（文章来源：证券日报） 龙软科技营业收入主要来源于LongRuanGIS智能矿山工业软件及数智化整体解决方案业务，目前公司以 LongRuanGIS时空信息平台为根基，向各行业应用领域拓展，已形成龙软科技工业软件为支撑的自主智 能矿山整体架构及技术体系，覆盖智能矿山建设的关键技术领域，并探索出高级智能化矿井的可行性建 设模式。 据披露，截至报告期末，国家能源局71个智能化煤矿建设示范矿，龙软科技参与建设的透明化地质保障 系统、智能化管控平台、采掘工作面自适应截割、智能化洗选等系统的煤矿数量为46个，市场渗透率领 先。LongRuanGIS全国煤矿装机已超过7万台，累计使用单位超过1800家，市场占有率大于85%。公司 在全国已累计实施地质保障系统120余对矿井、综合管控平台250余对矿井，为矿山的智能化、智慧化建 设提供了有力的技术支撑。 值得一提的是，目前龙软科技的主要收入来源于煤炭行业，面对未来的发展，龙软科技也在积极拓展非 煤行业的市场需求，并将对龙软云GIS结合客户需求进一步优化，加强对智能矿山及非煤矿山、CAE工 业软件的研发力度，进一步完善产品技术体系，积极拓展行业领域；同时加大时空智能技术在矿山 ...

Investment Rating - The industry rating is "Outperform" (maintained) [5] Core Insights - AI is expected to significantly reduce operational costs in the oil and gas sector, with estimates suggesting a potential reduction of approximately $7 per BOE, translating to a decrease of about 17.5% [43] - The coal industry is witnessing advancements in smart mining, with AI applications enhancing disaster warning systems and operational efficiency, potentially increasing profits by 7%-12% and improving ROI by 2%-3% [46] - In the chemical industry, AI is being integrated into material research and intelligent manufacturing, with significant potential for innovation and efficiency improvements [70] Summary by Sections AI in Oil and Gas Exploration - AI is being utilized in geophysical exploration, drilling, and reservoir engineering, with companies like EOG demonstrating a reduction in operational costs through AI integration [12][17][27] - Domestic oil and gas companies are leading in AI deployment, focusing on building high-quality data sets and AI models for exploration and operational optimization [22] AI in Coal Industry - The coal sector is advancing towards smart mining, with companies implementing AI technologies to enhance safety and operational efficiency [44] - The potential for profit enhancement through AI in coal mining is significant, with studies indicating a profit increase of 7%-12% [46] AI in Chemical Industry - AI is being applied in material research and synthesis, with the potential to accelerate the discovery of new materials and optimize production processes [50][70] - The integration of AI in chemical manufacturing is expected to improve production efficiency and quality control, with notable projects already underway [71]