Method and system for analyzing source of pathogenic microorganisms in water body

Abstract

The invention relates to the technical field of environmental monitoring, in particular to a water body pathogenic microorganism source analysis method and system, and the method comprises the following steps: based on water body sample collection information, identifying a plurality of moisture sources of a target water body through the sampling position of the water body, calculating the contribution degree of a plurality of source water bodies to a sampling point, and generating a water body position analysis result. According to the method, the prediction accuracy of the pollution risk area is improved by identifying the multiple sources of the water body, calculating the contribution degrees of the multiple water body sources to the sampling points and combining real-time meteorological and topographic data to simulate the diffusion path of pollution, and the prediction capability of the pollution source type is improved by evaluating the microbial population distribution mode in the water body sample. Data support is provided for pollution source position identification, multiple source positions of pathogenic microorganisms are identified through comparison with microorganism samples of multiple pollution sources, a scientific basis is provided for taking effective water quality protection and disease prevention measures, and the quality and efficiency of environment management are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of environmental monitoring, and in particular to a method and system for analyzing the sources of pathogenic microorganisms in water. Background Art

[0002] The field of environmental monitoring technology uses a variety of scientific methods and technical means to observe, evaluate and manage environmental quality and environmental changes, including monitoring of air, water, soil, and ecosystems. It aims to promptly detect environmental problems, evaluate the impact of human activities on the environment, and formulate corresponding environmental policies. It combines sensor technology, remote sensing technology, biological monitoring and chemical analysis to analyze environmental pollution levels, pollution trends and changes in environmental quality, and provide a scientific basis for environmental management and policy formulation.

[0003] Among them, the method for analyzing the source of pathogenic microorganisms in water bodies aims to identify and trace the sources of pathogenic microorganisms in water bodies. Through multiple steps such as microbial sampling, DNA extraction, high-throughput sequencing, and data analysis, the source of pathogens, including industrial emissions, agricultural activities, and natural water sources, is identified. This helps environmental protection agencies and public health departments take targeted measures to control and prevent water pollution, improve water quality, reduce the risk of waterborne diseases, ensure the safety and sustainable use of water resources, and protect public health and water resource safety.

[0004] Traditional analysis methods for the sources of pathogenic microorganisms in water rely on simple microbial sampling and laboratory analysis. They are poor in identifying the contribution of complex water sources and quickly tracking pollution sources. They lack efficient data integration capabilities and high-throughput biotechnology applications. They cannot provide immediate and accurate analysis results when responding to multi-source water pollution incidents. They cannot integrate a variety of environmental data when analyzing pollution diffusion paths, which affects the accuracy and timeliness of predictions, reduces the opportunities for environmental protection agencies to take effective measures in the early stages of pollution, and causes pollutants to affect wider areas. It is difficult to conduct in-depth identification of microbial species and community structure analysis, which affects the accurate prediction of pollution source types and limits the identification of pollution source locations. Summary of the invention

[0005] The purpose of the present invention is to solve the shortcomings of the prior art and to propose a method and system for analyzing the sources of pathogenic microorganisms in water.

[0006] In order to achieve the above object, the present invention adopts the following technical scheme, a method for analyzing the source of pathogenic microorganisms in water, comprising the following steps:

[0007] S1: Based on the water sample collection information, identify multiple water sources of the target water body through the sampling location of the water body, calculate the contribution of multiple source water bodies to the sampling point, and generate water body location analysis results;

[0008] S2: Based on the water body location analysis results, combined with meteorological and topographic data, simulate the propagation and diffusion path of the water body, predict the location of multiple pollution sources and the pollution impact range, and generate a pollution diffusion simulation path;

[0009] S3: Based on the pollution diffusion simulation path, DNA and RNA information of various microorganisms in the target water sample is collected, and serialized and standardized to generate microbial data processing records;

[0010] S4: Based on the microbial data processing record, identifying the types of multiple microorganisms in multiple water samples, analyzing the community distribution of microorganisms in the target water samples, and generating population distribution identification results;

[0011] S5: Based on the population distribution identification result, according to the population distribution pattern of microorganisms in the target water body, the type of pollution source is predicted, including domestic sewage, animal feces, and water body sediment, and a pollution source type prediction result is generated;

[0012] S6: Based on the pollution source type prediction result, collect microbial samples from multiple pollution sources and compare the distribution patterns of microbial communities to identify the locations of the sources of pathogenic microorganisms in the water body and generate source location identification results.

[0013] As a further scheme of the present invention, the water body location analysis results include multiple water source contribution information, water source geographic coordinate information, and environmental characteristics of associated water bodies; the pollution diffusion simulation path includes the predicted starting position of the pollutant, the diffusion direction calculation result, and the predicted end point area of ​​the diffusion; the microbial data processing record includes the extracted DNA sequenced data, the sequence quality score, and the data processing record; the population distribution identification result includes microbial species information, the proportion data of multiple types, and the population quantity statistics; the pollution source type prediction result includes the key pollution source type, the microbial distribution pattern corresponding to the pollution source type, and the pollution source hazard level score; the source location identification result includes the matching information of the microbial community and the pollution source, the source location coordinates, and the source path of the pathogen community.

