Aquatic ecological health assessment method based on fish environmental DNA experimental big data

Abstract

The present invention relates to the technical field of data processing and evaluation, and in particular to a water ecological health evaluation method based on fish environmental DNA experimental big data. The method comprises the following steps: obtaining water samples from a target area; filtering the water samples from the target area to generate water sample filtering data; centrifuging the water samples from the target area to generate water centrifuge data; extracting fish environmental DNA from the water samples from the target area based on the water sample filtering data and the water centrifuge data to obtain regional fish environmental DNA data; determining target DNA fragments from the regional fish environmental DNA data to obtain target DNA fragment information; the present invention uses data processing technology, high-throughput sequencing technology and machine learning algorithms to perform flow parameter and water level correlation analysis on a specific water area, and combines the fish community structure to perform index determination, thereby improving the accuracy of water ecological health evaluation.

Description

Technical Field

[0001] The present invention relates to the technical field of data processing and evaluation, and in particular to a method for evaluating water ecological health based on fish environmental DNA experimental big data. Background Art

[0002] Environmental DNA technology was originally a DNA technology that researchers used to detect and quantify the presence of various organisms by analyzing trace DNA in environmental samples such as water bodies. As the research on environmental DNA technology has gradually deepened, researchers have continuously optimized the sampling, extraction and detection techniques of environmental DNA technology, thereby improving the sensitivity of the technology. The application scope of environmental DNA technology has expanded from the initial species detection to multiple fields such as biodiversity assessment, water quality assessment, and water ecological health assessment. In particular, environmental DNA technology is applied in the field of water ecology for fish species diversity monitoring, endangered species monitoring, and fish biomass estimation, thereby conducting water ecological health assessments. However, the existing assessment methods lack flow parameter and water level correlation analysis for specific water body areas, and fail to combine fish community structure indicators, resulting in low accuracy in water ecological health assessments for specific water body areas. Summary of the invention

[0003] Based on this, it is necessary to provide a water ecological health assessment method based on fish environmental DNA experimental big data to solve at least one of the above technical problems.

[0004] To achieve the above purpose, a method for water ecological health assessment based on fish environmental DNA experimental big data is provided, the method comprising the following steps:

[0005] Step S1: obtaining water samples from the target area; filtering the water samples from the target area to generate water sample filtering data; centrifuging the water samples from the target area to generate water centrifugation data; extracting fish environmental DNA from the water samples from the target area based on the water sample filtering data and the water centrifugation data to obtain regional fish environmental DNA data;

[0006] Step S2: determining the target DNA fragment of the regional fish environmental DNA data to obtain the target DNA fragment information; amplifying the target DNA fragment of the fish environmental DNA data according to the target DNA fragment information to generate DNA fragment amplification data; performing high-throughput sequencing on the DNA fragment amplification data to obtain high-throughput sequencing data; identifying the fish species of the regional fish environmental DNA data through the high-throughput sequencing data to obtain regional fish species data; determining the fish community structure of the target area water sample according to the regional fish species data to obtain fish community structure data;

[0007] Step S3: monitoring water flow parameters of water samples in the target area to obtain regional water flow parameter information; measuring water levels of water samples in the target area through regional water flow parameter information to generate regional water level data; collecting regional surrounding environment data of water samples in the target area to generate regional environmental data; identifying influencing factors of regional environmental data based on regional water flow parameter information and regional water level data to generate regional environmental influencing factors; calculating fish environmental DNA abundance of regional fish environmental DNA data through regional environmental influencing factors to generate fish environmental DNA abundance data; determining fish community structure indicators of fish community structure data based on fish environmental DNA abundance data to obtain community structure indicator data;

[0008] Step S4: construct a water ecological health assessment model based on regional environmental impact factors, fish environmental DNA abundance data and community structure indicator data to obtain a water ecological health assessment model; use the water ecological health assessment model to conduct water ecological health assessment on water samples in the target area to generate water ecological health assessment data; visualize the water ecological health assessment data to generate a water ecological health assessment report.

[0009] The present invention can effectively extract fish environmental DNA from water samples by obtaining water samples of the target area and filtering and centrifuging them, thereby ensuring the accuracy of subsequent analysis, because filtration and centrifugation can remove impurities and non-target substances in the water samples, thereby improving the purity and quality of fish environmental DNA extraction; based on water sample filtration data and water body centrifugation data, fish environmental DNA is extracted from water samples in the target area, providing a high-quality template for subsequent target DNA fragment determination and amplification, thereby laying a foundation for accurate identification of fish species. Target DNA fragments are determined for regional fish environmental DNA data, and the DNA fragment information of fish can be accurately located; target DNA fragments are amplified for fish environmental DNA data according to the target DNA fragment information to generate DNA fragment amplification data; high-throughput sequencing is performed on DNA fragment amplification data to improve the efficiency and accuracy of fish species identification; fish species are identified for regional fish environmental DNA data through high-throughput sequencing data, and fish species in the region are accurately identified, thereby determining the fish community structure; fish community structure is determined for water samples in the target area according to regional fish species data, providing important information for understanding the diversity and distribution of fish in the region, and obtaining fish community structure data. Water flow parameters are monitored on water samples in the target area to accurately obtain regional water flow parameter information; water level is measured on water samples in the target area through regional water flow parameter information to reflect water level information; data on the surrounding environment of the target area is collected on water samples in the target area to clarify the status data of the regional environment; influencing factors of regional environmental data are identified based on regional water flow parameter information and regional water level data to identify environmental factors affecting the abundance of fish environmental DNA; fish environmental DNA abundance is calculated on regional fish environmental DNA data through regional environmental factors to accurately evaluate the richness of fish environmental DNA; fish community structure indicators are measured on fish community structure data based on fish environmental DNA abundance data to clarify the structure of fish communities. Based on regional environmental impact factors, fish environmental DNA abundance data and community structure index data, a water ecological health assessment model is constructed to obtain a water ecological health assessment model; the water ecological health assessment model is used to conduct a water ecological health assessment on water samples in the target area, providing a scientific and objective water ecological health assessment for water samples in the target area, and being able to comprehensively assess the water ecological health status of water samples in the target area; data visualization is performed on the water ecological health assessment data to generate a water ecological health assessment report, so that researchers can intuitively understand the health status of the water ecology. Therefore, the present invention uses data processing technology, high-throughput sequencing technology and machine learning algorithms to perform flow parameter water level correlation analysis on a specific water body area, and combines fish community structure to perform index determination, thereby improving the accuracy of water ecological health assessment.

[0010] Preferably, step S2 comprises the following steps:

[0011] Step S21: extracting sequence features from regional fish environmental DNA data to generate fish environmental DNA sequence features; performing sequence structure recognition on the fish environmental DNA sequence features to obtain sequence structure data; determining sequence structure domains of regional fish environmental DNA data using the sequence structure data to obtain DNA sequence structure domains; performing function detection on the DNA sequence structure domains to obtain DNA sequence function data;

[0012] Step S22: mapping the DNA sequence function data and the sequence structure data to function structure fragments to generate function fragment mapping data; determining the target DNA fragment of the regional fish environmental DNA data based on the function fragment mapping data to obtain the target DNA fragment information; performing sequence function alignment on the fish environmental DNA data according to the target DNA fragment information to generate sequence function alignment data;

[0013] Step S23: amplifying the target DNA structure fragment of the DNA sequence domain through the sequence function alignment data and the target DNA fragment information to generate DNA fragment amplification data; calculating the fragment length of the DNA fragment amplification data to generate amplified fragment length data; performing high-throughput sequencing on the DNA fragment amplification data in combination with the amplified fragment length data to obtain high-throughput sequencing data;

[0014] Step S24: fish species are identified on regional fish environmental DNA data through high-throughput sequencing data to obtain regional fish species data; fish community structure is determined on target area water samples based on the regional fish species data to obtain fish community structure data.

[0015] The present invention extracts sequence features from regional fish environmental DNA data to generate fish environmental DNA sequence features; performs sequence structure recognition on fish environmental DNA sequence features to clarify fish environmental DNA sequence structure features; determines sequence structure domains of regional fish environmental DNA data through sequence structure data to obtain DNA sequence structure domains; performs function detection on DNA sequence structure domains to reveal functional information of fish environmental DNA sequences; performs functional structure fragment mapping on DNA sequence function data and sequence structure data to reveal the relationship between specific DNA sequence fragments and their biological functions; determines target DNA fragments of regional fish environmental DNA data based on functional fragment mapping data to identify and locate key gene fragments that affect fish biological characteristics; and determines target DNA fragments based on target DNA fragment mapping data to identify and locate key gene fragments that affect fish biological characteristics. A fragment information is used to align the sequence functions of fish environmental DNA data, laying the foundation for subsequent DNA fragment amplification; the target DNA structure fragment is amplified on the DNA sequence domain through sequence function alignment data and target DNA fragment information, providing data support for subsequent research on fish community structure; the fragment length of the DNA fragment amplification data is calculated to generate amplified fragment length data; the DNA fragment amplification data is subjected to high-throughput sequencing in combination with the amplified fragment length data, so that the characteristics of different fish species can be quickly identified and compared in the future; the fish species of regional fish environmental DNA data are identified through high-throughput sequencing data, which can clarify the regional fish species situation; the fish community structure of the target area water samples is determined based on the regional fish species data, so that the fish community structure information is clarified.

[0016] Preferably, step S24 includes the following steps:

[0017] Step S241: performing sequencing function sequence matching on the functional fragment mapping data according to the high-throughput sequencing data to generate sequencing function sequence matching data; determining the function type of the DNA sequence function data based on the sequencing function sequence matching data to obtain sequence function type information;

[0018] Step S242: performing type grouping on the sequence function type information to generate sequence function type grouping data; performing sequence type component feature extraction on the sequence function type grouping data to obtain sequence type component features; performing fish feature correspondence on the regional fish environmental DNA data according to the sequence type component features to generate fish feature correspondence data; performing fish species identification on the fish feature correspondence data in combination with the sequence function type grouping data to obtain regional fish species data;

[0019] Step S243: performing fish composition type identification on the regional fish species data to obtain fish composition type data; performing composition type domain matching on the fish composition type data and the DNA sequence structure domain to obtain fish type structure domain data; performing fish species distribution feature extraction on the fish type structure domain data to generate fish species distribution features;

[0020] Step S244: Identify the fish distribution structure based on the fish species distribution characteristics to obtain fish distribution structure data; divide the fish distribution structure data into structural hierarchies to obtain fish structure hierarchy information; divide the target area water samples into fish community structures based on the fish structure hierarchy information and regional fish species data to obtain fish community structure data.

