Microscope system and device for intelligent analysis and verification of plankton

Abstract

The invention discloses a plankton intelligent analysis and verification microscope system and device, and relates to the technical field of biological detection.The plankton intelligent analysis and verification microscope system comprises a microscope body, a control panel, an eyepiece and a displayer, the control panel is arranged on one side of the bottom of the microscope body, the microscope body is connected with the bottom of a supporting table, and a slide is placed on the top of the supporting table; an adjusting knob is arranged on one side of the top of the microscope main body, an eyepiece is arranged at one end of the top of the microscope main body, the microscope system can carry out automatic sample detection, image acquisition, preprocessing and image analysis, so that the function of the microscope is expanded, and the system uses an artificial intelligence feature extraction and comparison technology to improve the detection accuracy. Compared with the prior art, the system has the advantages that similar species can be displayed according to similarity arrangement in a one-key image search mode, the system is more efficient and accurate compared with the prior art, meanwhile, the system can automatically store microphotographs, statistical identifiers and statistical data and export the microphotographs, the statistical identifiers and the statistical data into Excel and PDF formats, and more powerful data processing and reporting functions are provided.

Description

Technical Field

[0001] The present invention relates to the field of biological detection technology, in particular to a plankton intelligent analysis and verification microscope system and device. Background Art

[0002] Plankton is a key indicator of aquatic ecosystems and is sensitive to environmental changes. Intelligent analysis systems can monitor and analyze changes in plankton in real time, which is essential for tracking and understanding environmental changes.

[0003] After searching, the Chinese patent publication number CN116386090B discloses a plankton identification method, system and medium based on scanning atlas. The system identifies the basic information of plankton by combining multiple data, thereby improving the accuracy of the identification results. At the same time, it obtains the climate characteristic information of the target area and the basic species information of suitable plankton, and predicts the future growth trend of plankton in the target area. However, the system mainly focuses on image scanning and recognition technology, and the automated data processing and analysis process is poor, which means that more manual intervention is required when processing large amounts of data, thereby reducing the level of automation. In addition, the system does not take into account the need for regular updates of the system in actual applications to adapt to the needs of new species discoveries and classification changes, which will affect the long-term effectiveness and accuracy of the system. Summary of the invention

[0004] In view of the shortcomings of the prior art, the present invention provides a plankton intelligent analysis and calibration microscope system and device, which solves the problem mentioned in the background technology that the data processing and analysis process of the existing system is not automated enough, requires a lot of manual intervention, and does not fully consider the need for regular updates of the system in actual applications to adapt to the needs of new species discoveries and classification changes, affecting long-term effectiveness and accuracy.

[0005] To achieve the above objectives, the present invention is implemented through the following technical solutions: a plankton intelligent analysis and calibration microscope system and device, including a microscope body, a control panel, an eyepiece and a display, a control panel is provided on one side of the bottom of the microscope body, the microscope body is connected to the bottom of a support platform, a glass slide is placed on the top of the support platform, an adjustment knob is provided on one side of the top of the microscope body, and an eyepiece is provided at one end of the top of the microscope body, an objective lens is provided at one end of the bottom of the eyepiece, and the objective lens is located directly above the glass slide, the eyepiece is used to magnify the real image formed by the objective lens again through its internal lens system, and provide the observer with a more magnified virtual image, a display is provided above the eyepiece, and the bottom of the display is welded to the top of the microscope body.

[0006] Preferably, it includes an imaging module, a database module, an intelligent identification module, a counting and analysis module, a Microcystis analysis module and a data reporting module;

[0007] The imaging module is installed inside the microscope body, and includes a color CMOS camera unit, which is used to capture microscopic images of plankton and control the observation, shooting, and storage of microscopic images. The imaging module is electrically connected to a display, and the display is used to preview saturation warnings, automatic background correction, and multi-focus fusion 3D high-definition imaging in real time;

[0008] The database module contains a plankton expert library in Chinese and Latin, covering multiple major categories, genera and species, as well as a large number of valid libraries. Users can expand the library as needed, including count tables of freshwater algae, marine algae, etc., and can edit, export and import count tables by themselves;

[0009] The intelligent identification module is used to use artificial intelligence feature extraction and comparison technology to display similar species in descending order according to similarity in a one-click image search mode, and can search by phylum, morphological characteristics, and sub-image library;

