A water quality evaluation method and system for rain-source rivers

Abstract

The present invention relates to the field of water quality evaluation, and specifically to a water quality evaluation method and system for rain-source type rivers. The method monitors the micro-rainfall detection data of micro-rainfall events through sensors of a water quality monitoring system, the water quality monitoring system adjusts the sampling frequency of the sensor through an adaptive algorithm, and obtains water quality monitoring data through a micro-pollution mutation detection method; constructs a water quality recovery model of micro-rainfall events through nonlinear dynamic analysis and predicts the peak value and recovery time of water quality changes of micro-rainfall events; identifies significant mutation points of water quality changes through a chaos theory algorithm and adjusts the sampling frequency of the sensor through fuzzy logic control; obtains key pollutants according to the peak value and recovery time of water quality changes, and determines the pollution source through data correlation analysis of key pollutants and pollution source data. The method solves the problem that the impact and influence of micro-rainfall events on river water quality are not fully considered, resulting in insufficient timeliness and accuracy in water quality evaluation, and the inability to effectively capture water quality changes and analyze pollution sources.

Description

Technical Field

[0001] The invention relates to the field of water quality evaluation, in particular to a method and system for evaluating the water quality of a rain-source river. Background Art

[0002] Urban rain-source rivers are rivers in the rainy season and ditches in the dry season. They have small water environment capacity and lack dynamic water replenishment. They are easily disturbed by rainfall, resulting in water shortages and serious water pollution in the basin. In particular, the water quality of urban rain-source rivers will deteriorate sharply in a short period of time during small-scale intermittent rainfall or light rainfall. Due to the small amount of rainfall, the river cannot form an effective flow discharge, resulting in the accumulation of pollutants in the river, which leads to a slow recovery of river water quality.

[0003] In this case, conventional water quality monitoring methods are unable to capture the rapid accumulation of pollutants in a timely manner, and traditional water quality assessment often ignores the potential impact of this "micro-rainfall mutation" phenomenon on water quality. Therefore, in view of the short-term impact of micro-rainfall events on river water quality, a method is needed to capture water quality changes in real time and analyze the pollution source. Summary of the invention

[0004] (1) Technical issues to be resolved

[0005] The purpose of the present invention is to provide a water quality evaluation method and system for rain-source rivers to solve the problem that the impact and influence of micro-rain events on river water quality are not fully considered, resulting in water quality evaluation not being timely and accurate, and unable to effectively capture water quality changes and analyze pollution sources.

[0006] (2) Technical solution

[0007] To achieve the above object, on the one hand, the present invention provides a method for evaluating the water quality of a rain-fed river, the method comprising:

[0008] S1. Construct a water quality monitoring system according to the topographical characteristics of the river and the distribution of pollution sources, and monitor light rain events in real time through the sensors of the water quality monitoring system and obtain light rain detection data, wherein the light rain detection data includes rainfall intensity, runoff rate, and rainfall time;

[0009] S2, adjusting the sampling frequency of the sensor of the water quality monitoring system through an adaptive algorithm, and obtaining water quality monitoring data through a micro-pollution mutation detection method according to the sampling frequency;

[0010] S3, obtaining historical light rain detection data and water quality monitoring data, and constructing a water quality recovery model for light rain events through nonlinear dynamic analysis, and using the water quality recovery model to predict the peak value and recovery time of water quality changes in light rain events; identifying significant mutation points of water quality changes through chaos theory algorithms, and adjusting the sampling frequency of the sensor through fuzzy logic control based on the significant mutation points;

[0011] S4. Obtain key pollutants according to the peak value and recovery time of the water quality change; obtain pollution source data, and determine the pollution source through data correlation analysis based on the key pollutants and the pollution source data.

[0012] Furthermore, the method for determining the pollution source through data correlation analysis based on the key pollutants and the pollution source data includes:

[0013] Acquire the light rain detection data and water quality monitoring data of the water quality monitoring system to obtain the spatiotemporal distribution map of pollutants, perform spatial difference on the spatiotemporal distribution map of pollutants by Kriging interpolation method, predict the concentration of pollutants in the unmonitored river area, and obtain the spatial distribution map of pollutants;

[0014] Obtaining geographic information and emission of pollution sources in the river area and constructing a spatial distribution map of the pollution sources; fitting the spatial distribution map of the pollutants and the spatial distribution map of the pollution sources by the Kriging interpolation method to obtain the spatial distribution relationship between the pollutants and the pollution sources;

[0015] The spatial distribution relationship is analyzed through correlation analysis to obtain the correlation coefficient between pollutant concentration and pollution source emission, the correlation between pollutants and pollution sources is obtained through the correlation coefficient, and statistical analysis is performed to obtain a correlation distribution map of the river area; when the correlation coefficient is greater than a preset correlation threshold, it is determined that there is a significant positive correlation between the pollutant and the pollution source; if there is a significant positive correlation between the pollutant and the pollution source, the pollution source is determined by tracing the source through the pollutant diffusion model of the pollution source.

[0016] Furthermore, the method of determining the pollution source by performing source tracing analysis through the pollutant diffusion model of the pollution source includes:

[0017] Obtaining river environmental data and determining a diffusion model in combination with the diffusion behavior of pollutants; obtaining the physical and chemical properties of pollutants, river environmental parameters, and pollution source emission data and recording them as comprehensive parameters, and inputting the comprehensive parameters into the diffusion model to obtain the diffusion path and diffusion rate of pollutants;

[0018] Acquire light rain detection data of the light rain event, obtain the change of river flow velocity in the river environmental parameters during the light rain event by using the light rain detection data through the diffusion model, and correct the diffusion path and diffusion rate of the pollutant according to the change of the river flow velocity;

[0019] According to the diffusion path of the pollutants, a source tracing analysis is performed to obtain a predicted pollution source, the predicted pollution source is matched with the spatial distribution map of the pollution source, and the pollution source is determined through a geographic information system; the source tracing analysis is to reversely trace the diffusion path.

