Method and device for assessing risk of complex pollution of aquatic organisms, equipment and medium

Abstract

The application provides a water area biological composite pollution risk assessment method, device, equipment and medium, comprising: acquiring a pathogenic microorganism abundance table of a water area to be evaluated; constructing a biological pollution IBC index system with the pathogenic microorganism abundance as an index; determining the index score of each biological pollution IBC index based on the determined pathogenic microorganism abundance background value and the corrected pathogenic microorganism abundance value; determining the IBC value of each biological pollution IBC index based on the index score; calculating the water quality comprehensive evaluation index of the water area to be evaluated; constructing a multiple linear regression model with the water quality comprehensive evaluation index as the dependent variable and multiple IBC index scores as the independent variable; fitting the multiple linear regression model by using the least square method to obtain a regression coefficient; and after normalization processing of the regression coefficient, obtaining the weight of each pathogenic microorganism index; and based on the weight, performing weighted summation on the multiple IBC values to obtain a biological composite pollution risk assessment value.

Description

Technical Field

[0001] This invention relates to the field of wetland ecological environment protection technology, and to methods, devices, equipment and media for risk assessment of biological complex pollution in aquatic areas, but not limited to these. Background Technology

[0002] In recent years, the Microbiome-index of Biological Integrity (M-IBI) has provided a new tool for assessing the health of aquatic ecosystems by integrating parameters such as taxonomic abundance, diversity, and susceptible species. For example, the M-IBI constructed based on fungal communities has revealed a significant correlation between water quality and ecological health. This system can not only effectively assess the health status of urban river ecosystems but also serve as a standard for measuring the effectiveness of river ecological restoration. By combining the systematic pathogen detection advantages of metagenomics with the quantitative modeling of the IBI, it is hoped that the limitations of traditional monitoring methods can be overcome, and a comprehensive assessment system for biological pollution risk can be established. Summary of the Invention

[0003] To address the aforementioned technical problems in existing technologies, this invention provides a method, apparatus, equipment, and storage medium for risk assessment of aquatic biological complex pollution.

[0004] The technical method of this invention is implemented as follows:

[0005] In a first aspect, embodiments of the present invention provide a method for risk assessment of biological complex pollution in aquatic waters, the method comprising:

[0006] Obtain a pathogen abundance table for the water body to be assessed; the pathogens include bacterial pathogens, fungal pathogens, and protozoan pathogens.

[0007] Using the abundance of pathogenic microorganisms as an indicator, a biocontamination IBC indicator system was constructed;

[0008] Based on the determined baseline abundance value of pathogenic microorganisms and the corrected abundance value of pathogenic microorganisms, the index score of each of the aforementioned biocontamination IBC indicators is determined;

[0009] Based on the index scores, the IBC value of each of the aforementioned biological pollution IBC indicators is determined.

[0010] Calculate the comprehensive water quality evaluation index of the water area to be evaluated;

[0011] A multiple linear regression model is constructed by using the water quality comprehensive evaluation index as the dependent variable and the scores of multiple IBC indicators as independent variables.

[0012] The least squares method was used to fit the multiple linear regression model to obtain the regression coefficients; and the regression coefficients were normalized to obtain the weight of each pathogenic microorganism index.

[0013] Based on the weights, the multiple IBC values ​​are weighted and summed to obtain the risk assessment value for biocomplex pollution.

[0014] Secondly, the present invention provides a risk assessment device for aquatic biological complex pollution, the device comprising:

[0015] The acquisition module is used to acquire the abundance table of pathogenic microorganisms in the water area to be assessed; the pathogenic microorganisms include bacterial pathogenic microorganisms, fungal pathogenic microorganisms, and protozoan pathogenic microorganisms;

[0016] The module is used to construct an IBC (Indicator of Biocontamination) index system based on the abundance of pathogenic microorganisms.

[0017] The determination module is used to determine the index score of each of the biocontamination IBC indicators based on the determined background value of pathogenic microorganism abundance and the corrected pathogenic microorganism abundance value.

[0018] The determining module is further configured to determine the IBC value of each of the biopollution IBC indicators based on the indicator scores.

[0019] The calculation module is used to calculate the comprehensive water quality evaluation index of the water area to be evaluated;

[0020] The construction module is also used to construct a multiple linear regression model by taking the water quality comprehensive evaluation index as the dependent variable and the scores of multiple IBC indicators as independent variables.

[0021] The fitting module is used to fit the multiple linear regression model using the least squares method to obtain the regression coefficients; and after normalizing the regression coefficients, the weight of each pathogenic microorganism index is obtained.

[0022] The weighting module is used to perform a weighted summation of multiple IBC values ​​based on the weights to obtain a biocomplex pollution risk assessment value.

[0023] In some embodiments, the acquisition module is further configured to: uniformly set multiple sampling points within the water area to be evaluated; mix water samples collected from multiple sampling points to form a mixed water sample; perform metagenomic sequencing on the microorganisms in the mixed water sample to obtain raw metagenomic sequencing sequences; perform hydrochemical analysis on the mixed water sample to obtain hydrochemical analysis results; perform quality control on the raw sequences, sequentially removing low-quality sequences and human contamination gene sequences to obtain processed sequences; assemble the processed sequences with a preset high-quality sequence to obtain a contig sequence file, and assemble the genome into a MAG; remove duplicate MAGs from the MAG based on preset screening rules to obtain a non-redundant MAG dataset; calculate the normalized abundance of each MAG in the non-redundant MAG dataset to form a normalized abundance table; compare the non-redundant MAG dataset with a constructed pathogenic microorganism reference database to obtain pathogenic microorganisms; and merge the normalized abundance table and the pathogenic microorganisms into the pathogenic microorganism abundance table.

[0024] In some embodiments, the determining module is further configured to calculate the background values ​​of the bacterial pathogens, the fungal pathogens, and the protozoan pathogens based on the abundance of pathogens at multiple background locations; perform background correction on the abundance of pathogens at each sampling point to obtain the corrected abundance value of pathogens; and calculate the index score based on the background abundance value of pathogens and the corrected abundance value of pathogens.

[0025] In some embodiments, the calculation module is further configured to select DO and NH4. + -N, TP, COD, and TN are used as evaluation parameters for organic matter and nutrient pollution; the calculation formula for the Comprehensive Water Quality Index (CWQI) corresponding to each of the evaluation parameters is as follows:

[0026] When the evaluation parameter is DO, a larger value indicates a better water environment, calculated using the following formula: ;

[0027] When the evaluation parameter is NH4 + When -N, TP, COD, or TN are considered, a higher evaluation parameter value indicates a worse water environment, calculated using the following formula: ;

[0028] In the formula, The actual value of observed variable i (DO, COD, NH4) + -N, TN and TP); Refers to the reference value of observed variable i under the j-level standard; Refers to the reference value of observed variable i under the Class 1 standard for surface water; Refers to the reference value of observed variable i under the surface water j-1 standard; Refers to the reference value of observed variable i under the Class 5 standard for surface water;

[0029] Based on the comprehensive water quality evaluation index corresponding to the evaluation parameters, the comprehensive water quality evaluation index (CWQI) is determined, and the calculation formula for the CWQI is as follows:

[0030] .