[0010] The counting and analysis module is used to mark various plankton with symbols of different colors and sizes, click by category, and automatically accumulate counts; it can automatically calculate the Shannon-Wiener index, uniformity index, automatic conversion of algae density, automatic conversion of zooplankton abundance, and assist in calculating the biomass of plankton;

[0011] The counting analysis module is designed to improve the efficiency and accuracy of biodiversity assessment, reduce errors and labor intensity of manual counting, and provide standardized biodiversity index and biomass estimation methods;

[0012] The Microcystis analysis module is used to automatically learn and automatically analyze the number of cells in the Microcystis group, and can automatically count zooplankton such as granular or single-cell microalgae, chain-shaped microalgae cells, and nematodes;

[0013] The data report module is used to automatically save each batch of micrographs, statistical identification and statistical data. The analysis results can be exported to Excel and PDF formats, and audit tracking is also available.

[0014] Preferably, the intelligent identification module includes a feature extraction unit, a feature vectorization unit, a similarity calculation unit, an indexing unit and a sorting unit;

[0015] The feature extraction unit extracts features from the image through a deep learning model (such as a convolutional neural network (CNN)); the deep features in the image, including texture, shape, and pattern, are automatically learned through the model; for example, a pre-trained model such as ResNet or VGG can be used;

[0016] The feature vectorization unit is used to convert the extracted features into a feature vector of fixed length, wherein the vector is a mathematical representation of the image content and is used for subsequent similarity calculation;

[0017] The similarity calculation unit is used to evaluate the similarity of the images by calculating the similarity between the two feature vectors;

[0018] The index unit is used to organize and index feature vectors using a specific data structure; when searching, the system will search the index for the vector that is most similar to the query image feature and return the corresponding image, and the data structure is one of a KD tree, a hash table, and a Milvus;

[0019] The sorting unit is used to sort according to the similarity scores, and the most similar images will be returned to the counting analysis module in priority.

[0020] Preferably, the similarity calculation method includes cosine similarity and Euclidean distance; the cosine similarity evaluates the similarity of two vectors by calculating the angle between them, and the Euclidean distance measures the straight-line distance between vectors in a multidimensional space.

[0021] Preferably, the cosine similarity calculation method calculates the similarity of two feature vectors:

[0022]

[0023] Where A and B are eigenvectors, · represents the dot product, and ||A|| and ||B|| are the norms of the vectors.

[0024] Preferably, the Euclidean distance is used to calculate the distance between two feature vectors:

[0025]

[0026] Where A and B are eigenvectors and n is the dimension of the vector.

[0027] Preferably, the feature vectorization unit usually needs to normalize the features during the vectorization process to eliminate the scale differences between different features. The normalization is achieved by the following formula:

[0028]

[0029] Among them, v′ i is the normalized value, min(V) and max(V) are the minimum and maximum values ​​in the eigenvector, respectively.

[0030] Preferably, the counting and analysis module includes an image annotation unit, an automatic counting unit, a biodiversity index calculation unit, a density and abundance conversion unit, a biomass calculation unit, and a data output unit;

[0031] The image annotation unit is used to manually or automatically mark the plankton in the image using symbols of different colors and sizes, wherein the symbols are associated with the types or categories of the plankton;

[0032] The automatic counting unit is used to identify and count each symbol marked in the image to determine the number of each plankton;

[0033] The biodiversity index calculation unit is used to calculate the uniformity index to measure the uniform distribution of various species in the biological community;

[0034] The density and abundance conversion unit is used to calculate the number of algae in a unit volume according to the counting result and the sample volume, and to calculate the number of zooplankton in a unit volume according to the counting result and the sample volume;

[0035] The biomass calculation unit estimates the total biomass according to the number and individual biomass of plankton, wherein the individual biomass is obtained by an empirical formula, a literature value or experimental data;

[0036] The data output unit is used to output data to the data report module.

[0037] Preferably, the biodiversity index calculation formula is:

[0038]

[0039] Where, H′: the calculation result of Shannon-Wiener index, which represents the diversity of biological community; s: the total number of species in the community; p i : The proportion of individuals of the i-th species in the community to the total number of individuals; ln(p i ):p i The natural logarithm of is calculated according to the above formula to calculate the uniformity index;

[0040]

[0041] Among them, E: the calculation result of the uniformity index, which indicates the uniformity of the distribution of various species in the biological community; H′: the calculation result of the Shannon-Wiener index; ln(s): the natural logarithm of the total number of species s.

