Water quality evaluation method based on data analysis

Abstract

The invention relates to the technical field of water quality analysis, in particular to a water quality evaluation method based on data analysis, which comprises the following steps: based on a satellite remote sensing technology, analyzing water spectral characteristics by adopting a deep neural network algorithm, and generating water characteristic data. According to the method, the satellite remote sensing technology and the deep neural network are combined, the accuracy of water body spectral feature analysis is improved, particulate matter in water is classified through image recognition, the analysis speed is increased, the result accuracy is improved, environmental DNA sampling is used for evaluating the biological diversity, and basic data are generated for microbial activity analysis; a micro-fluidic chip and a fluorescence labeling method are used for improving the activity analysis sensitivity of microorganisms and algae, a support vector machine algorithm is used for improving the water quality detection accuracy, an autoregression integral moving average model and a random forest algorithm are combined for deep water quality change analysis, and a comprehensive risk assessment model is used for comprehensively assessing the water quality risk. And a basis is provided for accurate water quality management.

Description

Technical Field

[0001] The present invention relates to the technical field of water quality analysis, and in particular to a water quality assessment method based on data analysis. Background Art

[0002] Water quality analysis is a scientific and technological field that involves detecting and evaluating the content and properties of substances in water. It includes quantitative and qualitative analysis of chemicals, microorganisms, suspended matter, etc. in water to assess the degree of pollution, suitability, and impact on the environment and ecosystem of the water. Water quality analysis technology is widely used in environmental monitoring, drinking water safety, and industrial production to ensure the rational use and protection of water resources.

[0003] The water quality assessment method based on data analysis is a means of comprehensively assessing water quality by using a large amount of water quality data through data analysis methods such as statistics and machine learning. This method uses advanced computing technology to extract information about the state of water bodies from indicators obtained from water quality monitoring to assist decision-making, monitor water body change trends, and promptly detect abnormal situations and respond. Its purpose is to improve the efficiency and accuracy of water quality monitoring, reveal the laws and trends of water quality changes through data analysis, and achieve a more comprehensive and in-depth understanding of water quality conditions. Through this method, potential water quality problems can be identified in a timely manner, the management and governance capabilities of water pollution sources can be improved, the sustainable use of water resources can be ensured, and the ecological environment and human health can be protected. The water quality assessment method based on data analysis is mainly carried out through statistics, machine learning, and artificial intelligence technology. The large amount of data obtained from water quality monitoring is cleaned and preprocessed to eliminate outliers and noise. Statistical methods are used to identify patterns and trends in the data. Machine learning algorithms establish water quality models by learning a large amount of historical data to predict future water quality conditions. These methods combined make water quality assessment more comprehensive and scientific, and help formulate water quality management strategies.

[0004] Traditional water quality analysis methods rely on a single data source and technology, which limits the comprehensiveness and depth of the analysis. Traditional methods lack sufficient accuracy and sensitivity when processing complex water spectral data. Traditional biodiversity assessment and microbial activity analysis require time-consuming laboratory tests, which reduces analysis efficiency. In terms of water quality prediction and risk assessment, traditional methods lack data integration and advanced algorithm support, making it difficult to provide comprehensive and forward-looking analysis. These shortcomings limit the ability of traditional water quality analysis methods to quickly respond to environmental changes and prevent potential risks. Summary of the invention

[0005] The purpose of the present invention is to solve the shortcomings in the prior art and to propose a water quality assessment method based on data analysis.

[0006] In order to achieve the above object, the present invention adopts the following technical scheme: a water quality assessment method based on data analysis, comprising the following steps:

[0007] S1: Based on satellite remote sensing technology, a deep neural network algorithm is used to analyze the spectral characteristics of water bodies and generate water body characteristic data;

[0008] S2: Based on the water body characteristic data, an image recognition method is used to identify and classify suspended particles in the water body, and a particle classification report is generated;

[0009] S3: Based on the particulate matter classification report, a biodiversity analysis report is generated using environmental DNA sampling and analysis methods;

[0010] S4: Based on the biodiversity analysis report, use bioinformatics methods to generate a list of biological species and quantities;

[0011] S5: Based on the list of biological species and quantities, using a microfluidic chip and a fluorescent labeling method, perform microbial and algae activity analysis and generate a microbial activity analysis report;

[0012] S6: Based on the microbial activity analysis report, a support vector machine algorithm is used to perform water quality testing to generate a water quality classification and prediction report;

[0013] S7: Based on the water quality classification and prediction report, an autoregressive integrated moving average model and a random forest algorithm are used to analyze water quality changes and generate a future water quality prediction report;

[0014] S8: Based on the future water quality forecast report, a comprehensive risk assessment model is used to conduct water quality assessment and generate a water quality risk assessment report;

[0015] S9: Based on the water quality risk assessment report, generate a comprehensive water quality assessment report using data integration and visualization methods;

[0016] The water body characteristic data specifically include the color, turbidity, and spectral characteristics of the water body; the particulate matter classification report specifically includes the type and distribution of particulate matter; the biodiversity analysis report specifically refers to the species and distribution density of organisms in the water body; the list of species and quantity of organisms specifically refers to the species and quantity of organisms in the water body; the microbial activity analysis report specifically refers to the quantity and activity of microorganisms and algae; the water quality classification and forecast report specifically refers to the current status and future trends of water quality; the future water quality forecast report specifically refers to the future trend analysis of water quality based on historical data; the water quality risk assessment report specifically refers to future water quality environmental risks; and the comprehensive water quality assessment report specifically refers to water quality assessment results and recommendations.

