Core Insights - The article discusses the transformation in forest and grassland management through the integration of advanced technology, particularly AI and data systems, to enhance ecological protection efforts [1][4][11] Group 1: Challenges in the Industry - The forest and grassland management sector faces significant challenges, including data being underutilized, with vast amounts of satellite and drone imagery remaining unprocessed [2][3] - There is a lack of coordination among various data systems, leading to inefficiencies in responding to ecological crises such as forest fires and wildlife incidents [2] - Decision-making has historically relied on personal experience and fragmented information, hindering effective ecological restoration and risk assessment [2] Group 2: Technological Solutions - The introduction of a "thinking" digital brain aims to awaken dormant data, connect isolated systems, and transition from experience-based management to precise, data-driven decision-making [4][5] - A comprehensive monitoring network, integrating satellite, drone, infrared cameras, and IoT sensors, has been established to provide real-time insights into ecological conditions [4][7] - The system has demonstrated effectiveness in wildlife management, such as issuing over 13,000 alerts to mitigate human-elephant conflicts in Yunnan [4][5] Group 3: Upgrades in Management Practices - The management model has evolved from a flat, human-dependent approach to a three-dimensional monitoring system, allowing for comprehensive oversight of ecological areas [7] - The regulatory approach has shifted from reactive to proactive, with AI capabilities enabling early fire risk predictions and wildlife alerts [8] - Enforcement accuracy has improved, with real-time monitoring allowing for immediate responses to illegal activities, enhancing the effectiveness of ecological protection efforts [9] - A unified collaborative platform has been established to break down departmental silos, facilitating seamless communication and task management across various levels of governance [10] Group 4: Overall Impact - The transformation is characterized as a silent revolution, enhancing the ability to protect natural resources while minimizing human interference [6][10][11] - The integration of technology is revitalizing the mission of ecological guardianship, allowing for a more intelligent and efficient approach to environmental management [11][12]

我国首个林草行业大模型问世 金十数据4月21日讯，近日，中国林业科学研究院资源信息研究所成功研发我国首个林草行业大模型 ——"林龙大模型"，标志着我国"智慧林草"建设迈入新阶段。 目前，"林龙大模型"在行业文本处理、树 种类型识别、林木表型参数提取、野生动物识别、病虫害监测、林火识别、生态系统评估、经营管理决 策等八大应用场景落地生根，并应用于"三北"工程示范区、国家公园、国有林场等，运行状态稳定可 靠。未来，"林龙大模型"将通过优化升级、迭代更新，进一步深化与林草行业的融合，赋能"智慧林 草"发展。 ...

本报讯（特约记者温雅莉）近日，中国林业科学研究院资源信息研究所成功研发我国首个林草行业 大模型——"林龙大模型"，标志着我国"智慧林草"建设迈入新阶段。 据研发团队负责人、中国林业科学研究院首席科学家张怀清介绍，"林龙大模型"具备五大优势：一 是通过行业文本知识多智能体技术，有效融合林草领域知识，成功弥补了通用大模型在林草行业知识方 面的缺陷，使大模型对林草领域复杂问题的理解能力提升60％以上；二是针对林草行业数据和业务特 点，构建了林草多模态数据的时空大模型，打破了大语言模型在时空数据理解、分析和推理能力上的局 限，使林草业务计算和处理能力提升50％以上；三是实现了多模态大模型与专用小模型的协同融合，极 大降低开发成本，显著增强了模型的复用性、适用性和通用性，开发利用效率提升10倍以上；四是成功 解决了林草领域低资源条件下的多端兼容和国产化适配问题，摆脱了林草行业大模型对高算力的依赖， 提升了模型的易用性和普惠性；五是实现了行业自主产权的开放共享，具备强大的高扩展性，能支持功 能更新迭代与产品持续完善。 目前，"林龙大模型"在行业文本处理、树种类型识别、林木表型参数提取、野生动物识别、病虫害 监测、林火识别 ...