A system and method for monitoring water pollution

Abstract

The present invention discloses a system and method for monitoring water pollution, and relates to the technical field of water quality monitoring. The components of the system include: an ecological environment monitoring module, a water quality monitoring module, a data processing center module, a microbial monitoring module, and an early warning and response module. The system for monitoring water pollution of the present invention can comprehensively monitor the chemical and physical parameters of water quality and the ecological environment status, and provides multi-dimensional data monitoring and analysis means. By real-time monitoring of the behavior and sound signals of aquatic organisms, combined with accurate monitoring of microbial communities and traditional water quality parameter determination, it can more comprehensively and accurately reflect the health status of the water environment. In addition, the ability to obtain and process real-time data enables the system to respond to sudden changes in water quality in a timely manner, providing strong support for rapid decision-making.

Description

Technical Field

[0001] The present invention relates to the technical field of water quality monitoring, and in particular to a system and method for monitoring water pollution. Background Art

[0002] Water quality monitoring plays a vital role in environmental protection. It plays a key role in protecting human health and maintaining the balance of the ecosystem. With the development of society, the acceleration of industrialization and urbanization, water pollution problems are becoming increasingly serious, and higher requirements are placed on water quality monitoring technology. Traditional water quality monitoring methods mainly focus on the determination of chemical and physical parameters in water bodies, such as pH value, dissolved oxygen, chemical oxygen demand, etc., but these parameters are difficult to fully reflect the health of the water environment.

[0003] Existing water quality monitoring technology has many shortcomings. On the one hand, the monitoring indicators are relatively simple and only focus on chemical and physical parameters. They cannot fully reflect the impact of water quality changes on biological communities in the ecosystem and it is difficult to capture potential ecological risks. On the other hand, the monitoring frequency and scope are limited. Many systems can only conduct intermittent monitoring, making it difficult to grasp the dynamic changes in water quality in real time. The monitoring area is often difficult to cover the entire water area, and it is easy to miss key pollution points. In addition, data processing and analysis capabilities are relatively weak. When faced with large amounts of multi-source monitoring data, traditional methods lack effective integration and in-depth analysis methods, making it difficult to explore the laws and trends hidden behind the data.

[0004] To sum up, due to the lack of comprehensive monitoring of the ecological environment in existing technologies, when water bodies are polluted, it is difficult to accurately determine the source and extent of pollution by relying solely on changes in chemical indicators. At the same time, traditional monitoring technologies are difficult to quickly identify the source of pollution when responding to sudden pollution incidents, resulting in slow emergency response and difficulty in controlling the spread of pollution. Therefore, it is extremely urgent to develop a water quality pollution monitoring system that integrates ecological environment monitoring, efficient data processing, and intelligent early warning and emergency response capabilities. Summary of the invention

[0005] The purpose of the present invention is to make up for the shortcomings of the existing technology and provide a system and method for monitoring water pollution. It can monitor the chemical and physical parameters of water quality and the ecological environment, provide multi-dimensional data monitoring and analysis means, and monitor the behavior and sound signals of aquatic organisms in real time. Combined with traditional water quality parameter measurement, it can more comprehensively reflect the health status of the water environment. The real-time data acquisition and processing capabilities enable the system to respond to sudden changes in water quality in a timely manner and provide support for rapid decision-making.

[0006] In order to solve the above technical problems, the present invention provides the following technical solutions: On the one hand, a system for monitoring water pollution, the components of which include: an ecological environment monitoring module, a water quality monitoring module, a data processing center module, a microbial monitoring module, and an early warning and response module;

[0007] The ecological environment monitoring module combines artificial intelligence technology to monitor aquatic organisms in real time, accurately identify different types of aquatic animals, monitor their number, distribution and behavioral characteristics, and further understand their activity patterns and health status by collecting and analyzing the sound signals emitted by aquatic organisms;

[0008] The water quality monitoring module detects various chemical indicators of water quality in real time and accurately, including pH value, dissolved oxygen DO content, chemical oxygen demand COD, biochemical oxygen demand BOD, ammonia nitrogen NH3-N, total phosphorus TP, total nitrogen TN and various heavy metal contents, and accurately measures water temperature, water turbidity and conductivity parameters, and makes self-adjustments according to environmental conditions and usage time;

[0009] The data processing center module adopts a distributed storage architecture to efficiently store and manage data from water quality and ecological environment monitoring, uses support vector regression analysis technology to establish a complex correlation model between water quality indicators and ecological environment indicators, comprehensively analyzes the potential impact of water pollution on the ecological environment, generates an ecological risk assessment report, and provides decision support for environmental management;

[0010] The microbial monitoring module uses advanced molecular biology techniques to comprehensively monitor the microbial communities in the water, including bacteria, fungi, and viruses. Through gene sequencing and bioinformatics analysis, it accurately identifies the types and abundance of microorganisms and monitors the dynamic changes of microbial communities in real time.

[0011] The early warning and response module sets multi-level early warning thresholds. When the monitoring data exceeds the normal range, an alarm is immediately sent to the relevant departments, and a preliminary pollution source location and impact assessment report is provided. The system also has an emergency response function, which can quickly mobilize resources in an emergency and take effective measures to control the spread of pollution.