[0021] The present invention performs sequencing function sequence matching on functional fragment mapping data according to high-throughput sequencing data, and can accurately identify and match the functional characteristics of the target DNA sequence; determines the functional type of the DNA sequence functional data based on the sequencing function sequence matching data, and can further clarify the functional type of the fish environmental DNA; performs type grouping on the sequence functional type information, and effectively classifies the DNA sequences with similar functions, and can provide a clear biological marker for the correspondence between the subsequent regional fish environmental DNA data and the fish characteristics; extracts the sequence type component characteristics of the sequence functional type grouping data to obtain the sequence type component characteristics; performs fish characteristic correspondence on the regional fish environmental DNA data according to the sequence type component characteristics, and generates fish characteristic corresponding data; combines the sequence functional type grouping data with the fish characteristic corresponding data Fish species identification is carried out to reveal the diversity and distribution patterns of fish in the region; fish composition type identification is carried out on regional fish species data to obtain fish composition type data; fish composition type data is matched with DNA sequence domains to obtain type domain matching information; fish species distribution characteristics are extracted from fish type domain data to quickly identify and compare the distribution of different fish species; fish distribution structure identification is carried out on fish species distribution characteristics to provide data support for the subsequent clarification of fish community composition in water samples in the target area; fish distribution structure data is divided into structural hierarchies to clarify the specific composition information of fish structural hierarchies; fish community structure of water samples in the target area is divided based on fish structural hierarchy information and regional fish species data to obtain fish community structure data.

[0022] Preferably, step S3 comprises the following steps:

[0023] Step S31: Continuously track the flow of water samples in the target area to generate regional water sample flow tracking data; identify water flow parameter characteristics of the regional water sample flow tracking data to obtain water flow parameter characteristics; synchronously record water flow multi-parameters of the target area water samples according to the water flow parameter characteristics to generate regional water flow parameter information;

[0024] Step S32: establishing a flow-water level relationship for water samples in the target area based on the regional water flow parameter information to generate a flow-water level relationship network; performing regional flow parameter measurement on the flow-water level relationship network to obtain flow parameter measurement data; performing parameter change identification on the flow parameter measurement data to generate flow parameter change information; performing regional water level state mapping on the flow-water level relationship network according to the flow parameter change information to obtain regional water level data;

[0025] Step S33: collecting regional surrounding environmental data of target area water samples to generate regional environmental data; identifying influencing factors of regional environmental data based on regional water flow parameter information and regional water level data to generate regional environmental influencing factors; calculating fish environmental DNA abundance of regional fish environmental DNA data using regional environmental influencing factors to generate fish environmental DNA abundance data;

[0026] Step S34: Determine the fish community structure index of the fish community structure data according to the fish environmental DNA abundance data to obtain the community structure index data.

[0027] The present invention continuously tracks the flow of water samples in the target area, and can record the flow path information of the water samples in detail; identifies the water flow parameter characteristics of the regional water sample flow tracking data, and clarifies the key parameters affecting the water flow; synchronously records the water flow multi-parameters of the target area water samples according to the water flow parameter characteristics, and provides comprehensive parameter support for understanding the water flow; establishes the flow-water level relationship for the target area water samples based on the regional water flow parameter information, and reveals the relationship between the water flow parameters and the water level; measures the regional flow parameters on the flow-water level relationship network to obtain flow parameter measurement data; identifies parameter changes on the flow parameter measurement data, and further reveals the dynamic change trend of the flow parameters; and identifies the flow-water level relationship based on the flow parameter change information. The system network is used to map the regional water level status, which provides a data basis for water level status mapping, thereby obtaining regional water level data; the surrounding environmental data of the target area water samples are collected to comprehensively reflect the surrounding environmental conditions of the water samples; the influencing factors of the regional environmental data are identified based on the regional water flow parameter information and the regional water level data, and the key influencing factor indicators for the impact on the water ecology are provided; the fish environmental DNA abundance of the regional fish environmental DNA data is calculated through the regional environmental influencing factors, which can clarify the richness of the fish environmental DNA; the fish community structure index is determined based on the fish environmental DNA abundance data, which can provide a scientific basis for evaluating the fish community structure, thereby obtaining the community structure index data.

[0028] Preferably, step S33 includes the following steps:

[0029] Step S331: determining the regional environmental characteristics of the target area water sample to generate the regional environmental characteristics of the water sample; synchronously collecting multi-parameter environmental data of the regional environmental characteristics of the water sample to generate regional environmental data;

[0030] Step S332: determining the regional seasonal conditions of the regional environmental data based on the regional water flow parameter information and the regional water level data to generate regional seasonal condition data; identifying the water temperature state of the regional environmental data based on the regional seasonal condition data to obtain regional water temperature state information; detecting the pH value of the regional environmental data based on the regional water temperature state information to generate regional environmental pH value data; monitoring the water body biological activity of the regional environmental pH value data to obtain water body biological activity data; calculating the dissolved oxygen level of the water body biological activity data to obtain regional environmental dissolved oxygen data;

[0031] Step S333: extracting water temperature attributes from the regional water temperature status information to obtain regional water temperature attributes; determining the dissolved oxygen distribution area of ​​the regional environmental dissolved oxygen data based on the regional water temperature attributes to generate dissolved oxygen distribution area information; calculating the regional pH concentration of the regional environmental pH data based on the dissolved oxygen distribution area information to obtain regional pH concentration data; integrating the regional water temperature attributes, dissolved oxygen distribution area information and regional pH concentration data to obtain regional environmental impact factors;

[0032] Step S334: Calculate the fish environmental DNA abundance of regional fish environmental DNA data using regional environmental influencing factors to generate fish environmental DNA abundance data.

[0033] The present invention determines the regional environmental characteristics of water samples in the target area, and provides basic data for subsequent environmental monitoring; synchronously collects multi-parameter environmental data on the regional environmental characteristics of the water samples, and can comprehensively reflect the environmental characteristics of the water sample area; determines the regional seasonal conditions of regional environmental data based on regional water flow parameter information and regional water level data, and can reveal the law of seasonal changes in water environment; identifies the water temperature state of regional environmental data based on regional seasonal condition data, and provides a quantitative description of water temperature changes; detects the pH value of regional environmental data through regional water temperature state data, and provides parameter support for evaluating the acid-base balance of water bodies; monitors the water biological activity of regional environmental pH data to obtain water biological activity data; and analyzes the water biological activity data for dissolution. The degree of oxygen dissolution is calculated to obtain regional environmental dissolved oxygen data; the water temperature attributes are extracted from the regional water temperature status information to clarify the thermal characteristics of the water body; the dissolved oxygen distribution area of ​​the regional environmental dissolved oxygen data is determined according to the regional water temperature attributes, so as to clarify the spatial distribution of dissolved oxygen in the water body; the regional pH concentration of the regional environmental pH data is calculated based on the dissolved oxygen distribution area information to clarify the regional pH concentration; the regional water temperature attributes, dissolved oxygen distribution area information and regional pH concentration data are integrated to obtain the regional environmental impact factors; the fish environmental DNA abundance of the regional fish environmental DNA data is calculated through the regional environmental impact factors, which can reflect the richness of the fish environmental DNA and thus generate fish environmental DNA abundance data.

[0034] Preferably, step S334 includes the following steps:

[0035] Step S3341: extracting the water body temperature gradient of the regional water body temperature attribute to obtain the water body temperature gradient characteristics; identifying the temperature gradient factor of the regional water temperature state information according to the water body temperature gradient characteristics to generate the water body temperature gradient factor;

[0036] Step S3342: dividing the regional environmental data into environmental dissolved oxygen regions according to the dissolved oxygen distribution area information to generate dissolved oxygen regional situation data; measuring the dissolved oxygen concentration of each region of the dissolved oxygen regional situation data to obtain the dissolved oxygen concentration of each region; identifying the dissolved oxygen concentration factor of the dissolved oxygen distribution area information based on the dissolved oxygen concentration of each region to generate the dissolved oxygen concentration factor of the water body;

[0037] Step S3343: determining the water acidity and alkalinity of the target area water sample according to the regional pH concentration data to obtain the regional water acidity and alkalinity data; identifying the acidity and alkalinity factor of the regional pH concentration data through the regional water acidity and alkalinity data to generate the water acidity and alkalinity factor;

[0038] Step S3344: Combine the water temperature gradient factor, the water dissolved oxygen concentration factor and the water acidity and alkalinity factor to match the fish water sample environmental demand degree of the target area water sample, and generate the fish ecological demand matching degree; based on the fish ecological demand matching degree, calculate the number of species of regional fish species data to obtain matching fish quantity information; combine the matching fish quantity information and the fish community structure data to calculate the fish environmental DNA abundance of the regional fish environmental DNA data, and generate fish environmental DNA abundance data.

[0039] The present invention extracts the water temperature gradient of the regional water temperature attribute, which can reveal the spatial variation of the water temperature; identifies the temperature gradient factor of the regional water temperature status information according to the water temperature gradient characteristics, and provides a quantitative index for the water temperature variation; divides the regional environmental data into environmental dissolved oxygen regions according to the dissolved oxygen distribution area information, which can reveal the spatial distribution characteristics of dissolved oxygen in the water body; measures the dissolved oxygen concentration of each region on the dissolved oxygen regional situation data, and provides direct data for evaluating the biologically available oxygen level of the water body; identifies the dissolved oxygen concentration factor of the dissolved oxygen distribution area information based on the dissolved oxygen concentration of each region, and clarifies the key indicators of the dissolved oxygen concentration; determines the acidity and alkalinity of the water sample in the target area according to the regional pH concentration data , providing data basis for evaluating the acid-base balance of water bodies; identifying the acid-base factor of regional pH concentration data through regional water acid-base data to quantify the acidity and alkalinity of water bodies; combining water temperature gradient factor, water dissolved oxygen concentration factor and water acid-base factor to provide a comprehensive indicator for evaluating the adaptability of fish to water environmental conditions; matching the degree of environmental demand of fish water samples in the target area to clarify the matching degree of fish ecological needs; calculating the number of species of regional fish species data based on the matching degree of fish ecological needs to obtain matching fish number information; combining matching fish number information and fish community structure data to calculate the abundance of fish environmental DNA data for regional fish environmental DNA data to clarify the richness of fish environmental DNA.