[0020] Furthermore, the method further comprises:

[0021] Acquire light rain detection data and water quality monitoring data of historical light rain events and record them as first data, normalize the first data and input them into a nonlinear dynamic model, and use a numerical method to fit the nonlinear dynamic model to obtain a water quality restoration model;

[0022] Acquire the light rain detection data and water quality monitoring data of the water quality monitoring system and generate a time series of water quality changes according to the time sequence; identify the chaotic characteristics of the river water quality system by calculating the Lyapunov index of water quality changes; when the Lyapunov index is greater than zero, the river water quality system is in a chaotic state, and reconstruct the phase space of the water quality monitoring data by embedding dimension and time delay algorithm, analyze the phase space trajectory of the time series of water quality changes according to the space reconstruction, find the mutation position of the phase space trajectory by cross-section analysis and mark it as a significant mutation point;

[0023] When the water quality monitoring system detects a significant mutation point, the sensor sampling frequency is adjusted according to the preset fuzzy logic control.

[0024] Furthermore, the method for obtaining water quality monitoring data through a micro-pollution mutation detection method according to the sampling frequency includes:

[0025] The length of the sliding window is adjusted according to the rainfall intensity of the light rain event monitored in real time by the water quality monitoring system; the pollutant concentration in the sliding window is calculated by the mean to obtain the average change value of the pollutant concentration;

[0026] The average change value of the pollutant concentration of two adjacent sliding windows is obtained by numerical analysis. When the concentration difference value is greater than the preset difference threshold, the sampling frequency of the sensor is increased; when the concentration difference value is less than the preset difference threshold, the sampling frequency of the sensor is reduced.

[0027] Based on the same inventive concept, the present invention also provides a water quality evaluation system for rain-source rivers, the system comprising: a light rain detection data acquisition module, a water quality monitoring data acquisition module, a sampling frequency adjustment module, and a pollution source determination module, wherein each module is sequentially connected in communication;

[0028] A light rain detection data acquisition module is used to construct a water quality monitoring system according to the topographic characteristics of the river and the distribution of pollution sources, and to monitor light rain events in real time through the sensors of the water quality monitoring system and obtain light rain detection data, wherein the light rain detection data includes rainfall intensity, runoff rate, and rainfall time;

[0029] A water quality monitoring data acquisition module, used to adjust the sampling frequency of the sensor of the water quality monitoring system through an adaptive algorithm, and acquire water quality monitoring data through a micro-pollution mutation detection method according to the sampling frequency;

[0030] The sampling frequency adjustment module is used to obtain historical light rain detection data and water quality monitoring data, and to construct a water quality recovery model for light rain events through nonlinear dynamic analysis, and to predict the peak value and recovery time of water quality changes in light rain events through the water quality recovery model using the water quality monitoring data; to identify significant mutation points of water quality changes through chaos theory algorithms, and to adjust the sampling frequency of the sensor through fuzzy logic control using the significant mutation points;

[0031] The pollution source determination module is used to obtain key pollutants according to the peak value and recovery time of the water quality change; obtain pollution source data, and determine the pollution source through data correlation analysis based on the key pollutants and the pollution source data.

[0032] Furthermore, the pollution source determination module includes:

[0033] The pollutant spatial distribution map generation module is used to obtain the micro-rainfall detection data and water quality monitoring data of the water quality monitoring system to obtain the temporal and spatial distribution map of the pollutants, perform spatial difference on the temporal and spatial distribution map of the pollutants through the Kriging interpolation method, and predict the pollutant concentration in the unmonitored river area to obtain the spatial distribution map of the pollutants;

[0034] The spatial distribution relationship acquisition module is used to obtain the geographical information and emission of pollution sources in the river area and construct a spatial distribution map of the pollution sources; the spatial distribution map of the pollutants and the spatial distribution map of the pollution sources are fitted by the Kriging interpolation method to obtain the spatial distribution relationship between the pollutants and the pollution sources;

[0035] The pollution source tracing analysis module is used to obtain the correlation coefficient between the pollutant concentration and the pollution source emission through correlation analysis of the spatial distribution relationship, obtain the correlation between the pollutant and the pollution source through the correlation coefficient, and perform statistical analysis to obtain the correlation distribution map of the river area; when the correlation coefficient is greater than the preset correlation threshold, it is determined that there is a significant positive correlation between the pollutant and the pollution source; if there is a significant positive correlation between the pollutant and the pollution source, the pollution source is determined by tracing the source through the pollutant diffusion model of the pollution source.

[0036] Furthermore, the pollution source tracing analysis module includes the following steps:

[0037] Obtaining river environmental data and determining a diffusion model in combination with the diffusion behavior of pollutants; obtaining the physical and chemical properties of pollutants, river environmental parameters, and pollution source emission data and recording them as comprehensive parameters, and inputting the comprehensive parameters into the diffusion model to obtain the diffusion path and diffusion rate of pollutants;

[0038] Acquire light rain detection data of the light rain event, obtain the change of river flow velocity in the river environmental parameters during the light rain event by using the light rain detection data through the diffusion model, and correct the diffusion path and diffusion rate of the pollutant according to the change of the river flow velocity;

[0039] According to the diffusion path of the pollutants, a source tracing analysis is performed to obtain a predicted pollution source, the predicted pollution source is matched with the spatial distribution map of the pollution source, and the pollution source is determined through a geographic information system; the source tracing analysis is to reversely trace the diffusion path.