[0012] Furthermore, the ecological environment monitoring module includes:

[0013] Vegetation coverage monitoring submodule: obtain dynamic change data of regional vegetation coverage, set up ground fixed monitoring stations, and regularly measure key indicators such as vegetation reflectivity and chlorophyll content to accurately assess the health status of vegetation;

[0014] Soil quality monitoring submodule: real-time monitoring of soil moisture content, pH, organic matter, key nutrient indicators of nitrogen, phosphorus and potassium. Through long-term monitoring, analysis of soil quality change trends and their impact on the ecological environment;

[0015] Biodiversity monitoring submodule: monitors animal populations, bird migration, and insect diversity in the region to achieve rapid identification and statistics of biodiversity;

[0016] Meteorological environment monitoring submodule: real-time monitoring of regional meteorological conditions, namely temperature, humidity, wind speed, wind direction, and rainfall, to identify changes in the ecological environment and the degree of water pollution.

[0017] Furthermore, the process of the ecological environment monitoring module combining artificial intelligence technology to monitor aquatic organisms in real time is as follows:

[0018] Aquatic organism image recognition: Apply local binary pattern histogram to automatically identify aquatic organisms in images to accurately distinguish different types of aquatic animals;

[0019] Sound signal analysis: Capture underwater sound signals, extract frequency features through fast Fourier transform (FFT) in sound signal processing technology, classify and identify sound signals, and judge the activity status and health status of aquatic organisms;

[0020] Behavioral feature analysis: Based on continuous image sequences and sound signals, the movement trajectory, aggregation pattern, and foraging behavior of aquatic organisms are analyzed. The movement trajectory is determined by calculating the position change of the organism in the image, and the periodicity of the sound and its intensity change are used to infer its behavior pattern.

[0021] Multimodal data fusion: The species and quantity information obtained from biological image recognition, the activity and health status information obtained from sound signal analysis, and the results of behavioral feature analysis are integrated, and the image features and sound features are spliced ​​into a comprehensive feature vector to provide technical support for the correlation model between ecological environment and water quality indicators.

[0022] Furthermore, the environmental monitoring module extracts the characteristics of the types, quantity and distribution of aquatic organisms from the aquatic organism image recognition, uses the local binary pattern histogram to extract features and represent the biological data, and describes the local texture information by comparing the gray value of each pixel in the pixel neighborhood with the central pixel. The LBP value calculation formula is: ,in represents the number of sampling points, Represents the distance between the sampling point and the center point, Indicates The gray value of the sampling point, represents the gray value of the center pixel, is a symbolic function defined as , construct the LBPH histogram, and for each pixel in each image, calculate its corresponding LBP value, divide the image into multiple small blocks, construct a histogram for the LBP value in each small block, merge the histograms of all small blocks into a global histogram as the feature description of the image, and merge the histograms of all small blocks to form a global histogram as the feature vector of the image, that is, , is the ith entry of the histogram, is the total number of entries in the histogram. The extracted feature vector is compared with a large number of samples to determine the type of organisms in the image and count their number.

[0023] Furthermore, the sound signal analysis is performed by fast Fourier transform FFT to collect the sound signals of aquatic organisms, and the sound signals are converted to the frequency domain by fast Fourier transform FFT, and the frequency features are extracted, and the key features in the sound signals, namely, the frequency, amplitude, and rhythm, are identified, and the signals are framed and windowed, and the continuous signals are divided into short frames, and various features of the sound signals are extracted, including time domain features and frequency domain features. The algorithm formula is: , is the transformed spectral coefficient, are the elements of the original signal, Represents the index of the transformed spectral coefficient, n represents the index of the original signal element, N is the signal length, i is the imaginary unit, and the sound signal is classified through FFT to identify different biological types and behavior patterns.

[0024] Furthermore, the multimodal data fusion combines the type and quantity information obtained from biological image recognition, the activity and health status information obtained from sound signal analysis, and the results of behavioral feature analysis, and uses a generalized weighted fusion method to combine image features. , Sound characteristics And water quality parameters Spliced ​​into a comprehensive feature vector, for image features , for the sound characteristics , for water quality parameters , use the formula to express the fused feature vector ,Right now , , , are image features, sound features and water quality parameter weight coefficients, respectively, and , the features are fused into a comprehensive feature vector ,and , use the fused feature vector F to train the support vector regression SVR model, and apply the trained model for prediction and analysis.

[0025] Furthermore, the microbial monitoring module uses molecular biology technology to comprehensively monitor the microbial community in the water, including bacteria, fungi, and viruses. Through gene sequencing and bioinformatics analysis, it accurately identifies the types and abundance of microorganisms and monitors the dynamic changes of microbial communities in real time. This module can reveal the complex relationship between microbial communities and water pollution, and provide a new perspective for evaluating the health of water quality. The process is as follows:

[0026] Sample collection and processing: Collect water samples and perform pretreatment, using chemical reagents and physical methods to extract DNA from microbial cells to enrich microorganisms;

[0027] Gene sequencing sequences the extracted DNA to obtain the genetic information of the microorganism, cleans the sequencing data, removes low-quality sequences and contaminants, and uses a quality control algorithm formula, namely , where Q is the quality score, is the error probability;

[0028] Operational taxonomic unit clustering: The cleaned sequences were clustered into OTUs to distinguish different microbial species, using the average neighbor-joining clustering algorithm, i.e. ,in It is a sample and The distance between and are the two eigenvalues ​​of sample i, and The samples are The two eigenvalues ​​of

[0029] Species annotation: The OTUs were compared with known microbial databases, species annotation was performed, the number of sequences of each OTU was calculated, the abundance and diversity of the microbial community were analyzed, and the Shannon diversity index ecological indicator was used, i.e. , where H′ is the diversity index, It is The relative abundance of OTUs, T represents the total number of OTUs;

[0030] Data integration and correlation analysis: Integrate microbial monitoring data with data from the data processing center module to explore the correlation between microbial communities and water pollution, interpret the analysis results, and provide a scientific basis for water quality management and pollution control.

