3 results about "Seston" patented technology

The utility model belongs to environmental monitoring device technical field discloses a kind of surface water body multipoint simultaneous sampling device, including base and the liquid inlet being opened in the base, the base is fixedly connected with multiple mounting plates at equal angle circumferentially, the base is fixedly connected with negative pressure assembly, multiple the mounting plate is fixedly connected with water suction assembly and cleaning assembly on, the utility model is configured by each sampling point, and the cleaning assembly of backflushing can be reversed to water suction unit Reverse flushing, effectively remove the suspended particulate matter, algae, biological membrane and other impurities, such as jam, improve the water absorption efficiency of each point, ensure that sufficient water sample is smoothly extracted in each water suction interval, can also maintain the purity of subsequent sampling water sample, avoid the continuous pollution of subsequent sample due to dirt residual accumulation, ensure the accuracy of single point water sample detection data.

This invention relates to the field of intelligent formulation optimization technology for aquatic feed based on computer vision, and particularly to a method and system for optimizing aquatic feed formulation based on artificial intelligence. The method includes controlling an underwater vortex light source to emit a spiral phase light field into the water body, and acquiring a topological phase interference holographic image obtained from the backscattering of suspended particulate matter. By extracting the phase distortion matrix and combining it with the initial spiral phase matrix, the optical topological charge splitting step value is calculated, thereby separating the degraded background pixels caused by suspended bait and extracting the charge splitting visual tensor induced by the feeding of target organisms. The neural network transforms the charge splitting step frequency into dynamic penalty weights of hydrodynamic partial differential conditions, calculates and outputs mechanical kinetic energy intensity parameters, and adjusts the output aquatic feed formulation execution data. This solves the technical problem of artificial intelligence misjudging feeding behavior and outputting redundant attractant formulations due to underwater light curtain scattering.

The application provides an improved semi-analytical algorithm for inverting seawater inherent optical parameters. The method generates an inclusive synthetic dataset containing 200,000 data sets based on IOCCG report 5, and constructs an improved semi-analytical algorithm model (DQAA model) based on the QAA semi-analytical algorithm and a three-level deep learning model. The first-level deep learning model is fused to invert the particle backscattering coefficient b bp (λ), the second-level deep learning model introduces the ultraviolet band R rs (360)+ R rs (visible) to invert the key parameters ζ and ε, and the third-level deep learning model inverts the non-algal particle absorption coefficient a d (443) technology, and outputs the colored dissolved organic matter absorption coefficient a g (443) inversion result, and further obtains the phytoplankton absorption coefficient a ph (443). After being verified by the IOCCG simulation dataset and the NOMAD measured dataset, the method realizes low error (RMSD<0.13 m ‑1 , MARD<0.51) inversion, fully utilizes the UV band, and can effectively separate the water body phytoplankton, colored dissolved organic matter and inorganic suspended particle absorption coefficients in water color remote sensing, and has strong universality and robustness.