[0040] Furthermore, the sampling frequency adjustment module includes:

[0041] A water quality restoration model generation module is used to obtain light rain detection data and water quality monitoring data of historical light rain events and record them as first data, normalize the first data and input them into a nonlinear dynamic model, and use a numerical method to fit the nonlinear dynamic model to obtain a water quality restoration model;

[0042] The sampling frequency adjustment submodule is used to obtain the light rain detection data and water quality monitoring data of the water quality monitoring system and generate a time series of water quality changes according to the time sequence; the chaotic characteristics of the river water quality system are identified by calculating the Lyapunov index of water quality changes; when the Lyapunov index is greater than zero, the river water quality system is in a chaotic state, and the water quality monitoring data are reconstructed in phase space by embedding dimension and time delay algorithm, the phase space trajectory of the time series of water quality changes is analyzed according to the space reconstruction, and the mutation position of the phase space trajectory is found by cross-section analysis and marked as a significant mutation point;

[0043] When the water quality monitoring system detects a significant mutation point, the sensor sampling frequency is adjusted according to the preset fuzzy logic control.

[0044] Furthermore, the water quality monitoring data acquisition module includes the following steps:

[0045] The length of the sliding window is adjusted according to the rainfall intensity of the light rain event monitored in real time by the water quality monitoring system; the pollutant concentration in the sliding window is calculated by the mean to obtain the average change value of the pollutant concentration;

[0046] The average change value of the pollutant concentration of two adjacent sliding windows is obtained by numerical analysis. When the concentration difference value is greater than the preset difference threshold, the sampling frequency of the sensor is increased; when the concentration difference value is less than the preset difference threshold, the sampling frequency of the sensor is reduced.

[0047] (3) Beneficial effects

[0048] Compared with the prior art, the present invention has the following beneficial effects:

[0049] 1. Through the mechanism of adaptively adjusting the sampling frequency of the sensor, the sliding window length is dynamically adjusted according to the real-time monitoring of light rain events and water quality changes, and then the sampling frequency of the sensor is automatically adjusted according to the difference in pollutant concentration changes. It can respond quickly when the pollutant concentration fluctuates significantly, improve the sensitivity and accuracy of data collection, and reduce the sampling frequency when the water quality is stable to save resources, thereby improving the monitoring efficiency and adaptability of the water quality monitoring system under different environmental conditions.

[0050] 2. By constructing a water quality recovery model through nonlinear dynamic analysis, and combining the chaos theory algorithm to analyze the Lyapunov exponent and phase space reconstruction of water quality changes, it is possible to accurately identify the significant mutation points of water quality changes after light rain events, and effectively predict the peak value and recovery time of water quality after light rain events, thereby making a more comprehensive evaluation of river water quality. BRIEF DESCRIPTION OF THE DRAWINGS

[0051] Figure 1 A flowchart of a method for evaluating water quality of a rain-source river according to Embodiment 1 of the present invention;

[0052] Figure 2 This is a schematic diagram of the module composition of a water quality evaluation system for a rain-source river according to Example 2 of the present invention. DETAILED DESCRIPTION

[0053] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0054] Before giving examples, it is necessary to explain the application scenarios of the present invention. The present invention is a method and system for evaluating the water quality of rain-fed rivers, which are used for evaluating the water quality of rain-fed rivers.

[0055] Example 1: Figure 1 As shown, this embodiment provides a method for evaluating water quality of a rain-source river, the method comprising:

[0056] S1. A water quality monitoring system is constructed according to the topographical features of the river and the distribution of pollution sources. The sensors of the water quality monitoring system are used to monitor the light rain events in real time and obtain light rain detection data. The light rain detection data includes rainfall intensity, runoff rate, and rainfall time. The topographical features of the river include the depth, width, and flow rate of the river channel. The pollution sources include the types and locations of pollution sources. The main pollution sources of urban rivers include industrial wastewater, domestic sewage, agricultural wastewater, and rainwater. Sensors are installed in key areas to monitor the rainfall intensity, runoff rate, and rainfall time of light rain events in real time. These data can be used to study the changing laws of water quality during light rain events, especially in the early stage after the start of rainfall, when pollutants are usually more likely to enter the river with runoff. Light rain events usually cause short-term fluctuations in pollutants, which will affect water quality. The light rain event in this example refers to a rainfall event with a daily precipitation of light precipitation (0.1 mm / d≤P<10.0 mm / d). Sensors include flow rate sensors, chemical sensors (for detecting heavy metals, nitrogen, phosphorus, COD, etc.), etc.

[0057] S2. The water quality monitoring system adjusts the sampling frequency of the sensor through an adaptive algorithm, and obtains water quality monitoring data through a micro-pollution mutation detection method according to the sampling frequency; the adaptive algorithm adjusts the sampling frequency of the sensor according to the real-time changes of the light rain detection data of the light rain event, and the monitoring system can flexibly adapt to the dynamic changes of water quality to ensure the timeliness and accuracy of the data.