[0031] Furthermore, the data processing center module uses support vector regression analysis technology to explore the relationship between water quality parameters and ecological environment indicators, and predict the changing trend of water quality under specific conditions, understand the influence of various water quality parameters, namely pH value, dissolved oxygen DO, chemical oxygen demand, on the number, distribution and behavioral characteristics of aquatic organisms, and predict the future water quality status. Its operation process is:

[0032] Data preparation: From the collected water quality and ecological environment data, select independent variables, including chemical and physical indicators of water quality, and dependent variables, including the number of aquatic organisms and the health status of vegetation, and divide the data set into training and test sets;

[0033] Model construction: Use the support vector regression model to build the model according to the number of independent variables and the characteristics of the data. For linear SVR, use the RBF kernel function, that is, ,in is the weight vector, is the bias term, and the goal is to minimize the following optimization problem: , and is the slack variable, is the penalty coefficient, which is used to balance the model complexity and smoothness, and m is the number of samples in the training set;

[0034] Nonlinear SVR: For nonlinear VR, the RBF kernel function is used to map the data to a higher-dimensional space, in which the optimal hyperplane is found, defined as: , >0 is the kernel function parameter;

[0035] Model training: Use the least squares method to estimate the regression coefficients so that the sum of squared residuals between the predicted values ​​and the actual observed values ​​is minimized, that is, , is the model's observation The predicted value of , ,……, is the regression coefficient of the model, and Represents the value of the i-th sample on the 1st and nth features;

[0036] Model evaluation: Evaluate the model performance on the test set, using the mean square error (MSE) indicator to quantify the performance of the model and ensure that the model meets the requirements, that is, , n is the number of samples in the test set, is the true value of the i-th sample, is the model's predicted value for the i-th sample;

[0037] Model application: Apply the trained model to new monitoring data to predict the values ​​of ecological and environmental indicators. Based on the prediction results, evaluate the impact of water pollution on the ecological environment and generate corresponding reports and recommendations.

[0038] On the other hand, a method for monitoring water pollution comprises the following specific steps:

[0039] S100, Ecological Environment Monitoring: Combined with artificial intelligence technology, it monitors aquatic organisms in real time, accurately identifies different types of aquatic animals, monitors their number, distribution and behavioral characteristics, and further understands their activity patterns and health status by collecting and analyzing the sound signals emitted by aquatic organisms;

[0040] S200, water quality monitoring: real-time and accurate detection of various chemical indicators of water quality, including pH value, dissolved oxygen DO content, chemical oxygen demand COD, biochemical oxygen demand BOD, ammonia nitrogen NH3-N, total phosphorus TP, total nitrogen TN and various heavy metals, and accurately measure water temperature, water turbidity and conductivity parameters, and self-adjust according to environmental conditions and usage time;

[0041] S300, data processing center: uses a distributed storage architecture to efficiently store and manage data from water quality and ecological environment monitoring, uses support vector regression analysis technology to establish a complex correlation model between water quality indicators and ecological environment indicators, comprehensively analyzes the potential impact of water pollution on the ecological environment, generates ecological risk assessment reports, and provides decision support for environmental management;

[0042] S400, microbial monitoring: using advanced molecular biology techniques to comprehensively monitor the microbial communities in water, including bacteria, fungi, and viruses, accurately identifying the types and abundance of microorganisms through gene sequencing and bioinformatics analysis, and monitoring the dynamic changes of microbial communities in real time;

[0043] S500, early warning and response: Set multi-level early warning thresholds. When the monitoring data exceeds the normal range, an alarm is immediately sent to the relevant departments, and a preliminary pollution source location and impact assessment report is provided. The system also has an emergency response function to quickly mobilize resources in an emergency and take effective measures to control the spread of pollution.

[0044] Furthermore, the S500 sets a multi-level warning threshold to determine the threshold level according to the importance and harm degree of water quality and ecological environment indicators, namely:

[0045] Mild warning threshold: triggered when key indicators slightly deviate from the normal range but have not yet caused obvious harm to the ecosystem, that is, the chemical oxygen demand (COD) of water quality exceeds the standard value by 10%-20%, and the number of aquatic organisms decreases by 10%-20%;

[0046] Moderate warning threshold: Deviation of indicators has had a certain impact on the ecosystem, but is still within the controllable range, that is, COD exceeds the standard value by 20%-50%, and the number of aquatic organisms decreases by 20%-50%;

[0047] Severe warning threshold: The abnormality of the indicator has caused serious harm to the ecosystem and emergency measures need to be taken immediately, that is, COD exceeds the standard value by more than 50% and the number of key aquatic organisms decreases by more than 50%.