[0017] As a further solution of the present invention, based on satellite remote sensing technology, a deep neural network algorithm is used to analyze the spectral characteristics of water bodies and generate water body characteristic data in the following steps:

[0018] S101: Based on satellite remote sensing technology, image preprocessing algorithms are used to clean and standardize data to generate remote sensing image data;

[0019] S102: Based on the remote sensing image data, using a spectral analysis method to generate water body spectral characteristic data;

[0020] S103: Based on the water body spectral feature data, a deep learning algorithm is used to perform feature recognition and classification to generate a deep learning analysis result;

[0021] S104: Based on the deep learning analysis results, a data fusion method is used to integrate information to generate water body characteristic data.

[0022] As a further solution of the present invention, based on the water body characteristic data, an image recognition method is used to identify and classify suspended particles in the water body, and the steps of generating a particle classification report are specifically as follows:

[0023] S201: Based on the water body feature data, an image segmentation algorithm is used to analyze water body and non-water body regions to generate segmented image data;

[0024] S202: Based on the segmented image data, feature extraction is used to identify suspended particles to generate particle feature data;

[0025] S203: Based on the particle feature data, a machine learning classification algorithm is used to classify the particles and generate a preliminary particle classification result;

[0026] S204: Based on the preliminary particle classification result, a particle classification report is generated using a statistical analysis method.

[0027] As a further solution of the present invention, based on the particulate matter classification report, the steps of generating a biodiversity analysis report using environmental DNA sampling and analysis methods are as follows:

[0028] S301: Based on the particulate matter classification report, collect samples using an environmental DNA sampling method to generate an environmental DNA sample;

[0029] S302: Based on the environmental DNA sample, a DNA extraction method is used to extract DNA to generate DNA sample data;

[0030] S303: Based on the DNA sample data, using gene sequencing technology, DNA sequencing is performed to generate gene sequence data;

[0031] S304: Based on the gene sequence data, a bioinformatics analysis method is used to perform biological analysis to generate a biodiversity analysis report.

[0032] As a further solution of the present invention, based on the biodiversity analysis report, the steps of generating a list of biological species and quantities using bioinformatics methods are specifically as follows:

[0033] S401: Based on the biodiversity analysis report, a cluster analysis algorithm is used to classify species and generate preliminary biological classification data;

[0034] S402: Based on the preliminary biological classification data, a non-parametric statistical method is used to perform population density estimation and data integration to generate quantity estimation data;

[0035] S403: Based on the quantity estimation data, a sequence comparison method is used to accurately identify species and verify data to generate species identification data;

[0036] S404: Based on the species identification data, a biological information integration platform is used to integrate and review the data and generate a list of biological species and quantities.

[0037] As a further solution of the present invention, based on the list of biological species and quantities, a microfluidic chip and a fluorescent labeling method are used to perform microbial and algae activity analysis, and the steps of generating a microbial activity analysis report are specifically as follows:

[0038] S501: Based on the list of biological species and quantities, using a microfluidic method to separate and prepare samples to generate prepared samples;

[0039] S502: Based on the prepared sample, a fluorescent staining method is used to perform cell labeling and microscopic observation to generate a fluorescent labeled sample;

[0040] S503: Based on the fluorescent labeled sample, using fluorescence microscope imaging, activity evaluation and image analysis are performed to generate microbial activity observation data;

[0041] S504: Based on the microbial activity observation data, a data mining method is used to perform data analysis and integration, and a report is compiled to generate a microbial activity analysis report.

[0042] As a further solution of the present invention, based on the microbial activity analysis report, the support vector machine algorithm is used to detect water quality and generate a water quality classification and prediction report in the following steps:

[0043] S601: Based on the microbial activity analysis report, a preprocessed data set is generated using a data cleaning and standardization method;

[0044] S602: Based on the preprocessed data set, a support vector machine algorithm is used to perform feature learning to generate a water quality feature model;

[0045] S603: Based on the water quality characteristic model, cross validation and hyperparameter tuning are used to generate an optimized water quality classification model;

[0046] S604: Based on the optimized water quality classification model, a predictive analysis method is used to classify water quality grades and predict trends, and a water quality classification and prediction report is generated.

[0047] As a further solution of the present invention, based on the water quality classification and prediction report, the steps of using the autoregressive integrated moving average model and the random forest algorithm to analyze water quality changes and generate future water quality prediction reports are as follows:

[0048] S701: Based on the water quality classification and prediction report, time series decomposition and feature extraction are used to analyze data to generate time series feature data;

[0049] S702: Based on the time series characteristic data, an autoregressive integrated moving average model is used to perform historical trend analysis to generate historical trend analysis data;

[0050] S703: Based on the historical trend analysis data, a random forest algorithm is used to predict water quality and generate a water quality change prediction model;

[0051] S704: Based on the water quality change prediction model, a future water quality prediction report is generated by using comprehensive evaluation and report preparation.

[0052] As a further solution of the present invention, based on the future water quality forecast report, a comprehensive risk assessment model is used to conduct water quality assessment, and the steps of generating a water quality risk assessment report are specifically as follows:

[0053] S801: Based on the future water quality forecast report, a time series analysis algorithm is used to perform trend identification and risk determination, and a water quality trend analysis report is generated;

[0054] S802: Based on the water quality trend analysis report, a Monte Carlo simulation method is used to perform probability distribution calculation and risk quantification to generate a potential risk assessment report;

[0055] S803: Based on the potential risk assessment report, a multi-criteria decision analysis method is used to sort the risk levels and determine the risk levels, and a risk level determination report is generated;

[0056] S804: Based on the risk level determination report, a risk assessment model is used to conduct a comprehensive analysis and a comprehensive risk assessment to generate a water quality risk assessment report.