[0058] S3. Obtain historical light rain detection data and water quality monitoring data, and construct a water quality recovery model for light rain events through nonlinear dynamic analysis. The water quality monitoring data is used to predict the peak and recovery time of water quality changes in light rain events through the water quality recovery model; identify significant mutation points of water quality changes through chaos theory algorithms, and adjust the sampling frequency of the sensor through fuzzy logic control of the significant mutation points; combine historical light rain event detection data (such as rainfall intensity and runoff rate during different light rain events) with water quality monitoring data (such as changes in pollutant concentration over time), and construct a water quality recovery model through nonlinear dynamic analysis. The water quality recovery model analyzes when pollutants return to normal levels after the light rain event ends, and the peak and time points of pollutant concentrations. Chaos theory algorithms are used to identify seemingly random mutation points in water quality changes that are difficult to predict. By identifying these mutation points, the system can respond quickly. Once the mutation point is identified, the fuzzy logic control mechanism adjusts the sampling frequency of the sensor according to the trend of pollutant concentration changes, ensuring that the sampling frequency is increased at important mutation moments and reduced when stable, thereby improving monitoring efficiency.

[0059] S4. Obtain key pollutants based on the peak value and recovery time of the water quality change; obtain pollution source data, and determine the pollution source through data correlation analysis based on the key pollutants and the pollution source data. Key pollutants are pollutants whose concentrations increase significantly during light rain events and have a significant impact on water quality.

[0060] The method for determining the pollution source through data correlation analysis based on the key pollutants and the pollution source data includes:

[0061] The micro-rainfall detection data and water quality monitoring data of the water quality monitoring system are obtained to obtain the spatiotemporal distribution map of pollutants, the spatiotemporal distribution map of pollutants is spatially interpolated by the Kriging interpolation method, and the pollutant concentration in the unmonitored river area is predicted to obtain the spatial distribution map of pollutants.

[0062] The geographical information and emission of pollution sources in the river area are obtained and a spatial distribution map of the pollution sources is constructed; the spatial distribution map of the pollutants and the spatial distribution map of the pollution sources are fitted by the Kriging interpolation method to obtain the spatial distribution relationship between the pollutants and the pollution sources.

[0063] The spatial distribution relationship is analyzed through correlation analysis to obtain the correlation coefficient between pollutant concentration and pollution source emission, the correlation between pollutants and pollution sources is obtained through the correlation coefficient, and statistical analysis is performed to obtain a correlation distribution map of the river area; when the correlation coefficient is greater than a preset correlation threshold, it is determined that there is a significant positive correlation between the pollutant and the pollution source; if there is a significant positive correlation between the pollutant and the pollution source, the pollution source is determined by tracing the source through the pollutant diffusion model of the pollution source.

[0064] The spatiotemporal distribution map of pollutants reflects the changes in pollutant concentration over time and geographical location by integrating monitoring data in the time and space dimensions. For river areas that are not monitored by sensors, the Kriging interpolation method is used for spatial interpolation. The Kriging interpolation method predicts the distribution of pollutant concentration in unmonitored areas based on the pollutant concentration data of monitored points, and obtains the spatial distribution map of pollutants in the entire river area. The spatial distribution map of pollution sources reflects the distribution of pollution sources in the river area and the potential impact of each pollution source on the area. The spatial distribution map of pollutants and the spatial distribution map of pollution sources are fitted by the Kriging interpolation method respectively, and the two are correlated to obtain the relationship between the spatial distribution of pollutants and the emission distribution of pollution sources, and to analyze whether the changes in pollutant concentrations at different locations are associated with the location and emission of surrounding pollution sources. The fitting process will help discover the diffusion path of pollutants. The concentration data of pollutants are compared with the emission data of pollution sources through correlation analysis, and the correlation coefficient between the two is calculated. The correlation coefficient reflects the linear relationship between pollutant concentration and pollution source emission. The larger the value, the stronger the correlation between the two. A statistical analysis of the correlation of the entire river area is conducted to draw a correlation distribution map. The correlation distribution map will mark which areas have a high positive correlation between the pollutant concentration and the emission of surrounding pollution sources, so as to analyze and determine which pollution sources are mainly affected by the pollutants. When a preset correlation threshold is set, for example, a correlation coefficient greater than 0.7 indicates a significant positive correlation. If the correlation coefficient in the analysis result exceeds the threshold, it means that there is a significant positive correlation between the pollutant concentration in the area and the emission of certain pollution sources. It can be preliminarily determined that these pollution sources have a greater contribution to the water pollution in the area, thereby screening out which pollution sources have a high correlation with the concentration of pollutants, providing a basis for pollutant tracing.

[0065] The method for determining the pollution source by performing source tracing analysis through the pollutant diffusion model of the pollution source includes:

[0066] The environmental data of the river is obtained, and the diffusion model is determined in combination with the diffusion behavior of the pollutants; the physical and chemical properties of the pollutants, the environmental parameters of the river, and the emission data of the pollution sources are obtained and recorded as comprehensive parameters, and the comprehensive parameters are input into the diffusion model to obtain the diffusion path and diffusion rate of the pollutants.

[0067] The light rain detection data of the light rain event is obtained, and the change of the river flow velocity in the river environmental parameters during the light rain event is obtained by applying the light rain detection data to the diffusion model, and the diffusion path and diffusion rate of the pollutants are corrected according to the change of the river flow velocity.

[0068] According to the diffusion path of the pollutants, a source tracing analysis is performed to obtain a predicted pollution source, the predicted pollution source is matched with the spatial distribution map of the pollution source, and the pollution source is determined through a geographic information system; the source tracing analysis is to reversely trace the diffusion path.