[0040] Preferably, step S34 includes the following steps:

[0041] Step S341: performing fish species statistics on the fish environmental DNA abundance data to generate species quantity statistical information; constructing a fish species quantity heat map based on the species quantity statistical information to obtain a fish species quantity heat map; performing image color recognition on the fish species quantity heat map to obtain heat map color features; performing color depth determination on the heat map color features to generate heat map color depth data;

[0042] Step S342: Perform color depth type mapping on the heat map color depth data and the fish species data to generate color depth type mapping data; perform fish dominant species identification on the fish environmental DNA abundance data according to the color depth type mapping data to obtain fish dominant species information;

[0043] Step S343: combining the regional seasonal situation data with the information on fish dominant species to associate seasonal dominant species to obtain seasonal dominant species association data; monitoring the seasonal change trend of dominant species on the seasonal dominant species association data to generate seasonal change trend data;

[0044] Step S344: Based on the seasonal change trend data, the seasonal fish ecological distribution is determined for the fish environmental DNA abundance data to obtain seasonal fish ecological distribution data; and the fish community structure index is determined for the fish community structure data in combination with the seasonal fish ecological distribution data to obtain community structure index data.

[0045] The present invention performs fish species statistics on fish environmental DNA abundance data, which can provide a quantitative description of fish diversity in the region; constructs a fish species quantity heat map based on species quantity statistical information, which can intuitively display the number of fish species; performs image color recognition on the fish species quantity heat map, which can quantify the number of fish species; determines the color depth of the heat map color features, which can clarify the specific number of fish species; performs color depth-to-species mapping on the heat map color depth data and the fish species data, which can reveal the distribution of different fish species in the region; identifies the dominant fish species based on the fish environmental DNA abundance data according to the color depth-to-species mapping data, which can Identify the specific dominant species of fish; combine the regional seasonal situation data to associate the information of fish dominant species with seasonal dominant species, which can reveal the seasonal dynamic changes of fish populations; monitor the seasonal change trends of dominant species on the seasonal dominant species association data, so as to understand the status of fish population response to seasonal change trends; determine the seasonal fish ecological distribution based on the fish environmental DNA abundance data based on the seasonal change trend data, which can evaluate the seasonal habitat and migration pattern of fish populations; combine the seasonal fish ecological distribution data with the fish community structure data to determine the fish community structure indicators, which provides important ecological indicators for evaluating the health of water ecology.

[0046] Preferably, step S344 includes the following steps:

[0047] Step S3441: performing fish activity feature identification on the fish environmental DNA abundance data to obtain fish activity features; performing fish activity range determination on the fish activity features to generate fish activity range data; performing fish activity cycle identification on the fish activity range data to generate fish activity cycle information; performing seasonal fish ecological distribution determination on the fish environmental DNA abundance data based on the seasonal change trend data, the fish activity range data and the fish activity cycle information to obtain seasonal fish ecological distribution data;

[0048] Step S3442: identifying fish community diversity characteristics of fish community structure data in combination with seasonal fish ecological distribution data to generate fish community diversity characteristics; extracting diversity values ​​of fish community diversity characteristics to obtain community diversity values; calculating numerical indexes of community diversity values ​​to generate fish community diversity data;

[0049] Step S3443: extracting the dominant species of the fish community structure data according to the fish community diversity data and the fish dominant species information to obtain the dominant species data; performing community particle size structure identification on the dominant species data to generate the dominant community particle size structure information; performing particle size parameter calculation on the dominant community particle size structure information to obtain the dominant community particle size parameter data;

[0050] Step S3444: Calculate the fish community distribution uniformity of the fish community structure data based on the seasonal fish ecological distribution data to obtain community distribution uniformity data; determine the community stability of the community distribution uniformity data through the dominant community particle size parameter data and the seasonal fish ecological distribution data to generate fish community stability data;

[0051] Step S3445: Integrate the fish community diversity data, the dominant community particle size parameter data and the fish community stability data to obtain the community structure index data.

[0052] The present invention identifies fish activity characteristics based on fish environmental DNA abundance data, and can reveal the distribution and behavior patterns of fish in water bodies; determines fish activity range based on fish activity characteristics, and quantifies the activity range of fish in a specific area; identifies fish activity cycles based on fish activity range data, and can grasp the activity cycles of fish; determines seasonal fish ecological distribution based on fish environmental DNA abundance data based on seasonal change trend data, fish activity range data, and fish activity cycle information, and can clarify seasonal fish ecological distribution; identifies fish community diversity characteristics based on fish community structure data in combination with seasonal fish ecological distribution data, and can quantify the diversity level of fish communities in a region; extracts diversity values ​​from fish community diversity characteristics to obtain community diversity values; calculates numerical indexes for community diversity values, which is helpful for subsequent determination of indicators of fish community structure; and The fish community structure data are extracted from the community dominant species based on the diversity data and the information of dominant fish species, so as to clarify the fish species and their ecological niche sizes that dominate the specific area; the community particle size structure is identified for the community dominant species data, so as to clarify the community particle size structure information; the particle size parameters are calculated for the dominant community particle size structure information to obtain the dominant community particle size parameter data; the fish community distribution uniformity is calculated for the fish community structure data based on the seasonal fish ecological distribution data, so as to evaluate the spatial uniformity of the fish community; the community distribution uniformity data is measured through the dominant community particle size parameter data and the seasonal fish ecological distribution data, so as to understand the response and adaptability of the fish community to environmental changes; the fish community diversity data, the dominant community particle size parameter data and the fish community stability data are integrated to provide a comprehensive fish community indicator for comprehensively evaluating the health status of the aquatic ecosystem.

[0053] Preferably, step S41 includes the following steps:

[0054] Step S41: constructing a water ecological health assessment model according to regional environmental impact factors, fish environmental DNA abundance data and community structure index data to obtain a water ecological health assessment pre-model, and training the water ecological health assessment pre-model using regional environmental impact factors, fish environmental DNA abundance data and community structure index data to obtain a water ecological health assessment training model;

[0055] Step S42: performing a model cross-validation evaluation on the water ecological health assessment training model to obtain model evaluation data; adjusting the model parameters of the water ecological health assessment training model using the model evaluation data to obtain a water ecological health assessment model;

[0056] Step S43: Conduct water ecological health assessment on water samples in the target area based on the water ecological health assessment model to generate water ecological health assessment data; visualize the water ecological health assessment data to generate a water ecological health assessment report.

[0057] The present invention constructs a water ecological health assessment model according to regional environmental impact factors, fish environmental DNA abundance data and community structure indicator data to obtain a water ecological health assessment pre-model, and uses regional environmental impact factors, fish environmental DNA abundance data and community structure indicator data to train the water ecological health assessment pre-model, so as to further improve the accuracy of model evaluation; a model cross-validation evaluation is performed on the water ecological health assessment training model to verify the accuracy and reliability of the model; the model parameters of the water ecological health assessment training model are adjusted through the model evaluation data, so as to more accurately predict and evaluate the health status of the water ecosystem, thereby improving the credibility of the evaluation result; a water ecological health assessment is performed on water samples in the target area based on the water ecological health assessment model, so as to provide a quantitative evaluation of the health status of the water ecology; data visualization is performed on the water ecological health assessment data, so that the evaluation result is more intuitive, and a water ecological health assessment report is generated.

[0058] Preferably, step S43 includes the following steps:

[0059] Step S431: inputting water samples from the target area into the water ecological health assessment model to perform water ecological health prediction, and obtain water ecological health prediction data; performing water ecological health risk trend prediction on water samples from the target area according to the water ecological health prediction data, and obtain water ecological health trend prediction data;

[0060] Step S432: Perform risk assessment processing on the water ecological health trend prediction data to obtain water ecological risk assessment data; perform water ecological health assessment on water samples in the target area based on the water ecological health assessment model through the water ecological risk assessment data to generate water ecological health assessment data;

[0061] Step S433: Process the water ecological health assessment data into information charts to obtain water ecological health assessment charts; compile reports on the water ecological health assessment charts to obtain water ecological health assessment reports.

[0062] The present invention predicts water ecological health by inputting water samples from the target area into a water ecological health assessment model, thereby predicting the changing trend of the water ecological health status; predicting the water ecological health risk trend of water samples in the target area based on the water ecological health prediction data can reveal the potential risk trend of water ecological health; performing risk assessment processing on the water ecological health trend prediction data can assess the risk factors faced by water ecological health; performing water ecological health assessment on water samples in the target area through water ecological risk assessment data and based on the water ecological health assessment model, thereby providing a comprehensive description of the water ecological health status; processing the water ecological health assessment data into information charts, thereby intuitively displaying the water ecological health assessment results; and compiling a report on the water ecological health assessment charts to obtain a water ecological health assessment report, thereby enabling researchers to better understand the water ecological health status. BRIEF DESCRIPTION OF THE DRAWINGS

[0063] Figure 1 This is a schematic diagram of the steps of the water ecological health assessment method based on fish environmental DNA experimental big data;

[0064] Figure 2 for Figure 1 Detailed implementation steps of step S2 in the flowchart;

[0065] Figure 3 for Figure 2 Detailed implementation steps of step S24;

[0066] The realization of the purpose, functional features and advantages of the present invention will be further explained in conjunction with embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0067] The technical method of the present invention is described clearly and completely below in conjunction with the accompanying drawings. Obviously, the described embodiments are 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 technicians in this field without creative work are within the scope of protection of the present invention.

[0068] In addition, the accompanying drawings are only schematic illustrations of the present invention and are not necessarily drawn to scale. The same reference numerals in the figures represent the same or similar parts, and their repeated description will be omitted. Some of the block diagrams shown in the accompanying drawings are functional entities and do not necessarily correspond to physically or logically independent entities. The functional entities can be implemented in software form, or implemented in one or more hardware modules or integrated circuits, or implemented in different networks and / or processor methods and / or microcontroller methods.

[0069] It should be understood that, although the terms "first", "second", etc. may be used herein to describe various units, these units should not be limited by these terms. These terms are used only to distinguish one unit from another unit. For example, without departing from the scope of the exemplary embodiments, the first unit may be referred to as the second unit, and similarly the second unit may be referred to as the first unit. The term "and / or" used herein includes any and all combinations of one or more of the listed associated items.