[0031] In some embodiments, the multiple linear regression model is expressed as:

[0032] ;

[0033] In the formula, The intercept; , , These are the regression coefficients for bacterial pathogens, fungal pathogens, and protozoan pathogens, respectively. , , IBC values ​​for bacterial pathogens, fungal pathogens, and protozoan pathogens, respectively.

[0034] In some embodiments, the weight of each of the pathogenic microorganism indicators is represented as follows:

[0035] ;

[0036] ;

[0037] .

[0038] In some embodiments, the apparatus further includes: a division module, configured to sort the risk assessment values ​​of the bio-complex pollution from multiple sampling points, use the 95th percentile of the sorted risk assessment values ​​of the bio-complex pollution as the expected value, and divide the interval after the expected value into risk levels to obtain risk level division results.

[0039] Thirdly, embodiments of the present invention provide an electronic device, including: a memory for storing executable instructions; and a processor for executing the executable instructions stored in the memory to implement the above-mentioned method for assessing the risk of aquatic biological complex pollution.

[0040] Fourthly, embodiments of the present invention provide a computer-readable storage medium storing executable instructions for causing a processor to execute the executable instructions to implement the above-described method for assessing the risk of complex biological pollution in aquatic waters.

[0041] The method, apparatus, equipment, and medium for assessing the risk of aquatic biological complex pollution provided in this invention first obtain a pathogenic microorganism abundance table for the water area to be assessed; then, using pathogenic microorganism abundance as an indicator, a biological pollution IBC index system is constructed; secondly, based on the determined background value and corrected pathogenic microorganism abundance value, the index score of each of the biological pollution IBC indicators is determined; based on the index score, the IBC value of each biological pollution IBC indicator is determined; a comprehensive water quality evaluation index for the water area to be assessed is calculated; using the comprehensive water quality evaluation index as the dependent variable and multiple IBC indicator scores as independent variables, a multiple linear regression model is constructed; finally, the least squares method is used to fit the multiple linear regression model to obtain regression coefficients; after normalizing the regression coefficients, the weight of each pathogenic microorganism indicator is obtained; based on the weights, multiple IBC values ​​are weighted and summed to obtain the risk assessment value of biological complex pollution. Thus, this invention, based on metagenomic data, integrates the abundance of three types of pathogenic microorganisms—bacteria, fungi, and protozoa—into a biological pollution indicator system for the first time. This overcomes the limitations of traditional single biological indicators, enabling accurate identification and quantitative assessment of multiple pathogenic groups and significantly improving the detection sensitivity for hidden biological pollution risks. Secondly, based on a least squares regression model, the IBC indicators of the three types of pathogenic microorganisms are dynamically coupled with the comprehensive water quality index to determine the weight parameters of the IBC indicators. Finally, the biological composite pollution index is calculated. This model optimizes weight allocation through data-driven optimization, avoiding the bias of traditional subjective weighting methods, making the assessment results more consistent with actual pollution characteristics, and improving the accuracy and precision of the assessment. Thirdly, based on a dynamic risk level classification standard using quantile statistics, the Total-IBC value is divided into five equal intervals using the 95th percentile of the total sample as a benchmark, forming a gradient evaluation system from high risk to extremely low risk. This method adaptively adjusts the threshold based on data, solving the problem of poor applicability of traditional fixed thresholds to different wetland habitats, and providing managers with an intuitive basis for hierarchical management. Attached Figure Description

[0042] Figure 1 This is a schematic diagram of the structure of a water biological complex pollution risk assessment system provided in an embodiment of the present invention;

[0043] Figure 2 This is a flowchart illustrating a method for assessing the risk of biological complex pollution in aquatic areas, as provided in an embodiment of the present invention.

[0044] Figure 3 This is a regression analysis graph of CWQI and Total-IBC for all sample points provided in the embodiments of the present invention;

[0045] Figure 4 This is a schematic diagram of the composition and structure of the aquatic biological complex pollution risk assessment device provided in an embodiment of the present invention;

[0046] Figure 5 This is a schematic diagram of the composition structure of the electronic device provided in the embodiment of the present invention. Detailed Implementation

[0047] To make the objectives, technical solutions, and advantages of the present invention clearer, the present invention will be further described in detail below with reference to the accompanying drawings. The described embodiments should not be regarded as limitations on the present invention. All other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0048] In the following description, references to "some embodiments" refer to a subset of all possible embodiments; however, it is understood that "some embodiments" may be the same or different subsets of all possible embodiments and may be combined with each other without conflict. Unless otherwise defined, all technical and scientific terms used in the embodiments of the invention have the same meaning as commonly understood by one of ordinary skill in the art to which the embodiments of the invention pertain. The terminology used in the embodiments of the invention is for the purpose of describing the embodiments of the invention only and is not intended to limit the invention.

[0049] The following describes exemplary applications of the aquatic biological complex pollution risk assessment device according to embodiments of the present invention. The aquatic biological complex pollution risk assessment device provided in these embodiments can be implemented as a terminal or a server. In one implementation, the aquatic biological complex pollution risk assessment device provided in these embodiments can be implemented as various types of terminals such as laptops, tablets, desktop computers, and mobile devices. In another implementation, the aquatic biological complex pollution risk assessment device provided in these embodiments can also be implemented as a server. The server can be an independent physical server, a server cluster or distributed system composed of multiple physical servers, or a cloud server providing basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communication, middleware services, domain name services, security services, content delivery networks (CDNs), and big data and artificial intelligence platforms. The terminal and server can be directly or indirectly connected via wired or wireless communication, and this is not limited in these embodiments. The following will describe exemplary applications when the aquatic biological complex pollution risk assessment device is implemented as a server.

[0050] See Figure 1 , Figure 1This is a schematic diagram of the structure of the aquatic biological complex pollution risk assessment system 10 provided in an embodiment of the present invention. The present invention can provide an aquatic biological complex pollution risk assessment platform, which can be implemented as an aquatic biological complex pollution risk assessment application. The aquatic biological complex pollution risk assessment system 10 provided in this embodiment includes a terminal 110, a network 120, and a server 130, wherein the server 130 is the server for the aquatic biological complex pollution risk assessment application. The server 130 can constitute the aquatic biological complex pollution risk assessment device of this embodiment. The terminal 110 is connected to the server 130 through the network 120, which can be a wide area network (WAN), a local area network (LAN), or a combination of both.

[0051] In some embodiments, please refer to Figure 1 When assessing the risk of biological complex pollution in a water area, terminal 110 sends the risk assessment task to server 130 via network 120. Server 130 responds to the task by obtaining the pathogen abundance table for the water area to be assessed; constructs an IBC (Individual Biohazard) index system using pathogen abundance as an indicator; determines the index score for each IBC index based on the determined baseline and corrected pathogen abundance values; determines the IBC value for each IBC index based on the index score; calculates the comprehensive water quality evaluation index for the water area to be assessed; constructs a multiple linear regression model using the comprehensive water quality evaluation index as the dependent variable and multiple IBC index scores as independent variables; fits the multiple linear regression model using the least squares method to obtain regression coefficients; normalizes the regression coefficients to obtain the weight of each pathogen index; and weights and sums the multiple IBC values ​​based on the weights to obtain the risk assessment value for biological complex pollution. After obtaining the risk assessment value of biological complex pollution, server 130 sends the risk assessment value of biological complex pollution to terminal 110 through network 120.