[0014] As a further solution of the present invention, based on the water sample collection information, through the sampling location of the water body, multiple water sources of the target water body are identified, and the contribution of multiple source water bodies to the sampling point is calculated. The steps of generating the water body position analysis result are specifically as follows:

[0015] S101: Based on the water sample collection information, the geographical location information of the target water sample is analyzed, and the target water type is recorded to generate a sampling point location record;

[0016] S102: performing water source contribution analysis based on the sampling point location records, calculating contribution ratios of multiple water sources to the target sampling point, and generating water source contribution analysis results;

[0017] S103: Based on the water source contribution analysis result, through the contribution of multiple water sources corresponding to the sampling points, combined with the spatial position and distribution of multiple water bodies, a spatial distribution map of the water body sources is constructed to generate a water body position analysis result.

[0018] As a further solution of the present invention, based on the water body location analysis results, combined with meteorological and topographic data, the propagation and diffusion path of the water body is simulated, the location of multiple pollution sources and the pollution impact range are predicted, and the steps of generating the pollution diffusion simulation path are specifically as follows:

[0019] S201: Based on the water body location analysis result, historical meteorological data and terrain data of the target water body location are collected, wind direction, rainfall and terrain obstacles are analyzed, and meteorological and terrain data are generated;

[0020] S202: Based on the meteorological and topographic data, simulate the flow direction and speed, analyze the behavior of pollutants in the water body, including diffusion rate and path direction, and generate water flow dynamics simulation results;

[0021] S203: Based on the water flow dynamics simulation results, the diffusion paths of pollutants under various environmental conditions are simulated, the location of pollution sources and the scope of pollution impact are predicted, and a pollution diffusion simulation path is generated.

[0022] As a further solution of the present invention, based on the pollution diffusion simulation path, DNA and RNA information of multiple microorganisms in the target water sample is collected, and serialized and standardized to generate microbial data processing records. Specifically, the steps are as follows:

[0023] S301: Based on the pollution diffusion simulation path, collect and extract DNA and RNA information of microorganisms in the target water sample, record the collection time and location, and generate microbial sample information;

[0024] S302: Based on the microbial sample information, data cleaning and format standardization are performed on DNA and RNA data of the microorganism to generate sequence information records;

[0025] S303: Based on the sequence information record, classify the DNA and RNA data of various microorganisms to generate a microorganism data processing record.

[0026] As a further solution of the present invention, based on the microbial data processing record, the types of multiple microorganisms in multiple water samples are identified, the community distribution of microorganisms in the target water samples is analyzed, and the steps of generating population distribution identification results are specifically as follows:

[0027] S401: According to the microbial data processing record, by analyzing DNA and RNA information of multiple microorganisms, identifying and recording the types of multiple microorganisms in the water sample, and generating a microbial type record;

[0028] S402: Analyze the distribution density and pattern of multiple microorganisms in the target water sample based on the microorganism species record to generate a microorganism distribution density map;

[0029] S403: Based on the microbial distribution density map, by analyzing the community structure and distribution characteristics of multiple microorganisms, key populations and ecological functions in the community are identified to generate population distribution identification results.

[0030] As a further solution of the present invention, based on the population distribution identification result, according to the population distribution pattern of microorganisms in the target water body, the type of pollution source is predicted, including domestic sewage, animal feces, and water body sediment. The steps of generating the pollution source type prediction result are specifically as follows:

[0031] S501: Based on the population distribution identification result, analyzing the correlation between the microbial community pattern in the target water body and various pollution types, including domestic sewage, animal feces and water body sediments, and generating pollution type matching data;

[0032] S502: According to the pollution type matching data, a logistic regression algorithm is used to calculate the probability distribution of multiple pollution types and identify key pollution source types, and generate a probability distribution analysis result;

[0033] S503: Based on the probability distribution analysis result, predict the location information and impact range of various pollution sources, and generate a pollution source type prediction result.

[0034] As a further solution of the present invention, the logistic regression algorithm is according to the formula:

[0035]

[0036] Calculate the probability of multiple pollution sources, where p is the probability of the existence of the pollution type, e is the base of the natural logarithm, β0 is the intercept of the model, β1 is the weight coefficient of the microbial diversity index in the model, β2 is the weight coefficient of the target pathogen presence index in the model, β3 is the weight coefficient of the biological oxygen demand in the model, β4 is the weight coefficient of the number of antibiotic resistance genes in the model, β5 is the weight coefficient of the pathogen-specific gene expression level in the model, x1 represents the diversity index of the microbial community, x2 represents the presence of the target pathogen, x3 represents the biological oxygen demand in the water body, x4 represents the number of antibiotic resistance genes, and x5 represents the expression level of the pathogen-specific gene.

[0037] As a further solution of the present invention, based on the pollution source type prediction result, microbial samples of multiple pollution sources are collected and the distribution patterns of microbial communities are compared to identify the location of the source of pathogenic microorganisms in the water body. The steps of generating the source location identification result are specifically as follows:

[0038] S601: According to the pollution source type prediction result, collect microbial samples from multiple predicted pollution source locations to generate target pollution source sample information;

[0039] S602: Based on the target pollution source sample information, collect and extract DNA and RNA information of microorganisms in multiple pollution source samples, and compare them with the distribution pattern of microbial communities in the target water body sample to generate sample microbial comparison results;

[0040] S603: Using the sample microorganism comparison results, analyze and identify multiple pollution source locations of pathogenic microorganisms in the water body, and generate source location identification results.