[0048] Compared with the prior art, the system and method for monitoring water pollution have the following beneficial effects:

[0049] 1. The system of the present invention integrates an ecological environment monitoring module, a water quality monitoring module, a data processing center module, a microbial monitoring module and an early warning and response module, which can comprehensively monitor the chemical and physical parameters of water quality and the ecological environment status, and provide multi-dimensional data monitoring and analysis means. By real-time monitoring of the behavior and sound signals of aquatic organisms, combined with precise monitoring of microbial communities and traditional water quality parameter determination, it can more comprehensively and accurately reflect the health status of the water environment. In addition, the ability to acquire and process real-time data enables the system to respond to sudden changes in water quality in a timely manner, providing strong support for rapid decision-making.

[0050] 2. The data processing center module of the system of the present invention adopts a distributed storage architecture, which improves the efficiency and security of data processing. It uses support vector regression analysis technology to deeply explore the potential patterns and relationships in the data, establish a complex correlation model between water quality indicators and ecological environment indicators, comprehensively analyze the potential impact of water pollution on the ecological environment, generate accurate ecological risk assessment reports, and provide scientific decision-making support for environmental management.

[0051] 3. The introduction of the microbial monitoring module provides a new perspective for assessing the health of water quality. Through gene sequencing and bioinformatics analysis, it can accurately identify the types and abundance of microorganisms, monitor the dynamic changes of microbial communities in real time, reveal the complex relationship between microbial communities and water pollution, and provide a more comprehensive basis for the comprehensive assessment and control of water pollution.

[0052] Other advantages, objectives and features of the present invention will be set forth in part in the following description and, in part, will be apparent to those skilled in the art based on an examination of the following or may be taught from the practice of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0053] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the prior art descriptions are briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention, and for ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0054] Figure 1 The following is an operational flow chart of a system for monitoring water pollution.

[0055] Figure 2 A flow chart of a method for monitoring water pollution. DETAILED DESCRIPTION

[0056] The technical solutions in the embodiments of the present invention are described clearly and completely below. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of them. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0057] Embodiment 1

[0058] This embodiment is not limited to the determination of chemical and physical parameters in traditional water quality monitoring, but also integrates ecological environment monitoring functions, realizing real-time monitoring and evaluation of aquatic organism species, quantity, distribution, behavior and health status. This comprehensive and integrated monitoring method can more accurately reflect the overall health status of the water environment and provide a more comprehensive and scientific basis for the comprehensive management of water pollution.

[0059] In the specific implementation, first, implement the vegetation coverage monitoring submodule: set up ground fixed monitoring stations around the river, regularly measure the key indicators of vegetation reflectance and chlorophyll content, for example, measure once a week, record the data and transmit it to the data processing center module to accurately evaluate the health of vegetation; soil quality monitoring submodule: real-time monitoring of soil moisture content, pH, organic matter, nitrogen, phosphorus and potassium key nutrient indicators, through the sensors installed in the soil, collect data once an hour, analyze the soil quality change trend and its impact on the ecological environment; biodiversity monitoring submodule: use underwater cameras and acoustic monitoring equipment to monitor aquatic animal populations, bird migration, and insect diversity in the area, use artificial intelligence image recognition technology and sound signal analysis, use local binary pattern histograms to extract features and represent biological data, and describe local texture information by comparing the grayscale value of each pixel in the pixel neighborhood with the central pixel. The LBP value calculation formula is: , is a symbolic function defined as , to achieve rapid identification and statistics of biodiversity, summarize the data every day and send it to the data processing center module.

[0060] Then, water quality monitoring sensors are installed in the river to detect various chemical indicators of water quality in real time and accurately, including pH value, dissolved oxygen DO content, chemical oxygen demand COD, biochemical oxygen demand BOD, ammonia nitrogen NH3-N, total phosphorus TP, total nitrogen TN and various heavy metal contents, and accurately measure water temperature, water turbidity and conductivity parameters. The sensors adjust themselves according to environmental conditions and usage time to ensure the accuracy of the data, and the data is transmitted to the data processing center module in real time.

[0061] Subsequently, real-time data streams are received from the water quality monitoring module and the ecological environment monitoring module, and a distributed database system is used to store massive monitoring data. At the same time, metadata such as the source of the data, the time of collection, and the location are recorded to facilitate data tracking and verification. Key information such as the fluctuation amplitude of water quality parameters and the number of aquatic organisms in the feature vector are extracted. The generalized weighted fusion method is used to combine image features , Sound characteristics And water quality parameters Spliced ​​into a comprehensive feature vector, for image features , for the sound characteristics , for water quality parameters , use the formula to express the fused feature vector ,Right now , the features are fused into a comprehensive feature vector ,and , use the fused feature vector F to train the support vector regression SVR model, apply the trained model for prediction and analysis, and use the least squares method to estimate the regression coefficients to minimize the residual sum of squares between the predicted value and the actual observed value, that is, ,Comprehensively analyze the potential impact of water pollution on the ecological environment, evaluate the model performance on the test set, and use the mean square error MSE indicator to quantify the performance of the model to ensure that the model meets,that is, , generate ecological risk assessment reports, analyze and predict newly received data based on the established model, predict changing trends in water quality, assess the health of the ecosystem, and collect water samples and extract DNA, using high-throughput sequencing technology to comprehensively monitor microbial communities. Through gene sequencing and bioinformatics analysis, we can accurately identify the types and abundance of microorganisms and monitor the dynamic changes of microbial communities in real time.