[0052] This invention provides a method for risk assessment of biological complex pollution in aquatic waters. (See also...) Figure 2 , Figure 2 This is a flowchart illustrating the risk assessment method for aquatic biological complex pollution provided in this embodiment of the invention, which will be combined with... Figure 2 The steps shown are explained.

[0053] Step S210: Obtain the abundance table of pathogenic microorganisms in the water area to be evaluated; the pathogenic microorganisms include bacterial pathogenic microorganisms, fungal pathogenic microorganisms and protozoan pathogenic microorganisms.

[0054] In some embodiments, the water area to be assessed refers to any water body area whose water quality requires a biocontamination risk assessment, encompassing natural water bodies (such as rivers, lakes, and reservoirs), artificial water bodies (such as wetland parks, landscape water bodies, and industrial water systems), and water bodies for special purposes (such as drinking water sources and aquaculture water areas). In this invention, the water area to be assessed refers to the water area of ​​a wetland park.

[0055] In some embodiments, the pathogen abundance table is a structured table that records the abundance information of bacterial pathogens (such as Escherichia coli and Vibrio cholerae that cause intestinal diseases), fungal pathogens (such as Candida albicans and Aspergillus that can cause fungal infections in humans or animals and plants), and protozoan pathogens (such as Giardia lamblia and Cryptosporidium that cause diarrhea) in the water samples to be assessed. Unlike conventional tables that only record a single pathogen, this table integrates data from all three types of pathogens, comprehensively reflecting the biopollution status of the water area and providing a rich and systematic data foundation for subsequent assessments.

[0056] Step S220: Construct a biopollution IBC indicator system using pathogenic microorganism abundance as an indicator.

[0057] In some embodiments, pathogen abundance refers to the relative or absolute proportion of various pathogenic microorganisms in a specific sample. Relative abundance reflects the proportion of a particular microorganism in the total microorganisms, while absolute abundance represents the actual number of microorganisms per unit volume of sample. In this invention, metagenomic sequencing is used to detect pathogen abundance.

[0058] In some embodiments, the Biopolluting IBC index system records the abundance data of pathogenic microorganisms in the water samples to be evaluated in tabular form, including fields such as sample information, microbial species, abundance value, detection time and method.

[0059] Step S230: Based on the determined baseline abundance value of pathogenic microorganisms and the corrected abundance value of pathogenic microorganisms, determine the index score of each of the biocontamination IBC indicators.

[0060] In some embodiments, the pathogenic microorganism abundance background value refers to the normal abundance level of pathogenic microorganisms in waters that are not significantly polluted or in a natural state.

[0061] In some embodiments, the corrected pathogen abundance value refers to the pathogen abundance value after correction processing, which can be performed using a random forest algorithm based on machine learning.

[0062] Step S240: Based on the index scores, determine the IBC index score for each of the biopollution IBC indicators.

[0063] In some embodiments, the IBC value refers to the index score based on the IBC index system for biocontamination, which is obtained through a calculation model and reflects the biocontamination risk level of pathogenic microorganisms, taking into account the interaction between microorganisms and the degree of community disorder.

[0064] Step S250: Calculate the comprehensive water quality evaluation index of the water area to be evaluated.

[0065] In some embodiments, the comprehensive water quality evaluation index is calculated using a multimodal fusion assessment model that integrates biological, chemical, and ecological data. It integrates the IBC value of biological pollution, the environmental persistence and bioaccumulation index of chemical pollutants, and the species diversity and ecosystem service value of aquatic ecosystems, utilizing transfer learning and tensor decomposition techniques to achieve a comprehensive assessment and trend prediction of water quality status.

[0066] Step S260: Using the water quality comprehensive evaluation index as the dependent variable and the scores of multiple IBC indicators as independent variables, a multiple linear regression model is constructed.

[0067] Step S270: The least squares method is used to fit the multiple linear regression model to obtain the regression coefficients; and the regression coefficients are normalized to obtain the weight of each pathogenic microorganism index.

[0068] In some embodiments, the regression coefficients refer to the parameters obtained by fitting a multiple linear regression model using the least squares method, reflecting the magnitude and direction of the influence of each IBC index score on the comprehensive water quality evaluation index.

[0069] In some embodiments, the weights of pathogenic microorganism indicators refer to the values ​​obtained after normalizing the regression coefficients, which are dynamically adjusted in conjunction with the information entropy theory to reflect the relative importance of each pathogenic microorganism indicator in the comprehensive water quality evaluation. Cross-validation using generative adversarial networks ensures robustness.

[0070] Step S280: Based on the weights, the scores of multiple IBC indicators are weighted and summed to obtain the risk assessment value of biocomplex pollution.

[0071] In some embodiments, the scores of multiple IBC indicators are weighted and summed based on the pathogenic microorganism index weights, and a dynamic risk assessment framework is constructed by combining Monte Carlo simulation, scenario analysis and socio-economic factors to comprehensively assess the risk of bio-complex pollution from multiple dimensions.

[0072] The method, apparatus, equipment, and medium for assessing the risk of aquatic biological complex pollution provided in this invention first obtain a pathogenic microorganism abundance table for the water area to be assessed; then, using pathogenic microorganism abundance as an indicator, a biological pollution IBC index system is constructed; secondly, based on the determined background value and corrected pathogenic microorganism abundance value, the index score of each of the biological pollution IBC indicators is determined; based on the index score, the IBC value of each biological pollution IBC indicator is determined; a comprehensive water quality evaluation index for the water area to be assessed is calculated; using the comprehensive water quality evaluation index as the dependent variable and multiple IBC indicator scores as independent variables, a multiple linear regression model is constructed; finally, the least squares method is used to fit the multiple linear regression model to obtain regression coefficients; after normalizing the regression coefficients, the weight of each pathogenic microorganism indicator is obtained; based on the weights, multiple IBC values ​​are weighted and summed to obtain the risk assessment value of biological complex pollution. Thus, this invention, based on metagenomic data, integrates the abundance of three types of pathogenic microorganisms—bacteria, fungi, and protozoa—into a biological pollution indicator system for the first time. This overcomes the limitations of traditional single biological indicators, enabling accurate identification and quantitative assessment of multiple pathogenic groups and significantly improving the detection sensitivity for hidden biological pollution risks. Secondly, based on a least squares regression model, the IBC indicators of the three types of pathogenic microorganisms are dynamically coupled with the comprehensive water quality index to determine the weight parameters of the IBC indicators. Finally, the biological composite pollution index is calculated. This model optimizes weight allocation through data-driven optimization, avoiding the bias of traditional subjective weighting methods, making the assessment results more consistent with actual pollution characteristics, and improving the accuracy and precision of the assessment. Thirdly, based on a dynamic risk level classification standard using quantile statistics, the Total-IBC value is divided into five equal intervals using the 95th percentile of the total sample as a benchmark, forming a gradient evaluation system from high risk to extremely low risk. This method adaptively adjusts the threshold based on data, solving the problem of poor applicability of traditional fixed thresholds to different wetland habitats, and providing managers with an intuitive basis for hierarchical management.