[0057] As a further solution of the present invention, based on the water quality risk assessment report, the steps of generating a comprehensive water quality assessment report by using data integration and visualization methods are as follows:

[0058] S901: Based on the water quality risk assessment report, data processing is used to perform data error correction and data optimization to generate an optimized data set;

[0059] S902: Based on the optimized data set, a data fusion method is used to integrate the data to generate an integrated data set;

[0060] S903: Based on the integrated data set, using statistical analysis methods to extract information and analyze key indicators to generate a key information summary;

[0061] S904: Based on the key information summary, use data visualization tools to create charts and compile reports to generate a comprehensive water quality assessment report.

[0062] Compared with the prior art, the advantages and positive effects of the present invention are:

[0063] In the present invention, by combining satellite remote sensing technology and deep neural network algorithm, this scheme can more accurately analyze the spectral characteristics of water bodies and improve the accuracy and meticulousness of water body characteristic data. The image recognition method is used to classify suspended particles in water bodies, which not only improves the analysis speed, but also enhances the accuracy of the analysis results. The application of environmental DNA sampling and analysis methods provides an effective means for the accurate assessment of biodiversity. The list of biological species and quantities generated by bioinformatics methods provides detailed basic data for the subsequent analysis of microbial activity. The use of microfluidic chips and fluorescent labeling methods further improves the sensitivity and accuracy of microbial and algae activity analysis. The application of support vector machine algorithm in water quality detection improves the accuracy of water quality classification and prediction. The comprehensive application of autoregressive integral moving average model and random forest algorithm makes the analysis of water quality changes more comprehensive and in-depth. The use of comprehensive risk assessment model provides a solid foundation for the comprehensive assessment of water quality risks. BRIEF DESCRIPTION OF THE DRAWINGS

[0064] Figure 1 It is a schematic diagram of the main steps of the present invention;

[0065] Figure 2 It is a schematic diagram of the refinement of S1 of the present invention;

[0066] Figure 3 It is a schematic diagram of the refinement of S2 of the present invention;

[0067] Figure 4 It is a schematic diagram of the refinement of S3 of the present invention;

[0068] Figure 5 It is a schematic diagram of the refinement of S4 of the present invention;

[0069] Figure 6 It is a detailed schematic diagram of S5 of the present invention;

[0070] Figure 7 It is a detailed schematic diagram of S6 of the present invention;

[0071] Figure 8 It is a detailed schematic diagram of S7 of the present invention;

[0072] Fig. 9 It is a detailed schematic diagram of S8 of the present invention;

[0073] Fig.10 It is a detailed schematic diagram of S9 of the present invention. DETAILED DESCRIPTION

[0074] 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.

[0075] 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.

[0076] Embodiment 1

[0077] See also Figure 1 The present invention provides a technical solution: a water quality assessment method based on data analysis, comprising the following steps:

[0078] S1: Based on satellite remote sensing technology, a deep neural network algorithm is used to analyze the spectral characteristics of water bodies and generate water body characteristic data;

[0079] S2: Based on the water body characteristic data, the image recognition method is used to identify and classify suspended particles in the water body and generate a particle classification report;

[0080] S3: Generate a biodiversity analysis report based on the particulate matter classification report using environmental DNA sampling and analysis methods;

[0081] S4: Based on the biodiversity analysis report, use bioinformatics methods to generate a list of biological species and quantities;

[0082] S5: Based on the list of biological species and quantities, microfluidic chips and fluorescent labeling methods are used to analyze the activity of microorganisms and algae and generate a microbial activity analysis report;

[0083] S6: Based on the microbial activity analysis report, support vector machine algorithm is used to detect water quality and generate water quality classification and prediction report;

[0084] S7: Based on the water quality classification and prediction report, the autoregressive integrated moving average model and random forest algorithm are used to analyze water quality changes and generate future water quality prediction reports;

[0085] S8: Based on the future water quality forecast report, a comprehensive risk assessment model is used to conduct water quality assessment and generate a water quality risk assessment report;

[0086] S9: Based on the water quality risk assessment report, use data integration and visualization methods to generate a comprehensive water quality assessment report;

[0087] Water body characteristic data specifically include the color, turbidity, and spectral characteristics of the water body. The particulate matter classification report specifically includes the type and distribution of particulate matter. The biodiversity analysis report specifically refers to the species and distribution density of organisms in the water body. The list of biological species and quantity specifically refers to the species and quantity of organisms in the water body. The microbial activity analysis report specifically refers to the number and activity of microorganisms and algae. The water quality classification and prediction report specifically refers to the current status and future trends of water quality. The future water quality prediction report specifically refers to the analysis of future trends of water quality based on historical data. The water quality risk assessment report specifically refers to future water quality environmental risks. The comprehensive water quality assessment report specifically refers to the water quality assessment results and recommendations.

[0088] First, the spectral characteristics of water bodies are analyzed based on satellite remote sensing and deep neural network algorithms to generate water body characteristic data. Water body characteristics can be obtained from a wide range of angles, laying the foundation for subsequent analysis.

[0089] Secondly, image recognition methods are used to identify and classify suspended particles in water bodies, and then a particle classification report is generated. The classification report helps to accurately identify pollutants in water bodies and provide a more detailed description of environmental conditions.