[0042] The present invention provides a plankton intelligent analysis and calibration microscope system and device, which has the following beneficial effects:

[0043] (1) A plankton intelligent analysis and calibration microscope system and device, the microscope system can perform automatic sample detection, image acquisition, preprocessing and image analysis, thereby expanding the function of the microscope. The system uses artificial intelligence feature extraction and comparison technology to display similar species in a one-click image search manner by similarity, which is more efficient and accurate than existing technologies. At the same time, the system can automatically save microscopic photos, statistical identifiers and statistical data, and export them to Excel and PDF formats, providing more powerful data processing and reporting functions.

[0044] (2) This is a plankton intelligent analysis and calibration microscope system and device. The counting analysis module and Microcystis analysis module in the system can automatically mark and accumulate the counts of various plankton, automatically calculate ecological indexes and biomass, reduce manual operations, and improve the degree of automation. At the same time, the database module contains a bilingual plankton expert gallery, and users can expand the gallery as needed, providing better user experience and flexibility.

[0045] This solves the problem that the existing system's data processing and analysis processes are not automated enough, require a lot of manual intervention, and do not fully consider the need for regular updates of the system in actual applications to adapt to new species discoveries and classification changes, affecting long-term effectiveness and accuracy. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] Figure 1 It is a schematic diagram of the overall structure of the present invention;

[0047] Figure 2 It is a schematic diagram of the system module of the present invention.

[0048] In the figure, 1. microscope body; 2. control panel; 3. support table; 4. glass slide; 5. adjustment knob; 6. eyepiece; 7. display. DETAILED DESCRIPTION

[0049] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0050] See also Figure 1-2The embodiment of the present invention provides a technical solution: a plankton intelligent analysis and calibration microscope system and device, including a microscope body 1, a control panel 2, an eyepiece 6 and a display 7, a control panel 2 is arranged on one side of the bottom of the microscope body 1, the microscope body 1 is connected to the bottom of a support table 3, a glass slide 4 is placed on the top of the support table 3, an adjustment knob 5 is arranged on one side of the top of the microscope body 1, and an eyepiece 6 is arranged at one end of the top of the microscope body 1, an objective lens is arranged at one end of the bottom of the eyepiece 6, and the objective lens is located directly above the glass slide 4, the eyepiece 6 is used to magnify the real image formed by the objective lens again through the lens system inside it, and provide the observer with a more magnified virtual image, a display 7 is arranged above the eyepiece 6, and the bottom of the display 7 is welded to the top of the microscope body 1.

[0051] It includes imaging module, database module, intelligent identification module, counting analysis module, Microcystis analysis module and data report module;

[0052] The imaging module is installed inside the microscope body 1, and includes a color CMOS camera unit, which is used to capture the microscopic image of the plankton and control the observation, shooting and storage of the microscopic image. The imaging module is electrically connected to the display 7, and the display 7 is used for real-time preview of saturation warning, automatic background correction and multi-focus fusion 3D high-definition imaging;

[0053] The database module contains a plankton expert library in Chinese and Latin, covering multiple categories, genera and species, as well as a large number of valid libraries. Users can expand the library as needed, including freshwater algae and marine algae counting tables, and can edit, export and import counting tables by themselves;

[0054] The intelligent identification module uses artificial intelligence feature extraction and comparison technology to display similar species in descending order of similarity in a one-click image search mode. It can search by phylum, morphological characteristics, and sub-image library;

[0055] The counting analysis module is used to mark various plankton with symbols of different colors and sizes, click by category, and automatically accumulate counts; it can automatically calculate the Shannon-Wiener index, uniformity index, algae density, zooplankton abundance, and assist in calculating the biomass of plankton;

[0056] The Microcystis analysis module is used to automatically learn and automatically analyze the number of cells in the Microcystis group, and can automatically count granular or single-cell microalgae, chain-shaped microalgae cells, and nematode zooplankton;

[0057] The data report module is used to automatically save each batch of micrographs, statistical identification and statistical data. The analysis results can be exported to Excel, PDF format, and audit trail.