[0041] A water body pathogenic microorganism source analysis system, the water body pathogenic microorganism source analysis system is used to perform the water body pathogenic microorganism source analysis method, the system comprises:

[0042] The location information recording module analyzes the geographical location information of the target water body based on the water sample collection information, identifies and records the source of the water sample, including lakes, rivers, and groundwater, and generates a target water body data set;

[0043] The pollution path simulation module predicts the propagation path and impact range of pollutants based on the target water body data set and combines meteorological and topographic data to generate diffusion path analysis results;

[0044] The microbial information analysis module extracts the DNA and RNA of microorganisms from the target water sample according to the diffusion path analysis results, performs serialization and standardization processing, identifies the types of microorganisms in the sample, analyzes the community distribution, and generates community distribution data;

[0045] The pollution source characteristic comparison module predicts the type of pollution source, including domestic sewage, animal feces, and water body sediment, based on the community distribution data, by evaluating the population distribution pattern, and generates pollution source prediction information;

[0046] Based on the pollution source prediction information, the community distribution comparison module collects water samples from multiple predicted pollution sources and analyzes microbial information, compares community distribution patterns, identifies multiple source locations of pathogenic microorganisms, and generates source location identification results.

[0047] Compared with the prior art, the advantages and positive effects of the present invention are:

[0048] In the present invention, by identifying multiple sources of water bodies and calculating the contribution of multiple water sources to sampling points, the diffusion path of pollution is simulated in combination with real-time meteorological and topographic data, the prediction accuracy of pollution risk areas is improved, and the distribution pattern of microbial populations in water samples is evaluated to improve the prediction ability of pollution source types. Data support is provided for identifying the location of pollution sources, and combined with comparison with microbial samples from multiple pollution sources, multiple source locations of pathogenic microorganisms are identified, providing a scientific basis for taking effective water quality protection and disease prevention measures, thereby improving the quality and efficiency of environmental management. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] Figure 1 It is a schematic diagram of the workflow of the present invention;

[0050] Figure 2 This is a detailed flow chart of S1 of the present invention;

[0051] Figure 3 This is a detailed flow chart of S2 of the present invention;

[0052] Figure 4 This is a detailed flow chart of S3 of the present invention;

[0053] Figure 5 This is a detailed flow chart of S4 of the present invention;

[0054] Figure 6 This is a detailed flow chart of S5 of the present invention;

[0055] Figure 7 This is a detailed flow chart of S6 of the present invention;

[0056] Figure 8 It is a system flow chart of the present invention. DETAILED DESCRIPTION

[0057] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0058] In the description of the present invention, it should be understood that the terms "length", "width", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside" and the like indicate positions or positional relationships based on the positions or positional relationships shown in the drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as limiting the present invention. In addition, in the description of the present invention, "multiple" means two or more, unless otherwise clearly and specifically defined.

[0059] See also Figure 1 The present invention provides a technical solution, a method for analyzing the source of pathogenic microorganisms in water, comprising the following steps:

[0060] S1: Based on the water sample collection information, identify multiple water sources of the target water body through the sampling location of the water body, calculate the contribution of multiple source water bodies to the sampling point, and generate water body location analysis results;

[0061] S2: Based on the results of water body location analysis, combined with meteorological and topographic data, simulate the propagation and diffusion path of water bodies, predict the location of multiple pollution sources and the scope of pollution impact, and generate pollution diffusion simulation paths;

[0062] S3: Based on the pollution diffusion simulation path, DNA and RNA information of various microorganisms in the target water samples are collected, serialized and standardized, and microbial data processing records are generated;

[0063] S4: Based on the microbial data processing records, identify the types of multiple microorganisms in multiple water samples, analyze the community distribution of microorganisms in the target water samples, and generate population distribution identification results;

[0064] S5: Based on the population distribution identification results, according to the population distribution pattern of microorganisms in the target water body, the type of pollution source is predicted, including domestic sewage, animal feces, and water body sediments, and the pollution source type prediction results are generated;

[0065] S6: Based on the pollution source type prediction results, collect microbial samples from multiple pollution sources and compare the distribution patterns of microbial communities to identify the locations of the sources of pathogenic microorganisms in the water body and generate source location identification results.

[0066] The results of water body location analysis include contribution information of multiple water sources, geographical coordinate information of water sources, and environmental characteristics of associated water bodies. The pollution diffusion simulation path includes the predicted starting point of the pollutant, the calculation results of the diffusion direction, and the predicted end point area of ​​the diffusion. The microbial data processing records include the extracted DNA sequenced data, sequence quality scores, and data processing records. The population distribution identification results include microbial species information, proportion data of multiple types, and population quantity statistics. The pollution source type prediction results include key pollution source types, microbial distribution patterns corresponding to pollution source types, and pollution source hazard level scores. The source location identification results include the matching information of microbial communities and pollution sources, source location coordinates, and pathogen community source paths.

[0067] See also Figure 2Based on the water sample collection information, the multiple water sources of the target water body are identified through the sampling location of the water body, and the contribution of multiple source water bodies to the sampling point is calculated. The specific steps for generating the water body location analysis results are as follows:

[0068] S101: Based on the water sample collection information, the geographical location information of the target water sample is analyzed, and the target water type is recorded, and the specific process of generating the sampling point location record is as follows;

[0069] In sub-step S101, based on the water sample collection information, the geographical location of the target water sample is analyzed, including the longitude, latitude and elevation records. Through field visits and remote sensing technology, the surrounding environmental characteristics of the target water body are collected, the water body type and ecological characteristics are identified, including lakes, rivers, and artificial water bodies, and the water quality and biodiversity are evaluated. The location is marked and classified using geographic information system technology. The formula is: L = α s (x,y,z)+β s , where L represents the geographic location code of the water sample, α s is the geographic coordinate conversion coefficient, x, y, z are longitude, latitude and altitude respectively, β s To correct the deviation, the location and type information of the target water body is recorded and classified, and the sampling point location records are generated.