[0073] In some embodiments, step S210 can be implemented by the following steps S211 to S217:

[0074] Step S211: Multiple sampling points are evenly set up in the water area to be evaluated, and the water samples collected from the multiple sampling points are mixed to form a mixed water sample.

[0075] Step S212: Metagenomic sequencing is performed on the microorganisms in the mixed water sample to obtain the raw metagenomic sequencing sequence; and water chemical analysis is performed on the mixed water sample to obtain the water chemical analysis results.

[0076] Step S213: Perform quality control on the original sequence, sequentially removing low-quality sequences and human contamination gene sequences to obtain the processed sequence; splice the processed sequence with a preset high-quality sequence to obtain a contig sequence file, and assemble the genome into a MAG.

[0077] Step S214: Based on preset filtering rules, remove duplicate MAGs from the MAGs to obtain a non-redundant MAG dataset.

[0078] Step S215: Calculate the normalized abundance of each MAG in the non-redundant MAG dataset to form a normalized abundance table.

[0079] Step S216: Compare the non-redundant MAG dataset with the constructed pathogenic microorganism reference database to obtain the pathogenic microorganisms.

[0080] Step S217: Combine the standardized abundance table with the pathogenic microorganism to form the pathogenic microorganism abundance table.

[0081] In some embodiments, step S230 can be implemented by the following steps S231 to S233:

[0082] Step S231: Based on the abundance of pathogenic microorganisms at multiple background locations, calculate the background values ​​of the bacterial pathogenic microorganisms, the fungal pathogenic microorganisms, and the protozoan pathogenic microorganisms.

[0083] Step S232: Perform background correction on the abundance of pathogenic microorganisms at each sampling point to obtain the corrected abundance value of pathogenic microorganisms.

[0084] Step S233: Calculate the index score based on the baseline abundance value of the pathogenic microorganism and the corrected abundance value of the pathogenic microorganism.

[0085] In some embodiments, step S250 can be implemented by the following steps S251 to S254:

[0086] Step S251, select DO and NH4 + -N, TP, COD, and TN are used as evaluation parameters for organic matter and nutrient pollution.

[0087] Step S252: When the evaluation parameter is DO, a larger evaluation parameter value indicates a better water environment, calculated using the following formula:

[0088] .

[0089] Step S253, when the evaluation parameter is NH4 +When -N, TP, COD, or TN are considered, a higher evaluation parameter value indicates a worse water environment, calculated using the following formula:

[0090] .

[0091] In the formula, The observed variable i (including DO, COD, NH4) + The actual values ​​of -N, TN, and TP, Refers to the reference value of observed variable i under the j-level standard; Refers to the reference value of observed variable i under the Class 1 standard for surface water; Refers to the reference value of observed variable i under the surface water j-1 standard; This refers to the reference value of observed variable i under the Class 5 standard for surface water.

[0092] Step S254: Based on the comprehensive water quality evaluation index corresponding to the evaluation parameters, determine the comprehensive water quality evaluation index (CWQI). The calculation formula for the CWQI is as follows:

[0093] .

[0094] In some embodiments, the multiple linear regression model is expressed as:

[0095] ;

[0096] In the formula, The intercept; , , These are the regression coefficients for bacterial pathogens, fungal pathogens, and protozoan pathogens, respectively. , , IBC values ​​for bacterial pathogens, fungal pathogens, and protozoan pathogens, respectively.

[0097] In some embodiments, the weight of each of the pathogenic microorganism indicators is represented as follows:

[0098] ;

[0099] ;

[0100] .

[0101] In some embodiments, the method further includes:

[0102] First, the risk assessment values ​​of the bio-complex pollution from multiple sampling points are ranked, and the 95th percentile of the ranked risk assessment values ​​is used as the expected value. Second, the intervals following the expected value are classified into risk levels to obtain the risk level classification results.

[0103] The following will describe an exemplary application of the embodiments of the present invention in a practical application scenario.

[0104] This invention provides a method for assessing the risk of biological pollution in wetland park waters based on pathogenic microorganism indices. It constructs a biological pollution index system (Index of Biological Contamination, IBC) using the abundance of pathogenic microorganisms (including bacteria, fungi, and protozoa) obtained from metagenomic data as an indicator. First, the abundance of bacterial pathogenic microorganisms (…) is calculated… ), fungal pathogens ( ), protozoan pathogens ( Three categories of IBC individual indicators were used, and the least squares method was used to fit a regression model coupled with the comprehensive water quality index. After determining the weights, the Total-IBC of biological composite pollution in each wetland park was calculated. The Total-IBC values ​​of all sampling points were sorted from largest to smallest, and classified into high risk, relatively high risk, general risk, relatively low risk, and extremely low risk according to the scoring criteria of individual biological pollution indicators, thereby clarifying the areas for priority prevention and control.

[0105] The purpose of this invention is to provide a method based on bacterial pathogenic microorganisms ( ), fungal pathogens ( ), protozoan pathogens ( The system incorporates three categories of individual pollution indicators, including water quality indicators, and innovatively constructs a water biological pollution indicator system by coupling the comprehensive water quality labeling index with the least squares method, thereby achieving quantitative assessment and classification of biological compound pollution risks.

[0106] This invention provides a method for assessing the risk of biological pollution in wetland park waters based on pathogenic microorganism indices, comprising the following steps:

[0107] Step 1, Region Selection.

[0108] A certain region, characterized by its vibrant economy, dense population, abundant water resources, and large-scale wetland park, is an ideal location for studying biocomplex pollution in urban wetland parks. During the summer, water samples were collected from the urban wetland park's water areas (open recreational water areas). Five sampling points were evenly distributed within the sampling area (covering the area as much as possible), and water samples from all five points were mixed to form the final composite sample. The procedures were as follows: water samples were collected using a water sampler, and the temperature and dissolved oxygen of the water were measured in situ using a multi-functional portable water quality analyzer. From each sampling area, 10L of the composite water sample was placed in sterile plastic bottles and transported back to the laboratory in ice packs. 1000mL of water was passed through a 0.22 μm filter to collect microorganisms. Library construction was performed using the random shotgun method, and metagenomic sequencing (sequencing depth >20 Gb) was conducted using the Illumina HiSeq platform. The remaining sample was used for water chemical analysis (including permanganate index, total phosphorus, ammonia nitrogen, and total nitrogen).

[0109] Step 2: Local database construction, metagenomic sequencing data preprocessing, and pathogen annotation.

