A method and system for monitoring and sampling aquatic biodiversity

Abstract

The present application provides a method and system for monitoring sampling of aquatic biodiversity, which monitors the target waters, divides multiple sampling sub-areas according to water monitoring data; collects samples of aquatic organisms in the sampling sub-areas to obtain a biological sample set; extracts multiple sample clusters of the biological sample set based on the sample characteristics of the biological samples in the biological sample set, and performs diversity assessment on all sample clusters to obtain diversity indicators of aquatic biological communities in the sampling sub-areas; determines the relative density of each sample cluster, and determines the heterogeneity index of each sample cluster in spatial distribution according to all relative densities and water characteristics of the sampling sub-areas; determines the optimization degree of the sampling sub-areas based on all heterogeneity indices and diversity indices, and then determines the target sampling area for aquatic biodiversity monitoring according to all optimization degrees. The scheme of the present application can realize the accurate selection of sampling areas with high representativeness and high analytical value.

Description

Technical Field

[0001] The present application relates to the technical field of monitoring and sampling, and more specifically, to a method and system for monitoring and sampling of aquatic biodiversity. Background Art

[0002] Monitoring sampling technology occupies an important position in modern data collection and analysis, especially in the fields of ecological monitoring, health diagnosis, etc. Accurate sampling technology is crucial to ensure the reliability and scientificity of data. The core task of sample monitoring sampling is to obtain representative and analytically valuable samples from a larger population in a reasonable way to ensure that subsequent analysis can reflect the true situation of the population. With the development of technology, intelligent monitoring technology has been gradually introduced. With the support of big data and artificial intelligence, the sampling efficiency and accuracy have been improved, and the data collection process has been made more real-time and efficient, providing a more reliable basis for scientific decision-making.

[0003] When implementing sample monitoring sampling, it is first necessary to scientifically design a sampling plan, and the sampling plan is optimized according to different needs and monitoring environments. For example, in environmental monitoring, samples may come from multiple media such as the atmosphere, water quality, and soil. The influence of different environmental factors needs to be considered during collection to ensure that the selected samples can fully reflect the conditions of the target area. In food safety monitoring, samples need to representatively reflect the quality or pollutant content of different batches of products to protect the health of consumers. However, in the monitoring and sampling of water samples, due to the complexity of aquatic ecosystems and the diversity of aquatic organisms, traditional monitoring methods are often random and blind, resulting in a lack of representativeness in sampling results, making it difficult to accurately reflect the true state of aquatic communities. Therefore, how to achieve accurate selection of sampling areas with high representativeness and high analytical value has become a difficult problem faced by the industry. Summary of the invention

[0004] The present application provides a method and system for aquatic biodiversity monitoring sampling, which can achieve accurate selection of sampling areas with high representativeness and high analytical value.

[0005] In a first aspect, the present application provides a method for monitoring and sampling aquatic biodiversity, comprising the following steps: Monitor the target water area and divide the target water area into multiple sampling sub-areas based on the water body monitoring data of the target water area; Selecting a sampling sub-area as a selected sampling sub-area, collecting samples of aquatic organisms in the selected sampling sub-area, and obtaining a biological sample set of the selected sampling sub-area; Extracting multiple sample clusters of the biological sample set based on the sample characteristics of each biological sample in the biological sample set, performing diversity assessment on all sample clusters, and obtaining diversity indicators of aquatic biological communities in the selected sampling sub-areas; Determine the relative density of each sample cluster, and determine the heterogeneity index of each sample cluster in spatial distribution according to all relative densities and water body characteristics of the selected sampling sub-area; The priority of the selected sampling sub-area is determined based on all heterogeneity indices and the diversity index, and the priority of the remaining sampling sub-areas is further determined, and then the target sampling area for aquatic biodiversity monitoring is determined based on all the priority.

[0006] In some embodiments, dividing the target water area into a plurality of sampling sub-areas based on the water body monitoring data of the target water area specifically includes: Collect water monitoring data of target waters; The target water area is divided according to the water body monitoring data to obtain a plurality of sampling sub-areas.

[0007] In some embodiments, collecting samples of aquatic organisms in a selected sampling sub-area to obtain a biological sample set in the selected sampling sub-area specifically includes: The random sampling method was used to determine the sampling points in the selected sampling sub-areas; Arrange sampling equipment at the sampling point, wherein the sampling equipment includes a composite gill net and a ground cage; Collecting fish samples by using the sampling device; The collected fish samples are numbered and stored in sampling bottles, and the sampling records are marked. The sampling records include the sampling time, location, equipment, and monitoring parameters, so as to obtain a biological sample set of the selected sampling sub-area.

[0008] In some embodiments, extracting a plurality of sample clusters of the biological sample set based on the sample feature of each biological sample in the biological sample set specifically includes: determining a sample characteristic of each biological sample in the biological sample set; The biological samples are associated and clustered according to all the sample characteristics to obtain a plurality of sample clusters of the biological sample set.