[0058] The intelligent identification module includes a feature extraction unit, a feature vectorization unit, a similarity calculation unit, an indexing unit and a sorting unit;

[0059] The feature extraction unit extracts features from the image through a deep learning model (such as a convolutional neural network (CNN)). The model automatically learns deep features in the image, including texture, shape, and pattern.

[0060] The feature vectorization unit is used to convert the extracted features into a fixed-length feature vector, where the vector is a mathematical representation of the image content for subsequent similarity calculation;

[0061] The similarity calculation unit is used to evaluate the similarity of images by calculating the similarity between two feature vectors;

[0062] The index unit is used to organize and index feature vectors using a specific data structure. When searching, the system will find the vector most similar to the query image feature in the index and return the corresponding image. The data structure is one of KD tree, hash table, and Milvus.

[0063] The sorting unit is used to sort according to the similarity score, and the most similar images will be returned to the counting analysis module first.

[0064] Similarity calculation methods include cosine similarity and Euclidean distance; cosine similarity evaluates the similarity of two vectors by calculating the angle between them, while Euclidean distance measures the straight-line distance between vectors in multidimensional space.

[0065] The cosine similarity calculation method calculates the similarity of two feature vectors:

[0066]

[0067] Where A and B are eigenvectors, · represents the dot product, and ||A|| and ||B|| are the norms of the vectors.

[0068] Euclidean distance is used to calculate the distance between two feature vectors:

[0069]

[0070] Where A and B are eigenvectors and n is the dimension of the vector.

[0071] During the vectorization process, the feature vectorization unit usually needs to normalize the features to eliminate the scale differences between different features. Normalization is achieved through the following formula:

[0072]

[0073] Among them, v′ iis the normalized value, min(V) and max(V) are the minimum and maximum values ​​in the eigenvector, respectively.

[0074] The counting analysis module includes an image annotation unit, an automatic counting unit, a biodiversity index calculation unit, a density and abundance conversion unit, a biomass calculation unit, and a data output unit;

[0075] The image annotation unit is used to manually or automatically mark the plankton in the image using symbols of different colors and sizes, wherein the symbols are associated with the species or categories of the plankton;

[0076] An automatic counting unit is used to identify and count each symbol marked in the image to determine the number of each plankton species;

[0077] The biodiversity index calculation unit is used to calculate the evenness index to measure the even distribution of species in a biological community;

[0078] Density and abundance conversion units are used to calculate the number of algae per unit volume based on the count results and the sample volume, and to calculate the number of zooplankton per unit volume based on the count results and the sample volume;

[0079] The biomass calculation unit estimates the total biomass based on the number and individual biomass of plankton, and the individual biomass is obtained through empirical formulas, literature values ​​or experimental data;

[0080] The data output unit is used to output data to the data report module.

[0081] The calculation formula of biodiversity index is:

[0082]

[0083] Where, H′: the calculation result of Shannon-Wiener index, which represents the diversity of biological community; s: the total number of species in the community; p i : The proportion of individuals of the i-th species in the community to the total number of individuals; ln(p i ):p i The natural logarithm of is calculated according to the above formula to calculate the uniformity index;

[0084]

[0085] Among them, E: the calculation result of the uniformity index, which indicates the uniformity of the distribution of various species in the biological community; H′: the calculation result of the Shannon-Wiener index; ln(s): the natural logarithm of the total number of species s.