[0110] Reference genomes of prokaryotic and eukaryotic pathogenic microorganisms were downloaded from public databases such as the National Center for Biotechnology Information (NCBI) and the Pathogen Host Interactions (PHI-base) database, and a local biocontamination gene reference dataset was constructed using bioinformatics analysis scripts.

[0111] After obtaining the raw metagenomic sequencing sequences, Trimmomatic software was used for quality control to remove low-quality sequences. Bowtie2 software was then used to remove contaminating genes, including those from humans. High-quality sequences were then assembled using multiple bioinformatics tools, including Megahit / Metaspades, to obtain contig sequence files. Metawrapp software was then used to assemble the genome into MAGs (pure bacteria). ckeckM2 software was used to screen for high-quality MAGs with integrity greater than or equal to 90% and contamination less than 5% (if species abundance is low, the standard can be lowered to integrity greater than or equal to 70% and contamination less than 5%). Duplicate MAGs were then removed using dRep software with 95% nucleotide similarity to obtain a non-redundant MAG dataset. Finally, the Salmon algorithm was used to calculate the normalized abundance (e.g., Transcripts Per Million, TPM) for each MAG. CLARK software was used to align and annotate the non-redundant MAG dataset with a pathogenic microorganism (PHI-base) reference database. The annotation results were then merged with the above abundance table to generate the final pathogenic microorganism abundance table.

[0112] Step 3: Construction of the IBC indicator system for biofouling.

[0113] The abundance of pathogenic microorganisms (including bacteria, fungi, and protozoa) obtained from annotation is used as an indicator. Based on the calculation principle of biological integrity, a biological contamination (IBC) index system is constructed. The ratio method is used to calculate the score of each indicator. Since the abundance of pathogenic microorganisms shows a positive response to increased disturbance (human activity), the corresponding calculated score is: the difference between the maximum value of all indicators and the value of the sampled point indicator divided by the difference between the maximum value of all indicators and the 5th percentile value. The calculated score is between 0 and 1; otherwise, it is recorded as 1. The cumulative value of the scores for each indicator (human pathogenicity, animal pathogenicity, and plant pathogenicity) is the IBC value for that point. Bacterial pathogenic microorganisms (… ), fungal pathogens ( ), protozoan pathogens ( Three categories of IBC indicators were used. A least squares regression model was fitted and coupled with a comprehensive water quality index. After determining the weight of each indicator, the Total-IBC of the biopollution index for each wetland park's water area was calculated. The Total-IBC values ​​of all sampling points were sorted from largest to smallest, and the 95th percentile was taken as the expected value. The subsequent intervals were divided into five equal parts, representing the score ranges for high pollution, relatively high pollution, moderate pollution, relatively low pollution, and extremely low pollution, respectively, thus clarifying the biopollution risk of each sampling point's water area. Detailed calculations are as follows:

[0114] 1) To eliminate annotation errors for pathogenic microorganisms, three regions or source waters were selected as background values ​​for pathogenic microorganism abundance. It was assumed that the average abundance of bacterial, fungal, and protozoan pathogenic microorganisms in these background water bodies were respectively... , , The calculation is as follows:

[0115] (1)

[0116] (2)

[0117] (3)

[0118] In the formula, N is the number of samples of the background water body; The baseline value of bacteria and pathogenic microorganisms in the water body; The baseline value of fungal pathogenic microorganisms in water bodies; ...

[0119] Calculate the abundance values ​​of pathogenic bacteria in samples from the wetland park:

[0120] (4)

[0121] (5)

[0122] (6)

[0123] In the formula, , , These are the corrected abundances of bacterial pathogens, fungal pathogens, and protozoan pathogens in the wetland park's waters. , , This refers to the value of the i-th pathogenic microorganism index at the current sampling point;

[0124] 2) The indicator scores are calculated as follows:

[0125] (7)

[0126] (8)

[0127] (9)

[0128] In the formula, This represents the maximum value of the i-th index after correction among all sampling points in the wetland park's water area; This is the 5th percentile value of the i-th indicator after correction.

[0129] Constraints:

[0130] if >1, then let =1.

[0131] if If <0, then let = 0.

[0132] , The assignment rules are as above.

[0133] 3) The calculation of IBC for the three categories of pathogenic microorganisms (B-IBC), fungal pathogenic microorganisms (F-IBC), and protozoan pathogenic microorganisms (P-IBC) is as follows:

[0134] (10)

[0135] (11)

[0136] (12).

[0137] Step 4: Risk assessment system for combined biological pollution in aquatic areas.

[0138] In order to combine the three individual IBC risks (bacterial pathogens) Fungal pathogens Protozoan pathogens The risk assessment indicators for biological compound pollution were obtained through comprehensive calculation. This study used a weighted summation method and coupled it with the Comprehensive Water Quality Identification (CWQI) to determine the weight of each indicator. The specific calculation is as follows:

[0139] 1) Calculate the comprehensive water quality index of the sampling points in the wetland park.

[0140] Based on the national standard GB3838-2002, a higher CWQI index indicates worse water quality. This study selected DO and NH4+ as the primary water pollutants. + -N, TP, COD, and TN are used as evaluation parameters for organic matter and nutrient pollution. The CWQI calculation formula is as follows:

[0141] When a larger parameter value indicates a better water environment (such as DO), use formula (13): (13)

[0142] When a larger parameter value indicates a worse water environment (e.g., NH4), it means that the water environment is worse. + -N, TP, COD, and TN), use formula (14):

[0143] (14)

[0144] In the formula, The actual value of observed variable i (DO, COD, NH4) + -N, TN and TP); Refers to the reference value of observed variable i under the j-level standard; Refers to the reference value of observed variable i under the Class 1 standard for surface water; Refers to the reference value of observed variable i under the surface water j-1 standard; This refers to the reference value of observed variable i under the Class 5 standard for surface water.

[0145] The final water quality CWQI is the average of five variables:

[0146] (15)

[0147] 2) Determine the weights using the optimally fitted comprehensive water quality index:

[0148] Using the Comprehensive Water Quality Index (CWQI) as the dependent variable and the three individual IBC risks as independent variables, a multiple linear regression model was constructed:

[0149] (16)

[0150] in, The intercept; , , These are the regression coefficients for bacterial pathogens, fungal pathogens, and protozoan pathogens, respectively. , , IBC values ​​for bacterial pathogens, fungal pathogens, and protozoan pathogens, respectively.

[0151] The regression model was fitted using the least squares method to obtain the regression coefficients. , , Then, the regression coefficients are normalized to obtain the weights:

[0152] (17)

[0153] (18)

[0154] (19).

[0155] After determining the weights, the Total-IBC (Biological Complex Pollution Index) of each wetland park's water area was calculated.

[0156] 3) The three individual IBC risks are weighted and summed to obtain the final biocomplex pollution risk assessment index. The calculation formula is:

[0157] (20)

[0158] In the formula, , , , which represent the weights of bacterial pathogens, fungal pathogens, and protozoan pathogens, respectively.

