基于GQFD的无人智能化保障装备能力需求分析

Capability Analysis of Unmanned Intelligent Support Equipment based on GQFD

摘要:

着眼无人智能化保障装备发展问题，为构建智能高效的保障装备体系，在对智能化战争条件下装备保障“任务—能力”进行分析，确定感知战场态势、辅助指挥决策、装备检测维修、物资精准保障4项智能化保障任务和大数据分析处理能力、智能决策能力、多源数据采集能力等9种无人智能化保障装备能力的基础上，运用基于灰色关联分析的质量功能展开方法，建立包含灰色综合关联矩阵的无人智能化保障装备能力需求分析质量屋，得到无人智能化保障装备能力指标重要度排序，大数据分析处理能力重要度为6.8906居于首位，该能力是保障装备开展所有保障活动的基础，符合智能化装备保障“以智取胜”的致胜机理。结果表明该模型具有较好的可行性和适用性，验证了该方法在无人智能化保障装备能力需求分析中的可行性和有效性，为下一步无人智能化保障装备建设规划提供了有力参考。

Abstract:

Focusing on the development of unmanned intelligent support equipment, in order to build an intelligent and efficient support equipment system, the "task-capability" of equipment support under intelligent war conditions is analyzed. On the basis of determining four intelligent support tasks (battlefield situation perception, auxiliary command and decision making, equipment detection and maintenance, accurate material support) and nine unmanned intelligent support equipment capabilities (big data analysis and processing capability, intelligent decision making capability, multi-source data acquisition capability), the quality function expansion method based on grey correlation analysis is applied. A quality house for analysis of the capability requirements of unmanned intelligent support equipment including grey comprehensive correlation matrix was established, and the importance ranking of the capability indicators of unmanned intelligent support equipment was obtained. The importance of big data analysis and processing capability was 6.8906, ranking the first place. This capability is the basis for all support activities of the intelligent support equipment, and conforms to the winning mechanism of intelligent equipment support of "winning by intelligence". The results show that the model has good feasibility and applicability, which verifies the feasibility and effectiveness of the method in the capability requirement analysis of unmanned intelligent support equipment, and provides a strong reference for the next unmanned intelligent support equipment construction planning.