[0070] To achieve this, please refer to Figures 1 to 3 , a water ecological health assessment method based on fish environmental DNA experimental big data, the method comprising the following steps:

[0071] Step S1: obtaining water samples from the target area; filtering the water samples from the target area to generate water sample filtering data; centrifuging the water samples from the target area to generate water centrifugation data; extracting fish environmental DNA from the water samples from the target area based on the water sample filtering data and the water centrifugation data to obtain regional fish environmental DNA data;

[0072] Step S2: determining the target DNA fragment of the regional fish environmental DNA data to obtain the target DNA fragment information; amplifying the target DNA fragment of the fish environmental DNA data according to the target DNA fragment information to generate DNA fragment amplification data; performing high-throughput sequencing on the DNA fragment amplification data to obtain high-throughput sequencing data; identifying the fish species of the regional fish environmental DNA data through the high-throughput sequencing data to obtain regional fish species data; determining the fish community structure of the target area water sample according to the regional fish species data to obtain fish community structure data;

[0073] Step S3: monitoring water flow parameters of water samples in the target area to obtain regional water flow parameter information; measuring water levels of water samples in the target area through regional water flow parameter information to generate regional water level data; collecting regional surrounding environment data of water samples in the target area to generate regional environmental data; identifying influencing factors of regional environmental data based on regional water flow parameter information and regional water level data to generate regional environmental influencing factors; calculating fish environmental DNA abundance of regional fish environmental DNA data through regional environmental influencing factors to generate fish environmental DNA abundance data; determining fish community structure indicators of fish community structure data based on fish environmental DNA abundance data to obtain community structure indicator data;

[0074] Step S4: construct a water ecological health assessment model based on regional environmental impact factors, fish environmental DNA abundance data and community structure indicator data to obtain a water ecological health assessment model; use the water ecological health assessment model to conduct water ecological health assessment on water samples in the target area to generate water ecological health assessment data; visualize the water ecological health assessment data to generate a water ecological health assessment report.

[0075] The present invention can effectively extract fish environmental DNA from water samples by obtaining water samples of the target area and filtering and centrifuging them, thereby ensuring the accuracy of subsequent analysis, because filtration and centrifugation can remove impurities and non-target substances in the water samples, thereby improving the purity and quality of fish environmental DNA extraction; based on water sample filtration data and water body centrifugation data, fish environmental DNA is extracted from water samples in the target area, providing a high-quality template for subsequent target DNA fragment determination and amplification, thereby laying a foundation for accurate identification of fish species. Target DNA fragments are determined for regional fish environmental DNA data, and the DNA fragment information of fish can be accurately located; target DNA fragments are amplified for fish environmental DNA data according to the target DNA fragment information to generate DNA fragment amplification data; high-throughput sequencing is performed on DNA fragment amplification data to improve the efficiency and accuracy of fish species identification; fish species are identified for regional fish environmental DNA data through high-throughput sequencing data, and fish species in the region are accurately identified, thereby determining the fish community structure; fish community structure is determined for water samples in the target area according to regional fish species data, providing important information for understanding the diversity and distribution of fish in the region, and obtaining fish community structure data. Water flow parameters are monitored on water samples in the target area to accurately obtain regional water flow parameter information; water level is measured on water samples in the target area through regional water flow parameter information to reflect water level information; data on the surrounding environment of the target area is collected on water samples in the target area to clarify the status data of the regional environment; influencing factors of regional environmental data are identified based on regional water flow parameter information and regional water level data to identify environmental factors affecting the abundance of fish environmental DNA; fish environmental DNA abundance is calculated on regional fish environmental DNA data through regional environmental factors to accurately evaluate the richness of fish environmental DNA; fish community structure indicators are measured on fish community structure data based on fish environmental DNA abundance data to clarify the structure of fish communities. Based on regional environmental impact factors, fish environmental DNA abundance data and community structure index data, a water ecological health assessment model is constructed to obtain a water ecological health assessment model; the water ecological health assessment model is used to conduct a water ecological health assessment on water samples in the target area, providing a scientific and objective water ecological health assessment for water samples in the target area, and being able to comprehensively assess the water ecological health status of water samples in the target area; data visualization is performed on the water ecological health assessment data to generate a water ecological health assessment report, so that researchers can intuitively understand the health status of the water ecology. Therefore, the present invention uses data processing technology, high-throughput sequencing technology and machine learning algorithms to perform flow parameter water level correlation analysis on a specific water body area, and combines fish community structure to perform index determination, thereby improving the accuracy of water ecological health assessment.

[0076] In the embodiment of the present invention, reference Figure 1The above is a schematic diagram of the steps of the water ecological health assessment method based on fish environmental DNA experimental big data of the present invention. In this example, the water ecological health assessment method based on fish environmental DNA experimental big data includes the following steps:

[0077] Step S1: obtaining water samples from the target area; filtering the water samples from the target area to generate water sample filtering data; centrifuging the water samples from the target area to generate water centrifugation data; extracting fish environmental DNA from the water samples from the target area based on the water sample filtering data and the water centrifugation data to obtain regional fish environmental DNA data;

[0078] In an embodiment of the present invention, a water sample experimental collection device is used to collect water samples from a target area, thereby obtaining water samples from the target area; the water samples from the target area are filtered, specifically, a polyester membrane filter is used to separate suspended matter and microorganisms in the water samples, for example, a filter membrane with a pore size of 0.45 μm is used for filtering, and the filtering conditions and results are recorded to obtain water sample filtration data; the water samples from the target area are centrifuged, specifically, the filtered water samples are centrifuged at high speed using a centrifuge, for example, centrifuged at a speed of 4000 revolutions per minute, to separate the precipitate and supernatant in the water samples, and the collection efficiency of the precipitate is recorded to obtain water centrifugation data; based on the water sample filtration data and the water centrifugation data, a DNA extraction reagent is used, and the cetyltrimethylammonium bromide (CTAB) method is used to extract fish environmental DNA from the water samples in the target area, and the relevant characteristic information of the fish environmental DNA is recorded in detail, thereby obtaining regional fish environmental DNA data.

[0079] Step S2: determining the target DNA fragment of the regional fish environmental DNA data to obtain the target DNA fragment information; amplifying the target DNA fragment of the fish environmental DNA data according to the target DNA fragment information to generate DNA fragment amplification data; performing high-throughput sequencing on the DNA fragment amplification data to obtain high-throughput sequencing data; identifying the fish species of the regional fish environmental DNA data through the high-throughput sequencing data to obtain regional fish species data; determining the fish community structure of the target area water sample according to the regional fish species data to obtain fish community structure data;

[0080] In an embodiment of the present invention, target DNA fragments are determined for regional fish environmental DNA data, specifically, using polymerase chain reaction (PCR) technology; first, specific primers for fish environmental DNA data are designed, based on known fish DNA sequences, such as the control region of mitochondrial DNA, and the specific primers are complementary to the sequences at both ends of the target DNA fragment, so as to determine the target DNA fragment information; after determining the target DNA fragment, the target DNA fragment is amplified using PCR technology; specifically, the PCR reaction system includes a DNA template, specific primers, dNTPs (deoxynucleotide triphosphates), Taq DNA polymerase, and a thermal cycle program including denaturation, annealing and extension steps; and each cycle includes high-temperature denaturation, low-temperature annealing and medium-temperature extension, and multiple cycles are performed to achieve exponential amplification of the target DNA fragment. The DNA fragment amplification data is subjected to high-throughput sequencing, and the high-throughput sequencing technology of the Illumina platform is used to generate a large amount of short-read sequence or long-read sequence data in a short time through the sequencing by synthesis (SBS) method; the specific high-throughput sequencing includes quality control, sequence alignment and variation detection to obtain high-throughput sequencing data; the fish species are identified from the regional fish environmental DNA data through high-throughput sequencing data, and the sequencing results are specifically compared with the DNA database of known fish to determine the fish species present in the sample; based on the regional fish species data, the ecological method is used to determine the fish community structure of water samples in the target area, which specifically includes analyzing parameters such as the richness of fish species, diversity index, and biomass distribution to obtain fish community structure data.

[0081] Step S3: monitoring water flow parameters of water samples in the target area to obtain regional water flow parameter information; measuring water levels of water samples in the target area through regional water flow parameter information to generate regional water level data; collecting regional surrounding environment data of water samples in the target area to generate regional environmental data; identifying influencing factors of regional environmental data based on regional water flow parameter information and regional water level data to generate regional environmental influencing factors; calculating fish environmental DNA abundance of regional fish environmental DNA data through regional environmental influencing factors to generate fish environmental DNA abundance data; determining fish community structure indicators of fish community structure data based on fish environmental DNA abundance data to obtain community structure indicator data;

[0082] In an embodiment of the present invention, a Doppler flowmeter or an ultrasonic flowmeter is used to monitor the water samples in the target area in real time, and the flow velocity, flow direction, flow rate and other parameters are recorded to generate regional water flow parameter information; based on the obtained regional water flow parameter information, the water level is measured through the flow-water level relationship curve, and the correlation between the water flow and the water level is specifically simulated, so as to obtain quantitative data of water level changes and generate regional water level data; when collecting regional surrounding environment data of the target area water samples, the Internet of Things relay system carried by the unmanned aerial vehicle is used to collect data, and the collected regional surrounding environment data are summarized to obtain regional environmental data; based on the regional water flow parameter information and the regional water level data, the environmental factor analysis technology is used to identify the influencing factors of the regional environmental data and generate the regional environmental influencing factors; through the regional environmental influencing factors, the quantitative PCR technology is used to calculate the fish environmental DNA abundance of the regional fish environmental DNA data to generate the fish environmental DNA abundance data; according to the fish environmental DNA abundance data, the ecological data analysis method is used, such as the R software package vegan to perform diversity and structure analysis, and the fish community structure data is measured for the fish community structure index to obtain the community structure index data.

[0083] Step S4: construct a water ecological health assessment model based on regional environmental impact factors, fish environmental DNA abundance data and community structure indicator data to obtain a water ecological health assessment model; use the water ecological health assessment model to conduct water ecological health assessment on water samples in the target area to generate water ecological health assessment data; visualize the water ecological health assessment data to generate a water ecological health assessment report.