[0090] Subsequently, through environmental DNA sampling and analysis, a biodiversity analysis report is generated, which provides information on the water ecosystem, including biological species and distribution density, and helps to fully understand the ecological status of the water body.

[0091] Based on bioinformatics methods, an inventory of biological species and numbers is generated to provide an accurate record of the species and numbers of organisms in water bodies, which is crucial for assessing the biodiversity of water bodies and the existing ecosystem risks.

[0092] Furthermore, microfluidic chips and fluorescent labeling methods are used to analyze the activity of microorganisms and algae, and generate microbial activity analysis reports, which helps to understand the number and activity of microorganisms and algae in water bodies and provide more comprehensive data support for water quality assessment.

[0093] The subsequent water quality detection and classification prediction report provides water quality status and future trends through the support vector machine algorithm, allowing a clearer understanding of the current water quality status and providing a basis for future preventive measures.

[0094] The analysis of water quality changes based on historical data and the construction of a comprehensive risk assessment model can help predict water quality environmental risks and provide a scientific basis for the formulation of environmental protection measures.

[0095] Finally, a comprehensive water quality assessment report is generated through data integration and visualization methods, making the results more operational and understandable. The entire process not only provides comprehensive water quality information, but also provides multi-level, comprehensive analysis results and suggestions for environmental protection and supervision. This multi-angle, comprehensive analysis method helps to better protect and manage water resources and provide protection for the ecological environment and human health.

[0096] See also Figure 2 ,Based on satellite remote sensing technology, a deep neural network algorithm is used to analyze the spectral characteristics of water bodies. The specific steps for generating water body characteristic data are as follows:

[0097] S101: Based on satellite remote sensing technology, image preprocessing algorithms are used to clean and standardize data to generate remote sensing image data;

[0098] S102: Based on the remote sensing image data, a spectral analysis method is used to generate water body spectral characteristic data;

[0099] S103: Based on the water body spectral feature data, a deep learning algorithm is used to perform feature recognition and classification, and a deep learning analysis result is generated;

[0100] S104: Based on the deep learning analysis results, a data fusion method is used to integrate information and generate water body characteristic data.

[0101] First, satellite remote sensing image data is acquired. Subsequently, image preprocessing algorithms are applied to clean and standardize the data to eliminate the existing noise and cloud cover interference factors. Preprocessing also includes image georegistration to ensure that the data is spatially aligned and that the image data used is of high quality and reliable.

[0102] Based on the cleaned and standardized remote sensing image data, spectral analysis methods are used to extract the spectral characteristics of water bodies. This includes comparing and analyzing the spectral curves of the water body and the surrounding objects in the image in order to identify the unique characteristics of the water body. Spectral analysis can reveal the spectral response of the components in the water body by modeling the brightness values ​​of the bands.

[0103] A deep learning algorithm is used to identify and classify the spectral feature data. Deep neural networks include convolutional neural network structures, which are used to learn the complex relationships between spectral features. Training a deep learning model requires supervised learning using known water spectral data so that the model can accurately identify and classify water features in unknown data.

[0104] Data fusion involves superimposing or combining information from bands and deep learning results to more comprehensively describe the characteristics of water bodies, ensuring that information obtained from multiple data sources and analysis perspectives can provide a more accurate and comprehensive basis for subsequent water body characteristic analysis.

[0105] The overall practical operation process includes acquiring satellite remote sensing images, performing image preprocessing, performing spectral analysis, deep learning feature recognition and classification, and finally obtaining comprehensive characteristic data of water bodies through data fusion. The implementation covers multiple key areas such as satellite remote sensing technology, deep learning algorithms, and data fusion, providing a detailed and feasible operation plan for the analysis of water body spectral characteristics.

[0106] See also Figure 3 Based on the water body characteristic data, the image recognition method is used to identify and classify suspended particles in the water body. The specific steps for generating a particle classification report are as follows:

[0107] S201: Based on the water body feature data, an image segmentation algorithm is used to analyze the water body and non-water body areas to generate segmented image data;

[0108] S202: Based on the segmented image data, feature extraction is used to identify suspended particles to generate particle feature data;

[0109] S203: Based on the particle feature data, a machine learning classification algorithm is used to classify the particles and generate a preliminary particle classification result;

[0110] S204: Based on the preliminary particle classification results, a particle classification report is generated using a statistical analysis method.

[0111] First, based on the water body feature data, an image segmentation algorithm is used to analyze the water body and non-water body areas. This includes using threshold and region growing image segmentation techniques to separate the water body from the surrounding environment and generate segmented image data. By extracting the water body area, a clear background is provided for subsequent particle identification.

[0112] Based on the segmented image data, the feature extraction method is used to identify suspended particles. This involves extracting the shape, texture, and color features of the particles in the image to establish the feature data of the particles. The particles in the image are distinguished from the water background to provide feature data for subsequent classification.

[0113] Based on the extracted particle feature data, a machine learning classification algorithm is used to classify particles. This includes using support vector machines, decision trees, and deep learning methods to classify particles. The machine learning model needs to use known particle feature data for learning during the training phase so that it can accurately classify unknown data in practical applications.

[0114] Based on the preliminary particle classification results generated by the machine learning algorithm, statistical analysis methods are used for further analysis to generate a particle classification report, including the number and distribution of particles. It provides a comprehensive description of suspended particles in water bodies and provides decision support for environmental monitoring and management.

[0115] The overall practical operation process involves image segmentation, suspended particle identification, machine learning classification and statistical analysis to ensure that the solution can accurately identify suspended particles in water bodies in actual applications and generate detailed particle classification reports.

