccmtext. The cluster resource manager allocates the resources and creates an exciter on each running node. Sprkcontext distributes the program code and corresponding data trained by the model to the running node, and each work node allocates the current allocation the model training task is parallelized. Finally, the results of each node are collected to the driver program to realize the parallel training of the model.


## 4. Simulation Results and Analysis

### 4.1. Simulation Environment of Cloud Platform

This part mainly introduces the data server and Hadoop cluster server node environment. The relevant environment and configuration information of the data server and cluster server nodes are shown in Table1. All of our simulations are performed on the parallel cloud platform with one GTX-1080Ti card under CUDA9.0 and cudnnv7. In addition, the Sqoop component can realize the mutual conversion between the traditional relational database data and HDFS, HBase and other data, so as to ensure the efficient and safe import and export of data in different systems. The Spring-Boot OpcUa data acquisition server realizes the real-time data acquisition and monitoring management of remote equipment in the factory. Abp Core server is the basis of cloud platform visual management.Table 1
Overall deployment of the cloud platform.
Server typeOSMemory (GB)IP addressOther informationData server 1Windows 7410.33.46.45opc.tcp://10.33.46.45: 4401Data server 2Windows 7410.33.46.96opc.tcp://10.33.46.45: 4401Master nodeCentOS 7.56410.33.46.113Hadoop 2.7.3; Spark 1.5.1Slave node 1CentOS 7.56410.33.46.114Hadoop 2.7.3; Spark 1.5.1Slave node 2CentOS 7.56410.33.46.115Hadoop 2.7.3; Spark 1.5.1


### 4.2. Validation of GRU Evaluation Model

The simulation takes a group as an example, which mainly provides OEM services to famous overseas clothing brand companies. The export revenue has occupied the first place of knitting apparel enterprises for many years and also maintains the first position of export to Japan for many years.Planned production capacity, planned capacity index, service capacity index, timely delivery rate, order fulfilment rate, and average delivery period are the main indicators to evaluate a company. We use industrial heritage data for evaluation and visualization. In order to compare the advantages of our algorithm, we choose to compare it with SVM (support vector machine algorithm). The comparison results are shown in Figure6.Figure 6
Simulation results compared with the SVM algorithm.As can be seen from Figure6, external customers and internal management departments can provide reports intuitively and clearly on this unified interface so as to find out the changes of capacity indicators in time. The system will also rely on the interval early warning information to carry out automatic early warning prompt, meet the statistical data support of predictive scheduling, and monitor the manufacturing capacity under the contract. In addition, we can use the prediction model based on the GRU network to accurately predict the operation status of enterprises, and it is consistent with the actual production data. Compared with the SVM-based algorithm, our algorithm has higher prediction accuracy. The detailed prediction results are shown as in Figure 7.Figure 7
Simulation results compared with the SVM algorithm.From the results, it can be seen that the industrial heritage cloud platform model proposed in this paper has better prediction effect; it can more accurately capture the law of data change, can predict the trend of data change faster, can improve the prediction accuracy, and has high accuracy compared with the SVM prediction algorithm.In addition, the cloud platform of industrial heritage also needs to evaluate the possible risks in the actual production process. This paper mainly shows the risk warning function of the industrial heritage cloud platform, and the results are shown in Figure8.Figure 8
Enterprise operation risk prediction compared with SVM model.Risk early warning was from the material and plan conflict, fabric supply interruption, lack of planning information, abnormal expectations, production progress lag, and other five aspects of early warning. According to the contract number, the system can import production plan data and execution data according to the contract number, count the execution progress of all unfinished orders in real time, realize the automatic early warning of delivery date delay, and estimate the delayed delivery date under the existing constraints. As shown in the figure, our industrial heritage cloud platform can provide real-time early warning and visualization of possible problems in the production process so that enterprise decision makers can adjust their own production plans. In addition, the accuracy of our prediction model is much higher than that based on SVM, which achieves good prediction performance.


