The types and quantities of phytoplankton and zooplankton in water bodies, as well as their particle size distribution, are important basis for studying the water environment. Traditionally, manual judgment has been used, which is quite time-consuming and laborious. The Wan Shen AlgaeAC-22PF automatic classification and counting instrument for algae and plankton can effectively solve the pain point problem of users. It is mainly used in ecological investigation, fisheries, aquaculture, education and other industries to automatically classify and count phytoplankton (algae) and plankton samples in water, measure their size, classify their species, and determine their biomass. The AlgaeAC-22PF model also comes with an intelligent identification module for algae and planktonic animals, which helps reduce the heavy identification workload in the past and is an essential tool for ecological investigation and monitoring.

（1）★Imaging flux ≥ 4 counting frames, autofocus photography time ≤ 10 minutes for 4 counting frames (20X objective lens, 100 fields of view each, 32 megapixel high-resolution camera, capable of conducting 2 parallel sample tests simultaneously). Imaging supports a full range of objective lenses such as 10X, 20X, and 40X. The system is equipped with a 20X objective imaging algae automatic classification and recognition library, as well as 10X and 4X objective imaging zooplankton automatic classification and recognition libraries. The micro platform has a repeated positioning accuracy of less than 2 μ m in the X/Y axis, and has multi depth continuous automatic scanning characteristics. The automatic focusing algorithm optimized for micro plankton ensures clear scanning images. It can automatically stitch 400 automatic camera views into nearly 3 billion pixel super view images, effectively avoiding algae or plankton from being fragmented by the edges of each view. It can automatically scan sample images and store them, and record clear video images of samples.

（1）★Compliant with the technical regulations for monitoring phytoplankton in inland waters SL733-2016, the fifth part of the fourth edition of the supplementary edition of the Water and Wastewater Monitoring and Analysis Methods, Biological Monitoring Methods for Water and Wastewater (2002), GB17378-2007 Marine Monitoring Specification, GB/T12763-2007 Marine Survey Specification for algae monitoring, as well as the requirements of HJ 1216-2021 Water Quality Determination of Phytoplankton 0.1mL Counting Box Microscopic Counting Method and HJ 1215-2021 Water Quality Determination of Phytoplankton Membrane Microscopic Counting Method. After pre-treatment, the water sample is placed in the algae counting box, and the whole process of algae and plankton identification and classification counting analysis is automatically completed with one click (automatic moving field of view focusing scanning and photography, automatic classification recognition counting, and automatic generation of statistical reports).

(2) Imitating the process of detecting algae with an artificial microscope, imaging counting can be performed using five counting methods: whole slide counting, diagonal counting, grid counting, and random field counting.

(1) The system contains an automatic classification and recognition library for more than 105 common genera and species of algae in the phyla Cyanobacteria, Diatom, Chlorophyta, Naked Algae, Hidden Algae, Golden Algae, dinoflagellates, and Yellow Algae, obtained through AI enhanced deep learning. It also includes an automatic classification and recognition library for more than 31 major categories or genera of planktonic animals, which can be expanded to 120 genera and species according to local conditions; Support online updates of recognition libraries.

(2) Support dual process synchronization operation for photography and recognition analysis. It can automatically classify and analyze algae with a size of 3-1000 μ m. The automatic recognition and analysis time for each of the 4 algae counting boxes with 100 fields of view is ≤ 20 minutes (25-400 fields of view and the entire image can be selected), and the detection range is 10 ^ 5-10 ^ 10/liter. Automatic classification and analysis of planktonic animals ranging from 20 to 2000 μ m, with a maximum of 100 fields of view for each of the 4 planktonic counting boxes. The automatic stitching and recognition analysis time is ≤ 30 minutes (with an optional range of 25-400 fields of view).

(3) The automatic recognition rate of dominant species in the local classification recognition library is ≥ 90%, the comprehensive automatic recognition rate is ≥ 80%, and the final recognition rate after interactive correction can reach over 98%; At a concentration of 10 ^ 7-10 ^ 8 per liter, the repeatability error of automatic analysis is ≤ 5%.

(4) It can analyze and obtain morphological parameters such as area, perimeter, volume, length, width, major axis, minor axis, and equivalent diameter for each algae or planktonic animal.

(5) Can analyze and count the quantity, area, volume, and proportion of various algae or planktonic animals (by phylum, genus, species, or category); Sort and display the proportion of each category in a bar chart.

(6) Automatically calculate Shannon Wiener index, evenness index, richness index, individual density of algae or plankton, density of algae cells or plankton, biomass, etc.

(1) Automatically provide a classification and counting statistical report, indicating dominant species and dominance, and sorting by dominant species.

(2) The data can be exported as Excel for further statistical analysis.