[0116] See also Figure 4 Based on the particulate matter classification report, the steps to generate a biodiversity analysis report using environmental DNA sampling and analysis methods are as follows:

[0117] S301: Based on the particulate matter classification report, collect samples using the environmental DNA sampling method to generate environmental DNA samples;

[0118] S302: Based on the environmental DNA sample, a DNA extraction method is used to extract DNA to generate DNA sample data;

[0119] S303: Based on the DNA sample data, using gene sequencing technology, DNA sequencing is performed to generate gene sequence data;

[0120] S304: Based on the gene sequence data, bioinformatics analysis methods are used to perform biological analysis and generate a biodiversity analysis report.

[0121] According to the distribution of particulate matter in the particulate matter classification report, environmental DNA sampling methods are used to collect samples, including microorganisms and algae communities in water bodies. Sampling is carried out through water samples or appropriate methods to collect DNA samples representing the biodiversity of water bodies.

[0122] DNA is extracted from the collected environmental DNA samples. A special DNA extraction method is used to separate the environmental DNA collected from the water sample to obtain pure DNA sample data. The extraction method ensures the accuracy and effectiveness of subsequent gene sequencing.

[0123] The extracted DNA samples are sequenced using gene sequencing technology, including high-throughput sequencing technology, to obtain gene sequence data in DNA samples. Through sequencing, DNA sequence information of organisms in the water body is obtained.

[0124] The obtained gene sequence data are processed and analyzed using bioinformatics analysis methods. This involves sequence alignment, species identification, and diversity index calculation operations to understand the species, abundance, and diversity information of organisms in the water body. Based on these data, a biodiversity analysis report is generated, including information on the number of organisms, diversity indicators, and ecosystem stability.

[0125] The overall practical operation process involves multiple steps including sampling, DNA extraction, gene sequencing and bioinformatics analysis, ensuring that the DNA information obtained from the environment can accurately reflect the biodiversity in the water body and generate a detailed biodiversity analysis report. This method provides a way to gain a deeper understanding of the water ecosystem and provides an important basis for environmental protection and ecological management.

[0126] See also Figure 5 Based on the biodiversity analysis report, the steps to generate a list of biological species and quantities using bioinformatics methods are as follows:

[0127] S401: Based on the biodiversity analysis report, use cluster analysis algorithm to classify species and generate preliminary biological classification data;

[0128] S402: Based on preliminary biological taxonomic data, non-parametric statistical methods are used to estimate population density and integrate data to generate population estimation data;

[0129] S403: Based on the quantity estimation data, the sequence comparison method is used to accurately identify species and verify data to generate species identification data;

[0130] S404: Based on species identification data, use the biological information integration platform to integrate and review data and generate a list of biological species and quantities.

[0131] By using cluster analysis algorithms, the gene sequence data in the biodiversity analysis report is classified to determine the species present in the sample. Cluster analysis algorithms include K-means clustering, hierarchical clustering, and model clustering.

[0132] Based on the preliminary taxonomic data, non-parametric statistical methods are used to estimate population density, including statistical analysis of clustering results to estimate the relative number of each species in the sample. At the same time, the data is integrated to ensure that consistent data formats and standards are used in subsequent steps.

[0133] Use sequence comparison methods to accurately identify species in population estimate data. This involves comparing experimental data with known gene sequence databases to ensure the accuracy of preliminary classification and population estimates. Data verification is a key step to eliminate potential errors and confirm species identity.

[0134] Integrate species identification data into a bioinformatics integration platform for further data integration and review, including cross-validation, data cleaning, and removal of duplicate species to ensure that the generated list of biological species and quantities is accurate and reliable.

[0135] The overall practical operation process requires a high degree of precision and rigor to ensure the accuracy and reliability of each step. This method provides a systematic means to generate an inventory of biological species and quantities in a bioinformatic way, providing a powerful tool for ecological research and environmental monitoring.

[0136] See also Figure 6 Based on the list of biological species and quantities, microfluidic chips and fluorescent labeling methods are used to analyze the activity of microorganisms and algae. The specific steps for generating a microbial activity analysis report are as follows:

[0137] S501: Based on the list of biological species and quantities, use microfluidic methods to separate samples, prepare samples, and generate prepared samples;

[0138] S502: Based on the prepared sample, a fluorescent staining method is used to perform cell labeling and microscopic observation to generate a fluorescent labeled sample;

[0139] S503: Based on the fluorescent labeled samples, fluorescence microscopy imaging is used to perform activity evaluation and image analysis to generate microbial activity observation data;

[0140] S504: Based on the microbial activity observation data, data mining methods are used to analyze and integrate the data, and report preparation is performed to generate a microbial activity analysis report.

[0141] Based on the list of biological species and quantities, microfluidic methods are used to separate and prepare samples. Microfluidic chips enable precise control and separation of samples, ensuring the purity of the target microorganisms and algae for analysis, and generating prepared samples for subsequent fluorescent labeling.

[0142] The prepared samples are then labeled using fluorescent staining. Fluorescent staining can selectively label microbial and algal cells, making them easier to observe and analyze under a microscope. Completion of this step will result in a fluorescently labeled sample ready for microscopic observation for activity analysis.

[0143] Based on fluorescently labeled samples, fluorescence microscopy is used for imaging. Through microscopic observation, the activity of microorganisms and algae can be evaluated, including cell morphology, movement status, etc. Image analysis methods can be used to quantifying the number of active cells and further generate microbial activity observation data.