### 4.3. Production Data Visualization Based on Cloud Platform

The process flow of products corresponding to different orders is not completely the same, and the operations required by different products in the same process are also different, so the decision and arrangement of production balance are complex. At the same time, the volatility of orders is also obvious. Orders fluctuate frequently due to the influence of seasons, fashion trends, prices, emergencies, and other factors. In addition, the company’s OEM business is also affected by the order fluctuation. Different orders from different enterprises have different order placing time and delivery time, and the product types and quantities presented in the weekly and daily production plans are also different. Considering the order satisfaction rate oriented production balance adjustment, the factors need to be considered comprehensively are more complex, which is a typical mixed loading balance decision-making problem under uncertain environment.It can be seen from the figure that the production data are chaotic before processing, complex and disordered, as shown in Figure9(a). We need to carry out feature extraction and visualization through the cloud platform, and the visualization results are shown in Figure 9(b). Through the full mining of the industrial heritage cloud platform, the relevance of production data began to show. Production data are a complex high-dimensional and multi-dimensional data, which refer to data variables with multi-dimensional attributes. It widely exists in the application based on traditional relational database and data warehouse. The goal of high-dimensional multivariate data analysis is to explore the distribution rules and patterns of high-dimensional multivariate data items and to reveal the implicit relationship between different dimensional attributes. From the visualization results (Figure 9(b)), we can see that the production data mainly includes five aspects (planned production capacity, planned capacity index, service capacity index, timely delivery rate, order fulfilment rate, and average delivery period); we can figure out which leading force plays a leading role at what time and then deal with it according to the actual changes. Simulation results show that our industrial heritage big data platform has good data mining ability.Figure 9
Production data visualization based on the cloud platform. (a) Before processing and (b) after processing.
(a)(b)


## 4.1. Simulation Environment of Cloud Platform

This part mainly introduces the data server and Hadoop cluster server node environment. The relevant environment and configuration information of the data server and cluster server nodes are shown in Table1. All of our simulations are performed on the parallel cloud platform with one GTX-1080Ti card under CUDA9.0 and cudnnv7. In addition, the Sqoop component can realize the mutual conversion between the traditional relational database data and HDFS, HBase and other data, so as to ensure the efficient and safe import and export of data in different systems. The Spring-Boot OpcUa data acquisition server realizes the real-time data acquisition and monitoring management of remote equipment in the factory. Abp Core server is the basis of cloud platform visual management.Table 1
Overall deployment of the cloud platform.
Server typeOSMemory (GB)IP addressOther informationData server 1Windows 7410.33.46.45opc.tcp://10.33.46.45: 4401Data server 2Windows 7410.33.46.96opc.tcp://10.33.46.45: 4401Master nodeCentOS 7.56410.33.46.113Hadoop 2.7.3; Spark 1.5.1Slave node 1CentOS 7.56410.33.46.114Hadoop 2.7.3; Spark 1.5.1Slave node 2CentOS 7.56410.33.46.115Hadoop 2.7.3; Spark 1.5.1