（3）★Algae names can be directly marked on the collected images, and images of each algae or planktonic animal can be extracted and segmented, automatically classified and saved. Historical data can be viewed retrospectively.

（4）★It can be located and annotated on the map based on the geographic coordinates of the collection location, supporting various map sources such as Amap, Amap, Google Maps, and Google Satellite Maps.

（1）★A bilingual display of plankton expert libraries in Chinese and Latin: 15 phyla, 1719 genera, and 15832 species of algae; There are 26 major categories, 2002 genera, and 9845 species of planktonic animals. Covering common algae and planktonic animals in various river basins and sea areas in China. There are currently over 292471 valid image libraries, and each library's species and content can be expanded on their own. Expanded images can be searched in real-time.

(2) Contains sub libraries of Chinese freshwater algae, common planktonic diatoms in Chinese waters, red tide algae in Chinese coastal waters, Chinese freshwater branchiopods, Chinese freshwater copepods, and four major marine planktonic copepods. Users can create their own or generate a sub library of their local watershed through a counting table.

(3) You can search by door, genus, species, or by keywords such as species name, genus name, and textual description.

（1）★Comparison of artificial intelligence feature extraction, displaying similar species in descending order of similarity through a one click image search method. Can intelligently search and identify non planktonic organisms such as algae, plankton, pollen, fungi, etc. that are prone to appear in samples through image search. Being able to search and identify copepods based on P5 chest scan.

(2) It has three search modes: one click search, regular search, and advanced search, and can search by door, shape feature, and sub gallery.

(3) The search results can be filtered by species name, genus name, textual description, number of images, etc.

(4) For easily confused species with similar morphology, comparative images and textual descriptions can be developed on the same interface.

(1) Use symbols of different colors and sizes to mark various plankton, click by class, and automatically accumulate and count.

(2) Automatic sorting of dominant species, sorting by phylum (class), and percentage analysis of dominant community composition.

(3) It can automatically calculate Shannon Wiener index, uniformity index, algae density conversion, and planktonic animal abundance conversion.

(4) Assist in calculating the biomass of plankton using a large number of shape models (with 34 built-in geometric models, individual/cell volume can be calculated by measuring a small number of parameters).

（5）★Built in counting tables for common freshwater algae, common marine algae, etc., and can be edited, exported, and imported by oneself.

(6) Automatically estimate the number of cells in multicellular algae such as clumps and blocks based on the area of daughter cells, population area, and number of layers; The cell count of chain algae can be automatically estimated based on segment length and chain length.

(7) Equipped with a counter mode for quick counting under the eyepiece.

(1) It can measure the area of algae, individual area of planktonic animals, cell diameter, length of algal filaments and flagella, body length of planktonic animals, toe claw, branch angle, etc.

(2) The Microcystis analysis module can automatically learn and analyze the cell count of clustered Microcystis populations, and can automatically count planktonic animals such as granular or single-cell microalgae, chain microalgae cells, nematodes, etc.

(3) The color and shape of algae and planktonic animals have automatic learning and classification characteristics, which can monitor and correct the conversion of algae and planktonic animal categories, and perform secondary learning and preservation of classification features.

(4) It has the automatic image extraction feature of planktonic cells, which can quickly extract their main edge feature images. Has the ability to clarify blurry and overlapping images of planktonic organisms.

(1) Automatically save each batch of micrographs, statistical labels, and statistical data.

(2) The analysis results can be exported in Excel and PDF formats.

(3) Can merge different magnification counting results and multiple sample counting results.

(1) Multi user login system, each account forms independent data, and the data is permanently saved.

(2) The statistical results are output in PDF format, and the original data cannot be changed.

(3) The operator automatically records the operation software on the software for subsequent traceability of result data.

1. Wanshen Algae and Plankton Automatic Classification and Counting Instrument Software (including Plankton Intelligent Identification System) 1 set

2. 1 set of automatic digital microscopy imaging scanning system (Olympus BX43 three eye biological microscope (including BX43 frame, three eye observation tube, 5-hole objective turntable, mirror arm, Olympus 40X flat field semi achromatic objective, 10x wide field adjustable eyepiece, Sunny 20X, 10X, and 4X flat field semi achromatic objective), high-precision electronic XYZ automatic scanning platform with 4 fluxes+controller+32 million pixel camera)

3. 1 branded computer (11th generation or above Core i7 CPU/32GB memory/8GB or above GPU card with CUDA support/256GB solid-state drive+2T hard drive/23 "color display, 1 USB 3.0 port+3 USB 2.0 ports, running on Windows 10 or 11 Professional Edition)

1. The manufacturer provides assistance in establishing a free local classification initial recognition database service.

2. Free remote assistance and guidance services are provided.

1. In this technical proposal★The payment must be responsive, otherwise it is a significant deviation.