[0144] Based on the observed data of microbial activity, data mining methods are used for data analysis and integration, including statistical analysis of the number of active cells and the temporal and spatial distribution of activity. Microbial activity analysis reports are compiled through integrated analysis results, providing detailed activity information and association with biological species and quantity lists.

[0145] The overall practical operation process requires strict sample preparation, labeling and microscopic observation techniques to ensure the accuracy of activity analysis. This method provides a high-resolution observation method for the activity of microorganisms and algae, providing a powerful tool for environmental monitoring and ecological research.

[0146] See also Figure 7 Based on the microbial activity analysis report, the support vector machine algorithm is used to detect water quality. The specific steps for generating water quality classification and prediction reports are as follows:

[0147] S601: Based on the microbial activity analysis report, a preprocessed data set is generated using data cleaning and standardization methods;

[0148] S602: Based on the preprocessed data set, a support vector machine algorithm is used to perform feature learning to generate a water quality feature model;

[0149] S603: Based on the water quality characteristic model, cross validation and hyperparameter tuning are used to generate an optimized water quality classification model;

[0150] S604: Based on the optimized water quality classification model, a predictive analysis method is used to classify water quality grades and predict trends, and a water quality classification and prediction report is generated.

[0151] First, data preprocessing, including data cleaning and standardization, is performed based on the microbial activity analysis report. Missing values ​​and outliers are processed, and the data is standardized to generate a standardized preprocessed data set to ensure data quality and consistency.

[0152] Next, the preprocessed data set was used to perform feature learning using the support vector machine algorithm to generate a water quality feature model. The support vector machine can learn the features in the microbial activity analysis report and map them to specific patterns of water quality status, providing a basis for subsequent water quality classification.

[0153] Based on feature learning, an optimized water quality classification model is generated through cross-validation and hyperparameter tuning. The model performance is evaluated through cross-validation, and the accuracy and generalization ability of the model are improved by adjusting the hyperparameters to better adapt to the complex characteristics of microbial activity data.

[0154] Finally, based on the optimized water quality classification model, the predictive analysis method is used to classify water quality grades and predict trends, and a water quality classification and prediction report is generated. The report includes the classification results of water quality grades, trend analysis, and model accuracy assessment, so that decision makers can better understand the water quality situation.

[0155] In actual operation, the data of the microbial activity analysis report needs to be checked in detail and the appropriate data processing method should be selected. In the model optimization stage, the grid search method can be used for hyperparameter tuning to ensure that the optimal model parameters are found. The final report should be presented in an interpretable and easy-to-understand way, including visual charts and classification accuracy information to improve users' understanding of water quality. This meticulous implementation process will help improve the accuracy and interpretability of water quality detection models.

[0156] See also Figure 8 Based on the water quality classification and prediction report, the autoregressive integrated moving average model and random forest algorithm are used to analyze water quality changes. The specific steps for generating future water quality prediction reports are as follows:

[0157] S701: Based on the water quality classification and prediction report, time series decomposition and feature extraction are used to analyze data and generate time series feature data;

[0158] S702: Based on the time series characteristic data, an autoregressive integrated moving average model is used to perform historical trend analysis and generate historical trend analysis data;

[0159] S703: Based on historical trend analysis data, a random forest algorithm is used to predict water quality and generate a water quality change prediction model;

[0160] S704: Based on the water quality change prediction model, a comprehensive assessment and report preparation are used to generate a future water quality forecast report.

[0161] First, based on the water quality classification and prediction report, time series decomposition and feature extraction are performed to generate time series feature data containing trend, seasonality and residual information. These features are designed to better capture the change pattern of water quality data and provide more specific input for subsequent modeling.

[0162] Based on the generated time series characteristic data, the autoregressive integrated moving average model was used for historical trend analysis. The ARIMA model fits the trend, seasonality and residuals of the time series data, providing a detailed understanding of past water quality changes.

[0163] Using historical trend analysis data, a water quality change prediction model was established using the random forest algorithm. Random forests have strong generalization capabilities by integrating the prediction results of multiple decision trees, and are suitable for processing complex water quality change patterns.

[0164] Based on the established water quality change prediction model, comprehensive assessment and report preparation are carried out to generate a future water quality prediction report. The report includes the trend of future water quality changes, key influencing factors, and the prediction accuracy evaluation of the model, providing decision makers with comprehensive future water quality information.

[0165] In actual operation, it is necessary to pay attention to selecting appropriate time series feature extraction methods, adjusting the parameters of the ARIMA model and the random forest model, and conducting rigorous model validation and optimization. The final report should be presented in clear visual charts and easy-to-understand language to ensure that decision makers can quickly understand and respond to future changes in water quality. This detailed implementation process helps to improve the accuracy and reliability of water quality prediction models.

[0166] See also Fig. 9 , based on the future water quality forecast report, a comprehensive risk assessment model is used to conduct water quality assessment. The specific steps for generating a water quality risk assessment report are as follows:

[0167] S801: Based on the future water quality forecast report, use the time series analysis algorithm to identify trends and determine risks, and generate a water quality trend analysis report;

[0168] S802: Based on the water quality trend analysis report, the Monte Carlo simulation method is used to calculate the probability distribution and quantify the risk to generate a potential risk assessment report;

[0169] S803: Based on the potential risk assessment report, a multi-criteria decision analysis method is used to sort and determine the risk level, and a risk level determination report is generated;

[0170] S804: Based on the risk level determination report, a risk assessment model is used to conduct a comprehensive analysis and a comprehensive risk assessment to generate a water quality risk assessment report.