## 4.2. Validation of GRU Evaluation Model

The simulation takes a group as an example, which mainly provides OEM services to famous overseas clothing brand companies. The export revenue has occupied the first place of knitting apparel enterprises for many years and also maintains the first position of export to Japan for many years.Planned production capacity, planned capacity index, service capacity index, timely delivery rate, order fulfilment rate, and average delivery period are the main indicators to evaluate a company. We use industrial heritage data for evaluation and visualization. In order to compare the advantages of our algorithm, we choose to compare it with SVM (support vector machine algorithm). The comparison results are shown in Figure6.Figure 6
Simulation results compared with the SVM algorithm.As can be seen from Figure6, external customers and internal management departments can provide reports intuitively and clearly on this unified interface so as to find out the changes of capacity indicators in time. The system will also rely on the interval early warning information to carry out automatic early warning prompt, meet the statistical data support of predictive scheduling, and monitor the manufacturing capacity under the contract. In addition, we can use the prediction model based on the GRU network to accurately predict the operation status of enterprises, and it is consistent with the actual production data. Compared with the SVM-based algorithm, our algorithm has higher prediction accuracy. The detailed prediction results are shown as in Figure 7.Figure 7
Simulation results compared with the SVM algorithm.From the results, it can be seen that the industrial heritage cloud platform model proposed in this paper has better prediction effect; it can more accurately capture the law of data change, can predict the trend of data change faster, can improve the prediction accuracy, and has high accuracy compared with the SVM prediction algorithm.In addition, the cloud platform of industrial heritage also needs to evaluate the possible risks in the actual production process. This paper mainly shows the risk warning function of the industrial heritage cloud platform, and the results are shown in Figure8.Figure 8
Enterprise operation risk prediction compared with SVM model.Risk early warning was from the material and plan conflict, fabric supply interruption, lack of planning information, abnormal expectations, production progress lag, and other five aspects of early warning. According to the contract number, the system can import production plan data and execution data according to the contract number, count the execution progress of all unfinished orders in real time, realize the automatic early warning of delivery date delay, and estimate the delayed delivery date under the existing constraints. As shown in the figure, our industrial heritage cloud platform can provide real-time early warning and visualization of possible problems in the production process so that enterprise decision makers can adjust their own production plans. In addition, the accuracy of our prediction model is much higher than that based on SVM, which achieves good prediction performance.


## 4.3. Production Data Visualization Based on Cloud Platform

The process flow of products corresponding to different orders is not completely the same, and the operations required by different products in the same process are also different, so the decision and arrangement of production balance are complex. At the same time, the volatility of orders is also obvious. Orders fluctuate frequently due to the influence of seasons, fashion trends, prices, emergencies, and other factors. In addition, the company’s OEM business is also affected by the order fluctuation. Different orders from different enterprises have different order placing time and delivery time, and the product types and quantities presented in the weekly and daily production plans are also different. Considering the order satisfaction rate oriented production balance adjustment, the factors need to be considered comprehensively are more complex, which is a typical mixed loading balance decision-making problem under uncertain environment.It can be seen from the figure that the production data are chaotic before processing, complex and disordered, as shown in Figure9(a). We need to carry out feature extraction and visualization through the cloud platform, and the visualization results are shown in Figure 9(b). Through the full mining of the industrial heritage cloud platform, the relevance of production data began to show. Production data are a complex high-dimensional and multi-dimensional data, which refer to data variables with multi-dimensional attributes. It widely exists in the application based on traditional relational database and data warehouse. The goal of high-dimensional multivariate data analysis is to explore the distribution rules and patterns of high-dimensional multivariate data items and to reveal the implicit relationship between different dimensional attributes. From the visualization results (Figure 9(b)), we can see that the production data mainly includes five aspects (planned production capacity, planned capacity index, service capacity index, timely delivery rate, order fulfilment rate, and average delivery period); we can figure out which leading force plays a leading role at what time and then deal with it according to the actual changes. Simulation results show that our industrial heritage big data platform has good data mining ability.Figure 9
Production data visualization based on the cloud platform. (a) Before processing and (b) after processing.
(a)(b)


## 5. Conclusion

In order to solve the practical application problems of the above industrial big data technology and the performance problems of data mining algorithms, this paper constructs a small private cloud platform, which is built on the current mainstream Hadoop distributed computing platform. At the same time, to solve the algorithm performance problems of cloud platform analysis, it uses the gated recurrent unit recurrent neural network combined with a spark distributed computing engine to realize the prediction and analysis of industrial time series big data, and the effective information is displayed by data visualization method. The simulation shows the effectiveness of the visualization platform compared with the SVM algorithm.



---

*Source: 8885191-2020-10-28.xml*