[0086] The device realizes the whole process from image capture to data analysis through the collaborative work of multiple modules. First, the imaging module is installed inside the microscope, and the color CMOS camera unit is used to capture the microscopic images of plankton. These images can be observed, photographed and stored, and real-time preview is achieved through electrical connection with the display, including saturation warning, automatic background correction and multi-focus fusion 3D high-definition imaging; then, the database module comes into play, which contains a bilingual (Chinese and Latin) plankton expert library covering multiple major categories, genera and species. Users can expand the library as needed, including count tables of freshwater algae and marine algae, and can edit, export and import count tables to provide reference for intelligent identification, and use deep learning models such as convolutional neural networks (CNN) to automatically extract deep features in the image, including texture, shape and pattern. These features are then converted into fixed-length feature vectors by the feature vectorization unit to prepare for similarity calculation. The similarity calculation unit evaluates the similarity of images by comparing the similarity between feature vectors, while the indexing unit uses data structures such as KD trees, hash tables or Milvus to organize and index these feature vectors for fast retrieval. When the user places the sample on the slide 4, the color CMOS camera unit of the imaging module captures the microscopic image of the plankton. The system quickly finds the most similar image features through the indexing unit, and the sorting unit sorts them according to the similarity score to ensure that the most similar images are displayed first. The counting analysis module uses symbols of different colors and sizes to mark various plankton in the image and automatically accumulates the counts. It can also automatically calculate the Shannon-Wiener index, uniformity index, algae density and zooplankton abundance, and assist in calculating the biomass of plankton, providing users with an efficient and accurate counting and analysis method. Finally, the data report module automatically saves each batch of microscopic photos, statistical identification and statistical data, and exports the analysis results to Excel and PDF formats, while providing an audit trail function to ensure the accuracy and traceability of the data. The workflow of the entire system is coherent. From image capture to data analysis to result output, each module plays an important role and together they constitute an efficient plankton intelligent analysis solution.

[0087] Table 1 below is the analysis and verification table of algae samples by this system:

[0088]

[0089]

[0090] Table 1

[0091] Table 1 below is the analysis and verification table of zooplankton samples by this system:

[0092]

[0093] Table 2

[0094] From the table above, we can see that the system can automatically count and classify through the microscope's intelligent identification module, reducing the need for manual operation and improving efficiency and accuracy. The system can calculate biodiversity index and abundance in real time, providing researchers with rapid data analysis capabilities.

[0095] The above shows and describes the basic principles and main features of the present invention and the advantages of the present invention. It is obvious to those skilled in the art that the present invention is not limited to the details of the above exemplary embodiments, and the present invention can be implemented in other specific forms without departing from the spirit or basic features of the present invention. Therefore, no matter from which point of view, the embodiments should be regarded as exemplary and non-restrictive. The scope of the present invention is defined by the attached claims rather than the above description, and it is intended that all changes falling within the meaning and scope of the equivalent elements of the claims are included in the present invention. Any figure mark in the claims should not be regarded as limiting the claims involved.

[0096] In addition, it should be understood that although the present specification is described according to implementation modes, not every implementation mode contains only one independent technical solution. This description of the specification is only for the sake of clarity. Those skilled in the art should regard the specification as a whole. The technical solutions in each embodiment may also be appropriately combined to form other implementation modes that can be understood by those skilled in the art.

Claims

1. A plankton intelligent analysis and calibration microscope system and device, characterized in that: The invention comprises a microscope body (1), a control panel (2), an eyepiece (6) and a display (7), wherein a control panel (2) is arranged on one side of the bottom of the microscope body (1), the microscope body (1) is connected to the bottom of a support platform (3), a glass slide (4) is placed on the top of the support platform (3), an adjustment knob (5) is arranged on one side of the top of the microscope body (1), an eyepiece (6) is arranged at one end of the top of the microscope body (1), an objective lens is arranged at one end of the bottom of the eyepiece (6), and the objective lens is located directly above the glass slide (4), the eyepiece (6) is used to magnify the real image formed by the objective lens again through the lens system inside the eyepiece (6), and provide a more magnified virtual image to the observer, and a display (7) is arranged above the eyepiece (6), and the bottom of the display (7) is welded to the top of the microscope body (1).

2. According to claim 1, a plankton intelligent analysis and calibration microscope system is characterized by: It includes imaging module, database module, intelligent identification module, counting analysis module, Microcystis analysis module and data report module; The imaging module is installed inside the microscope body (1), and includes a color CMOS camera unit, which is used to capture microscopic images of plankton and control the observation, shooting and storage of microscopic images. The imaging module is electrically connected to a display 7, and the display 7 is used to preview saturation warning, automatic background correction and multi-focus fusion 3D high-definition imaging in real time; The database module contains a plankton expert library in Chinese and Latin, covering multiple major categories, genera and species, as well as a large number of valid libraries. Users can expand the library as needed, including freshwater algae and marine algae counting tables, and can edit, export and import counting tables by themselves; The intelligent identification module is used to use artificial intelligence feature extraction and comparison technology to display similar species in descending order according to similarity in a one-click image search mode, and can search by phylum, morphological characteristics, and sub-image library; The counting and analysis module is used to mark various plankton with symbols of different colors and sizes, click by category, and automatically accumulate counts; it can automatically calculate the Shannon-Wiener index, uniformity index, automatic conversion of algae density, automatic conversion of zooplankton abundance, and assist in calculating the biomass of plankton; The Microcystis analysis module is used to automatically learn and automatically analyze the number of cells in a group of Microcystis, and can automatically count granular or single-cell microalgae, chain-shaped microalgae cells, and nematode zooplankton; The data report module is used to automatically save each batch of micrographs, statistical identification and statistical data. The analysis results can be exported to Excel and PDF formats, and audit tracking is also available.