[0171] First, data is extracted from future water quality forecast reports and the trend of water quality is confirmed using time series analysis algorithms. This step includes using trend graphs and seasonal analysis techniques to identify future water quality change patterns and make risk assessments. The results of the trend analysis will be compiled into a water quality trend analysis report to explore the potential trend changes in water quality in detail.

[0172] Next, the Monte Carlo simulation method is used to calculate the probability distribution of potential risks, taking into account various uncertainties. This process will convert the probability distribution into quantifiable data to assess the extent of potential risks and form a potential risk assessment report to explain the risks faced.

[0173] The multi-criteria decision analysis method is used to sort and classify potential risks and determine different risk levels. This process generates a risk level determination report that details the risk implications of the levels and the corresponding risk thresholds.

[0174] Finally, a comprehensive analysis is conducted using the risk assessment model to comprehensively consider the combined impact of factors on water quality. This stage generates a water quality risk assessment report that describes the comprehensive risk of water quality in detail, including risk sources, levels, potential impacts, and response recommendations.

[0175] In actual operation, each step requires careful design of data processing methods and model selection to ensure the accuracy and reliability of the results. The participation of professionals and data verification are key to ensuring the credibility of the assessment results. In addition, the definition of risk levels and the setting of judgment criteria need to consider industry standards and professional advice to ensure the operability and practicality of the assessment results.

[0176] See also Fig.10 Based on the water quality risk assessment report, the steps to generate a comprehensive water quality assessment report using data integration and visualization methods are as follows:

[0177] S901: Based on the water quality risk assessment report, data processing is used to correct data errors and optimize data to generate optimized data sets;

[0178] S902: Based on the optimized data set, a data fusion method is used to integrate the data to generate an integrated data set;

[0179] S903: Based on the integrated data set, statistical analysis methods are used to extract information, analyze key indicators, and generate key information summaries;

[0180] S904: Based on the summary of key information, use data visualization tools to create charts and reports to generate a comprehensive water quality assessment report.

[0181] First, based on the water quality risk assessment report, data processing is performed to correct and optimize data to ensure data accuracy and completeness, including processing missing values, outliers, and data standardization to generate an optimized data set to provide high-quality input for subsequent integration and analysis.

[0182] Using the optimized data set, data fusion methods are used for data integration. This involves combining data from the sources to obtain more comprehensive information. Ensure that data consistency is maintained during the integration process to generate an integrated data set that contains key information from multiple data sources.

[0183] Based on the integrated data set, statistical analysis methods are used to extract information and analyze key indicators. Key information is extracted from the data and key indicators of water quality assessment are analyzed. A summary of key information is generated to clearly present the main results of the comprehensive water quality assessment.

[0184] Use data visualization tools to create charts and reports using key information summaries. Use charts and visualization elements to present key information in an intuitive way, making the comprehensive water quality assessment easier to understand. Generate a visual report of the comprehensive water quality assessment, including charts, trend analysis and key conclusions.

[0185] In actual operation, each step requires careful selection and adjustment of data processing methods, integration techniques, statistical analysis methods and visualization tools to ensure the accuracy and reliability of the results. Professional participation and data verification are key to ensuring the credibility of the evaluation results. In addition, the design and visualization of the report should focus on conveying clear, concise and easy-to-understand information to improve the practicality and decision-making support capabilities of the report.

[0186] 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 water quality assessment method based on data analysis, characterized in that: The following steps are involved: Based on satellite remote sensing technology, a deep neural network algorithm is used to analyze the spectral characteristics of water bodies and generate water body characteristic data; Based on the water body characteristic data, an image recognition method is used to identify and classify suspended particles in the water body, and a particle classification report is generated; Based on the particulate matter classification report, a biodiversity analysis report is generated using environmental DNA sampling and analysis methods; Based on the biodiversity analysis report, a list of biological species and quantities is generated using bioinformatics methods; Based on the list of biological species and quantities, using microfluidic chips and fluorescent labeling methods, analyze the activity of microorganisms and algae and generate a microbial activity analysis report; Based on the microbial activity analysis report, a support vector machine algorithm is used to perform water quality testing to generate a water quality classification and prediction report; Based on the water quality classification and prediction report, the autoregressive integrated moving average model and random forest algorithm are used to analyze water quality changes and generate future water quality prediction reports; Based on the future water quality forecast report, a comprehensive risk assessment model is used to conduct water quality assessment and generate a water quality risk assessment report; Based on the water quality risk assessment report, a comprehensive water quality assessment report is generated using data integration and visualization methods; The water body characteristic data specifically include the color, turbidity, and spectral characteristics of the water body; the particulate matter classification report specifically includes the type and distribution of particulate matter; the biodiversity analysis report specifically refers to the species and distribution density of organisms in the water body; the list of species and quantity of organisms specifically refers to the species and quantity of organisms in the water body; the microbial activity analysis report specifically refers to the quantity and activity of microorganisms and algae; the water quality classification and forecast report specifically refers to the current status and future trends of water quality; the future water quality forecast report specifically refers to the future trend analysis of water quality based on historical data; the water quality risk assessment report specifically refers to future water quality environmental risks; and the comprehensive water quality assessment report specifically refers to water quality assessment results and recommendations.

2. The water quality assessment method based on data analysis according to claim 1, characterized in that: Based on satellite remote sensing technology, a deep neural network algorithm is used to analyze the spectral characteristics of water bodies. The specific steps to generate water body characteristic data are as follows: Based on satellite remote sensing technology, image preprocessing algorithms are used to clean and standardize data to generate remote sensing image data; Based on the remote sensing image data, a spectral analysis method is used to generate water body spectral characteristic data; Based on the water body spectral feature data, a deep learning algorithm is used to perform feature recognition and classification to generate a deep learning analysis result; Based on the deep learning analysis results, a data fusion method is used to integrate information and generate water body characteristic data.