[0009] In some embodiments, the biological samples are clustered according to all sample characteristics to obtain multiple sample clusters of the biological sample set, specifically including: Normalizing each sample feature to obtain a standardized feature corresponding to each sample feature; For each standardized feature, determining a similarity index between the standardized feature and other standardized features, and then obtaining a similarity index corresponding to each standardized feature; Each biological sample is clustered based on all similarity indicators to obtain a plurality of sample clusters of the biological sample set.

[0010] In some embodiments, determining the preference of the selected sampling sub-region based on all heterogeneity indices and the diversity index specifically includes: A composite indicator of the spatial heterogeneity of the distribution of aquatic organisms in the selected sampling subareas was determined based on all heterogeneity indices; The preference of the selected sampling sub-area is determined according to the comprehensive index and the diversity index.

[0011] In some embodiments, the target water area is monitored by a water quality sensor.

[0012] In a second aspect, the present application provides an aquatic biodiversity monitoring sampling system, comprising: A monitoring module is used to monitor the target water area and divide the target water area into multiple sampling sub-areas based on the water body monitoring data of the target water area; A processing module is used to select a sampling sub-area as a selected sampling sub-area, collect samples of aquatic organisms in the selected sampling sub-area, and obtain a biological sample set of the selected sampling sub-area; The processing module is further used to extract multiple sample clusters of the biological sample set based on the sample characteristics of each biological sample in the biological sample set, perform diversity assessment on all sample clusters, and obtain diversity indicators of aquatic biological communities in the selected sampling sub-areas; The processing module is further used to determine the relative density of each sample cluster, and determine the heterogeneity index of each sample cluster in spatial distribution according to all relative densities and water body characteristics of the selected sampling sub-area; The execution module is used to determine the preference of the selected sampling sub-area based on all heterogeneity indices and the diversity index, continue to determine the preference of the remaining sampling sub-areas, and then determine the target sampling area for aquatic biodiversity monitoring based on all the preference.

[0013] In a third aspect, the present application provides a computer device, comprising a memory and a processor, wherein the memory stores codes, and the processor is configured to obtain the codes and execute the above-mentioned aquatic biodiversity monitoring sampling method.

[0014] In a fourth aspect, the present application provides a computer-readable storage medium storing a computer program, which implements the above-mentioned aquatic biodiversity monitoring sampling method when executed by a processor.

[0015] The technical solution provided by the embodiments disclosed in this application has the following beneficial effects: In the aquatic biodiversity monitoring sampling method and system provided by the present application, first, the target water area is monitored, and the target water area is divided into multiple sampling sub-areas based on the water body monitoring data of the target water area; secondly, a sampling sub-area is selected as the selected sampling sub-area, and samples of aquatic organisms in the selected sampling sub-area are collected to obtain a biological sample set of the selected sampling sub-area; then, based on the sample characteristics of each biological sample in the biological sample set, multiple sample clusters of the biological sample set are extracted, and diversity evaluation is performed on all sample clusters to obtain diversity indicators of the aquatic biological community in the selected sampling sub-area; in addition, the relative density of each sample cluster is determined, and the heterogeneity index of each sample cluster in spatial distribution is determined according to all relative densities and water body characteristics of the selected sampling sub-area; finally, the preference of the selected sampling sub-area is determined based on all heterogeneity indices and the diversity index, and the preference of the remaining sampling sub-areas is continued to be determined, and then the target sampling area for aquatic biodiversity monitoring is determined according to all the preference degrees.

[0016] It can be seen that the present application can realize the accurate selection of sampling areas with high representativeness and high analytical value; firstly, the target water area is divided into multiple sampling sub-areas based on the water body monitoring data of the target water area, which is conducive to studying the local ecology of the water area, thereby reflecting the structure and diversity distribution of aquatic biological communities in the entire water area; secondly, multiple sample clusters of the biological sample set are extracted based on the sample characteristics of each biological sample in the biological sample set to enhance the group parsing ability of biological distribution characteristics, thereby improving the accuracy of biodiversity assessment; then, a diversity assessment is performed on all sample clusters to obtain the diversity index of the sampling sub-area to measure the species richness of the sampling sub-area, thereby effectively evaluating the ecological diversity of the sampling sub-area. estimation; further, the heterogeneity index of the spatial distribution of the sample clusters is determined according to the relative density of the sample clusters and the water characteristics of the sampling sub-areas, so as to reflect the distribution pattern of the community in the spatial area, thereby providing support for the selection strategy of optimizing the sampling area; finally, the preference of the sampling sub-areas is determined based on the heterogeneity index and the diversity index, and then the target sampling area for aquatic biodiversity monitoring is determined according to the preference, so as to select sampling areas with high representativeness and high analytical value, thereby more accurately reflecting the information of aquatic biological communities, and thus facilitating biodiversity research and ecological environmental protection work; in summary, the technical solution provided in this application can realize the accurate selection of sampling areas with high representativeness and high analytical value. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 is an exemplary flow chart of a sampling method for monitoring aquatic biodiversity according to some embodiments of the present application; Figure 2 is an exemplary flow chart of determining diversity indicators according to some embodiments of the present application; Figure 3 is an exemplary flow chart for determining a heterogeneity index according to some embodiments of the present application; Figure 4 is a schematic diagram of the structure of an aquatic biodiversity monitoring and sampling system according to some embodiments of the present application; Figure 5 It is a schematic diagram of the structure of a computer device for implementing a method for monitoring and sampling aquatic biodiversity according to some embodiments of the present application. DETAILED DESCRIPTION

[0018] In order to better understand the technical solution of the present application, the technical solution of the present application will be described in detail below in conjunction with the accompanying drawings and specific implementation methods.