[0062] Finally, set up multi-level warning thresholds, and determine the threshold level according to the importance and degree of harm of water quality and ecological environment indicators. When the monitoring data exceeds the normal range, an alarm will be sent to the relevant departments immediately. For example, when the chemical oxygen demand (COD) of water quality exceeds the standard value by 10%-20% or the number of aquatic organisms decreases by 10%-20%, a mild warning will be triggered; when COD exceeds the standard value by 20%-50% or the number of aquatic organisms decreases by 20%-50%, a moderate warning will be triggered; when COD exceeds the standard value by more than 50% or the number of key aquatic organisms decreases by more than 50%, a severe warning will be triggered. At the same time, the system provides preliminary pollution source location and impact assessment reports. In emergency situations, the system has emergency response functions, which can quickly mobilize resources and take effective measures to control the spread of pollution. Once a severe warning is triggered, the system immediately notifies the relevant departments to take measures to stop surrounding sewage discharge and start sewage treatment equipment.

[0063] A system for monitoring water pollution described in this embodiment can simultaneously monitor the chemical and physical parameters of water quality and the ecological environment, and provides a multi-dimensional data monitoring and analysis method. By real-time monitoring of the behavior and sound signals of aquatic organisms and adopting an advanced distributed storage architecture, it not only improves the efficiency of data processing, but also realizes deep mining and intelligent analysis of data by integrating data analysis algorithms and machine learning models.

[0064] Embodiment 2

[0065] This embodiment describes in detail the implementation process of a method for monitoring water pollution. The method can simultaneously monitor water quality parameters and ecological environment conditions. Through multi-faceted monitoring of aquatic organisms and accurate detection of water quality chemical indicators, a more comprehensive and accurate water quality assessment system is established, and multi-level early warning thresholds are set. When water pollution just appears, an alarm is issued in time to buy more response time for relevant departments. At the same time, a preliminary pollution source location and impact assessment report is provided, making emergency response more targeted and efficient, and being able to control the spread of pollution more quickly and reduce environmental and economic losses.

[0066] First, enter the ecological environment monitoring stage (S100), install water quality monitoring sensors, deploy underwater cameras and sound collection equipment for image and sound signal monitoring of aquatic organisms, set up a data processing center module, including a distributed database system and data analysis algorithm, to ensure that real-time data streams can be received and processed efficiently. According to local water quality standards and ecological environment health indicators, set mild, moderate and severe warning thresholds. Sensors collect water quality chemical indicators in real time, cameras capture images of aquatic organisms, and sound collection equipment records underwater sound signals for identifying the species, quantity, distribution and behavioral characteristics of aquatic organisms. Water quality parameters and ecological environment data are integrated to extract key features, such as the fluctuation pattern of water quality parameters and the changing trend of the number of aquatic organisms.

[0067] Then, we enter the water quality monitoring stage (S200), use data fusion technology to integrate the data of water quality parameters and ecological environment indicators, extract the fluctuation pattern of water quality parameters and the changing trend of aquatic organisms over time, build a linear regression model, explore the quantitative relationship between water quality parameters and ecological environment indicators, and predict the impact of water quality changes on the ecological environment.

[0068] Subsequently, the data processing center stage (S300) is entered, and the data processing center module is used to apply support vector regression analysis technology to establish a correlation model between water quality indicators and ecological environmental indicators. The model is trained using historical data to explore the quantitative relationship between water quality parameters and ecological environmental indicators.

[0069] Next, we enter the microbial monitoring stage (S400), using advanced molecular biology techniques to comprehensively monitor the microbial communities in the water, including bacteria, fungi, and viruses. Through gene sequencing and bioinformatics analysis, we can accurately identify the types and abundance of microorganisms, monitor the dynamic changes of microbial communities in real time, and feed the monitoring and evaluation results back to the data processing center to optimize the parameters and algorithms of the monitoring system, improve monitoring equipment and technology, continuously optimize the performance and adaptability of the monitoring system, and enhance data processing capabilities and early warning response speed.

[0070] Finally, the early warning and response stage (S500) is entered. Multi-level early warning thresholds are set. When the monitoring data exceeds the normal range, the alarm mechanism is immediately triggered, and an alarm message is sent to the environmental protection department. A preliminary pollution source location and impact assessment report is provided to assist the decision-making department to respond quickly. In an emergency, the system can automatically mobilize resources, such as emergency equipment and manpower, to take effective measures to control the spread of pollution, comprehensively analyze the long-term impact of water pollution on the ecological environment, and assess the ecological risk level. Based on the monitoring results and analysis, scientific strategies and suggestions are provided for water quality improvement and ecological environment protection. Through the monitoring results, more specific and detailed ecological risk assessment reports are provided to relevant departments and enterprises to guide the formulation of water environment protection strategies and promote the sustainable management of the water environment.

