数字孪生林业解决方案

数字孪生林业解决方案主要针对林业治理中资源监测精度不足、森林资源管控粗放、森林经营规划滞后等堵点难点问题，围绕加强林业生态价值系统建设，推动林业生态体系发展的目标，构建林下经济、防灾减灾、林业大脑、数字运营、林业工程等场景，提升林业空间治理能力，为林业管理者提供科学的决策依据。 方案采用四大核心技术，包括： 数字孪生技术，以林业森防业务为切入，构建多源异构时空数据融合底座、病虫害AI识别，林业病虫害趋势预测分析智能化平台，不断完善优化病虫害监测预报管理体系。 IOT技术：以无人机、激光雷达、多（高）光谱、物联传感等数据采集技术，在林业森防、林下经济、林业共富、林业碳汇、生物多样性等方面积累了成熟的案例和丰富的项目经验。 森防时序大模型：利用计算机视觉、机器学习等人工智能技术，实现林业运管数据感知-智能识别分析-知识图谱-自主学习优化-智能决策的循环，推动智慧林业的优化发展，满足管理需要，满足日常监测、决策、指挥的需要。 知识图谱技术：集成数字孪生与林业数据，建设打造林业全生命周期管理，实现区域生态农林及其周边地域的数字空间构建，具备动态数据采集、实时数据处理、智能数据分析、未来发展推演、可视化决策支撑等覆盖数字孪生全产业链的服务能力。 以上述技术为核心，通过IOT设备的接入，在生态区内动植物等实现一体化的全景实时观测，实现森林场景还原；对生态区内动植物、环境等要素构建一体化的监测和感知体系，构建生长模型，对植物自然生长后的状态进行仿真模拟与监测；赋能林下经济、国储林建设、林业大脑，真正实现林业多场景应用。

The Digital Twin Forestry Solution mainly targets the pain points and difficult issues in forestry governance, such as insufficient accuracy of resource monitoring, extensive management and control of forest resources, and lagging forest management planning. Focusing on the goals of strengthening the construction of forest ecological value systems and promoting the development of forest ecological systems, it builds scenarios including understory economy, disaster prevention and mitigation, Forestry Brain, digital operation, and forestry engineering, so as to improve forest spatial governance capabilities and provide scientific decision-making basis for forestry managers. The solution adopts four core technologies, including: 1. Digital Twin Technology: Taking forestry pest prevention and control services as the entry point, it builds a multi-source heterogeneous spatiotemporal data fusion platform, AI-based pest and disease identification, and an intelligent platform for forestry pest and disease trend prediction and analysis, continuously improving and optimizing the pest and disease monitoring, forecasting and management system. 2. IoT Technology: With data collection technologies such as UAVs, LiDAR, multi-/hyperspectral sensors, and IoT sensing devices, it has accumulated mature cases and rich project experience in areas such as forestry pest prevention and control, understory economy, shared prosperity in forestry, forestry carbon sequestration, and biodiversity. 3. Pest Prevention Sequential Large Model: Leveraging artificial intelligence technologies such as computer vision and machine learning, it realizes the cycle of forestry operation and management data perception - intelligent recognition and analysis - knowledge graph - autonomous learning and optimization - intelligent decision-making, promoting the optimized development of smart forestry to meet management needs, as well as the needs of daily monitoring, decision-making and command. 4. Knowledge Graph Technology: Integrating digital twin and forestry data, it constructs full-lifecycle management of forestry, realizes the construction of digital spaces for regional ecological agriculture, forestry and their surrounding areas, and possesses service capabilities covering the entire digital twin industrial chain, including dynamic data collection, real-time data processing, intelligent data analysis, future development deduction, and visualized decision support. Taking the above technologies as the core, through the access of IoT devices, it realizes integrated panoramic real-time observation of animals and plants in the ecological zone and restores forest scenes; it builds an integrated monitoring and perception system for elements such as animals, plants and the environment in the ecological zone, constructs growth models to simulate and monitor the post-natural-growth status of plants; it empowers understory economy, national reserve forest construction and Forestry Brain, and truly realizes multi-scenario applications in forestry.

搜集汇总

背景与挑战

背景概述

该数据集'数字孪生林业解决方案'专注于解决林业治理中的资源监测精度不足、管控粗放和规划滞后等难点，旨在通过构建林下经济、防灾减灾、林业大脑等场景提升林业空间治理能力。它采用数字孪生、IOT、森防时序大模型和知识图谱四大核心技术，实现森林场景还原、一体化监测感知和生长模型仿真，为林业管理者提供科学决策支持，赋能多场景应用。