## 6 Results and Discussion
### Data Extraction and Results  
With the development of the Industry 4.0 architecture, each microservice is equipped with a corresponding monitoring mechanism. When a failure occurs, the system can notify relevant personnel and access corresponding information in the meantime. The configuration logic of the database will affect the completeness of information analysis during data collection. We will delete the comparison datasets based on the causal analysis results by concatenating different data tables. The data on the product side are related to external environmental factors, not service system issues, and will not be considered. The problems of the service system may be difficult to identify in the data, and detailed data analysis is required. The MTTF and failure ratio can be defined and measured after data cleaning and concatenation. Table 1 will plan the data arrangement using one of the microservice systems as an example. In the measurement, the data will be arranged by different electronic serial number, and the failure rate of the microservice system will be integrated and analyzed in real time. Furthermore, the data collation list shows the individual failures and accumulated time. In the construction of reliability functions and AI prediction models, the impact of censored data must be evaluated in the simulation of the final system. Table 2 shows the system reliability assessment conclusion constructed by the Kaplan-Meier methodology. Using the Microservice 2 as an example, the censoring code shows the initial mastery of the data acquisition system on the manufacturing status. In the estimation of the survival rate, system data were processed in the discrimination stage, so the estimated value varies according to the characteristics of each microservice. For the estimation analysis, we can analyze the confidence interval of the estimated value with upper and lower bound to define the boundary value. We use the nonparametric MLP model to predict the reliability of the corresponding service after the definition of the reliability function is completed. The results in Table 3 show the predicted reliability of each microservice system. Because  
Figure 8: Procedure of the model development. In the configuration, the values of hidden nodes and conjugate gradient are shown in the setting field. To improve the model's explanatory power, the hidden nodes of microservices are three, and the iterations are 10,000 times. In terms of error evaluation, the analysis of residuals shows that this nonparametric MLP model has a lower error value. In the definition of the AI model, we will use MSE as the loss function to confirm the accuracy of the model. The MSE of this model is within 0.00032 (Table 3), and the average is only 0.00024. However, to clearly define the frequent failure ratio of microservice, we refer to the design documents, PFMEA, DEMEA and historical data to obtain the reliability of each service system [70]. Regarding the design guidelines of Firm B and C, the malfunction defined in the microservice system can be closer to the actual situation. The simulation and analysis results of three main service scenarios are shown in Figure 9. The service provider Firm A must provide four microservices to Firm B and C for production operations. The reliability of the four microservice systems has been defined in related documents and prediction models and is shown in Figure 9. Considering the individual requirement of each company, the system architecture is moving toward modular design in terms of improvement and integration [71]. After partitioning the system with the architecture shown in Figure 5, various microservices deliver different solutions. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Events** & **Service 1** &  
In the original architecture, the cascade of systems will affect the overall reliability of each service, resulting in only 86.79%. Under the demand-oriented design for Firm B, the reliability is greatly improved owing to its parallel structure.