[0069] The environmental data of the river include key parameters such as the river's topography, flow rate, flow rate, water temperature, etc., which will directly affect the diffusion behavior of pollutants in the river. According to these environmental data, a suitable diffusion model can be selected, such as a one-dimensional, two-dimensional or three-dimensional diffusion model, to simulate the propagation of pollutants in the river. This embodiment adopts a two-dimensional diffusion model. Environmental parameters such as the flow rate and depth of the river play a role in determining the diffusion range and speed of pollutants in the diffusion model. The physical and chemical properties of pollutants include diffusion coefficients, reaction rates, sedimentation rates, etc., and the emission data of pollution sources include emissions, emission rates, etc. The comprehensive parameters are input into the diffusion model, and the diffusion model will simulate the diffusion path and diffusion rate of pollutants in the river based on these parameters. In a light rain event, the flow rate and flow of the river will change significantly, affecting the diffusion of pollutants. The light rain detection data is input into the diffusion model, and the model will automatically adjust the flow rate parameters of the river, and accordingly correct the diffusion path and diffusion rate of pollutants. The core of the traceability analysis is to find the starting point of the pollutant, that is, the possible source of pollution, along the reverse path according to the direction and speed of the pollutant diffusion. The predicted pollution source location is matched with the existing pollution source spatial distribution map. Through the Geographic Information System (GIS), the predicted pollution source location can be compared with the actual pollution source location on the spatial distribution map to confirm a more accurate pollution source. Reverse tracing of the diffusion path is to analyze in the opposite direction, from the pollutant location that has been detected downstream of the river, upstream along the path of pollutant diffusion, and gradually approach the pollution source. It is particularly suitable for flowing environments such as rivers.

[0070] The method further comprises:

[0071] The light rain detection data and water quality monitoring data of historical light rain events are obtained and recorded as first data, the first data are normalized and input into a nonlinear dynamic model, and a numerical method is used to fit the nonlinear dynamic model to obtain a water quality restoration model.

[0072] The light rain detection data and water quality monitoring data of the water quality monitoring system are obtained and a time series of water quality changes is generated according to the time sequence; the chaotic characteristics of the river water quality system are identified by calculating the Lyapunov exponent of the water quality change; when the Lyapunov exponent is greater than zero, the river water quality system is in a chaotic state, and the water quality monitoring data are reconstructed in phase space by embedding dimension and time delay algorithm, the phase space trajectory of the time series of water quality changes is analyzed according to the space reconstruction, and the mutation position of the phase space trajectory is found by cross-section analysis and marked as a significant mutation point.

[0073] When the water quality monitoring system detects a significant mutation point, the sensor sampling frequency is adjusted according to the preset fuzzy logic control.

[0074] Normalization scales the data to the same dimensional range (usually 0 to 1), thereby reducing the impact of dimensional differences on the model. The nonlinear dynamic model is fitted by numerical methods. The fitting process uses historical data to optimize the model parameters so that the model can accurately describe the recovery process of river water quality after a micro-rainfall event, thereby forming a water quality recovery model. The water quality change time series is used to describe the dynamic changes in river water quality during micro-rainfall events, which plays a key role in the subsequent chaotic characteristic analysis and phase space reconstruction. The Lyapunov exponent is a key indicator for detecting the chaotic characteristics of river water quality systems. The Lyapunov exponent is calculated by analyzing the water quality change time series. If the Lyapunov exponent is greater than zero, it means that the river water quality system has the characteristic of sensitive dependence on initial conditions, which means that a small initial disturbance will cause a great difference in the system in the future. In a chaotic state, the behavior of the river water quality system will become more complex and unpredictable, so it is necessary to identify the mutation point of the river water quality system. Phase space reconstruction is a tool used to analyze the complex behavior of time series. By embedding the algorithm of dimension and time delay, the one-dimensional time series data can be reconstructed into a multidimensional phase space to form a trajectory diagram of water quality change. The trajectory in the phase space can reveal the state of the system at different time points. By analyzing the evolution of these trajectories, the key points in the change of river water quality can be identified. Cross-sectional analysis is to find mutation points by analyzing the intersection of trajectories. These mutation points indicate the moment when the state of the system changes dramatically. These mutation points are marked as significant mutation points, indicating that the water quality at these locations may be significantly polluted or affected by the environment. Phase space analysis and cross-sectional analysis identify significant mutation points in water quality. Through the preset fuzzy logic control rules, the water quality monitoring system will automatically adjust the sampling frequency of the sensor. For example, when the mutation point indicates that the water quality changes rapidly, the system can increase the sampling frequency of the sensor to obtain more accurate data; when the water quality is relatively stable, the sampling frequency can be reduced to save energy and computing resources. The preset fuzzy logic control rules are based on the historical data of the river and are preset according to the empirical analysis method.

[0075] The method for obtaining water quality monitoring data by a micro-pollution mutation detection method according to the sampling frequency includes:

[0076] The length of the sliding window is adjusted according to the rainfall intensity of the light rain event monitored in real time by the water quality monitoring system; the pollutant concentration in the sliding window is calculated by the mean to obtain the average change value of the pollutant concentration;

[0077] The average change value of the pollutant concentration of two adjacent sliding windows is obtained by numerical analysis. When the concentration difference value is greater than the preset difference threshold, the sampling frequency of the sensor is increased; when the concentration difference value is less than the preset difference threshold, the sampling frequency of the sensor is reduced.