[0084] In the embodiment of the present invention, based on regional environmental impact factors, fish environmental DNA abundance data and community structure index data, multivariate statistical analysis methods, such as principal component analysis (PCA), are used to identify key factors affecting water ecological health; these key factors are used as input variables to construct a water ecological health assessment model; the model will be constructed using machine learning methods, such as Random Forest (Random Forest) or support vector machine (SVM) to obtain a water ecological health assessment model; after the model is built, the model is used to conduct water ecological health assessment on water samples in the target area to generate water ecological health assessment data; specifically, the actual monitoring data is input into the model to obtain the water ecological health score or classification of each assessment point; for example, a lake ecosystem health evaluation index system is used, including the trophic index (TMI) of large aquatic plants, continuity indicators such as the number of artificial barriers set up for migratory fish, and hydrological status indicators such as the allowable water level fluctuation, so as to generate water ecological health assessment data; next, the water ecological health assessment data is visualized; and geographic information system (GIS) technology is used to combine the assessment results with spatial location information to generate intuitive maps and charts, such as graded color maps, to show the water ecological health status of different areas; finally, the visualization results are organized into a water ecological health assessment report.

[0085] As an example of the present invention, refer to Figure 2 As shown, in this example, step S2 includes:

[0086] Step S21: extracting sequence features from regional fish environmental DNA data to generate fish environmental DNA sequence features; performing sequence structure recognition on the fish environmental DNA sequence features to obtain sequence structure data; determining sequence structure domains of regional fish environmental DNA data using the sequence structure data to obtain DNA sequence structure domains; performing function detection on the DNA sequence structure domains to obtain DNA sequence function data;

[0087] Step S22: mapping the DNA sequence function data and the sequence structure data to function structure fragments to generate function fragment mapping data; determining the target DNA fragment of the regional fish environmental DNA data based on the function fragment mapping data to obtain the target DNA fragment information; performing sequence function alignment on the fish environmental DNA data according to the target DNA fragment information to generate sequence function alignment data;

[0088] Step S23: amplifying the target DNA structure fragment of the DNA sequence domain through the sequence function alignment data and the target DNA fragment information to generate DNA fragment amplification data; calculating the fragment length of the DNA fragment amplification data to generate amplified fragment length data; performing high-throughput sequencing on the DNA fragment amplification data in combination with the amplified fragment length data to obtain high-throughput sequencing data;

[0089] Step S24: fish species are identified on regional fish environmental DNA data through high-throughput sequencing data to obtain regional fish species data; fish community structure is determined on target area water samples based on the regional fish species data to obtain fish community structure data.

[0090] In the embodiment of the present invention, in the process of extracting sequence features from regional fish environmental DNA data, specifically, a high-throughput sequencing technology, such as the Illumina NovaSeq 6000 platform, is used to sequence environmental DNA samples to generate a large amount of short-read sequence data; the short-read sequence data is compared and analyzed to identify the DNA sequence features unique to fish and generate fish environmental DNA sequence feature data; sequence structure recognition is performed on the fish environmental DNA sequence features, and bioinformatics tools, such as BLAST or MEGA-X software, are used to perform sequence comparison and analysis to determine the conserved regions and variable regions of the DNA sequence, thereby obtaining sequence structure data; combined with the sequence structure data, specific domains in the sequence, such as signal peptides, transmembrane regions or enzyme active sites, are identified to obtain DNA sequence domains; functional detection of the DNA sequence domains is performed, such as in vitro enzyme activity assay; functional structure fragments are mapped between the DNA sequence function data and the sequence structure data to generate functional fragment mapping data; based on the functional fragment mapping data, the target DNA fragments of the regional fish environmental DNA data can be determined, specifically using multiple sequence alignment software, such as MegAlign Pro, perform DNA sequence comparison; according to the target DNA fragment information, perform sequence function alignment on the fish environmental DNA data, specifically by comparing the target DNA fragment with the known functional sequence for sequence function alignment; through the sequence function alignment data and the target DNA fragment information, use PCR technology to use specific primers to amplify the target DNA fragment to generate DNA fragment amplification data; calculate the fragment length of the DNA fragment amplification data, and determine the length of the amplified fragment through gel electrophoresis analysis, such as agarose gel electrophoresis; perform high-throughput sequencing on the DNA fragment amplification data in combination with the amplified fragment length data, and use a high-throughput sequencing platform to sequence the amplified DNA fragments to generate a large amount of short-read sequence data; identify fish species for regional fish environmental DNA data through high-throughput sequencing data, specifically through OTU clustering analysis and database comparison to identify and classify fish species in sequencing data; according to the regional fish species data, determine the fish community structure of water samples in the target area, such as diversity index calculation and principal coordinate analysis (PCoA), to evaluate the diversity and spatial distribution pattern of fish communities, and obtain fish community structure data.

[0091] As an example of the present invention, refer to Figure 3As shown, in this example, step S24 includes:

[0092] Step S241: performing sequencing function sequence matching on the functional fragment mapping data according to the high-throughput sequencing data to generate sequencing function sequence matching data; determining the function type of the DNA sequence function data based on the sequencing function sequence matching data to obtain sequence function type information;

[0093] Step S242: performing type grouping on the sequence function type information to generate sequence function type grouping data; performing sequence type component feature extraction on the sequence function type grouping data to obtain sequence type component features; performing fish feature correspondence on the regional fish environmental DNA data according to the sequence type component features to generate fish feature correspondence data; performing fish species identification on the fish feature correspondence data in combination with the sequence function type grouping data to obtain regional fish species data;

[0094] Step S243: performing fish composition type identification on the regional fish species data to obtain fish composition type data; performing composition type domain matching on the fish composition type data and the DNA sequence structure domain to obtain fish type structure domain data; performing fish species distribution feature extraction on the fish type structure domain data to generate fish species distribution features;

[0095] Step S244: Identify the fish distribution structure based on the fish species distribution characteristics to obtain fish distribution structure data; divide the fish distribution structure data into structural hierarchies to obtain fish structure hierarchy information; divide the target area water samples into fish community structures based on the fish structure hierarchy information and regional fish species data to obtain fish community structure data.

[0096] In an embodiment of the present invention, a high-throughput sequencing technology, such as the Illumina NovaSeq platform, is used to sequence environmental DNA samples to generate a large amount of short-read sequence data; a bioinformatics tool, such as the pattern matching function (matchPattern and vmatchPattern) in the Biostrings software package, is used to perform sequencing function sequence matching on functional fragment mapping data to generate sequencing function sequence matching data; based on the sequencing function sequence matching data, a bioinformatics software, such as ClustalW or MAFFT, is used to perform multiple sequence alignment on the DNA sequence function data to specifically determine the homology and difference between the sequences; the sequence function type information is grouped by type based on the alignment results, and specifically a principal component analysis (PCA) multivariate statistical analysis method is used to extract sequence type component features from the sequence function type grouping data to obtain sequence type component features; fish characteristics are matched to regional fish environmental DNA data according to the sequence type component features, and fish species are identified in combination with the sequence function type grouping data to obtain to regional fish species data; identify the fish composition types of the regional fish species data, specifically, match the fish composition type data with the DNA sequence domain to obtain the fish type domain data; use ecological data analysis methods, such as the non-metric multidimensional scaling (NMDS) sorting method in the R software package vegan, to extract the fish species distribution characteristics of the fish type domain data and generate the fish species distribution characteristics; use NMDS sorting and permutation multivariate analysis of variance (PERMANOVA) test to identify the fish distribution structure of the fish species distribution characteristics and obtain the fish distribution structure data; divide the fish distribution structure data into structural levels and obtain the fish structure level information; based on the fish structure level information and regional fish species data, use the species diversity index calculation in the R software package vegan, such as the Shannon-Wiener diversity index, to divide the fish community structure of the target area water samples and obtain the fish community structure data.

[0097] Preferably, step S3 comprises the following steps:

[0098] Step S31: Continuously track the flow of water samples in the target area to generate regional water sample flow tracking data; identify water flow parameter characteristics of the regional water sample flow tracking data to obtain water flow parameter characteristics; synchronously record water flow multi-parameters of the target area water samples according to the water flow parameter characteristics to generate regional water flow parameter information;

[0099] Step S32: establishing a flow-water level relationship for water samples in the target area based on the regional water flow parameter information to generate a flow-water level relationship network; performing regional flow parameter measurement on the flow-water level relationship network to obtain flow parameter measurement data; performing parameter change identification on the flow parameter measurement data to generate flow parameter change information; performing regional water level state mapping on the flow-water level relationship network according to the flow parameter change information to obtain regional water level data;

[0100] Step S33: collecting regional surrounding environmental data of target area water samples to generate regional environmental data; identifying influencing factors of regional environmental data based on regional water flow parameter information and regional water level data to generate regional environmental influencing factors; calculating fish environmental DNA abundance of regional fish environmental DNA data using regional environmental influencing factors to generate fish environmental DNA abundance data;

[0101] Step S34: Determine the fish community structure index of the fish community structure data according to the fish environmental DNA abundance data to obtain the community structure index data.

[0102] In an embodiment of the present invention, a continuous flow tracking technology, such as an acoustic Doppler current profiler (ADCP) or an electromagnetic current meter, is used to continuously track the flow of water samples in the target area, and real-time data of flow velocity and flow direction are recorded, so as to obtain detailed flow characteristics of the water flow and generate regional water sample flow tracking data; water flow parameter characteristics are identified for the regional water sample flow tracking data, and data processing software such as Matlab or Python is used for data analysis. By identifying the flow velocity profile and flow direction changes, water flow parameter characteristics are obtained, including key parameters such as average flow velocity, maximum flow velocity and flow direction changes; according to the water flow parameter characteristics, a data acquisition system (such as YSI Sondes or Hobo U20 hydrological recorder) is used to synchronously record the water flow multi-parameters of the water samples in the target area, specifically including flow velocity parameters at different times and locations. Based on the regional water flow parameter information, a flow-water level relationship network is established to measure the regional flow parameters of the flow-water level relationship network. Specifically, the water level situation is simulated through the regional water flow parameter information to obtain the flow parameter measurement data; the flow parameter measurement data is identified for parameter changes, and the change trend of the flow parameters is analyzed using statistical analysis methods (such as the R software package vegan), and the regional water level state is mapped to the flow-water level relationship network according to the flow parameter change information to obtain the regional water level data; the target area water samples are collected for the surrounding environment data of the area, specifically using the multi-parameter water quality monitor or automatic weather station carried by the drone for data collection. The regional environmental data were generated by using a set of data. Based on the regional water flow parameter information and the regional water level data, multivariate statistical analysis methods were used to identify the influencing factors of the regional environmental data and generate regional environmental influencing factors. The fish environmental DNA abundance of the regional fish environmental DNA data was calculated using quantitative PCR technology, and fluorescently labeled probes were used to quantify the DNA abundance by comparing the CT value (the threshold cycle number was set to 10). Based on the fish environmental DNA abundance data, ecological data analysis methods (such as the R software package vegan) were used to determine the fish community structure index of the fish community structure data to obtain the community structure index data.