[0070] S102: Based on the sampling point location records, water source contribution analysis is performed to calculate the contribution ratio of multiple water sources to the target sampling point. The specific process of generating the water source contribution analysis result is as follows;

[0071] In sub-step S102, based on the sampling point location records, stable isotope analysis and chemical composition comparison are applied to evaluate the impact and contribution of different surrounding water sources to the target sampling point, calculate the contribution ratio of each water source to the sampling point, and use the mixed model algorithm to analyze the water sample data from different sources. The formula is: in, For the i c The water source contribution of each sampling point, s j For the jth c The stability index of a water source, For the jth c The water source is c The contribution weight of each sampling point is calculated to quantify the contribution of each water source to the target sampling point and generate the water source contribution analysis results.

[0072] S103: Based on the water source contribution analysis result, the spatial distribution map of the water source is constructed by combining the contribution of the sampling points corresponding to the multiple water sources with the spatial positions and distribution of the multiple water bodies. The specific process of generating the water body position analysis result is as follows;

[0073] In sub-step S103, based on the water source contribution analysis results, combined with the spatial location data of water samples and the impact weights of multiple water sources, a water source spatial distribution map is constructed, and multiple data points are integrated using spatial analysis tools and hydrological and geographical data models. The formula is: Where D represents the spatial distribution map of water sources, is a positional parameter, For the kth g The geographic information weight of each water source is used to map the spatial contribution of water sources to sampling points, show the location relationship and source influence of water bodies, and generate water body location analysis results.

[0074] See also Figure 3 Based on the results of water body location analysis, combined with meteorological and topographic data, the propagation and diffusion paths of water bodies are simulated to predict the locations of multiple pollution sources and the scope of pollution impact. The specific steps for generating pollution diffusion simulation paths are as follows:

[0075] S201: Based on the water body location analysis result, historical meteorological data and terrain data of the target water body location are collected, wind direction, rainfall and terrain obstacles are analyzed, and the specific process of generating meteorological and terrain data is as follows;

[0076] In sub-step S201, based on the results of the water body location analysis, historical meteorological data and terrain data associated with the target water body location are collected, including wind direction, rainfall, and terrain obstacles in the past few years, including mountains and buildings. The meteorological and terrain parameters are encoded and analyzed through meteorological station and satellite remote sensing data, and the data are processed using a multivariate statistical model. The formula is: M = γ·(w,r,t)+δ f , where M represents the comprehensive meteorological and topographic influence analysis results, γ is the environmental factor influence coefficient, w, r and t represent the quantitative indicators of wind direction, rainfall and topographic obstruction factors respectively, and δ f It is a model error correction term that records and predicts the impact of meteorology and topography on the location of water bodies and generates meteorological and topographic data.

[0077] S202: Based on meteorological and topographic data, simulate the flow direction and speed, analyze the behavior of pollutants in the water body, including diffusion rate and path direction, and generate the flow dynamics simulation results. The specific process is as follows;

[0078] In sub-step S202, based on meteorological and topographic data, a fluid dynamics model is used to simulate the direction and speed of water flow, analyze the diffusion rate and path direction of pollutants in the water body, and combine hydrodynamics and pollutant propagation theory to construct a mathematical model for simulation. The formula is: Where V represents the result of water flow dynamics simulation, For nth l The hydrodynamic coefficients of the parameters, is the water velocity vector, To consider pollutant diffusion factors, analyze water flow velocity and pollutant behavior patterns, and generate water flow dynamics simulation results.

[0079] S203: Based on the results of the hydrodynamic simulation, the diffusion paths of pollutants under various environmental conditions are simulated, the location of the pollution source and the scope of pollution impact are predicted, and the specific process of generating the pollution diffusion simulation path is as follows;

[0080] In sub-step S203, based on the results of hydrodynamic simulation, environmental simulation software is used to simulate the diffusion path of pollutants under different environmental conditions, predict the location of pollution sources and the scope of pollution impact, and combine the geographic information system and simulation results to develop a composite model to predict pollution diffusion. The formula is:

[0081] Where P represents the pollution diffusion simulation path, φ m is the adjustment coefficient of the diffusion model, and Represents the i m The influencing factors and diffusion sensitivity indicators of environmental conditions are used to predict the behavior and diffusion path of pollutants under different conditions and generate pollution diffusion simulation paths.