[0078] When the rainfall intensity changes, the system will adaptively adjust the length of the sliding window. Sliding windows are a common method for processing time series data, and the window length determines the time span of each analysis. When the rainfall intensity is large, the pollutant concentration may change rapidly, and the system can shorten the sliding window to update the analysis more frequently; conversely, when the rainfall intensity is small, the length of the sliding window can be extended to improve the system's sensitivity to dynamic changes and optimize the timeliness of the analysis. In order to capture the drastic changes in pollutant concentration in a timely manner, a preset difference threshold is set. When the difference value of the pollutant concentration of adjacent sliding windows exceeds the threshold, it means that the pollutant concentration changes rapidly, and the system will automatically increase the sampling frequency of the sensor. Increasing the sampling frequency ensures that the system obtains data more frequently, thereby capturing the fluctuations in pollutant concentration more accurately. When the difference value of the pollutant concentration of adjacent sliding windows indicates that the pollutant concentration does not change much and the water quality is relatively stable, the system will automatically reduce the sampling frequency of the sensor.

[0079] Embodiment 2: Based on the same inventive concept, this embodiment also provides a water quality evaluation system for a rain-source river, including: a light rain detection data acquisition module, a water quality monitoring data acquisition module, a sampling frequency adjustment module, and a pollution source determination module, wherein each module is sequentially connected in communication;

[0080] The light rain detection data acquisition module is used to construct a water quality monitoring system according to the topographic characteristics of the river and the distribution of pollution sources. The sensors of the water quality monitoring system monitor light rain events in real time and obtain light rain detection data. The light rain detection data includes rainfall intensity, runoff rate, and rainfall time.

[0081] The water quality monitoring data acquisition module is used to adjust the sampling frequency of the sensor of the water quality monitoring system through an adaptive algorithm, and obtain water quality monitoring data through a micro-pollution mutation detection method according to the sampling frequency.

[0082] The sampling frequency adjustment module is used to obtain historical light rain detection data and water quality monitoring data, and to construct a water quality recovery model for light rain events through nonlinear dynamic analysis. The water quality monitoring data is used to predict the peak value and recovery time of water quality changes in light rain events through the water quality recovery model; the significant mutation points of water quality changes are identified through chaos theory algorithms, and the sampling frequency of the sensor is adjusted through fuzzy logic control based on the significant mutation points.

[0083] The pollution source determination module is used to obtain key pollutants according to the peak value and recovery time of the water quality change; obtain pollution source data, and determine the pollution source through data correlation analysis based on the key pollutants and the pollution source data.

[0084] The pollution source determination module comprises:

[0085] The spatial distribution map generation module of pollutants is used to obtain the micro-rainfall detection data and water quality monitoring data of the water quality monitoring system to obtain the spatiotemporal distribution map of pollutants, perform spatial difference on the spatiotemporal distribution map of pollutants through the Kriging interpolation method, predict the pollutant concentration in the unmonitored river area to obtain the spatial distribution map of pollutants.

[0086] The spatial distribution relationship acquisition module is used to obtain the geographical information and emission of pollution sources in the river area and construct a spatial distribution map of the pollution sources; the spatial distribution map of the pollutants and the spatial distribution map of the pollution sources are fitted by the Kriging interpolation method to obtain the spatial distribution relationship between the pollutants and the pollution sources.

[0087] The pollution source tracing analysis module is used to obtain the correlation coefficient between the pollutant concentration and the pollution source emission through correlation analysis of the spatial distribution relationship, obtain the correlation between the pollutant and the pollution source through the correlation coefficient, and perform statistical analysis to obtain the correlation distribution map of the river area; when the correlation coefficient is greater than the preset correlation threshold, it is determined that there is a significant positive correlation between the pollutant and the pollution source; if there is a significant positive correlation between the pollutant and the pollution source, the pollution source is determined by tracing the source through the pollutant diffusion model of the pollution source.

[0088] The pollution source tracing analysis module includes the following steps:

[0089] The environmental data of the river is obtained, and the diffusion model is determined in combination with the diffusion behavior of the pollutants; the physical and chemical properties of the pollutants, the environmental parameters of the river, and the emission data of the pollution sources are obtained and recorded as comprehensive parameters, and the comprehensive parameters are input into the diffusion model to obtain the diffusion path and diffusion rate of the pollutants.

[0090] The light rain detection data of the light rain event is obtained, and the change of the river flow velocity in the river environmental parameters during the light rain event is obtained by applying the light rain detection data to the diffusion model, and the diffusion path and diffusion rate of the pollutants are corrected according to the change of the river flow velocity.

[0091] According to the diffusion path of the pollutants, a source tracing analysis is performed to obtain a predicted pollution source, the predicted pollution source is matched with the spatial distribution map of the pollution source, and the pollution source is determined through a geographic information system; the source tracing analysis is to reversely trace the diffusion path.

[0092] The sampling frequency adjustment module comprises:

[0093] The water quality restoration model generation module is used to obtain the light rain detection data and water quality monitoring data of historical light rain events and record them as first data, normalize the first data and input them into the nonlinear dynamic model, and use numerical methods to fit the nonlinear dynamic model to obtain the water quality restoration model.

[0094] The sampling frequency adjustment submodule is used to obtain the light rain detection data and water quality monitoring data of the water quality monitoring system and generate a time series of water quality changes according to the time sequence; the chaotic characteristics of the river water quality system are identified by calculating the Lyapunov exponent of water quality changes; when the Lyapunov exponent is greater than zero, the river water quality system is in a chaotic state, and the water quality monitoring data are reconstructed in phase space by embedding dimension and time delay algorithm, the phase space trajectory of the time series of water quality changes is analyzed according to the space reconstruction, and the mutation position of the phase space trajectory is found by cross-section analysis and marked as a significant mutation point.

[0095] When the water quality monitoring system detects a significant mutation point, the sensor sampling frequency is adjusted according to the preset fuzzy logic control.