[0159] 4) Sort the Total-IBC values of all sampling points from largest to smallest. Take the 95th percentile of the sorted Total-IBC values as the expected value (i.e., the upper limit of the high-risk state). Divide the interval after the expected value (i.e., the interval where the Total-IBC value is less than the 95th percentile) into five equal parts, corresponding to the score ranges of high pollution, relatively high pollution, general pollution, relatively low pollution, and extremely low pollution in turn.

[0160] Scoring range:

[0161] High pollution: Total-IBC ≥ expected value.

[0162] Relatively high pollution: expected value - Δ < Total-IBC < expected value.

[0163] General pollution: expected value - 2Δ < Total-IBC < expected value - Δ.

[0164] Relatively low pollution: expected value - 3Δ < Total-IBC < expected value - 2Δ.

[0165] Extremely low pollution: Total-IBC < expected value - 3Δ.

[0166] Where: Δ = (expected value - minimum Total-IBC value) / 5.

[0167] Next, the exemplary application of the embodiments of the present invention in another actual application scenario will be described.

[0168] 1. Sample collection and metagenomic sequencing used in the present invention.

[0169] 1.1. Sample collection.

[0170] In a region, 10 wetland park water sampling areas (site1 - 10) were selected, and the water bodies of drinking water source areas were collected simultaneously as background values (site11 - 13). Five sampling points were evenly set within the sampling areas, and the water bodies of the five sampling points were mixed as the final mixed sample. In situ, a multi-functional portable water quality analyzer was used to measure the temperature and dissolved oxygen of the water in situ. For each sampling area, 10 L of the mixed water sample was filled into a sterilized plastic bottle for physicochemical index detection (including indicators such as permanganate index, total phosphorus, ammonia nitrogen, total nitrogen, etc.). Separately, 1000 mL of water sample was taken in a 1000 mL sterilized sample bottle on site and immediately transported back to the laboratory in an ice box.

[0171] 1.2. DNA extraction and metagenomic sequencing.

[0172] DNA was extracted from water samples using a sample extraction kit (MoBio, CA, USA) and dissolved in 50 μL of elution buffer. DNA concentration and purity were then checked using a Nanodrop-2000 (Thermo Fisher Scientific, MA, USA) (260 / 280 ≥ 1.8, 260 / 230 ≥ 1.7). DNA concentration was accurately determined, and DNA integrity and conformity were verified by agarose gel electrophoresis. Subsequently, metagenomic sequencing was performed using the HiSeq platform of the Illumina sequencing platform, with a sequencing depth >20 Gb for each sample.

[0173] 2. Bioinformatics analysis of sequencing data.

[0174] 2.1 Local database construction.

[0175] Reference genomes of prokaryotic and eukaryotic pathogens were downloaded from public databases such as NCBI and the Pathogen Host Interactions (PHI-base) database. This database contains 878 pathogens, of which 572 are prokaryotes and 304 are eukaryotes. A local biocontamination gene reference dataset was constructed using CLARK software and related bioinformatics analysis scripts.

[0176] 2.2 Sequencing data preprocessing.

[0177] After obtaining the raw metagenomic sequencing sequences, Trimmomatic software was used for quality control to remove low-quality sequences; then bowtie2 software was used to remove contaminating genes such as human genes to obtain high-quality reads.

[0178] 2.3 Genome assembly.

[0179] Then, multiple bioinformatics tools, including Megahit and metaSPAdes, were used to assemble high-quality sequences into contig sequence files. The genomes were then assembled into MAGs (pure bacteria) using MetaWrap software. High-quality MAGs with integrity greater than or equal to 90% and contamination less than 5% were selected using ckeckM2 software (if the species abundance is low, the standard can be lowered to integrity greater than or equal to 70% and contamination less than 5%). Duplicate MAGs were then removed using dRep software with 95% nucleotide similarity to obtain a non-redundant MAG dataset. Finally, the Salmon algorithm software was used to calculate the normalized abundance (coverage per million sequences) of each MAG.

[0180] 2.4. Annotations on pathogenic microorganisms.

[0181] The non-redundant MAG dataset was aligned and annotated with the pathogenic microorganism (PHI-base) reference database using CLARK software, resulting in MAGs for a total of 63 pathogenic microorganisms. The annotation results were then merged with the abundance table above to generate the final pathogenic microorganism abundance table. Among these, bacterial pathogenic microorganisms (…) 59 species; fungal pathogenic microorganisms ( ) 2 types; protozoan pathogenic microorganisms ( ) 2 types.

[0182] 3. Risk analysis of biological compound pollution.

[0183] 3.1 Background value calculation.

[0184] Based on the abundance of pathogenic microorganisms at three background sites (site11-13), the baseline averages of bacteria (Bb), fungi (Bf), and protozoa (Bp) were calculated, as shown in Table 1:

[0185] Table 1 Summary of Background Average Calculations for Background Points

[0186]

[0187] 3.2 Calculation of the corrected abundance.

[0188] Background correction was performed on the abundance of pathogenic microorganisms at each sampling point (site1-site10), as shown in Table 2:

[0189] Table 2. Example of pathogenic microorganism abundance correction at sampling points

[0190]

[0191] Note: If the abundance is negative after correction, force it to be set to 0.

[0192] 3.3 Calculation of indicator scores.

[0193] Taking the human pathogens in site1 as an example, calculate SBi:

[0194] 1) Determine the maximum value and the 5th percentile:

[0195] (Mycobacterium Malmö from site 7).

[0196] .

[0197] 2) Calculate the score:

[0198] ;

[0199] 3) Constraints:

[0200] If the score is greater than 1, set it to 1; if it is less than 0, set it to 0.

[0201] The calculation process for pathogenic bacteria and animal bacteria and pathogenic bacteria and plant bacteria in site1 is similar.

[0202] 3.4. Calculation of individual IBCs.

[0203] The scores of the three pathogenic indicators are summed (Formula 10-12), and the IBC values ​​of all points are calculated.

[0204] The complete IBC value table is shown in Table 3:

[0205] Table 3. Complete IBC values ​​for all locations.

[0206]

[0207] 3.5 Total-IBC calculation.

[0208] Based on weight ( =0.266, =0.513, =0.221), calculate the Total-IBC value for each point, as shown in Table 4:

[0209] Table 4 Total-IBC values ​​for all locations

[0210]

[0211] 3.6 Risk Assessment: Determining the scoring criteria.

[0212] 1) Sorting Total-IBC values:

[0213] The Total-IBC values ​​of the 13 sampling points are sorted from largest to smallest, as shown in Table 5:

[0214] Table 5. Ranking of Total-IBC values ​​for all sampling points

[0215]

[0216] 2) Calculate the 95th percentile (expected value):

[0217] Sample size: 13;

[0218] Quantile position: 0.95 × 13 = 12.35, take the 12th place (rounded).

