On April 7, the Hunan Institute of Science and Technology Information convened a science and technology achievement evaluation meeting in Changsha for the project “Precision Fixed-Depth Sampling and In-Situ Abundance Assessment Technology and Equipment for Deep-Sea Polymetallic Nodules,” completed by the Changsha Research Institute of Mining and Metallurgy (CRIMM). The expert review panel, chaired by Gui Weihua, academician of the Chinese Academy of Engineering, unanimously concluded that the project’s overall technology has reached an internationally leading level and provides China’s deep-sea mineral resource exploration with independently controlled core technology and equipment.
Targeting industry-wide problems in deep-sea polymetallic nodule exploration, low sampling efficiency, difficulty in assessing vertical abundance, and insufficient in-situ identification accuracy, the project team, working under the resource exploration tasks of the China Minmetals polymetallic nodule contract area, developed China’s first in-situ polymetallic nodule abundance assessment equipment with engineering-scale application capability.
The team designed an innovative integrated sampling-and-recovery structure for deep-sea polymetallic nodules and built a purely mechanical sampling mechanism with continuous action and a compact layout, integrating a “low-position collection, high-position discharge” function that significantly improved the reliability and efficiency of deep-sea nodule sampling operations. The team also broke through the technical bottleneck of precise fixed-depth sampling in complex deep-sea environments. Through multi-source information fusion and intelligent control algorithms, it achieved precise closed-loop control of the sampling motion parameters, with sampling accuracy at the centimeter level.
To address the difficulty of identifying nodules in complex deep-sea conditions, the project team independently developed high-precision in-situ image recognition and abundance assessment technology, building a closed-loop learning model of image recognition, abundance assessment, and model optimization. This achieved precise matching and calibration of image recognition results against in-situ measured data, with nodule identification accuracy at an industry-leading level, providing key technical support for efficient exploration of deep-sea polymetallic nodule resources.
The research results have already been successfully applied on multiple China Ocean deep-sea resource exploration cruises, completing precision sampling and abundance assessment at multiple stations in 6,000-meter-class deep-sea environments with notable results. The project has been granted 10 patents (including 8 invention patents), has 2 patents pending, and has produced 5 high-level academic papers.