[0019] refer to Figure 1 , which is an exemplary flow chart of a method for monitoring and sampling aquatic biodiversity according to some embodiments of the present application. The method 100 for monitoring and sampling aquatic biodiversity mainly includes the following steps: In step 101, a target water area is monitored, and the target water area is divided into a plurality of sampling sub-areas based on water body monitoring data of the target water area.

[0020] In specific implementation, the target water area is monitored by deploying a water quality sensor network. These sensors transmit the monitoring data to a centralized management platform via wireless communication, thereby ensuring the continuity and accuracy of monitoring.

[0021] In some embodiments, the target water area may be divided into a plurality of sampling sub-areas based on the water body monitoring data of the target water area in the following manner, namely: Collect water monitoring data of target waters; The target water area is divided according to the water body monitoring data to obtain a plurality of sampling sub-areas.

[0022] In specific implementation, water body monitoring data of the target water area is collected, that is: multiple sensors are deployed at different depths and locations of the target water area to monitor the physical and chemical parameters in the target water area in real time to obtain water body monitoring data. For example, the conductivity of the water body is measured by a conductivity sensor, and the pH value of the water body is measured by a pH sensor. The water body monitoring data includes pH value, dissolved oxygen, temperature, turbidity, and nutrient concentration, etc. In addition, in other embodiments, other water body data can also be collected according to actual application requirements, which is not limited here. By collecting water body monitoring data, the water environment ecology of the target water area can be accurately reflected.

[0023] In a specific implementation, the target water area is divided according to the water monitoring data to obtain a plurality of sampling sub-areas, namely: first, the collected water monitoring data is imported into the Geographic Information System (GIS) software, and in the GIS environment, the water monitoring data is pre-processed using spatial analysis tools to fill in possible missing data values, for example, abnormal data is smoothed using a moving average algorithm to improve data quality; then, the K-Means clustering algorithm is used in combination with the spatial position coordinates of the target water area to divide the spatial area corresponding to the data points with similar water environment into a sampling sub-area, thereby obtaining a plurality of sampling sub-areas.

[0024] It should be noted that the sampling sub-area in this application represents a smaller area in the target waters. Dividing the sampling sub-areas is helpful for studying the local ecology of the waters, thereby reflecting the structure and diversity distribution of aquatic biological communities in the entire waters.

[0025] In step 102, a sampling sub-area is selected as a selected sampling sub-area, and samples of aquatic organisms in the selected sampling sub-area are collected to obtain a biological sample set of the selected sampling sub-area.

[0026] It should be noted that the biological sample set in the present application refers to a collection of a group of aquatic biological samples collected. Specifically, the biological sample set in the present application refers to a collection of a group of fish samples collected from the selected sampling sub-area. By collecting the biological sample set, basic data materials can be provided for subsequent aquatic biodiversity monitoring. In addition, in other embodiments, other aquatic organisms can be collected according to actual needs, such as plankton, benthic organisms, algae, etc., which are not limited here. As a preferred embodiment, samples of aquatic organisms in the selected sampling sub-area are collected to obtain the biological sample set of the selected sampling sub-area. The following method can be used, namely: The random sampling method was used to determine the sampling points in the selected sampling sub-areas; Arrange sampling equipment at the sampling point, wherein the sampling equipment includes a composite gill net and a ground cage; Collecting fish samples by using the sampling device; The collected fish samples are numbered and stored in sampling bottles, and the sampling records are marked. The sampling records include the sampling time, location, equipment, and monitoring parameters, so as to obtain a biological sample set of the selected sampling sub-area.

[0027] In step 103, multiple sample clusters of the biological sample set are extracted based on the sample characteristics of each biological sample in the biological sample set, and diversity evaluation is performed on all sample clusters to obtain diversity indicators of the aquatic biological community in the selected sampling sub-area.

[0028] In some embodiments, extracting multiple sample clusters of the biological sample set based on the sample feature of each biological sample in the biological sample set may be performed in the following manner, namely: determining a sample characteristic of each biological sample in the biological sample set; The biological samples are associated and clustered according to all the sample characteristics to obtain a plurality of sample clusters of the biological sample set.