[0096] The water quality monitoring data acquisition module comprises the following steps:

[0097] The length of the sliding window is adjusted according to the rainfall intensity of the light rain event monitored in real time by the water quality monitoring system; the pollutant concentration in the sliding window is calculated by the mean to obtain the average change value of the pollutant concentration.

[0098] The average change value of the pollutant concentration of two adjacent sliding windows is obtained by numerical analysis. When the concentration difference value is greater than the preset difference threshold, the sampling frequency of the sensor is increased; when the concentration difference value is less than the preset difference threshold, the sampling frequency of the sensor is reduced.

[0099] It should be noted that, regarding the system in the above embodiment, the specific manner in which each module performs operations has been described in detail in the embodiment of the method, and will not be elaborated here.

[0100] Finally, it should be noted that: Although the present invention has been described in detail with reference to the aforementioned embodiments, it is still possible for those skilled in the art to modify the technical solutions described in the aforementioned embodiments, or to make equivalent substitutions for some of the technical features therein. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A method for evaluating the water quality of a rain-fed river, characterized in that: The method comprises: A water quality monitoring system is constructed according to the topographical features of the river and the distribution of pollution sources, and light rain events are monitored in real time through sensors of the water quality monitoring system to obtain light rain detection data, wherein the light rain detection data includes rainfall intensity, runoff rate, and rainfall time; The water quality monitoring system adjusts the sampling frequency of the sensor through an adaptive algorithm, and obtains water quality monitoring data through a micro-pollution mutation detection method according to the sampling frequency; Acquire historical light rain detection data and water quality monitoring data, and construct a water quality recovery model for light rain events through nonlinear dynamic analysis, and use the water quality monitoring data to predict the peak value and recovery time of water quality changes in light rain events through the water quality recovery model; identify significant mutation points of water quality changes through chaos theory algorithms, and adjust the sampling frequency of the sensor through fuzzy logic control based on the significant mutation points; Obtain key pollutants according to the peak value and recovery time of the water quality change; obtain pollution source data, and determine the pollution source through data correlation analysis based on the key pollutants and the pollution source data; Acquire light rain detection data and water quality monitoring data of historical light rain events and record them as first data, normalize the first data and input them into a nonlinear dynamic model, and use a numerical method to fit the nonlinear dynamic model to obtain a water quality restoration model; Acquire the light rain detection data and water quality monitoring data of the water quality monitoring system and generate a time series of water quality changes according to the time sequence; identify the chaotic characteristics of the river water quality system by calculating the Lyapunov index of water quality changes; when the Lyapunov index is greater than zero, the river water quality system is in a chaotic state, and reconstruct the phase space of the water quality monitoring data by embedding dimension and time delay algorithm, analyze the phase space trajectory of the time series of water quality changes according to the space reconstruction, find the mutation position of the phase space trajectory by cross-section analysis and mark it as a significant mutation point; When the water quality monitoring system detects a significant mutation point, the sensor sampling frequency is adjusted according to the preset fuzzy logic control; The method for obtaining water quality monitoring data by a micro-pollution mutation detection method according to the sampling frequency includes: The length of the sliding window is adjusted according to the rainfall intensity of the light rain event monitored in real time by the water quality monitoring system; the pollutant concentration in the sliding window is calculated by the mean to obtain the average change value of the pollutant concentration; The average change value of the pollutant concentration of two adjacent sliding windows is obtained by numerical analysis. When the concentration difference value is greater than the preset difference threshold, the sampling frequency of the sensor is increased; when the concentration difference value is less than the preset difference threshold, the sampling frequency of the sensor is reduced.

2. The water quality evaluation method for a rain-source river according to claim 1, characterized in that: The method for determining the pollution source through data correlation analysis based on the key pollutants and the pollution source data includes: Acquire the light rain detection data and water quality monitoring data of the water quality monitoring system to obtain the spatiotemporal distribution map of pollutants, perform spatial difference on the spatiotemporal distribution map of pollutants by Kriging interpolation method, predict the concentration of pollutants in the unmonitored river area, and obtain the spatial distribution map of pollutants; Obtaining geographic information and emission of pollution sources in the river area and constructing a spatial distribution map of the pollution sources; fitting the spatial distribution map of the pollutants and the spatial distribution map of the pollution sources by the Kriging interpolation method to obtain the spatial distribution relationship between the pollutants and the pollution sources; The spatial distribution relationship is analyzed through correlation analysis to obtain the correlation coefficient between pollutant concentration and pollution source emission, the correlation between pollutants and pollution sources is obtained through the correlation coefficient, and statistical analysis is performed to obtain a correlation distribution map of the river area; when the correlation coefficient is greater than a preset correlation threshold, it is determined that there is a significant positive correlation between the pollutant and the pollution source; if there is a significant positive correlation between the pollutant and the pollution source, the pollution source is determined by tracing the source through the pollutant diffusion model of the pollution source.

3. The water quality evaluation method for a rain-source river according to claim 2, characterized in that: The method for determining the pollution source by performing source tracing analysis through the pollutant diffusion model of the pollution source includes: Obtaining river environmental data and determining a diffusion model in combination with the diffusion behavior of pollutants; obtaining the physical and chemical properties of pollutants, river environmental parameters, and pollution source emission data and recording them as comprehensive parameters, and inputting the comprehensive parameters into the diffusion model to obtain the diffusion path and diffusion rate of pollutants; Acquire light rain detection data of the light rain event, obtain the change of river flow velocity in the river environmental parameters during the light rain event by using the light rain detection data through the diffusion model, and correct the diffusion path and diffusion rate of the pollutant according to the change of the river flow velocity; According to the diffusion path of the pollutants, a source tracing analysis is performed to obtain a predicted pollution source, the predicted pollution source is matched with the spatial distribution map of the pollution source, and the pollution source is determined through a geographic information system; the source tracing analysis is to reversely trace the diffusion path.