[0103] Preferably, step S33 includes the following steps:

[0104] Step S331: determining the regional environmental characteristics of the target area water sample to generate the regional environmental characteristics of the water sample; synchronously collecting multi-parameter environmental data of the regional environmental characteristics of the water sample to generate regional environmental data;

[0105] Step S332: determining the regional seasonal conditions of the regional environmental data based on the regional water flow parameter information and the regional water level data to generate regional seasonal condition data; identifying the water temperature state of the regional environmental data based on the regional seasonal condition data to obtain regional water temperature state information; detecting the pH value of the regional environmental data based on the regional water temperature state information to generate regional environmental pH value data; monitoring the water body biological activity of the regional environmental pH value data to obtain water body biological activity data; calculating the dissolved oxygen level of the water body biological activity data to obtain regional environmental dissolved oxygen data;

[0106] Step S333: extracting water temperature attributes from the regional water temperature status information to obtain regional water temperature attributes; determining the dissolved oxygen distribution area of ​​the regional environmental dissolved oxygen data based on the regional water temperature attributes to generate dissolved oxygen distribution area information; calculating the regional pH concentration of the regional environmental pH data based on the dissolved oxygen distribution area information to obtain regional pH concentration data; integrating the regional water temperature attributes, dissolved oxygen distribution area information and regional pH concentration data to obtain regional environmental impact factors;

[0107] Step S334: Calculate the fish environmental DNA abundance of regional fish environmental DNA data using regional environmental influencing factors to generate fish environmental DNA abundance data.

[0108] In an embodiment of the present invention, the environmental characteristics of the target water sample area, including topography, water flow direction, vegetation coverage, etc., are determined through field investigation and remote sensing technology to generate water sample area environmental characteristics; a multi-parameter water quality monitor, such as YSISondes, is used to synchronously collect multi-parameter environmental data, including water temperature, dissolved oxygen, and pH value, etc., so as to obtain regional environmental data; based on regional water flow parameter information and regional water level data, a statistical analysis method, such as correlation analysis, is used to determine the regional seasonal conditions of the regional environmental data; based on the regional seasonal condition data, a temperature monitoring technology, such as a temperature sensor, is used to identify the water temperature state of the regional environmental data; through the regional water temperature state data, a pH value detection technology, such as an electrode method, is used to detect the pH value of the water sample of the regional environmental data; the regional environmental pH value data is monitored for water body biological activity, and a biological monitoring method, such as a biodiversity index calculation, is used to obtain water body biological activity data; the dissolved oxygen level of the water body biological activity data is measured, and a dissolved oxygen meter, such as a film electrode method or a fluorescence method, is used to obtain regional environmental dissolved oxygen data. The water temperature attributes are extracted from the regional water temperature status information, and the temperature time series analysis method is used to obtain the regional water temperature attributes; the regional environmental dissolved oxygen data is used to determine the dissolved oxygen distribution area according to the regional water temperature attributes, and the spatial interpolation technology is used to obtain the dissolved oxygen distribution area information; the regional environmental pH value data is used to calculate the regional pH concentration based on the dissolved oxygen distribution area information, and the water quality analysis software is used to obtain the regional pH concentration data; the regional water temperature attributes, dissolved oxygen distribution area information and regional pH concentration data are integrated, and multivariate statistical analysis methods such as principal component analysis are used to obtain the regional environmental impact factors; through the regional environmental impact factors, the quantitative PCR technology is used to calculate the fish environmental DNA abundance of the regional fish environmental DNA data to generate fish environmental DNA abundance data; specifically, quantitative PCR technology, as well as bioinformatics analysis methods, such as sequence alignment and abundance estimation algorithms, are used to extract fish abundance information from environmental DNA data.

[0109] Preferably, step S334 includes the following steps:

[0110] Step S3341: extracting the water body temperature gradient of the regional water body temperature attribute to obtain the water body temperature gradient characteristics; identifying the temperature gradient factor of the regional water temperature state information according to the water body temperature gradient characteristics to generate the water body temperature gradient factor;

[0111] Step S3342: dividing the regional environmental data into environmental dissolved oxygen regions according to the dissolved oxygen distribution area information to generate dissolved oxygen regional situation data; measuring the dissolved oxygen concentration of each region of the dissolved oxygen regional situation data to obtain the dissolved oxygen concentration of each region; identifying the dissolved oxygen concentration factor of the dissolved oxygen distribution area information based on the dissolved oxygen concentration of each region to generate the dissolved oxygen concentration factor of the water body;

[0112] Step S3343: determining the water acidity and alkalinity of the target area water sample according to the regional pH concentration data to obtain the regional water acidity and alkalinity data; identifying the acidity and alkalinity factor of the regional pH concentration data through the regional water acidity and alkalinity data to generate the water acidity and alkalinity factor;

[0113] Step S3344: Combine the water temperature gradient factor, the water dissolved oxygen concentration factor and the water acidity and alkalinity factor to match the fish water sample environmental demand degree of the target area water sample, and generate the fish ecological demand matching degree; based on the fish ecological demand matching degree, calculate the number of species of regional fish species data to obtain matching fish quantity information; combine the matching fish quantity information and the fish community structure data to calculate the fish environmental DNA abundance of the regional fish environmental DNA data, and generate fish environmental DNA abundance data.

[0114] In the embodiment of the present invention, the water body temperature gradient is extracted from the regional water body temperature attribute, and the temperature profile analysis technology is used to divide the water body into temperature gradients according to the regional water body temperature attribute, such as dividing the temperature gradient into three layers; according to the water body temperature gradient characteristics, the temperature gradient factor of the regional water temperature status information is identified by a statistical analysis method to generate a water body temperature gradient factor; the environmental dissolved oxygen region is divided into regional environmental data according to the dissolved oxygen distribution area information, and the spatial analysis technology is used to divide the regions according to the different characteristics of each environmental dissolved oxygen region; the dissolved oxygen concentration of each region is measured for the dissolved oxygen regional situation data, and a dissolved oxygen meter is used to measure to obtain the dissolved oxygen concentration of each region; based on the dissolved oxygen concentration of each region, the dissolved oxygen concentration factor of the dissolved oxygen distribution area information is identified by a multivariate statistical analysis method to generate a water body dissolved oxygen concentration factor. The method comprises the following steps: determining the acidity and alkalinity of water samples in the target area according to the regional pH concentration data, measuring with a pH meter, determining the acidity and alkalinity of water samples in the target area based on the specific conditions of the pH concentration and in combination with an acidity and alkalinity indicator, and obtaining the regional water acidity and alkalinity data; identifying the acidity and alkalinity factors of the regional pH concentration data through the regional water acidity and alkalinity data, and generating the water acidity and alkalinity factors; matching the environmental requirements of fish water samples in the target area with the matching degree of the fish ecological requirements by combining the water temperature gradient factor, the water dissolved oxygen concentration factor and the water acidity and alkalinity factor, and generating the matching degree of the fish ecological requirements; calculating the number of species of regional fish species data based on the matching degree of fish ecology requirements; calculating the fish environmental DNA abundance of regional fish environmental DNA data by combining the matching degree of fish quantity information and the fish community structure data, and generating the fish environmental DNA abundance data.

[0115] Preferably, step S34 includes the following steps:

[0116] Step S341: performing fish species statistics on the fish environmental DNA abundance data to generate species quantity statistical information; constructing a fish species quantity heat map based on the species quantity statistical information to obtain a fish species quantity heat map; performing image color recognition on the fish species quantity heat map to obtain heat map color features; performing color depth determination on the heat map color features to generate heat map color depth data;

[0117] Step S342: Perform color depth type mapping on the heat map color depth data and the fish species data to generate color depth type mapping data; perform fish dominant species identification on the fish environmental DNA abundance data according to the color depth type mapping data to obtain fish dominant species information;

[0118] Step S343: combining the regional seasonal situation data with the information on fish dominant species to associate seasonal dominant species to obtain seasonal dominant species association data; monitoring the seasonal change trend of dominant species on the seasonal dominant species association data to generate seasonal change trend data;

[0119] Step S344: Based on the seasonal change trend data, the seasonal fish ecological distribution is determined for the fish environmental DNA abundance data to obtain seasonal fish ecological distribution data; and the fish community structure index is determined for the fish community structure data in combination with the seasonal fish ecological distribution data to obtain community structure index data.

[0120] In an embodiment of the present invention, the fish environmental DNA abundance data is statistically analyzed, and bioinformatics software such as Mothur or QIIME2 is used to perform operations to obtain species quantity statistical information; a data visualization tool such as Python's Matplotlib library is used to construct a fish species quantity heat map according to the species quantity statistical information to obtain a fish species quantity heat map; an image processing technology such as the OpenCV library is used to perform image color recognition on the fish species quantity heat map to obtain the heat map color features; and then a color recognition technology such as RGB color space conversion technology is used to determine the color depth of the heat map color features; the heat map color depth data and the fish species data are mapped to color depth types, specifically, the color depth data and the fish species data are mapped to each other to generate color depth type mapping data; based on the color depth type mapping data, a multivariate statistical analysis method such as principal component analysis (PCA) is used to analyze the fish environmental DNA A abundance data is used to identify dominant fish species and obtain information on dominant fish species; in combination with regional seasonal situation data, time series analysis methods, such as seasonal decomposition time series (STL), are used to associate the information on dominant fish species with seasonal dominant species; time series analysis techniques, such as autocorrelation function (ACF) and partial autocorrelation function (PACF), are used to monitor the seasonal change trend of dominant species in seasonal dominant species association data and generate seasonal change trend data; based on seasonal change trend data, such as Pearson chi-square test technology, seasonal fish ecological distribution is determined for fish environmental DNA abundance data, and the seasonal fish ecological distribution of fish environmental DNA abundance is specifically determined; in combination with seasonal fish ecological distribution data, ecological indicator calculation methods, such as Shannon-Wiener diversity index, are used to measure fish community structure indicators for fish community structure data and obtain community structure indicator data.

