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煤矿智能化建设市场现状及发展趋势研究

Research of the present situation and development trend of intelligent coal mine construction market

  • 摘要: 煤矿智能化建设市场现状及未来发展趋势是行业管理部门、煤炭生产企业、科技支撑企业、其他潜在企业等关注的对象,也是行业内资本投入、技术研发和人才培养的重要指导方向。构建基于生产要素、需求条件、支持性产业、企业战略以及行业机遇和政府支持政策的六维波特钻石模型,宏观分析煤矿智能化建设市场的竞争优势;以2013年—2023年的煤矿数量、产能、采选业固定资产投资及智能化建设总投资、建设成效等历史数据为基础,选取数量指标、产能指标和投资指标等不同维度的数据,计算煤矿智能化建设市场渗透率并进行曲线拟合,根据煤炭企业规模集中度、煤炭产能分布集中度和煤炭企业属性集中度对拟合的曲线进一步修正;为进一步衡量煤矿智能化建设市场活力,以国家首批71处智能化示范煤矿为样本,预测2025年—2035年煤矿智能化市场容量,在难以全面精准获得煤矿智能化全部子系统历年市场数据的情况下,利用SAC、SAM、SAP 3项产品市场统计数据对煤矿智能化市场分析及预测结果进行可靠性验证;根据政策导向、技术现状和建设实际,提出了煤矿智能化建设市场发展萌芽破土、培育试点、示范建设、全面建设和高级智能5个阶段,基于杰弗里摩尔鸿沟理论,分析了智能化建设各阶段特征及早期市场和主流市场特点,发掘了煤矿智能化建设市场鸿沟及其产生因素,给出跨越鸿沟的具体措施。结果表明:我国煤矿智能化建设市场具有强且持续的竞争力;2023年市场渗透率约为4.74%,预计至2026年渗透率将达10%,是社会资本投入黄金时期,可用较低投入撬动未来较高市场份额;煤矿智能化建设市场容量将于2025年突破3200亿元,2035年将累计达到万亿级;当前煤矿智能化建设处于早期市场和主流市场之间的鸿沟区,预计用2—3年时间将完成鸿沟跨越。

     

    Abstract: The current situation and future development trend of the intelligent construction market in coal mines are the focus of attention for industry management departments, coal production enterprises, technology support enterprises, and other potential enterprises. It is also an important guiding direction for capital investment, technology research and development, and talent cultivation in the industry. Construct a six dimensional Porter diamond model based on production factors, demand conditions, supporting industries, enterprise strategies, industry opportunities, and government support policies to macroscopically analyze the competitive advantages of the coal mine intelligent construction market; Based on historical data such as the number of coal mines, production capacity, fixed assets investment in mining and dressing industry, total investment in intelligent construction, and construction results from 2013 to 2023, select data in different dimensions such as quantity indicators, production capacity indicators, and investment indicators, calculate the market penetration rate of intelligent construction of coal mines and perform curve fitting, and further modify the fitted curve according to the concentration of coal enterprise scale, concentration of coal production capacity distribution, and concentration of coal enterprise attributes; To further measure the market vitality of coal mine intelligent construction, taking the first batch of 71 intelligent demonstration coal mines in China as samples, the market capacity of coal mine intelligent construction from 2025 to 2035 is predicted. In the case where it is difficult to obtain comprehensive and accurate market data of all subsystems of coal mine intelligent construction over the years, SAC, SAM, and SAP product market statistical data are used to verify the reliability of coal mine intelligent market analysis and prediction results; Based on policy guidance, technological status, and actual construction, five stages of the development of the coal mine intelligent construction market have been proposed: budding, cultivating pilot projects, demonstration construction, comprehensive construction, and advanced intelligence. Based on the Jeffrey Moore divide theory, the characteristics of each stage of intelligent construction and the characteristics of early and mainstream markets have been analyzed, and the market gap and its factors in coal mine intelligent construction have been explored. Specific measures to bridge the gap have been proposed. China’s coal mine intelligent construction market has strong and sustained competitiveness; The market penetration rate is expected to reach 10% by 2026, with a market penetration rate of approximately 4.74% in 2023. This is a golden period for social capital investment, and lower investment can be used to leverage higher market share in the future; The market capacity for intelligent construction of coal mines is expected to exceed 320 billion yuan by 2025 and reach trillions of yuan by 2035; The current intelligent construction of coal mines is in a gap area between the early market and the mainstream market, and it is expected to cross the gap in 2—3 years.

     

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