[0082] See also Figure 4 Based on the pollution diffusion simulation path, DNA and RNA information of various microorganisms in the target water samples are collected, and serialized and standardized. The specific steps to generate microbial data processing records are as follows:

[0083] S301: Based on the pollution diffusion simulation path, collect and extract DNA and RNA information of microorganisms in the target water sample, record the collection time and location, and generate the microorganism sample information. The specific process is as follows;

[0084] In sub-step S301, based on the pollution diffusion simulation path, the DNA and RNA information of microorganisms in the target water samples are collected, and the representativeness and integrity of the samples are ensured by using on-site sampling and laboratory analysis methods, including using sterile sampling techniques to collect samples, recording the collection time and location information, ensuring the accuracy and traceability of sample data, and using molecular biology techniques to extract and amplify the genetic material of microorganisms. The formula is: E = η ks ·(D DNA ,D RNA )+ξ ks , where E represents the efficiency of genetic information extraction from microbial samples, η ks is the extraction coefficient, D DNA and D RNA represent the initial concentrations of DNA and RNA, respectively, ksIt is the experimental error correction term and generates microbial sample information.

[0085] S302: Based on the microbial sample information, the DNA and RNA data of the microorganism are cleaned and formatted, and the specific process of generating sequence information records is as follows;

[0086] In sub-step S302, based on the microbial sample information, the DNA and RNA data of the microorganisms are cleaned and formatted, including removing contaminated readings and nonspecific signals during the experiment, converting the data format to match bioinformatics analysis, and applying bioinformatics software for data processing. The formula is: Among them, S represents the sequence information record, λ1 is the data cleaning efficiency, is the original reading of the k1th data point, A quality adjustment factor is applied to each read to ensure data accuracy and consistency, and a sequence information record is generated.

[0087] S303: Based on the sequence information record, the DNA and RNA data of various microorganisms are classified to generate the microorganism data processing record. The specific process is as follows;

[0088] In sub-step S303, based on the sequence information records, the DNA and RNA data of various microorganisms are classified and analyzed. High-throughput sequencing technology and alignment algorithms are used to match the sequence data with the known microbial database to determine the types of microorganisms present in the sample. The classification results are verified and optimized using statistical analysis methods. The formula is: Where C represents the microbial data processing record, σ2 is the classification accuracy coefficient, is the sequence depth of the m2th data point, The matching degree of the sequence in the database is used to identify and record the DNA and RNA information of various microorganisms, and generate microbial data processing records.

[0089] See also Figure 5 Based on the microbial data processing records, the types of multiple microorganisms in multiple water samples are identified, the community distribution of microorganisms in the target water samples is analyzed, and the steps for generating population distribution identification results are as follows:

[0090] S401: According to the microbial data processing record, by analyzing the DNA and RNA information of multiple microorganisms, the types of multiple microorganisms in the water sample are identified and recorded, and the specific process of generating the microbial type record is as follows;

[0091] In sub-step S401, according to the microbial data processing record, the DNA and RNA information of various microorganisms are analyzed to identify and record the various microbial species present in the water sample, and the sequence data are classified using bioinformatics tools, compared with the microbial database and the species are identified, and biodiversity analysis software is used. The formula is: Among them, K represents the microbial species record, μ3 is the recognition accuracy coefficient, is the genetic identifier of the i3th microbial sequence, To identify the confidence level, the microbial species in the water sample are recorded and classified to generate a microbial species record.

[0092] S402: Based on the microbial species records, the distribution density and pattern of multiple microorganisms in the target water sample are analyzed to generate a microbial distribution density map. Specifically, the process is as follows;

[0093] In sub-step S402, based on the records of microbial species, the distribution density and pattern of various microorganisms in the target water samples are analyzed, spatial analysis techniques and ecological statistical methods are applied to evaluate the spatial distribution patterns of different microorganisms, and density maps are constructed. The distribution density map is drawn using geostatistical methods. The formula is: Among them, D z represents the microbial distribution density map, ω z is the distribution density weight coefficient, For the jth z The frequency of occurrence of a microorganism in a sample, The spatial distribution weights were used to analyze the distribution density and spatial distribution pattern of microorganisms and generate a microbial distribution density map.

[0094] S403: Based on the microbial distribution density map, by analyzing the community structure and distribution characteristics of multiple microorganisms, identifying key populations and ecological functions in the community, and generating population distribution identification results, the specific process is as follows;

[0095] In sub-step S403, based on the microbial distribution density map, the key microbial populations and ecological functions in the water body are identified through community structure and distribution characteristics analysis. The key populations are identified and their contribution to ecosystem services is evaluated using community ecology methods and functional diversity analysis. The formula is: Among them, F d Represents the population distribution identification result, ρ d is the ecological function analysis coefficient, For the kth d The abundance of a population, For its ecological function importance index, the structure and function of key microbial communities are identified and recorded, and population distribution identification results are generated.

[0096] See also Figure 6 Based on the population distribution identification results, according to the population distribution pattern of microorganisms in the target water body, the type of pollution source is predicted, including domestic sewage, animal feces, and water body sediment. The specific steps for generating the pollution source type prediction results are as follows:

[0097] S501: Based on the population distribution identification results, the correlation between the microbial community pattern in the target water body and various pollution types, including domestic sewage, animal feces and water body sediments, is analyzed, and the specific process of generating pollution type matching data is as follows;

[0098] In sub-step S501, based on the population distribution identification results, the correlation between the microbial community pattern in the target water body and various pollution types such as domestic sewage, animal feces and water body sediments is analyzed. The methods of environmental microbiology and pollution ecology are applied to construct a data model to analyze the response and adaptability of the microbial community to different pollution sources. The formula is: Among them, R represents the pollution type matching data, ζ q is the correlation analysis coefficient, For a q The impact indicators of each pollution type are In order to correspond to the responsiveness of the microbial community, the interaction between various pollution types and the target microbial community was analyzed to generate pollution type matching data.