[0121] Preferably, step S344 includes the following steps:

[0122] Step S3441: performing fish activity feature identification on the fish environmental DNA abundance data to obtain fish activity features; performing fish activity range determination on the fish activity features to generate fish activity range data; performing fish activity cycle identification on the fish activity range data to generate fish activity cycle information; performing seasonal fish ecological distribution determination on the fish environmental DNA abundance data based on the seasonal change trend data, the fish activity range data and the fish activity cycle information to obtain seasonal fish ecological distribution data;

[0123] Step S3442: identifying fish community diversity characteristics of fish community structure data in combination with seasonal fish ecological distribution data to generate fish community diversity characteristics; extracting diversity values ​​of fish community diversity characteristics to obtain community diversity values; calculating numerical indexes of community diversity values ​​to generate fish community diversity data;

[0124] Step S3443: extracting the dominant species of the fish community structure data according to the fish community diversity data and the fish dominant species information to obtain the dominant species data; performing community particle size structure identification on the dominant species data to generate the dominant community particle size structure information; performing particle size parameter calculation on the dominant community particle size structure information to obtain the dominant community particle size parameter data;

[0125] Step S3444: Calculate the fish community distribution uniformity of the fish community structure data based on the seasonal fish ecological distribution data to obtain community distribution uniformity data; determine the community stability of the community distribution uniformity data through the dominant community particle size parameter data and the seasonal fish ecological distribution data to generate fish community stability data;

[0126] Step S3445: Integrate the fish community diversity data, the dominant community particle size parameter data and the fish community stability data to obtain the community structure index data.

[0127] In an embodiment of the present invention, environmental DNA technology is used to analyze fish environmental DNA abundance data, and bioinformatics software such as Mothur is used to specifically identify fish activity characteristic information; fish activity range is measured based on fish activity characteristics according to the fish activity characteristic information, and the status of the fish activity range is specifically monitored; time series analysis methods, such as seasonal decomposition time series (STL) analysis, are used to identify fish activity cycles from fish activity range data; seasonal fish ecological distribution is determined from fish environmental DNA abundance data using redundancy analysis (RDA) technology in combination with seasonal change trend data, fish activity range data and fish activity cycle information, to obtain seasonal fish ecological distribution data; combined with seasonal fish ecological distribution data, ecological data analysis methods, such as diversity index calculation in R software package vegan, are used to identify fish community diversity characteristics from fish community structure data; numerical extraction techniques, such as principal component analysis (PCA), are used to extract diversity values ​​from fish community diversity characteristics; based on community diversity values, numerical index calculation methods, such as Shannon-Wiener diversity index, are used to calculate community diversity values ​​to generate fish community diversity data. Based on the fish community diversity data and the information of dominant fish species, pattern recognition technology is used to identify the dominant species of the fish community structure data; morphological analysis methods, such as particle size spectrum analysis, are used to identify the community particle size structure of the dominant species data; parameter calculation methods, such as linear regression analysis, are used to calculate the particle size parameters of the dominant community particle size structure information to obtain the dominant community particle size parameter data; based on the seasonal fish ecological distribution data, spatial analysis technology is used to measure the uniformity of fish community distribution on the fish community structure data to obtain the community distribution uniformity data; through the dominant community particle size parameter data and seasonal fish ecological distribution data, the Pearson chi-square test technology is used to measure the community stability of the community distribution uniformity data to generate fish community stability data; fish community diversity data, dominant community particle size parameter data and fish community stability data are integrated, and multivariate statistical analysis methods, such as principal component analysis (PCA), are used to obtain community structure indicator data.

[0128] Preferably, step S41 includes the following steps:

[0129] Step S41: constructing a water ecological health assessment model according to regional environmental impact factors, fish environmental DNA abundance data and community structure index data to obtain a water ecological health assessment pre-model, and training the water ecological health assessment pre-model using regional environmental impact factors, fish environmental DNA abundance data and community structure index data to obtain a water ecological health assessment training model;

[0130] Step S42: performing a model cross-validation evaluation on the water ecological health assessment training model to obtain model evaluation data; adjusting the model parameters of the water ecological health assessment training model using the model evaluation data to obtain a water ecological health assessment model;

[0131] Step S43: Conduct water ecological health assessment on water samples in the target area based on the water ecological health assessment model to generate water ecological health assessment data; visualize the water ecological health assessment data to generate a water ecological health assessment report.

[0132] In the embodiment of the present invention, a water ecological health assessment model is constructed according to regional environmental impact factors, fish environmental DNA abundance data and community structure indicator data; specifically, the model is constructed using a random forest algorithm, in which the depth of each decision tree is set to 10 and the minimum sample segmentation value is set to 2 to maximize the classification accuracy of the model; the pre-model is trained using the same data set, and the model parameters are adjusted, such as increasing the number of trees to 100, to improve the stability and predictive ability of the model, and obtain a water ecological health assessment training model; the training model is cross-validated and evaluated, using k-fold cross validation (k=5), and the results are calculated. Calculate the accuracy, recall rate and F1 score of the model; adjust the model parameters according to the evaluation data, for example, adjust the tree depth to 20 and the minimum sample segmentation value to 5, so as to obtain the water ecological health assessment model; use the water ecological health assessment model with adjusted parameters to evaluate the water samples in the target area; input the actual monitoring data, including the latest water samples in the target area, to generate water ecological health assessment data; visualize the water ecological health assessment data, specifically use Python's Matplotlib library to generate charts, intuitively display the key indicators of water ecological health, and generate a water ecological health assessment report.

[0133] Preferably, step S43 includes the following steps:

[0134] Step S431: inputting water samples from the target area into the water ecological health assessment model to perform water ecological health prediction, and obtain water ecological health prediction data; performing water ecological health risk trend prediction on water samples from the target area according to the water ecological health prediction data, and obtain water ecological health trend prediction data;

[0135] Step S432: Perform risk assessment processing on the water ecological health trend prediction data to obtain water ecological risk assessment data; perform water ecological health assessment on water samples in the target area based on the water ecological health assessment model through the water ecological risk assessment data to generate water ecological health assessment data;

[0136] Step S433: Process the water ecological health assessment data into information charts to obtain water ecological health assessment charts; compile reports on the water ecological health assessment charts to obtain water ecological health assessment reports.

[0137] In an embodiment of the present invention, water samples of the target area are input into a water ecological health assessment model to perform water ecological health prediction to obtain water ecological health prediction data; water ecological health risk trend prediction is performed on water samples of the target area according to the water ecological health prediction data, specifically, time series analysis of environmental data is performed, and seasonal decomposition time series (STL) analysis method is used to identify trends and seasonal patterns to obtain water ecological health trend prediction data; risk assessment processing is performed on the water ecological health trend prediction data, specifically, risk thresholds and classification standards are set according to a limit value scoring table of water quality parameters, thereby obtaining water ecological risk assessment data; water ecological health assessment is performed on water samples of the target area based on the water ecological risk assessment data and the water ecological health assessment model to obtain water ecological health assessment data; information graph processing is performed on the water ecological health assessment data, specifically, data visualization tools are used, such as Python's Matplotlib library to generate charts, such as bar charts and line charts, to display water ecological health scores and environmental parameter changes in different regions; and the charts are reported to obtain a water ecological health assessment report.

[0138] Therefore, the embodiments should be regarded as illustrative and non-restrictive from all points, and the scope of the present invention is limited by the appended claims rather than the above description, and it is intended that all changes falling within the meaning and range of equivalent elements of the application documents are included in the present invention.

[0139] The above description is only a specific embodiment of the present invention, so that those skilled in the art can understand or implement the present invention. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to the embodiments shown herein, but should conform to the widest scope consistent with the principles and novel features invented herein.

Claims

1. A method for assessing water ecological health based on fish environmental DNA experimental big data, characterized in that: The following steps are involved: Step S1: Obtain water samples from the target area; Filter water samples in the target area to generate water sample filtering data; Perform water centrifugation on water samples in the target area to generate water centrifugation data; Based on the water sample filtration data and water body centrifugation data, fish environmental DNA is extracted from water samples in the target area to obtain regional fish environmental DNA data; Step S2: determining the target DNA fragment of the regional fish environmental DNA data to obtain the target DNA fragment information; amplifying the target DNA fragment of the fish environmental DNA data according to the target DNA fragment information to generate DNA fragment amplification data; performing high-throughput sequencing on the DNA fragment amplification data to obtain high-throughput sequencing data; identifying the fish species of the regional fish environmental DNA data through the high-throughput sequencing data to obtain regional fish species data; determining the fish community structure of the target area water sample according to the regional fish species data to obtain fish community structure data; Step S3: monitoring water flow parameters of water samples in the target area to obtain regional water flow parameter information; The water level of the target area water samples is measured through the regional water flow parameter information to generate regional water level data; the regional surrounding environment data of the target area water samples is collected to generate regional environmental data; the influencing factors of the regional environmental data are identified based on the regional water flow parameter information and the regional water level data to generate regional environmental influencing factors; the fish environmental DNA abundance of the regional fish environmental DNA data is calculated through the regional environmental influencing factors to generate fish environmental DNA abundance data; the fish community structure index of the fish community structure data is measured based on the fish environmental DNA abundance data to obtain community structure index data; Step S4: construct a water ecological health assessment model based on regional environmental impact factors, fish environmental DNA abundance data and community structure indicator data to obtain a water ecological health assessment model; use the water ecological health assessment model to conduct water ecological health assessment on water samples in the target area to generate water ecological health assessment data; visualize the water ecological health assessment data to generate a water ecological health assessment report.

2. The water ecological health assessment method based on fish environmental DNA experimental big data according to claim 1 is characterized in that: Step S2 includes the following steps: Step S21: extracting sequence features from regional fish environmental DNA data to generate fish environmental DNA sequence features; performing sequence structure recognition on the fish environmental DNA sequence features to obtain sequence structure data; determining sequence structure domains of regional fish environmental DNA data using the sequence structure data to obtain DNA sequence structure domains; performing function detection on the DNA sequence structure domains to obtain DNA sequence function data; Step S22: mapping the DNA sequence function data and the sequence structure data to function structure fragments to generate function fragment mapping data; determining the target DNA fragment of the regional fish environmental DNA data based on the function fragment mapping data to obtain the target DNA fragment information; performing sequence function alignment on the fish environmental DNA data according to the target DNA fragment information to generate sequence function alignment data; Step S23: amplifying the target DNA structure fragment of the DNA sequence domain through the sequence function alignment data and the target DNA fragment information to generate DNA fragment amplification data; calculating the fragment length of the DNA fragment amplification data to generate amplified fragment length data; performing high-throughput sequencing on the DNA fragment amplification data in combination with the amplified fragment length data to obtain high-throughput sequencing data; Step S24: fish species are identified on regional fish environmental DNA data through high-throughput sequencing data to obtain regional fish species data; fish community structure is determined on target area water samples based on the regional fish species data to obtain fish community structure data.