[0029] In some embodiments, the sample characteristics of each biological sample in the biological sample set may be determined in the following manner, namely: Acquire a sample image of each biological sample in the biological sample set; Feature extraction is performed on the sample image of each biological sample to obtain the sample feature of each biological sample.

[0030] In a specific implementation, a sample image of each biological sample in the biological sample set is obtained, that is, each biological sample in the biological sample set is photographed by a high-resolution camera, and then the photographed image is preprocessed to enhance the detail clarity of the biological sample, thereby obtaining a sample image of each biological sample in the biological sample set.

[0031] In the specific implementation, the sample image of each biological sample is feature extracted by a computer vision technology algorithm to obtain the sample features of each biological sample. Specifically, first, the Canny edge detection algorithm is used to identify the edge contour of the biological sample in each sample image; then, the image is converted from the RGB color space to the HSV color space, and the histograms of different color channels (H, S, V) are counted to obtain the distribution characteristics of each biological sample in different color components; secondly, the gray-level co-occurrence matrix is ​​generated by the gray-level co-occurrence matrix algorithm, and then the texture feature parameters such as energy, entropy, and contrast are extracted from the gray-level co-occurrence matrix; finally, the extracted edge contour, color, and texture features are integrated to obtain the sample features of each biological sample.

[0032] It should be noted that the sample characteristics in this application represent the numerical information describing the biological samples. By determining the sample characteristics, the biological characteristics of the biological samples in morphology, color, and texture can be reflected, thereby providing important data support for subsequent sample analysis and diversity assessment.

[0033] In some embodiments, the following method can be used to perform association clustering on each biological sample according to all sample characteristics to obtain multiple sample clusters of the biological sample set, namely: Normalizing each sample feature to obtain a standardized feature corresponding to each sample feature; For each standardized feature, determining a similarity index between the standardized feature and other standardized features, and then obtaining a similarity index corresponding to each standardized feature; Each biological sample is clustered based on all similarity indicators to obtain a plurality of sample clusters of the biological sample set.

[0034] In specific implementation, each sample feature is normalized by the Min-Max normalization method to obtain the standardized feature corresponding to each sample feature. In addition, in other embodiments, other methods can be used for normalization, for example, the Z-score method, which is not limited here. By determining the standardized features, the influence between different feature dimensions can be eliminated, so that all features can be kept within a comparable range.

[0035] In specific implementation, the similarity index between the standardized feature and other standardized features is determined, that is, a standardized feature is selected, the cosine similarity between the standardized feature and other remaining standardized features is calculated, and the calculation result is used as the similarity index between the standardized feature and other standardized features. By determining the similarity index, the similarity between biological samples can be measured.

[0036] In a specific implementation, each biological sample is clustered based on all similarity indicators to obtain multiple sample clusters of the biological sample set, that is, all similarity indicators are used as input parameters, and each biological sample is clustered by a density-based DBSCAN algorithm to obtain multiple sample clusters, thereby obtaining multiple sample clusters of the biological sample set. In addition, in other embodiments, other algorithms may be used for clustering, such as hierarchical clustering, which is not limited here.

[0037] It should be noted that the sample cluster in this application represents a collection of multiple biological samples. The multiple biological samples in each sample cluster have similar morphological and texture characteristics. By determining the sample clusters, the group analysis capability of biological distribution characteristics can be improved, thereby improving the accuracy of biodiversity assessment.

[0038] In some embodiments, reference Figure 2 As shown in FIG. 1 , this figure is an exemplary flow chart of determining diversity indicators according to some embodiments of the present application. In this embodiment, diversity evaluation is performed on all sample clusters to obtain the diversity indicators of the aquatic biological community in the selected sampling sub-area, which can be achieved by the following steps: First, in step 1031, a sample cluster is selected as a selected sample cluster, and the species of aquatic organisms in the selected sample cluster are counted to obtain the number of species in the selected sample cluster, and the number of species is used as the species richness of the selected sample cluster; Then, in step 1032, the relative abundance of each species in the selected sample cluster is determined; Then, in step 1033, the distribution coefficient of aquatic organisms in the selected sample cluster is determined based on the relative abundance and the species richness, and the distribution coefficient of aquatic organisms in the remaining sample clusters is continued to be determined; Finally, in step 1034, the diversity index of the aquatic biological community in the selected sampling sub-area is determined based on all the distribution coefficients.

[0039] In specific implementation, the species of aquatic organisms in the selected sample cluster are counted to obtain the number of species in the selected sample cluster, that is, the sample features of each biological sample in the selected sample cluster are obtained, and the trained convolutional neural network model is called, and the sample features of each biological sample are input into the convolutional neural network model. The model will automatically output the prediction results of the species to which each biological sample belongs, and then the number of species in the selected sample cluster is obtained.

[0040] It should be noted that the convolutional neural network model called in this embodiment has used a large amount of aquatic biological characteristic information of labeled species as a training set. During the training process, the convolutional neural network model continuously learns the characteristic patterns in the information to identify different species.

