Process models are frequently used in software engineering to describe business requirements, guide software testing and control system improvement. However, traditional process modeling methods often require the participation of numerous experts, which is expensive and time-consuming. Therefore, the exploration of a more efficient and cost-effective automated modeling method has emerged as a focal point in current research. This article explores a framework for automatically generating process models with multi-agent orchestration (MAO), aiming to enhance the efficiency of process modeling and offer valuable insights for domain experts. Our framework MAO leverages large language models as the cornerstone for multi-agent, employing an innovative prompt strategy to ensure efficient collaboration among multi-agent. Specifically, 1) Generation: The first phase of MAO is to generate a slightly rough process model from the text description; 2) Refinement: The agents would continuously refine the initial process model through multiple rounds of dialogue; 3) Reviewing: Large language models are prone to hallucination phenomena among multi-turn dialogues, so the agents need to review and repair semantic hallucinations in process models; 4) Modifying: The agents utilize external tools to test whether the generated process model contains format errors, and then adjust the process model to conform to the output paradigm. The experiments demonstrate that the process models generated by our framework outperform existing methods and surpass manual modeling by 89%, 61%, 52% and 75% on four different processes, respectively.

The proliferation of wireless sensors enables the effective collection of human behavior trajectories, especially in the smart home domain. Studying human behavior patterns can not only improve the design of products and services but also assist healthcare professionals in better managing and treating illnesses. However, existing research on human behavior patterns suffers from non-visualization of the analysis process and time-consuming issues. In this paper, we employ graph convolutional neural networks for the analysis of human behavior patterns based on PIR sensor networks. Specifically, 1) we preprocess the human location data collected by sensors and create a directed graph that visually represents the movement of people within their homes; 2) we use graph convolutional networks to extract features from each graph and incorporate the time proportion of each room into the features to enhance the clustering accuracy; 3) we utilize the K-Means algorithm to cluster different directed graphs, and present distinct behavior patterns of individuals during different time intervals in a calendar format for clear visualization. The experiments demonstrate that our method effectively distinguishes between different human behavior patterns indoors, allowing to us detect patterns happening on special days.

Based on information contained in the third population census and the second industrial investigation, and illustrated by maps, this paper discusses the conceptual design, methodology and procedures used in the establishment and application or the geographic mapping database. The statistical cartographic expert system is specifically introduced as an application.