3. The water ecological health assessment method based on fish environmental DNA experimental big data according to claim 2 is characterized in that: Step S24 includes the following steps: Step S241: performing sequencing function sequence matching on the functional fragment mapping data according to the high-throughput sequencing data to generate sequencing function sequence matching data; determining the function type of the DNA sequence function data based on the sequencing function sequence matching data to obtain sequence function type information; Step S242: performing type grouping on the sequence function type information to generate sequence function type grouping data; performing sequence type component feature extraction on the sequence function type grouping data to obtain sequence type component features; performing fish feature correspondence on the regional fish environmental DNA data according to the sequence type component features to generate fish feature correspondence data; performing fish species identification on the fish feature correspondence data in combination with the sequence function type grouping data to obtain regional fish species data; Step S243: performing fish composition type identification on the regional fish species data to obtain fish composition type data; performing composition type domain matching on the fish composition type data and the DNA sequence structure domain to obtain fish type structure domain data; performing fish species distribution feature extraction on the fish type structure domain data to generate fish species distribution features; Step S244: Identify the fish distribution structure based on the fish species distribution characteristics to obtain fish distribution structure data; divide the fish distribution structure data into structural hierarchies to obtain fish structure hierarchy information; divide the target area water samples into fish community structures based on the fish structure hierarchy information and regional fish species data to obtain fish community structure data.

4. The water ecological health assessment method based on fish environmental DNA experimental big data according to claim 1 is characterized in that: Step S3 includes the following steps: Step S31: Continuously track the flow of water samples in the target area to generate regional water sample flow tracking data; identify water flow parameter characteristics of the regional water sample flow tracking data to obtain water flow parameter characteristics; synchronously record water flow multi-parameters of the target area water samples according to the water flow parameter characteristics to generate regional water flow parameter information; Step S32: establishing a flow-water level relationship for water samples in the target area based on the regional water flow parameter information to generate a flow-water level relationship network; performing regional flow parameter measurement on the flow-water level relationship network to obtain flow parameter measurement data; performing parameter change identification on the flow parameter measurement data to generate flow parameter change information; performing regional water level state mapping on the flow-water level relationship network according to the flow parameter change information to obtain regional water level data; Step S33: collecting regional surrounding environmental data of target area water samples to generate regional environmental data; identifying influencing factors of regional environmental data based on regional water flow parameter information and regional water level data to generate regional environmental influencing factors; calculating fish environmental DNA abundance of regional fish environmental DNA data using regional environmental influencing factors to generate fish environmental DNA abundance data; Step S34: Determine the fish community structure index of the fish community structure data according to the fish environmental DNA abundance data to obtain the community structure index data.

5. The water ecological health assessment method based on fish environmental DNA experimental big data according to claim 4 is characterized in that: Step S33 includes the following steps: Step S331: determining the regional environmental characteristics of the target area water sample to generate the regional environmental characteristics of the water sample; synchronously collecting multi-parameter environmental data of the regional environmental characteristics of the water sample to generate regional environmental data; Step S332: determining the regional seasonal conditions of the regional environmental data based on the regional water flow parameter information and the regional water level data to generate regional seasonal condition data; identifying the water temperature state of the regional environmental data based on the regional seasonal condition data to obtain regional water temperature state information; detecting the pH value of the regional environmental data based on the regional water temperature state information to generate regional environmental pH value data; monitoring the water body biological activity of the regional environmental pH value data to obtain water body biological activity data; calculating the dissolved oxygen level of the water body biological activity data to obtain regional environmental dissolved oxygen data; Step S333: extracting water temperature attributes from the regional water temperature status information to obtain regional water temperature attributes; determining the dissolved oxygen distribution area of ​​the regional environmental dissolved oxygen data based on the regional water temperature attributes to generate dissolved oxygen distribution area information; calculating the regional pH concentration of the regional environmental pH data based on the dissolved oxygen distribution area information to obtain regional pH concentration data; integrating the regional water temperature attributes, dissolved oxygen distribution area information and regional pH concentration data to obtain regional environmental impact factors; Step S334: Calculate the fish environmental DNA abundance of regional fish environmental DNA data using regional environmental influencing factors to generate fish environmental DNA abundance data.

6. The water ecological health assessment method based on fish environmental DNA experimental big data according to claim 5 is characterized in that: Step S334 includes the following steps: Step S3341: extracting the water body temperature gradient of the regional water body temperature attribute to obtain the water body temperature gradient characteristics; identifying the temperature gradient factor of the regional water temperature state information according to the water body temperature gradient characteristics to generate the water body temperature gradient factor; Step S3342: dividing the regional environmental data into environmental dissolved oxygen regions according to the dissolved oxygen distribution area information to generate dissolved oxygen regional situation data; measuring the dissolved oxygen concentration of each region of the dissolved oxygen regional situation data to obtain the dissolved oxygen concentration of each region; identifying the dissolved oxygen concentration factor of the dissolved oxygen distribution area information based on the dissolved oxygen concentration of each region to generate the dissolved oxygen concentration factor of the water body; Step S3343: determining the water acidity and alkalinity of the target area water sample according to the regional pH concentration data to obtain the regional water acidity and alkalinity data; identifying the acidity and alkalinity factor of the regional pH concentration data through the regional water acidity and alkalinity data to generate the water acidity and alkalinity factor; Step S3344: Combine the water temperature gradient factor, the water dissolved oxygen concentration factor and the water acidity and alkalinity factor to match the fish water sample environmental demand degree of the target area water sample, and generate the fish ecological demand matching degree; based on the fish ecological demand matching degree, calculate the number of species of regional fish species data to obtain matching fish quantity information; combine the matching fish quantity information and the fish community structure data to calculate the fish environmental DNA abundance of the regional fish environmental DNA data, and generate fish environmental DNA abundance data.

7. The water ecological health assessment method based on fish environmental DNA experimental big data according to claim 4 is characterized in that: Step S34 includes the following steps: Step S341: performing fish species statistics on the fish environmental DNA abundance data to generate species quantity statistical information; constructing a fish species quantity heat map based on the species quantity statistical information to obtain a fish species quantity heat map; performing image color recognition on the fish species quantity heat map to obtain heat map color features; performing color depth determination on the heat map color features to generate heat map color depth data; Step S342: Perform color depth type mapping on the heat map color depth data and the fish species data to generate color depth type mapping data; perform fish dominant species identification on the fish environmental DNA abundance data according to the color depth type mapping data to obtain fish dominant species information; Step S343: combining the regional seasonal situation data with the information on fish dominant species to associate seasonal dominant species to obtain seasonal dominant species association data; monitoring the seasonal change trend of dominant species on the seasonal dominant species association data to generate seasonal change trend data; Step S344: Based on the seasonal change trend data, the seasonal fish ecological distribution is determined for the fish environmental DNA abundance data to obtain seasonal fish ecological distribution data; and the fish community structure index is determined for the fish community structure data in combination with the seasonal fish ecological distribution data to obtain community structure index data.

8. The water ecological health assessment method based on fish environmental DNA experimental big data according to claim 7 is characterized in that: Step S344 includes the following steps: Step S3441: performing fish activity feature identification on the fish environmental DNA abundance data to obtain fish activity features; performing fish activity range determination on the fish activity features to generate fish activity range data; performing fish activity cycle identification on the fish activity range data to generate fish activity cycle information; performing seasonal fish ecological distribution determination on the fish environmental DNA abundance data based on the seasonal change trend data, the fish activity range data and the fish activity cycle information to obtain seasonal fish ecological distribution data; Step S3442: identifying fish community diversity characteristics of fish community structure data in combination with seasonal fish ecological distribution data to generate fish community diversity characteristics; extracting diversity values ​​of fish community diversity characteristics to obtain community diversity values; calculating numerical indexes of community diversity values ​​to generate fish community diversity data; Step S3443: extracting the dominant species of the fish community structure data according to the fish community diversity data and the fish dominant species information to obtain the dominant species data; performing community particle size structure identification on the dominant species data to generate the dominant community particle size structure information; performing particle size parameter calculation on the dominant community particle size structure information to obtain the dominant community particle size parameter data; Step S3444: Calculate the fish community distribution uniformity of the fish community structure data based on the seasonal fish ecological distribution data to obtain community distribution uniformity data; determine the community stability of the community distribution uniformity data through the dominant community particle size parameter data and the seasonal fish ecological distribution data to generate fish community stability data; Step S3445: Integrate the fish community diversity data, the dominant community particle size parameter data and the fish community stability data to obtain the community structure index data.

9. The water ecological health assessment method based on fish environmental DNA experimental big data according to claim 1 is characterized in that: Step S4 includes the following steps: Step S41: constructing a water ecological health assessment model according to regional environmental impact factors, fish environmental DNA abundance data and community structure index data to obtain a water ecological health assessment pre-model, and training the water ecological health assessment pre-model using regional environmental impact factors, fish environmental DNA abundance data and community structure index data to obtain a water ecological health assessment training model; Step S42: performing a model cross-validation evaluation on the water ecological health assessment training model to obtain model evaluation data; adjusting the model parameters of the water ecological health assessment training model using the model evaluation data to obtain a water ecological health assessment model; Step S43: Conduct water ecological health assessment on water samples in the target area based on the water ecological health assessment model to generate water ecological health assessment data; visualize the water ecological health assessment data to generate a water ecological health assessment report.

10. The water ecological health assessment method based on fish environmental DNA experimental big data according to claim 9 is characterized in that: Step S43 includes the following steps: Step S431: inputting water samples from the target area into the water ecological health assessment model to perform water ecological health prediction, and obtain water ecological health prediction data; performing water ecological health risk trend prediction on water samples from the target area according to the water ecological health prediction data, and obtain water ecological health trend prediction data; Step S432: Perform risk assessment processing on the water ecological health trend prediction data to obtain water ecological risk assessment data; perform water ecological health assessment on water samples in the target area based on the water ecological health assessment model through the water ecological risk assessment data to generate water ecological health assessment data; Step S433: Process the water ecological health assessment data into information charts to obtain water ecological health assessment charts; compile reports on the water ecological health assessment charts to obtain water ecological health assessment reports.

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