[0041] In specific implementation, the relative abundance of each species in the selected sample cluster is determined, that is, the sample number of each species' biological samples in the selected sample cluster is extracted, and the quotient of the sample number of each species and the total sample number of all biological samples in the selected sample cluster is taken as the relative abundance of each species in the selected sample cluster.

[0042] It should be noted that the relative abundance in this embodiment indicates the ratio of the number of individuals of a species to the total number of individuals of all species. Specifically, the relative abundance in this embodiment refers to the ratio of the number of individuals of a specific species in a sample cluster to the total number of individuals of all species in the sample cluster. By determining the relative abundance, the accuracy of subsequent diversity monitoring can be improved.

[0043] In a specific implementation, the distribution coefficient of aquatic organisms in the selected sample cluster is determined based on the relative abundance and the species richness, that is, the relative abundance and the species richness are substituted as known parameters into the Shannon-Wiener index calculation formula for calculation, and the calculation result is used as the distribution coefficient of aquatic organisms in the selected sample cluster.

[0044] It should be noted that the Shannon-Wiener index calculation formula in this embodiment represents a method for measuring the ecological diversity of species, which will not be repeated here. In addition, it should be noted that the distribution coefficient in this embodiment represents an indicator for measuring the level of species diversity. Specifically, the distribution coefficient in this embodiment refers to an indicator for measuring the diversity level of species in a sample cluster. In this embodiment, the Shannon-Wiener index is used to characterize the distribution coefficient.

[0045] In specific implementation, the diversity index of the aquatic biological community in the selected sampling sub-area is determined according to all the distribution coefficients, that is, the mean value of all the distribution coefficients is used as the diversity index of the aquatic biological community in the selected sampling sub-area.

[0046] It should be noted that the diversity index in this application represents a measurement value for measuring the species diversity of aquatic biological communities. Specifically, the diversity index in this application refers to a quantitative index for measuring the species richness in a sampling sub-area. The larger the diversity index, the higher the species richness in the sampling sub-area, and the smaller the diversity index, the lower the species richness in the sampling sub-area. By determining the diversity index, the ecological diversity of the sampling sub-area can be effectively evaluated, thereby providing data support for subsequent ecological monitoring and management.

[0047] In step 104, the relative density of each sample cluster is determined, and the heterogeneity index of each sample cluster in spatial distribution is determined according to all relative densities and water body characteristics of the selected sampling sub-area.

[0048] In some embodiments, the relative density of each sample cluster may be determined in the following manner, namely: Obtain the total number of samples of aquatic organisms in each sample cluster; Determine the spatial distribution area corresponding to all sample clusters; The relative density of each sample cluster is determined based on the total number of samples of aquatic organisms in each sample cluster and the spatial distribution area.

[0049] In specific implementation, the spatial distribution area corresponding to all sample clusters is determined, that is, the spatial position coordinates of the selected sampling sub-area are extracted from the spatial position coordinate data set when the sampling sub-area is divided, and the area of ​​the selected sampling sub-area is calculated using an area calculation algorithm using a geographic information system (GIS), and the calculation result is used as the spatial distribution area corresponding to all sample clusters. In addition, in other embodiments, other methods can also be used to calculate the spatial distribution area, for example, the minimum convex hull algorithm, etc., which are not limited here.

[0050] It should be noted that the spatial distribution area in this embodiment represents the coverage of the aquatic biological sample cluster in the geographical space. Specifically, the spatial distribution area in this embodiment refers to the coverage of the aquatic biological sample cluster in the sampling sub-area within the sampling sub-area.

[0051] In specific implementation, the relative density of each sample cluster is determined based on the total number of aquatic organism samples in each sample cluster and the spatial distribution area, that is, the quotient of the total number of aquatic organism samples in each sample cluster and the spatial distribution area is calculated as the relative density of each sample cluster.

[0052] It should be noted that the relative density in this application represents the proportion of biological individuals in the sample cluster in the entire group space area. By determining the relative density, the scale of the ecological space occupied by the sample cluster can be reflected.

[0053] In some embodiments, reference Figure 3 As shown in FIG. 1 , this figure is an exemplary flow chart of determining the heterogeneity index according to some embodiments of the present application. In this embodiment, the heterogeneity index of each sample cluster in spatial distribution is determined according to all relative densities and water body characteristics of the selected sampling sub-area, which can be implemented by the following steps: First, in step 1041, the selected sampling sub-area is gridded to obtain a plurality of spatial grids of the selected sampling sub-area; Then, in step 1042, water body monitoring data of each spatial grid is extracted from the water body monitoring data; Then, in step 1043, the water body characteristics of the selected sampling sub-area are determined based on the water body monitoring data of each spatial grid; Finally, in step 1044, the heterogeneity index of each sample cluster in spatial distribution is determined based on the relative density of each sample cluster and the water body characteristics.