[0071] It will be apparent to those skilled in the art that the invention is not limited to the details of the exemplary embodiments described above and that the invention can be implemented in other specific forms without departing from the spirit or essential features of the invention. Therefore, the embodiments should be considered exemplary and non-limiting in all respects, and the scope of the invention is defined by the appended claims rather than the foregoing description, and it is intended that all variations falling within the meaning and scope of the equivalent elements of the claims be included in the invention. Any reference numeral in a claim should not be considered as limiting the claim to which it relates.

Claims

1. A system for monitoring water pollution, characterized in that: The components of the system include: ecological environment monitoring module, water quality monitoring module, data processing center module, microbial monitoring module, early warning and response module; The ecological environment monitoring module combines artificial intelligence technology to monitor aquatic organisms in real time, accurately identify different types of aquatic animals, monitor their number, distribution and behavioral characteristics, and further understand their activity patterns and health status by collecting and analyzing the sound signals emitted by aquatic organisms. The ecological environment monitoring module specifically includes: Vegetation coverage monitoring submodule: obtain dynamic change data of regional vegetation coverage, set up ground fixed monitoring stations, and regularly measure key indicators such as vegetation reflectivity and chlorophyll content to accurately assess the health status of vegetation; Soil quality monitoring submodule: real-time monitoring of soil moisture content, pH, organic matter, key nutrient indicators of nitrogen, phosphorus and potassium. Through long-term monitoring, analysis of soil quality change trends and their impact on the ecological environment; Biodiversity monitoring submodule: monitors animal populations, bird migration, and insect diversity in the region to achieve rapid identification and statistics of biodiversity; Meteorological environment monitoring submodule: real-time monitoring of regional meteorological conditions, namely temperature, humidity, wind speed, wind direction, and rainfall, and identification of ecological environment changes and water pollution levels; The process of the ecological environment monitoring module combining artificial intelligence technology to monitor aquatic organisms in real time is as follows: Aquatic organism image recognition: Apply local binary pattern histogram to automatically identify aquatic organisms in images to accurately distinguish different types of aquatic animals; Sound signal analysis: Capture underwater sound signals, extract frequency features through fast Fourier transform (FFT) in sound signal processing technology, classify and identify sound signals, and judge the activity status and health status of aquatic organisms; Behavioral feature analysis: Based on continuous image sequences and sound signals, the movement trajectory, aggregation pattern, and foraging behavior of aquatic organisms are analyzed. The movement trajectory is determined by calculating the position change of the organism in the image, and the periodicity of the sound and its intensity change are used to infer its behavior pattern. Multimodal data fusion: Fusion of species and quantity information obtained from biological image recognition, activity and health status information obtained from sound signal analysis, and behavioral feature analysis results, splicing image features and sound features into a comprehensive feature vector to provide technical support for the association model between ecological environment and water quality indicators; The water quality monitoring module detects various chemical indicators of water quality in real time and accurately, including pH value, dissolved oxygen DO content, chemical oxygen demand COD, biochemical oxygen demand BOD, ammonia nitrogen NH3-N, total phosphorus TP, total nitrogen TN and various heavy metal contents, and accurately measures water temperature, water turbidity and conductivity parameters, and makes self-adjustments according to environmental conditions and usage time; The data processing center module adopts a distributed storage architecture to efficiently store and manage data from water quality and ecological environment monitoring, uses support vector regression analysis technology to establish a complex correlation model between water quality indicators and ecological environment indicators, comprehensively analyzes the potential impact of water pollution on the ecological environment, generates an ecological risk assessment report, and provides decision support for environmental management. The data processing center module uses support vector regression analysis technology to explore the relationship between water quality parameters and ecological environment indicators, and predict the changing trend of water quality under specific conditions, understand the impact of various water quality parameters, namely pH value, dissolved oxygen DO, chemical oxygen demand, on the number, distribution and behavioral characteristics of aquatic organisms, and predict future water quality status. Its operation process is: Data preparation: From the collected water quality and ecological environment data, select independent variables, including chemical and physical indicators of water quality, and dependent variables, including the number of aquatic organisms and the health status of vegetation, and divide the data set into training and test sets; Model construction: Use the support vector regression model to build the model according to the number of independent variables and the characteristics of the data. For linear SVR, use the RBF kernel function, that is, ,in is the weight vector, is the bias term, and the goal is to minimize the following optimization problem: , and is the slack variable, is the penalty coefficient, which is used to balance model complexity and smoothness; Nonlinear SVR: For nonlinear VR, the RBF kernel function is used to map the data to a higher-dimensional space, in which the optimal hyperplane is found, defined as: , >0 is the kernel function parameter; Model training: Use the least squares method to estimate the regression coefficients so that the sum of squared residuals between the predicted values ​​and the actual observed values ​​is minimized, that is, , is the model's observation The predicted value of Model evaluation: Evaluate the model performance on the test set, using the mean square error (MSE) indicator to quantify the model performance and ensure that the model meets the requirements, that is, , n is the number of samples in the test set, is the true value of the i-th sample, is the model's predicted value for the i-th sample; Model application: Apply the trained model to new monitoring data to predict the values ​​of ecological and environmental indicators. Based on the prediction results, evaluate the impact of water pollution on the ecological environment and generate corresponding reports and recommendations. The microbial monitoring module uses advanced molecular biology techniques to comprehensively monitor the microbial communities in the water, including bacteria, fungi, and viruses. Through gene sequencing and bioinformatics analysis, it accurately identifies the types and abundance of microorganisms and monitors the dynamic changes of microbial communities in real time. The early warning and response module sets multi-level early warning thresholds. When the monitoring data exceeds the normal range, an alarm is immediately sent to the relevant departments, and a preliminary pollution source location and impact assessment report is provided. The system also has an emergency response function, which can quickly mobilize resources in an emergency and take effective measures to control the spread of pollution.