[0219] The 12th value after sorting: 0.226 (site13);

[0220] Expected value: 0.226 (actual value is the minimum Total-IBC value);

[0221] 3) Calculate the Δ value:

[0222]

[0223] 4) The risk level classification is shown in Table 6 below:

[0224] Table 6 Risk Level Classification Reference Table

[0225]

[0226] 5) The risk assessment results for each sampling point are shown in Table 7 below:

[0227] Table 7 Risk Level Classification of All Sampling Points

[0228]

[0229] Figure 3 The regression analysis plots of CWQI and Total-IBC for all sample points provided in the embodiments of the present invention are as follows: Figure 3 As shown, the correlation analysis between the biological IBC index and the comprehensive water quality index (CWQI) indicates that the IBC index of the sample is significantly positively correlated with the CWQI index, meaning that the worse the water quality, the higher the biological risk.

[0230] This invention presents a biocontamination assessment system based on pathogenic microorganism indices. It proposes, for the first time, an integrated biocontamination index (IBC) system that combines the abundance of three types of pathogenic microorganisms—bacteria, fungi, and protozoa—based on metagenomic data, overcoming the limitations of traditional single-indicator systems. The system calculates the abundance of bacterial pathogenic microorganisms (…) separately. ), fungal pathogens ( ), protozoan pathogens ( The system employs a multi-pathogen index to achieve accurate identification and quantitative assessment of multiple pathogen groups, significantly improving the detection sensitivity of hidden biological pollution risks. On the other hand, a dynamic weighted coupling model for constructing a composite pollution index is proposed. Specifically, based on a least squares regression model, the IBC indicators of three types of pathogenic microorganisms are dynamically coupled with the Comprehensive Water Quality Index (CWQI) to scientifically determine weight parameters, ultimately generating the Total-IBC index. This model optimizes weight allocation through data-driven methods, avoiding the biases of traditional subjective weighting methods and making the assessment results more closely reflect actual pollution characteristics. Thirdly, a multi-dimensional risk rating system and a dynamic threshold classification method are developed. Based on the dynamic risk level classification standard of quantile statistics, the Total-IBC value is divided into five equal intervals using the 95th percentile of the total sample as the benchmark, forming a gradient evaluation system from high risk to extremely low risk. This method adaptively adjusts the threshold through data, solving the problem of poor applicability of traditional fixed thresholds to different wetland habitats, and providing managers with an intuitive basis for hierarchical management.

[0231] Figure 4 This is a schematic diagram of the structural composition of the aquatic biological complex pollution risk assessment device provided in an embodiment of the present invention, as shown below. Figure 4 As shown, the aquatic biological complex pollution risk assessment device 400 includes: an acquisition module 401, used to acquire a pathogenic microorganism abundance table of the water area to be assessed; the pathogenic microorganisms include bacterial pathogenic microorganisms, fungal pathogenic microorganisms, and protozoan pathogenic microorganisms; a construction module 402, used to construct a biological pollution IBC index system using pathogenic microorganism abundance as an indicator; and a determination module 403, used to determine the index score of each of the biological pollution IBC indicators based on the determined background value of pathogenic microorganism abundance and the corrected pathogenic microorganism abundance value; the determination module 403 is also used to determine the index score of each of the biological pollution IBC indicators based on the index score. The system includes: an IBC value for the BC index; a calculation module 404 for calculating the comprehensive water quality evaluation index of the water area to be evaluated; a construction module 402 for constructing a multiple linear regression model using the comprehensive water quality evaluation index as the dependent variable and the scores of multiple IBC indicators as independent variables; a fitting module 405 for fitting the multiple linear regression model using the least squares method to obtain regression coefficients; and a normalization process for the regression coefficients to obtain the weight of each pathogenic microorganism indicator; and a weighting module 406 for weighted summation of multiple IBC values ​​based on the weights to obtain a risk assessment value for biological compound pollution.

[0232] It should be noted that, in the embodiments of the present invention, if the above-mentioned risk assessment method for aquatic biological complex pollution is implemented as a software functional module and sold or used as an independent product, it can also be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the embodiments of the present invention, or the part that contributes to related technologies, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a terminal to execute all or part of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), magnetic disks, or optical disks. Thus, the embodiments of the present invention are not limited to any specific hardware and software combination.

[0233] This invention provides an electronic device. Figure 5 This is a schematic diagram of the composition structure of the electronic device provided in the embodiments of the present invention, such as... Figure 5 As shown, the electronic device 500 includes at least a processor 501 and a computer-readable storage medium 502 configured to store executable instructions, wherein the processor 501 generally controls the overall operation of the electronic device 500. The computer-readable storage medium 502 is configured to store instructions and applications executable by the processor 501, and may also cache data to be processed or processed by various modules in the processor 501 and the electronic device 500, and may be implemented using flash memory or random access memory (RAM).

[0234] This invention provides a storage medium storing executable instructions. When these executable instructions are executed by a processor, they cause the processor to perform the method provided in this invention, for example... Figure 2 The method shown.

[0235] In some embodiments, the storage medium may be a computer-readable storage medium, such as a ferromagnetic random access memory (FRAM), a read-only memory (ROM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), an electrically erasable programmable read-only memory (EEPROM), flash memory, magnetic surface memory, optical disc, or a compact disk-read-only memory (CD-ROM); or it may be a device that includes one or any combination of the above-mentioned memories.

[0236] In some embodiments, executable instructions may take the form of a program, software, software module, script, or code, written in any form of programming language (including compiled or interpreted languages, or declarative or procedural languages), and may be deployed in any form, including as a standalone program or as a module, component, subroutine, or other unit suitable for use in a computing environment.

[0237] As an example, executable instructions may, but do not necessarily, correspond to files in a file system. They may be stored as part of a file containing other programs or data, for example, in one or more scripts within a Hyper Text Markup Language (HTML) document, in a single file dedicated to the program in question, or in multiple collaborating files (e.g., files storing one or more modules, subroutines, or code sections). As an example, executable instructions may be deployed to execute on a single electronic device, or on multiple electronic devices located in one location, or on multiple electronic devices distributed across multiple locations and interconnected via a communication network.

[0238] The above description is merely an embodiment of the present invention and is not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, and improvements made within the spirit and scope of the present invention are included within the scope of protection of the present invention.

[0239] It should be understood that the phrase "one embodiment" or "an embodiment" throughout the specification means that a specific feature, structure, or characteristic related to the embodiment is included in at least one embodiment of the invention. Therefore, "in one embodiment" or "in an embodiment" appearing throughout the specification does not necessarily refer to the same embodiment. Furthermore, these specific features, structures, or characteristics can be combined in any suitable manner in one or more embodiments. It should be understood that in the various embodiments of the invention, the sequence numbers of the above-described processes do not imply a sequential order of execution; the execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the invention. The sequence numbers of the above-described embodiments of the invention are merely descriptive and do not represent the superiority or inferiority of the embodiments.

[0240] It should be noted that, in this document, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes that element. In the several embodiments provided by this invention, it should be understood that the disclosed devices and methods can be implemented in other ways. The device embodiments described above are merely illustrative; for example, the division of units is only a logical functional division, and in actual implementation, there may be other division methods, such as: multiple units or components may be combined, or integrated into another system, or some features may be ignored or not performed.