[0054] In the specific implementation, the selected sampling sub-area is gridded through the geographic information system to obtain multiple spatial grids of the selected sampling sub-area. Specifically, based on the geographic information system technology and the spatial position coordinates of the selected sampling sub-area, the selected sampling sub-area is divided into grid units of uniform size in the horizontal and vertical directions, and the coordinate information of each grid unit in space is recorded to obtain multiple spatial grids of the selected sampling sub-area.

[0055] In the specific implementation, the water body characteristics of the selected sampling sub-area are determined according to the water body monitoring data of each spatial grid, that is, the water body characteristics of the selected sampling sub-area are constructed by the water body monitoring data of each spatial grid. Specifically, the water body monitoring data of each spatial grid are arranged in rows and the data are aligned through the Numpy component in Python to obtain a data feature matrix, which is used as the water body characteristics of the selected sampling sub-area. In addition, in other embodiments, other methods can be used to determine the water body characteristics of the sampling sub-area, which are not limited here.

[0056] It should be noted that the water body characteristics in this application are data feature matrices that describe the ecological and physical characteristics of different water spaces. Specifically, the water body characteristics in this application are data feature matrices that describe the ecological and physical characteristics of different spatial grids in the sampling sub-area. By determining the water body characteristics, the water body characteristics of the sampling sub-area can be reflected, which is conducive to accurate analysis of the spatial distribution characteristics of aquatic organisms.

[0057] In specific implementation, the heterogeneity index of spatial distribution of each sample cluster is determined based on the relative density of each sample cluster and the water body characteristics, that is: a sample cluster is selected, and the relative density of the sample cluster is respectively subjected to multivariate linear fitting with each row of data in the water body characteristics, so as to obtain the heterogeneity index of spatial distribution of the sample cluster. Specifically, first, the multivariate linear regression model is called, and the relative density of the sample cluster and each row of data in the water body characteristics are used as input parameters; secondly, the input parameters are standardized by the Z-score algorithm to ensure the comparability of the data; then, the standardized input parameters are subjected to multivariate linear fitting by the multivariate linear regression model, and the fitting results are used as the spatial distribution index of the sample cluster; finally, the mean of all spatial distribution indices is used as the heterogeneity index of spatial distribution of the sample cluster, so as to obtain the heterogeneity index of spatial distribution of each sample cluster.

[0058] It should be noted that the Z-score algorithm in this embodiment is a standardized technology for data processing. In addition, the multivariate linear regression model belongs to the existing technology. In this embodiment, only the input parameters are selected and the output results are analyzed and processed, and the details are not repeated.

[0059] It should also be noted that the spatial distribution index in this embodiment represents the processing result of linear fitting and only appears as an intermediate variable in calculation. In addition, the heterogeneity index in this application represents an indicator for measuring the spatial distribution differences of biological communities. Specifically, the heterogeneity index in this application refers to the spatial distribution differences of aquatic organisms in the sample cluster in the sampling sub-area. The higher the heterogeneity index, the more concentrated the distribution of aquatic organisms in the sample cluster in the sampling sub-area. By determining the heterogeneity index, the distribution pattern of the community in the spatial area can be reflected, thereby providing support for optimizing the sampling area and evaluating ecological diversity.

[0060] In step 105, the preference of the selected sampling sub-area is determined based on all heterogeneity indices and the diversity index, and the preference of the remaining sampling sub-areas is further determined, and then the target sampling area for aquatic biodiversity monitoring is determined based on all the preference.

[0061] In some embodiments, the preference of the selected sampling sub-region may be determined based on all heterogeneity indices and the diversity index in the following manner, namely: A composite indicator of the spatial heterogeneity of the distribution of aquatic organisms in the selected sampling subareas was determined based on all heterogeneity indices; The preference of the selected sampling sub-area is determined according to the comprehensive index and the diversity index.

[0062] In specific implementation, a comprehensive index of the spatial heterogeneity of the distribution of aquatic organisms in the selected sampling sub-area is determined based on all the heterogeneity indices, that is, the mean of all the heterogeneity indices is used as the comprehensive index of the spatial heterogeneity of the distribution of aquatic organisms in the selected sampling sub-area. In addition, in other embodiments, other methods can be used to calculate the comprehensive index, which is not limited here. The comprehensive index represents an index that comprehensively measures the spatial heterogeneity of the distribution of aquatic biological communities in the sampling sub-area, thereby providing a more scientific data basis for subsequent analysis and sampling work.

[0063] In specific implementation, the preference of the selected sampling sub-area is determined according to the comprehensive index and the diversity index, that is, the comprehensive index and the diversity index are weightedly summed, and the calculation result of the weighted summation is used as the preference of the selected sampling sub-area. The weight distribution in the specific weighted summation process can be set according to actual needs. For example, the weight of the comprehensive index is set to 0.65, and the weight of the diversity index is set to 0.35.

[0064] It should be noted that the preference in this application represents a comprehensive indicator to measure the relative importance and representativeness of the sampling sub-area in aquatic biodiversity monitoring. The sampling sub-area with a higher preference has a higher monitoring value. By determining the preference, more representative data support can be provided for subsequent sampling work, thereby improving the reliability of aquatic biodiversity monitoring.