3. The plankton intelligent analysis and calibration microscope system according to claim 1, characterized in that: The intelligent identification module includes a feature extraction unit, a feature vectorization unit, a similarity calculation unit, an index unit and a sorting unit; The feature extraction unit extracts features from the image through a deep learning model (such as a convolutional neural network (CNN)); the deep features in the image, including texture, shape and pattern, are automatically learned through the model; The feature vectorization unit is used to convert the extracted features into a feature vector of fixed length, wherein the vector is a mathematical representation of the image content and is used for subsequent similarity calculation; The similarity calculation unit is used to evaluate the similarity of the images by calculating the similarity between the two feature vectors; The index unit is used to organize and index feature vectors using a specific data structure; when searching, the system will search the index for the vector that is most similar to the query image feature and return the corresponding image, and the data structure is one of a KD tree, a hash table, and a Milvus; The sorting unit is used to sort according to the similarity scores, and the most similar images will be returned to the counting analysis module in priority.

4. The plankton intelligent analysis and calibration microscope system according to claim 3, characterized in that: The similarity calculation method includes cosine similarity and Euclidean distance; the cosine similarity evaluates the similarity of two vectors by calculating the angle between them, and the Euclidean distance measures the straight-line distance of vectors in multidimensional space.

5. The plankton intelligent analysis and calibration microscope system according to claim 4, characterized in that: The cosine similarity calculation method calculates the similarity of two feature vectors: Where A and B are eigenvectors, · represents the dot product, and ||A|| and ||B|| are the norms of the vectors.

6. The plankton intelligent analysis and calibration microscope system according to claim 4, characterized in that: The Euclidean distance is used to calculate the distance between two feature vectors: Where A and B are eigenvectors and n is the dimension of the vector.

7. The plankton intelligent analysis and calibration microscope system according to claim 4, characterized in that: During the vectorization process, the feature vectorization unit usually needs to normalize the features to eliminate the scale differences between different features. Normalization is achieved by the following formula: Among them, v i is the normalized value, min(V) and max(V) are the minimum and maximum values ​​in the eigenvector, respectively.

8. The plankton intelligent analysis and calibration microscope system according to claim 1, characterized in that: The counting and analysis module includes an image annotation unit, an automatic counting unit, a biodiversity index calculation unit, a density and abundance conversion unit, a biomass calculation unit, and a data output unit; The image annotation unit is used to manually or automatically mark the plankton in the image using symbols of different colors and sizes, wherein the symbols are associated with the types or categories of the plankton; The automatic counting unit is used to identify and count each symbol marked in the image to determine the number of each plankton; The biodiversity index calculation unit is used to calculate the uniformity index to measure the uniform distribution of various species in the biological community; The density and abundance conversion unit is used to calculate the number of algae in a unit volume according to the counting result and the sample volume, and to calculate the number of zooplankton in a unit volume according to the counting result and the sample volume; The biomass calculation unit estimates the total biomass according to the number and individual biomass of plankton, wherein the individual biomass is obtained by an empirical formula, a literature value or experimental data; The data output unit is used to output data to the data report module.

9. The plankton intelligent analysis and calibration microscope system according to claim 8, characterized in that: The biodiversity index calculation formula is: Where, H′: the calculation result of Shannon-Wiener index, which represents the diversity of biological community; s: the total number of species in the community; p i : The proportion of individuals of the i-th species in the community to the total number of individuals; ln(p i ):p i The natural logarithm of is calculated according to the above formula to calculate the uniformity index; Among them, E: the calculation result of the uniformity index, which indicates the uniformity of the distribution of various species in the biological community; H′: the calculation result of the Shannon-Wiener index; ln(s): the natural logarithm of the total number of species s.

Images