4. A water quality evaluation system for a rain-fed river, characterized in that: The system comprises: a light rain detection data acquisition module, a water quality monitoring data acquisition module, a sampling frequency adjustment module, and a pollution source determination module, and each module is sequentially connected in communication; A light rain detection data acquisition module is used to construct a water quality monitoring system according to the topographic characteristics of the river and the distribution of pollution sources, and to monitor light rain events in real time through the sensors of the water quality monitoring system and obtain light rain detection data, wherein the light rain detection data includes rainfall intensity, runoff rate, and rainfall time; A water quality monitoring data acquisition module, used to adjust the sampling frequency of the sensor of the water quality monitoring system through an adaptive algorithm, and acquire water quality monitoring data through a micro-pollution mutation detection method according to the sampling frequency; The sampling frequency adjustment module is used to obtain historical light rain detection data and water quality monitoring data, and to construct a water quality recovery model for light rain events through nonlinear dynamic analysis, and to predict the peak value and recovery time of water quality changes in light rain events through the water quality recovery model using the water quality monitoring data; to identify significant mutation points of water quality changes through chaos theory algorithms, and to adjust the sampling frequency of the sensor through fuzzy logic control using the significant mutation points; A pollution source determination module is used to obtain key pollutants according to the peak value and recovery time of the water quality change; obtain pollution source data, and determine the pollution source through data correlation analysis based on the key pollutants and the pollution source data; The sampling frequency adjustment module comprises: A water quality restoration model generation module is used to obtain light rain detection data and water quality monitoring data of historical light rain events and record them as first data, normalize the first data and input them into a nonlinear dynamic model, and use a numerical method to fit the nonlinear dynamic model to obtain a water quality restoration model; The sampling frequency adjustment submodule is used to obtain the light rain detection data and water quality monitoring data of the water quality monitoring system and generate a time series of water quality changes according to the time sequence; the chaotic characteristics of the river water quality system are identified by calculating the Lyapunov index of water quality changes; when the Lyapunov index is greater than zero, the river water quality system is in a chaotic state, and the water quality monitoring data are reconstructed in phase space by embedding dimension and time delay algorithm, the phase space trajectory of the time series of water quality changes is analyzed according to the space reconstruction, and the mutation position of the phase space trajectory is found by cross-section analysis and marked as a significant mutation point; When the water quality monitoring system detects a significant mutation point, the sensor sampling frequency is adjusted according to the preset fuzzy logic control; The water quality monitoring data acquisition module comprises the following steps: The length of the sliding window is adjusted according to the rainfall intensity of the light rain event monitored in real time by the water quality monitoring system; the pollutant concentration in the sliding window is calculated by the mean to obtain the average change value of the pollutant concentration; The average change value of the pollutant concentration of two adjacent sliding windows is obtained by numerical analysis. When the concentration difference value is greater than the preset difference threshold, the sampling frequency of the sensor is increased; when the concentration difference value is less than the preset difference threshold, the sampling frequency of the sensor is reduced.

5. A water quality evaluation system for a rain-source river according to claim 4, characterized in that: The pollution source determination module comprises: The pollutant spatial distribution map generation module is used to obtain the micro-rainfall detection data and water quality monitoring data of the water quality monitoring system to obtain the temporal and spatial distribution map of the pollutants, perform spatial difference on the temporal and spatial distribution map of the pollutants through the Kriging interpolation method, and predict the pollutant concentration in the unmonitored river area to obtain the spatial distribution map of the pollutants; The spatial distribution relationship acquisition module is used to obtain the geographical information and emission of pollution sources in the river area and construct a spatial distribution map of the pollution sources; the spatial distribution map of the pollutants and the spatial distribution map of the pollution sources are fitted by the Kriging interpolation method to obtain the spatial distribution relationship between the pollutants and the pollution sources; The pollution source tracing analysis module is used to obtain the correlation coefficient between the pollutant concentration and the pollution source emission through correlation analysis of the spatial distribution relationship, obtain the correlation between the pollutant and the pollution source through the correlation coefficient, and perform statistical analysis to obtain the correlation distribution map of the river area; when the correlation coefficient is greater than the preset correlation threshold, it is determined that there is a significant positive correlation between the pollutant and the pollution source; if there is a significant positive correlation between the pollutant and the pollution source, the pollution source is determined by tracing the source through the pollutant diffusion model of the pollution source.

6. A water quality evaluation system for a rain-source river according to claim 5, characterized in that: The pollution source tracing analysis module includes the following steps: Obtaining river environmental data and determining a diffusion model in combination with the diffusion behavior of pollutants; obtaining the physical and chemical properties of pollutants, river environmental parameters, and pollution source emission data and recording them as comprehensive parameters, and inputting the comprehensive parameters into the diffusion model to obtain the diffusion path and diffusion rate of pollutants; Acquire light rain detection data of the light rain event, obtain the change of river flow velocity in the river environmental parameters during the light rain event by using the light rain detection data through the diffusion model, and correct the diffusion path and diffusion rate of the pollutant according to the change of the river flow velocity; According to the diffusion path of the pollutants, a source tracing analysis is performed to obtain a predicted pollution source, the predicted pollution source is matched with the spatial distribution map of the pollution source, and the pollution source is determined through a geographic information system; the source tracing analysis is to reversely trace the diffusion path.

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