2. A system for monitoring water pollution according to claim 1, characterized in that: The environmental monitoring module extracts the characteristics of the types, quantity and distribution of aquatic organisms from the aquatic organism image recognition, uses the local binary pattern histogram to extract features and represent the biological data, and describes the local texture information by comparing the gray value of each pixel in the pixel neighborhood with the central pixel. The LBP value calculation formula is: ,in represents the number of sampling points, Represents the distance between the sampling point and the center point, Indicates The gray value of the sampling point, represents the gray value of the center pixel, is a symbolic function defined as , construct the LBPH histogram, and for each pixel in each image, calculate its corresponding LBP value, divide the image into multiple small blocks, construct a histogram for the LBP value in each small block, merge the histograms of all small blocks into a global histogram as the feature description of the image, and merge the histograms of all small blocks to form a global histogram as the feature vector of the image, that is, , is the ith entry of the histogram, is the total number of entries in the histogram. The extracted feature vector is compared with a large number of samples to determine the type of organisms in the image and count their number.

3. A system for monitoring water pollution according to claim 1, characterized in that: The sound signal analysis is performed by fast Fourier transform (FFT), the sound signals of aquatic organisms are collected, the sound signals are converted to the frequency domain by fast Fourier transform (FFT), the frequency features are extracted, the key features in the sound signals, namely, the frequency, amplitude, and rhythm, the signals are framed and windowed, the continuous signals are divided into short frames, and various features of the sound signals, including time domain features and frequency domain features, are extracted. The algorithm formula is: , is the transformed spectral coefficient, are the elements of the original signal, Represents the index of the transformed spectral coefficient, n represents the index of the original signal element, N is the signal length, i is the imaginary unit, and the sound signal is classified through FFT to identify different biological types and behavior patterns.

4. A system for monitoring water pollution according to claim 1, characterized in that: The multimodal data fusion combines the type and quantity information obtained by biological image recognition, the activity and health status information obtained by sound signal analysis, and the results of behavioral feature analysis, and uses a generalized weighted fusion method to combine image features. , Sound characteristics And water quality parameters Spliced ​​into a comprehensive feature vector, for image features , for the sound characteristics , for water quality parameters , use the formula to express the fused feature vector ,Right now , , , are image features, sound features and water quality parameter weight coefficients, respectively, and , the features are fused into a comprehensive feature vector ,and , use the fused feature vector F to train the support vector regression SVR model, and apply the trained model for prediction and analysis.

5. A system for monitoring water pollution according to claim 1, characterized in that: The microbial monitoring module uses molecular biology technology to comprehensively monitor the microbial community in the water, including bacteria, fungi, and viruses. Through gene sequencing and bioinformatics analysis, it accurately identifies the types and abundance of microorganisms and monitors the dynamic changes of microbial communities in real time. This module can reveal the complex relationship between microbial communities and water pollution, and provide a new perspective for evaluating the health of water quality. The process is as follows: Sample collection and processing: Collect water samples and perform pretreatment, using chemical reagents and physical methods to extract DNA from microbial cells to enrich microorganisms; Gene sequencing sequences the extracted DNA to obtain the genetic information of the microorganism, cleans the sequencing data, removes low-quality sequences and contaminants, and uses a quality control algorithm formula, namely , where Q is the quality score, is the error probability; Operational taxonomic unit clustering: The cleaned sequences were clustered into OTUs to distinguish different microbial species, using the average neighbor-joining clustering algorithm, i.e. ,in It is a sample and The distance between and are the two eigenvalues ​​of sample i, and The samples are The two eigenvalues ​​of Species annotation: The OTUs were compared with known microbial databases, species annotation was performed, the number of sequences of each OTU was calculated, the abundance and diversity of the microbial community were analyzed, and the Shannon diversity index ecological indicator was used, i.e. , where H′ is the diversity index, It is The relative abundance of OTUs, T represents the total number of OTUs; Data integration and correlation analysis: Integrate microbial monitoring data with data from the data processing center module to explore the correlation between microbial communities and water pollution, interpret the analysis results, and provide a scientific basis for water quality management and pollution control.