[0099] S502: According to the pollution type matching data, a logistic regression algorithm is used to calculate the probability distribution of multiple pollution types and identify the key pollution source types. The specific process of generating the probability distribution analysis results is as follows;

[0100] In sub-step S502, data is matched according to pollution types, and a logistic regression model is used to calculate the probability distribution of various pollution types, identify key pollution source types, and build and optimize the logistic regression model through statistical analysis and machine learning methods. The types of key pollution sources are identified and predicted through quantitative analysis, and the probability distribution analysis results are generated.

[0101] Logistic regression algorithm, according to the formula:

[0102]

[0103] Calculate the probability of multiple pollution sources, where p is the probability of the existence of the pollution type, which is used to indicate the probability of the occurrence of the target pollution type; e is the base of the natural logarithm, which is used to provide a continuous growth curve in the logistic regression formula; β0 is the intercept of the model, which is used to adjust the baseline of the model output probability; β1 is the weight coefficient of the microbial diversity index in the model, which is used to measure the impact of this index on the prediction results; β2 is the weight coefficient of the target pathogen presence index in the model, which is used to adjust the contribution of this factor to the probability of predicting the pollution source; β3 is the weight coefficient of the biological oxygen demand in the model, which reflects the importance of this parameter in predicting the pollution source; β4 is the number of antibiotic resistance genes in the model. The weight coefficient is a key parameter for evaluating the impact of domestic sewage. β5 is the weight coefficient of the pathogen-specific gene expression level in the model, which is used to evaluate the impact of pathogen activity on pollution prediction. x1 represents the diversity index of the microbial community, which is used to measure the richness of microbial species in the water body. x2 represents the presence of the target pathogen, which is obtained through genetic testing and is used to indicate the presence of specific pathogens. x3 represents the biological oxygen demand in the water body, which is used to evaluate the activity of microorganisms in decomposing organic matter. x4 represents the number of antibiotic resistance genes, which is used to reflect the pollution level of domestic sewage. x5 represents the expression level of pathogen-specific genes, which is used to identify the activity of target pathogens.

[0104] The specific execution process of the formula is as follows:

[0105] Through environmental monitoring data, we obtain the microbial diversity index, target pathogen detection results, biological oxygen demand test values, antibiotic resistance gene counts and pathogen-specific gene expression data. Based on the target data, we use the logistic regression model to obtain the numerical values ​​of multiple parameters. The parameters are substituted into the formula to calculate the probability of existence of each pollution type, which is used to predict and evaluate the type and impact of pollution sources.

[0106] S503: Based on the probability distribution analysis results, the location information and impact range of various pollution sources are predicted, and the specific process of generating the pollution source type prediction result is as follows;

[0107] In sub-step S503, based on the results of probability distribution analysis, geographic information system and environmental simulation tools are used to predict the location information and impact range of various pollution sources, and a spatial prediction model is constructed by combining geographic and environmental data. The formula is: Among them, L p Represents the predicted location information and impact range of the pollution source, is the spatial distribution probability of the i4th pollution source type, The geographical influencing factors of this type of pollution are combined with probability and spatial data to predict the distribution and impact range of pollution sources and generate pollution source type prediction results.

[0108] See also Figure 7Based on the pollution source type prediction results, microbial samples from multiple pollution sources are collected and the distribution patterns of microbial communities are compared to identify the locations of the sources of pathogenic microorganisms in the water body. The specific steps for generating the source location identification results are as follows:

[0109] S601: According to the pollution source type prediction result, the process of collecting microbial samples from multiple predicted pollution source locations and generating target pollution source sample information is as follows;

[0110] In sub-step S601, based on the pollution source type prediction results, the field team is organized to systematically collect microbial samples at multiple predicted pollution source locations, using standardized sampling methods and equipment to ensure that representative and reliable microbial samples are collected. The specific geographic coordinates and time of each sample collection point will be recorded to ensure accurate traceability of sample data and the effectiveness of subsequent analysis. The sampling team follows relevant regulations on environmental protection and biosafety to ensure the purity and integrity of the samples. The formula is: Among them, S x Represents the sample information of the target pollution source, is the sampling efficiency of sampling point p5, is the position coordinate, For time recording, record and classify microbial sample information of multiple pollution sources, and generate target pollution source sample information.

[0111] S602: Based on the target pollution source sample information, collect and extract DNA and RNA information of microorganisms in multiple pollution source samples, and compare them with the distribution pattern of microbial communities in the target water body samples to generate sample microbial comparison results. The specific process is as follows;

[0112] In sub-step S602, based on the target pollution source sample information, the DNA and RNA information of microorganisms are collected and extracted, and the genetic materials in multiple samples are extracted using molecular biology techniques. The target data are compared with the distribution pattern of microbial communities in the target water samples. The genetic sequences are processed and compared using bioinformatics software and data analysis tools. The formula is: Among them, C v Represents the sample microbial comparison results, is the DNA and RNA extraction efficiency of the i6th sample, The genetic information of the sample is obtained, the relationship between different pollution sources and microbial communities in water samples is analyzed, and sample microbial comparison results are generated.