[0065] In specific implementation, the target sampling area for aquatic biodiversity monitoring is determined based on all the preferences, that is, the maximum preference is extracted from all the preferences, and the sampling sub-area corresponding to the extracted preference is used as the target sampling area for aquatic biodiversity monitoring, and then subsequent monitoring and analysis work is carried out.

[0066] It should be noted that the target sampling area in this application represents a sampling area with high ecological representativeness and monitoring value. By determining the target sampling area, the information of aquatic biological communities can be reflected more accurately and comprehensively, which is beneficial to biodiversity research and ecological environmental protection work.

[0067] In addition, in another aspect of the present application, in some embodiments, the present application provides an aquatic biodiversity monitoring sampling system, referring to Figure 4 , which is a schematic diagram of the structure of an aquatic biodiversity monitoring and sampling system according to some embodiments of the present application. The aquatic biodiversity monitoring and sampling system 200 includes: a monitoring module 201, a processing module 202 and an execution module 203, which are described as follows: Monitoring module 201: In the present application, monitoring module 201 is mainly used to monitor the target water area and divide the target water area into multiple sampling sub-areas based on the water body monitoring data of the target water area; Processing module 202, in the present application, the processing module 202 is mainly used to select a sampling sub-area as a selected sampling sub-area, collect samples of aquatic organisms in the selected sampling sub-area, and obtain a biological sample set of the selected sampling sub-area; The processing module 202 is further used to extract multiple sample clusters of the biological sample set based on the sample characteristics of each biological sample in the biological sample set, perform diversity evaluation on all sample clusters, and obtain a diversity index of the aquatic biological community in the selected sampling sub-area; In addition, the processing module 202 is also used to determine the relative density of each sample cluster, and determine the heterogeneity index of each sample cluster in spatial distribution according to all relative densities and water body characteristics of the selected sampling sub-area; Execution module 203. In the present application, execution module 203 is mainly used to determine the preference of the selected sampling sub-area based on all heterogeneity indices and the diversity index, continue to determine the preference of the remaining sampling sub-areas, and then determine the target sampling area for aquatic biodiversity monitoring based on all the preference.

[0068] In addition, the present application also provides a computer device, which includes a memory and a processor, wherein the memory stores codes, and the processor is configured to obtain the codes and execute the above-mentioned aquatic biodiversity monitoring sampling method.

[0069] In some embodiments, reference Figure 5 , which is a schematic diagram of the structure of a computer device for implementing a method for monitoring and sampling aquatic biodiversity according to some embodiments of the present application. The method for monitoring and sampling aquatic biodiversity in the above embodiments can be performed by Figure 5 The computer device 300 shown in the figure is implemented, and the computer device 300 includes at least one processor 301, a communication bus 302, a memory 303 and at least one communication interface 304.

[0070] The processor 301 may be a general-purpose central processing unit (CPU), or an application-specific integrated circuit (ASIC) or one or more processors for controlling the execution of the aquatic biodiversity monitoring sampling method of the present application.

[0071] The communication bus 302 may be used to transmit information between the above-mentioned components.

[0072] The memory 303 may be a read-only memory (ROM) or other types of static storage devices that can store static information and instructions, a random access memory (RAM) or other types of dynamic storage devices that can store information and instructions, or an electrically erasable programmable read-only memory (EEPROM), a compact disc read-only memory (CD-ROM) or other optical disc storage, an optical disc storage (including a compressed optical disc, a laser disc, an optical disc, a digital versatile disc, a Blu-ray disc, etc.), a magnetic disk or other magnetic storage device, or any other medium that can be used to carry or store the desired program code in the form of an instruction or data structure and can be accessed by a computer, but is not limited thereto. The memory 303 may exist independently and be connected to the processor 301 via the communication bus 302. The memory 303 may also be integrated with the processor 301.

[0073] The memory 303 is used to store the program code for executing the solution of the present application, and the execution is controlled by the processor 301. The processor 301 is used to execute the program code stored in the memory 303. The program code may include one or more software modules. The determination of the sampling method for monitoring aquatic biodiversity in the above embodiment can be implemented by the processor 301 and one or more software modules in the program code in the memory 303.

[0074] The communication interface 304 uses any transceiver or other device for communicating with other devices or communication networks, such as Ethernet, radio access network (RAN), wireless local area networks (WLAN), etc.

[0075] In a specific implementation, as an embodiment, a computer device may include multiple processors, each of which may be a single-CPU processor or a multi-CPU processor. The processor here may refer to one or more devices, circuits, and / or processing cores for processing data (e.g., computer program instructions).

[0076] The above-mentioned computer device may be a general-purpose computer device or a special-purpose computer device. In a specific implementation, the computer device may be a desktop computer, a portable computer, a network server, a personal digital assistant (PDA), a mobile phone, a tablet computer, a wireless terminal device, a communication device or an embedded device. The embodiment of the present application does not limit the type of computer device.