6. A method for monitoring water pollution, characterized in that: The method comprises the following steps: S100, Ecological Environment Monitoring: Combined with artificial intelligence technology, use image recognition, sound signal analysis and behavior characteristic analysis technology to monitor aquatic organisms in real time, accurately identify different types of aquatic animals, monitor their number, distribution and behavior characteristics, and collect and analyze the sound signals emitted by aquatic organisms to further understand their activity patterns and health status. The specific steps of the ecological environment monitoring are as follows: Obtain dynamic data on regional vegetation coverage, set up fixed ground monitoring stations, and regularly measure key indicators such as vegetation reflectivity and chlorophyll content to accurately assess vegetation health; Real-time monitoring of soil moisture content, pH, organic matter, nitrogen, phosphorus and potassium key nutrient indicators, and through long-term monitoring, analysis of soil quality change trends and their impact on the ecological environment; Monitor animal populations, bird migration, and insect diversity in the region to quickly identify and count biodiversity; Real-time monitoring of regional meteorological conditions, namely temperature, humidity, wind speed, wind direction, and rainfall, to identify changes in the ecological environment and the degree of water pollution; The process of real-time monitoring of aquatic organisms by combining ecological environment monitoring with artificial intelligence technology is as follows: Aquatic organism image recognition: Apply local binary pattern histogram to automatically identify aquatic organisms in images to accurately distinguish different types of aquatic animals; Sound signal analysis: Capture underwater sound signals, extract frequency features through fast Fourier transform (FFT) in sound signal processing technology, classify and identify sound signals, and judge the activity status and health status of aquatic organisms; Behavioral feature analysis: Based on continuous image sequences and sound signals, the movement trajectory, aggregation pattern, and foraging behavior of aquatic organisms are analyzed. The movement trajectory is determined by calculating the position change of the organism in the image, and the periodicity of the sound and its intensity change are used to infer its behavior pattern. Multimodal data fusion: Fusion of species and quantity information obtained from biological image recognition, activity and health status information obtained from sound signal analysis, and behavioral feature analysis results, splicing image features and sound features into a comprehensive feature vector to provide technical support for the association model between ecological environment and water quality indicators; S200, water quality monitoring: real-time and accurate detection of various chemical indicators of water quality, including pH value, dissolved oxygen DO content, chemical oxygen demand COD, biochemical oxygen demand BOD, ammonia nitrogen NH3-N, total phosphorus TP, total nitrogen TN and various heavy metals, and accurately measure water temperature, water turbidity and conductivity parameters, and self-adjust according to environmental conditions and usage time; S300, data processing center: adopts distributed storage architecture to efficiently store and manage data from water quality and ecological environment monitoring, uses support vector regression analysis technology to establish a complex correlation model between water quality indicators and ecological environment indicators, comprehensively analyzes the potential impact of water pollution on the ecological environment, generates ecological risk assessment reports, and provides decision support for environmental management. The data processing center uses support vector regression analysis technology to explore the relationship between water quality parameters and ecological environment indicators, predict the changing trend of water quality under specific conditions, understand the impact of various water quality parameters, namely pH value, dissolved oxygen DO, chemical oxygen demand, on the number, distribution and behavioral characteristics of aquatic organisms, and predict future water quality status. Its operation process is: Data preparation: From the collected water quality and ecological environment data, select independent variables, including chemical and physical indicators of water quality, and dependent variables, including the number of aquatic organisms and the health status of vegetation, and divide the data set into training and test sets; Model construction: Use the support vector regression model to build the model according to the number of independent variables and the characteristics of the data. For linear SVR, use the RBF kernel function, that is, ,in is the weight vector, is the bias term, and the goal is to minimize the following optimization problem: , and is the slack variable, is the penalty coefficient, which is used to balance model complexity and smoothness; Nonlinear SVR: For nonlinear VR, the RBF kernel function is used to map the data to a higher-dimensional space, in which the optimal hyperplane is found, defined as: , >0 is the kernel function parameter; Model training: Use the least squares method to estimate the regression coefficients so that the sum of squared residuals between the predicted values ​​and the actual observed values ​​is minimized, that is, , is the model's observation The predicted value of Model evaluation: Evaluate the model performance on the test set, using the mean square error (MSE) indicator to quantify the model performance and ensure that the model meets the requirements, that is, , n is the number of samples in the test set, is the true value of the i-th sample, is the model's predicted value for the i-th sample; Model application: Apply the trained model to new monitoring data to predict the values ​​of ecological and environmental indicators. Based on the prediction results, evaluate the impact of water pollution on the ecological environment and generate corresponding reports and recommendations. S400, microbial monitoring: using advanced molecular biology techniques to comprehensively monitor the microbial communities in water, including bacteria, fungi, and viruses, accurately identifying the types and abundance of microorganisms through gene sequencing and bioinformatics analysis, and monitoring the dynamic changes of microbial communities in real time; S500, early warning and response: Set multi-level early warning thresholds. When the monitoring data exceeds the normal range, an alarm is immediately sent to the relevant departments, and a preliminary pollution source location and impact assessment report is provided. The system also has an emergency response function to quickly mobilize resources in an emergency and take effective measures to control the spread of pollution.

7. A method for monitoring water pollution according to claim 6, characterized in that: The S500 sets a multi-level warning threshold to determine the threshold level according to the importance and harm degree of water quality and ecological environment indicators, namely: Mild warning threshold: triggered when key indicators slightly deviate from the normal range but have not yet caused obvious harm to the ecosystem, that is, the chemical oxygen demand (COD) of water quality exceeds the standard value by 10%-20%, and the number of aquatic organisms decreases by 10%-20%; Moderate warning threshold: Deviation of indicators has had a certain impact on the ecosystem, but is still within the controllable range, that is, COD exceeds the standard value by 20%-50%, and the number of aquatic organisms decreases by 20%-50%; Severe warning threshold: The abnormality of the indicator has caused serious harm to the ecosystem and emergency measures need to be taken immediately, that is, COD exceeds the standard value by more than 50% and the number of key aquatic organisms decreases by more than 50%.

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