[0113] S603: Using the sample microorganism comparison results, analyzing and identifying multiple pollution source locations of pathogenic microorganisms in the water body, and generating the source location identification results are specifically as follows;

[0114] In sub-step S603, the sample microbial comparison results are used to analyze and identify multiple pollution source locations of pathogenic microorganisms in water bodies through data analysis and statistical methods, and the geographic information system and environmental microbiology are applied to identify the key pathogenic microorganism sources. The formula is: Among them, P h Represents the source location identification result, is the abundance index of pathogenic microorganisms of the j7th pollution source, To predict the location of pollution sources, identify the locations of pollution sources and take corresponding control and prevention measures to generate source location identification results.

[0115] See also Figure 8 A water pathogenic microorganism source analysis system is used to implement the water pathogenic microorganism source analysis method, and the system includes:

[0116] The location information recording module analyzes the geographical location information of the target water body based on the water sample collection information, identifies and records the source of the water sample, including lakes, rivers, and groundwater, and generates a target water body data set;

[0117] The pollution path simulation module predicts the propagation path and impact range of pollutants based on the target water body data set, combined with meteorological and topographic data, and generates diffusion path analysis results;

[0118] The microbial information analysis module extracts the DNA and RNA of microorganisms from the target water samples according to the diffusion path analysis results, performs serialization and standardization processing, identifies the microbial species in the samples, analyzes the community distribution, and generates community distribution data;

[0119] The pollution source characteristics comparison module is based on community distribution data. It predicts the type of pollution source by evaluating the population distribution pattern, including domestic sewage, animal feces, and water sediments, and generates pollution source prediction information;

[0120] The community distribution comparison module collects water samples from multiple predicted pollution sources based on the pollution source prediction information and analyzes the microbial information, compares the community distribution patterns, identifies multiple source locations of pathogenic microorganisms, and generates source location identification results.

[0121] The above are only preferred embodiments of the present invention and are not intended to limit the present invention in other forms. Any technician familiar with the profession may use the technical contents disclosed above to change or modify them into equivalent embodiments with equivalent changes and apply them to other fields. However, any simple modification, equivalent change and modification made to the above embodiments based on the technical essence of the present invention without departing from the technical solution of the present invention still falls within the protection scope of the technical solution of the present invention.

Claims

1. A method for analyzing the source of pathogenic microorganisms in water, characterized in that: The following steps are involved: Based on the water sample collection information, the multiple water sources of the target water body are identified through the sampling location of the water body, and the contribution of multiple source water bodies to the sampling point is calculated to generate the water body location analysis results; Based on the water body location analysis results, combined with meteorological and topographic data, the propagation and diffusion paths of the water body are simulated, the locations of multiple pollution sources and the pollution impact range are predicted, and a pollution diffusion simulation path is generated; Based on the pollution diffusion simulation path, DNA and RNA information of various microorganisms in the target water sample is collected, and serialized and standardized to generate microbial data processing records; Based on the microbial data processing records, identifying the types of multiple microorganisms in multiple water samples, analyzing the community distribution of microorganisms in the target water samples, and generating population distribution identification results; Based on the population distribution identification results, according to the population distribution pattern of microorganisms in the target water body, the type of pollution source is predicted, including domestic sewage, animal feces, and water body sediment, and a pollution source type prediction result is generated; Based on the pollution source type prediction results, microbial samples from multiple pollution sources are collected and the distribution patterns of microbial communities are compared to identify the locations of the sources of pathogenic microorganisms in the water body and generate source location identification results.

2. The method for analyzing the source of pathogenic microorganisms in water according to claim 1, characterized in that: The water body location analysis results include multiple water source contribution information, water source geographic coordinate information, and environmental characteristics of associated water bodies. The pollution diffusion simulation path includes the predicted starting position of the pollutant, the diffusion direction calculation result, and the predicted end point area of ​​the diffusion. The microbial data processing record includes the extracted DNA sequenced data, the sequence quality score, and the data processing record. The population distribution identification result includes microbial species information, the proportion data of multiple types, and population quantity statistics. The pollution source type prediction result includes key pollution source types, microbial distribution patterns corresponding to pollution source types, and pollution source hazard level scores. The source location identification result includes the matching information of microbial communities and pollution sources, source location coordinates, and pathogen community source paths.

3. The method for analyzing the source of pathogenic microorganisms in water according to claim 1, characterized in that: Based on the water sample collection information, the multiple water sources of the target water body are identified through the sampling location of the water body, and the contribution of multiple source water bodies to the sampling point is calculated. The specific steps for generating the water body location analysis results are as follows: Based on the water sample collection information, the geographical location information of the target water sample is analyzed, and the target water type is recorded to generate the sampling point location record; Based on the sampling point location records, water source contribution analysis is performed to calculate the contribution ratios of multiple water sources to the target sampling point, and a water source contribution analysis result is generated; Based on the water source contribution analysis results, through the contribution of multiple water sources corresponding to sampling points, combined with the spatial position and distribution of multiple water bodies, a spatial distribution map of water body sources is constructed to generate water body position analysis results.

4. The method for analyzing the source of pathogenic microorganisms in water according to claim 1, characterized in that: Based on the water body location analysis results, combined with meteorological and topographic data, the propagation and diffusion path of the water body is simulated, the location of multiple pollution sources and the pollution impact range are predicted, and the steps of generating the pollution diffusion simulation path are specifically as follows: Based on the water body location analysis results, historical meteorological data and terrain data of the target water body location are collected, wind direction, rainfall and terrain obstacles are analyzed, and meteorological and terrain data are generated; Based on the meteorological and topographic data, simulate the direction and speed of water flow, analyze the behavior of pollutants in the water body, including diffusion rate and path direction, and generate water flow dynamics simulation results; Based on the water flow dynamics simulation results, the diffusion paths of pollutants under various environmental conditions are simulated, the location of pollution sources and the scope of pollution impact are predicted, and the pollution diffusion simulation path is generated.