[0077] In addition, the present application also provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the above-mentioned aquatic biodiversity monitoring sampling method is implemented.

[0078] Although the preferred embodiments of the present application have been described, those skilled in the art may make other changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications falling within the scope of the present application.

[0079] Obviously, those skilled in the art can make various changes and modifications to the present application without departing from the spirit and scope of the present application. Thus, if these modifications and variations of the present application fall within the scope of the claims of the present application and their equivalents, the present application is also intended to include these modifications and variations.

Claims

1. A method for sampling aquatic biodiversity monitoring, characterized in that: The steps include: Monitor the target water area and divide the target water area into multiple sampling sub-areas based on the water body monitoring data of the target water area; Selecting a sampling sub-area as a selected sampling sub-area, collecting samples of aquatic organisms in the selected sampling sub-area, and obtaining a biological sample set of the selected sampling sub-area; Extracting multiple sample clusters of the biological sample set based on the sample characteristics of each biological sample in the biological sample set, performing diversity assessment on all sample clusters, and obtaining diversity indicators of aquatic biological communities in the selected sampling sub-areas; Determine the relative density of each sample cluster, and determine the heterogeneity index of each sample cluster in spatial distribution according to all relative densities and water body characteristics of the selected sampling sub-area; The priority of the selected sampling sub-area is determined based on all heterogeneity indices and the diversity index, and the priority of the remaining sampling sub-areas is further determined, and then the target sampling area for aquatic biodiversity monitoring is determined based on all the priority.

2. The method according to claim 1, characterized in that Based on the water monitoring data of the target water area, the target water area is divided into multiple sampling sub-areas, including: Collect water monitoring data of target waters; The target water area is divided according to the water body monitoring data to obtain a plurality of sampling sub-areas.

3. The method according to claim 1, characterized in that Samples of aquatic organisms in the selected sampling sub-area are collected to obtain a biological sample set of the selected sampling sub-area, specifically including: The random sampling method was used to determine the sampling points in the selected sampling sub-areas; Arrange sampling equipment at the sampling point, wherein the sampling equipment includes a composite gill net and a ground cage; Collecting fish samples by using the sampling device; The collected fish samples are numbered and stored in sampling bottles, and the sampling records are marked. The sampling records include the sampling time, location, equipment, and monitoring parameters, so as to obtain a biological sample set of the selected sampling sub-area.

4. The method according to claim 1, characterized in that Extracting a plurality of sample clusters of the biological sample set based on the sample feature of each biological sample in the biological sample set specifically includes: determining a sample characteristic of each biological sample in the biological sample set; The biological samples are associated and clustered according to all the sample characteristics to obtain a plurality of sample clusters of the biological sample set.

5. The method according to claim 4, characterized in that The biological samples are clustered according to all sample characteristics to obtain multiple sample clusters of the biological sample set, specifically including: Normalizing each sample feature to obtain a standardized feature corresponding to each sample feature; For each standardized feature, determining a similarity index between the standardized feature and other standardized features, and then obtaining a similarity index corresponding to each standardized feature; Each biological sample is clustered based on all similarity indicators to obtain a plurality of sample clusters of the biological sample set.

6. The method according to claim 1, characterized in that Determining the preference of the selected sampling sub-area based on all heterogeneity indices and the diversity index specifically includes: A composite indicator of the spatial heterogeneity of the distribution of aquatic organisms in the selected sampling subareas was determined based on all heterogeneity indices; The preference of the selected sampling sub-area is determined according to the comprehensive index and the diversity index.

7. The method according to claim 1, characterized in that The target water area is monitored through water quality sensors.

8. An aquatic biodiversity monitoring and sampling system, characterized in that: include: A monitoring module is used to monitor the target water area and divide the target water area into multiple sampling sub-areas based on the water body monitoring data of the target water area; A processing module is used to select a sampling sub-area as a selected sampling sub-area, collect samples of aquatic organisms in the selected sampling sub-area, and obtain a biological sample set of the selected sampling sub-area; The processing module is further used to extract multiple sample clusters of the biological sample set based on the sample characteristics of each biological sample in the biological sample set, perform diversity assessment on all sample clusters, and obtain diversity indicators of aquatic biological communities in the selected sampling sub-areas; The processing module is further used to determine the relative density of each sample cluster, and determine the heterogeneity index of each sample cluster in spatial distribution according to all relative densities and water body characteristics of the selected sampling sub-area; The execution module is used to determine the preference of the selected sampling sub-area based on all heterogeneity indices and the diversity index, continue to determine the preference of the remaining sampling sub-areas, and then determine the target sampling area for aquatic biodiversity monitoring based on all the preference.

9. A computer device, characterized in that: The computer device comprises a memory and a processor, wherein the memory stores codes, and the processor is configured to obtain the codes and execute the aquatic biodiversity monitoring sampling method according to any one of claims 1 to 7.

10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the aquatic biodiversity monitoring sampling method according to any one of claims 1 to 7 is implemented.

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