3. The water quality assessment method based on data analysis according to claim 1, characterized in that: Based on the water body characteristic data, the image recognition method is used to identify and classify suspended particles in the water body. The specific steps for generating a particle classification report are as follows: Based on the water body feature data, an image segmentation algorithm is used to analyze water body and non-water body regions to generate segmented image data; Based on the segmented image data, feature extraction is used to identify suspended particles to generate particle feature data; Based on the particle feature data, a machine learning classification algorithm is used to classify the particles and generate preliminary particle classification results; Based on the preliminary particle classification results, a particle classification report is generated using a statistical analysis method.

4. The water quality assessment method based on data analysis according to claim 1, characterized in that: Based on the particulate matter classification report, the steps to generate a biodiversity analysis report using environmental DNA sampling and analysis methods are as follows: Based on the particulate matter classification report, an environmental DNA sampling method is used to collect samples to generate environmental DNA samples; Based on the environmental DNA sample, a DNA extraction method is used to extract DNA to generate DNA sample data; Based on the DNA sample data, using gene sequencing technology to perform DNA sequencing to generate gene sequence data; Based on the gene sequence data, a bioinformatics analysis method is used to perform biological analysis and generate a biodiversity analysis report.

5. The water quality assessment method based on data analysis according to claim 1, characterized in that: Based on the biodiversity analysis report, the steps to generate a list of biological species and quantities using bioinformatics methods are as follows: Based on the biodiversity analysis report, a cluster analysis algorithm is used to classify species and generate preliminary biological taxonomy data; Based on the preliminary taxonomic data, non-parametric statistical methods are used to estimate population density and integrate data to generate population estimation data; Based on the population estimation data, sequence comparison methods are used to accurately identify species and verify data to generate species identification data; Based on the species identification data, a biological information integration platform is used to integrate and review the data and generate a list of biological species and quantities.

6. The water quality assessment method based on data analysis according to claim 1, characterized in that: Based on the list of biological species and quantities, the microfluidic chip and fluorescent labeling method are used to analyze the activity of microorganisms and algae. The specific steps for generating a microbial activity analysis report are as follows: Based on the list of biological species and quantities, using a microfluidic method to separate samples and prepare samples to generate prepared samples; Based on the prepared sample, a fluorescent staining method is used to perform cell labeling and microscopic observation to generate a fluorescent labeled sample; Based on the fluorescent labeled samples, fluorescence microscopy imaging is used to perform activity evaluation and image analysis to generate microbial activity observation data; Based on the microbial activity observation data, a data mining method is used to perform data analysis and integration, and a report is compiled to generate a microbial activity analysis report.

7. The water quality assessment method based on data analysis according to claim 1, characterized in that: Based on the microbial activity analysis report, the support vector machine algorithm is used to detect water quality and generate water quality classification and prediction reports in the following steps: Based on the microbial activity analysis report, a preprocessed data set is generated using data cleaning and standardization methods; Based on the preprocessed data set, a support vector machine algorithm is used to perform feature learning to generate a water quality feature model; Based on the water quality characteristic model, cross validation and hyperparameter tuning are used to generate an optimized water quality classification model; Based on the optimized water quality classification model, a predictive analysis method is used to classify water quality grades and predict trends, and a water quality classification and prediction report is generated.

8. The water quality assessment method based on data analysis according to claim 1, characterized in that: Based on the water quality classification and prediction report, the autoregressive integrated moving average model and random forest algorithm are used to analyze water quality changes. The specific steps for generating future water quality prediction reports are as follows: Based on the water quality classification and prediction report, time series decomposition and feature extraction are used to analyze data to generate time series feature data; Based on the time series characteristic data, an autoregressive integrated moving average model is used to perform historical trend analysis to generate historical trend analysis data; Based on the historical trend analysis data, a random forest algorithm is used to predict water quality and generate a water quality change prediction model; Based on the water quality change prediction model, comprehensive assessment and report preparation are adopted to generate future water quality prediction reports.

9. The water quality assessment method based on data analysis according to claim 1, characterized in that: Based on the future water quality forecast report, a comprehensive risk assessment model is used to conduct water quality assessment. The specific steps for generating a water quality risk assessment report are as follows: Based on the future water quality forecast report, a time series analysis algorithm is used to perform trend identification and risk assessment to generate a water quality trend analysis report; Based on the water quality trend analysis report, a Monte Carlo simulation method is used to calculate probability distribution and quantify risks to generate a potential risk assessment report; Based on the potential risk assessment report, a multi-criteria decision analysis method is used to sort and determine the risk level, and a risk level determination report is generated; Based on the risk level determination report, a risk assessment model is used to conduct a comprehensive analysis and a comprehensive risk assessment to generate a water quality risk assessment report.

10. The water quality assessment method based on data analysis according to claim 1, characterized in that: Based on the water quality risk assessment report, the steps to generate a comprehensive water quality assessment report using data integration and visualization methods are as follows: Based on the water quality risk assessment report, data processing is used to perform data error correction and data optimization to generate an optimized data set; Based on the optimized data set, a data fusion method is used to integrate the data to generate an integrated data set; Based on the integrated data set, statistical analysis methods are used to extract information and analyze key indicators to generate a key information summary; Based on the key information summary, data visualization tools are used to create charts and reports to generate a comprehensive water quality assessment report.

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