[0241] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in the present invention should be included within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A method for risk assessment of biological complex pollution in aquatic waters, characterized in that, The method includes: Obtain a pathogen abundance table for the water body to be assessed; the pathogens include bacterial pathogens, fungal pathogens, and protozoan pathogens. Using the abundance of pathogenic microorganisms as an indicator, a biocontamination IBC indicator system was constructed; Based on the determined baseline abundance value of pathogenic microorganisms and the corrected abundance value of pathogenic microorganisms, the index score of each of the aforementioned biocontamination IBC indicators is determined; Specifically, this includes: calculating the background values ​​of the bacterial pathogens, the fungal pathogens, and the protozoan pathogens based on the abundance of pathogens at multiple background locations; Background correction is performed on the abundance of pathogenic microorganisms at each sampling point to obtain the corrected abundance value of pathogenic microorganisms; The index score is calculated based on the baseline abundance value of the pathogenic microorganism and the corrected abundance value of the pathogenic microorganism; Based on the index scores, the IBC index score for each of the aforementioned biological pollution IBC indicators is determined. Calculate the comprehensive water quality evaluation index of the water area to be evaluated; A multiple linear regression model is constructed by using the water quality comprehensive evaluation index as the dependent variable and the scores of multiple IBC indicators as independent variables. The IBC score is obtained using the following method: Construct bacterial pathogens separately Fungal pathogens Protozoan pathogenic microorganisms The three categories of IBC indicators; the sum of the scores of the indicators for human pathogenicity, animal pathogenicity, and plant pathogenicity is the IBC indicator score for that sampling point. The least squares method was used to fit the multiple linear regression model to obtain the regression coefficients; and the regression coefficients were normalized to obtain the weight of each pathogenic microorganism index. Based on the aforementioned weights, the scores of multiple IBC indicators are weighted and summed to obtain the risk assessment value for biocomplex pollution.

2. The method according to claim 1, characterized in that, The acquisition of pathogenic microorganism abundance in the water body to be assessed includes: Multiple sampling points are evenly set up in the water area to be evaluated, and the water samples collected from the multiple sampling points are mixed to form a mixed water sample. Metagenomic sequencing was performed on the microorganisms in the mixed water sample to obtain the raw metagenomic sequencing sequences; and water chemical analysis was performed on the mixed water sample to obtain the water chemical analysis results. The original sequence is subjected to quality control, and low-quality sequences and human contamination gene sequences are removed in sequence to obtain the processed sequence; the processed sequence is spliced ​​with a preset high-quality sequence to obtain a contig sequence file, and the genome is assembled into a MAG; Based on preset filtering rules, duplicate MAGs are removed from the MAGs to obtain a non-redundant MAG dataset; Calculate the normalized abundance of each MAG in the non-redundant MAG dataset to form a normalized abundance table; The non-redundant MAG dataset is compared with the constructed pathogenic microorganism reference database to obtain the pathogenic microorganisms; The standardized abundance table and the pathogenic microorganism are combined to form the pathogenic microorganism abundance table.

3. The method according to claim 1, characterized in that, The calculation of the comprehensive water quality evaluation index of the water area to be evaluated includes: Select DO and NH4 + -N, TP, COD and TN are used as evaluation parameters for organic matter and nutrient pollution; The formula for calculating the Comprehensive Water Quality Index (CWQI) corresponding to each of the aforementioned evaluation parameters is as follows: When the evaluation parameter is DO, a larger value indicates a better water environment, calculated using the following formula: ； When the evaluation parameter is NH4 + When -N, TP, COD, or TN are considered, a higher evaluation parameter value indicates a worse water environment, calculated using the following formula: ； In the formula, The actual value of observed variable i; Refers to the reference value of observed variable i under the j-level standard; Refers to the reference value of observed variable i under the Class 1 standard for surface water; Refers to the reference value of observed variable i under the surface water j-1 standard; Refers to the reference value of observed variable i under the Class 5 standard for surface water; Based on the comprehensive water quality evaluation index corresponding to the evaluation parameters, the comprehensive water quality evaluation index (CWQI) is determined, and the calculation formula for the CWQI is as follows: 。 4. The method according to claim 1, characterized in that, The multiple linear regression model is expressed as follows: ； In the formula, The intercept; , , These are the regression coefficients for bacterial pathogens, fungal pathogens, and protozoan pathogens, respectively. , , IBC values ​​for bacterial pathogens, fungal pathogens, and protozoan pathogens, respectively.

5. The method according to claim 1, characterized in that, The weight of each of the pathogenic microorganism indicators is represented as follows: ； ； 。 6. The method according to claim 1, characterized in that, The method further includes: The risk assessment values ​​of the bio-complex pollution from multiple sampling points are sorted, and the 95th percentile of the sorted bio-complex pollution risk assessment values ​​is taken as the expected value. The risk level is determined by classifying the interval following the expected value.

7. A risk assessment device for aquatic biological complex pollution, characterized in that, The device includes: The acquisition module is used to acquire the abundance table of pathogenic microorganisms in the water area to be assessed; the pathogenic microorganisms include bacterial pathogenic microorganisms, fungal pathogenic microorganisms, and protozoan pathogenic microorganisms; The module is used to construct an IBC (Indicator of Biocontamination) index system based on the abundance of pathogenic microorganisms. The determination module is used to determine the index score of each of the biopollution IBC indicators based on the determined background values ​​of pathogenic microorganism abundance and the corrected pathogenic microorganism abundance values. Specifically, it includes: calculating the background values ​​of the bacterial pathogenic microorganisms, fungal pathogenic microorganisms, and protozoan pathogenic microorganisms based on the pathogenic microorganism abundance of multiple background points; performing background correction on the pathogenic microorganism abundance of each sampling point to obtain the corrected pathogenic microorganism abundance value; and calculating the index score based on the background values ​​of pathogenic microorganism abundance and the corrected pathogenic microorganism abundance values. The determining module is further configured to determine the IBC index score of each of the biopollution IBC indicators based on the index score. The calculation module is used to calculate the comprehensive water quality evaluation index of the water area to be evaluated; The construction module is further configured to construct a multiple linear regression model using the comprehensive water quality evaluation index as the dependent variable and multiple IBC index scores as independent variables; wherein, the IBC index scores are obtained through the following method: constructing bacterial pathogenic microorganisms respectively. Fungal pathogens Protozoan pathogenic microorganisms The three categories of IBC indicators; the sum of the scores of the indicators for human pathogenicity, animal pathogenicity, and plant pathogenicity is the IBC indicator score for that sampling point. The fitting module is used to fit the multiple linear regression model using the least squares method to obtain the regression coefficients; and after normalizing the regression coefficients, the weight of each pathogenic microorganism index is obtained. The weighting module is used to sum the scores of multiple IBC indicators based on the weights to obtain the risk assessment value of biocomplex pollution.

8. An electronic device, characterized in that, include: Memory, used to store executable instructions; The processor, when executing executable instructions stored in the memory, implements the risk assessment method for aquatic biological complex pollution according to any one of claims 1 to 6.

9. A computer-readable storage medium, characterized in that, The device stores executable instructions that, when executed by a processor, implement the risk assessment method for aquatic biological complex pollution as described in any one of claims 1 to 6.

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