5. The method for analyzing the source of pathogenic microorganisms in water according to claim 1, characterized in that: Based on the pollution diffusion simulation path, DNA and RNA information of various microorganisms in the target water sample is collected, and serialized and standardized to generate microbial data processing records. The specific steps are: Based on the pollution diffusion simulation path, DNA and RNA information of microorganisms in target water samples are collected and extracted, the collection time and location are recorded, and microbial sample information is generated; Based on the microbial sample information, data cleaning and format standardization are performed on the DNA and RNA data of the microorganism to generate sequence information records; Based on the sequence information records, multiple microbial DNA and RNA data are classified to generate microbial data processing records.

6. The method for analyzing the source of pathogenic microorganisms in water according to claim 1, characterized in that: Based on the microbial data processing record, the steps of identifying the types of multiple microorganisms in multiple water samples, analyzing the community distribution of microorganisms in the target water samples, and generating population distribution identification results are specifically as follows: According to the microbial data processing record, by analyzing DNA and RNA information of multiple microorganisms, identifying and recording the types of multiple microorganisms in the water sample, and generating a microbial type record; Based on the microbial species records, analyzing the distribution density and pattern of multiple microorganisms in the target water sample to generate a microbial distribution density map; Based on the microbial distribution density map, by analyzing the community structure and distribution characteristics of multiple microorganisms, key populations and ecological functions in the community are identified, and population distribution identification results are generated.

7. The method for analyzing the source of pathogenic microorganisms in water according to claim 1, characterized in that: Based on the population distribution identification results, according to the population distribution pattern of microorganisms in the target water body, the type of pollution source is predicted, including domestic sewage, animal feces, and water body sediment. The steps for generating the pollution source type prediction result are specifically as follows: Based on the population distribution identification results, analyzing the correlation between the microbial community patterns in the target water body and various pollution types, including domestic sewage, animal feces and water body sediments, to generate pollution type matching data; According to the pollution type matching data, a logistic regression algorithm is used to calculate the probability distribution of multiple pollution types and identify key pollution source types, and generate probability distribution analysis results; Based on the probability distribution analysis results, the location information and impact range of various pollution sources are predicted, and pollution source type prediction results are generated.

8. The method for analyzing the source of pathogenic microorganisms in water according to claim 7, characterized in that: The logistic regression algorithm is based on the formula: Calculate the probability of multiple pollution sources, where p is the probability of the existence of the pollution type, e is the base of the natural logarithm, β0 is the intercept of the model, β1 is the weight coefficient of the microbial diversity index in the model, β2 is the weight coefficient of the target pathogen presence index in the model, β3 is the weight coefficient of the biological oxygen demand in the model, β4 is the weight coefficient of the number of antibiotic resistance genes in the model, β5 is the weight coefficient of the pathogen-specific gene expression level in the model, x1 represents the diversity index of the microbial community, x2 represents the presence of the target pathogen, x3 represents the biological oxygen demand in the water body, x4 represents the number of antibiotic resistance genes, and x5 represents the expression level of the pathogen-specific gene.

9. The method for analyzing the source of pathogenic microorganisms in water according to claim 1, characterized in that: Based on the pollution source type prediction result, microbial samples of multiple pollution sources are collected and the distribution patterns of microbial communities are compared to identify the locations of the sources of pathogenic microorganisms in the water body. The steps of generating the source location identification result are specifically as follows: According to the pollution source type prediction result, collecting microbial samples from multiple predicted pollution source locations to generate target pollution source sample information; Based on the target pollution source sample information, DNA and RNA information of microorganisms in multiple pollution source samples are collected and extracted, and compared with the distribution pattern of microbial communities in target water samples to generate sample microbial comparison results; The sample microbial comparison results are used to analyze and identify multiple pollution source locations of pathogenic microorganisms in the water body, and generate source location identification results.

10. A system for analyzing the source of pathogenic microorganisms in water, characterized in that: According to any one of claims 1 to 9, the method for analyzing the source of pathogenic microorganisms in water bodies comprises: The location information recording module analyzes the geographical location information of the target water body based on the water sample collection information, identifies and records the source of the water sample, including lakes, rivers, and groundwater, and generates a target water body data set; The pollution path simulation module predicts the propagation path and impact range of pollutants based on the target water body data set and combines meteorological and topographic data to generate diffusion path analysis results; The microbial information analysis module extracts the DNA and RNA of microorganisms from the target water sample according to the diffusion path analysis results, performs serialization and standardization processing, identifies the types of microorganisms in the sample, analyzes the community distribution, and generates community distribution data; The pollution source characteristic comparison module predicts the type of pollution source, including domestic sewage, animal feces, and water body sediment, based on the community distribution data, by evaluating the population distribution pattern, and generates pollution source prediction information; Based on the pollution source prediction information, the community distribution comparison module collects water samples from multiple predicted pollution sources and analyzes microbial information, compares community distribution patterns, identifies multiple source locations of pathogenic microorganisms, and generates source location identification results.

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