Purpose

The purpose is to analyze and discuss the sustainable development (SD) and financing risk assessment (FRA) of resource-based industrial clusters under the Internet of Things (IoT) economy and promote the application of Machine Learning methods and intelligent optimization algorithms in FRA.

Design/methodology/approach

This study used the Support Vector Machine (SVM) algorithm that is analyzed together with the Genetic Algorithm (GA) and Ant Colony Optimization (ACO) algorithm. First, Yulin City in Shaanxi Province is selected for case analysis. Then, resource-based industrial clusters are studied, and an SD early-warning model is implemented. Then, the financing Risk Assessment Index System is established from the perspective of construction-operation-transfer. Finally, the risk assessment results of Support Vector Regression (SVR) and ACO-based SVR (ACO-SVR) are analyzed.

Findings

The results show that the overall sustainability of resource-based industrial clusters and IoT industrial clusters is good in the Yulin City of Shaanxi Province, and the early warning model of GA-based SVR (GA-SVR) has been achieved good results. Yulin City shows an excellent SD momentum in the resource-based industrial cluster, but there are still some risks. Therefore, it is necessary to promote the industrial structure of SD and improve the stability of the resource-based industrial cluster for Yulin City.

Originality/value

The results can provide a direction for the research on the early warning and evaluation of the SD-oriented resource-based industrial clusters and the IoT industrial clusters, promoting the application of SVM technology in the engineering field.

中文翻译：

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基于优化算法的资源型产业集群可持续发展预警与融资风险管理

目的

目的是分析和探讨物联网（IoT）经济下资源型产业集群的可持续发展（SD）和融资风险评估（FRA），促进机器学习方法和智能优化算法在FRA中的应用。

设计/方法/方法

本研究使用了支持向量机 (SVM) 算法，该算法与遗传算法 (GA) 和蚁群优化 (ACO) 算法一起分析。一是选取陕西省榆林市进行案例分析。然后，研究了基于资源的产业集群，并实施了SD预警模型。然后，从建设-运营-转让的角度建立了融资风险评估指标体系。最后，分析了支持向量回归（SVR）和基于ACO的SVR（ACO-SVR）的风险评估结果。

发现

结果表明，陕西省榆林市资源型产业集群和物联网产业集群的整体可持续性良好，基于遗传算法的SVR（GA-SVR）预警模型取得了较好的效果。榆林市资源型产业集群发展势头良好，但仍存在一定风险。因此，有必要推进可持续发展产业结构，提高榆林市资源型产业集群的稳定性。

原创性/价值

研究结果可为面向SD的资源型产业集群和物联网产业集群的预警与评价研究提供方向，推动SVM技术在工程领域的应用。