A method for tracing the source of river sewage outlets based on grid-based water quality monitoring

Abstract

The present invention relates to the technical field of water quality monitoring, and in particular to a method for tracing the source of a river sewage outlet based on grid-based water quality monitoring, comprising the following steps: S1, adaptive grid optimization based on watershed characteristics: dynamically adjusting the density and distribution of water quality monitoring grids; S2, water quality parameter monitoring and collection: deploying water quality sensors and monitoring water quality parameters in the water body in real time; S3, pollutant diffusion model establishment and tracing source analysis: simulating the propagation path, diffusion speed and concentration changes of pollutants in a river, and predicting the potential location of pollution sources and the intensity of pollutant emissions; S4, pollution source tracing and positioning: determining the location of the pollution source, the emission amount and the diffusion pattern of the sewage outlet; the present invention generates the concentration distribution of pollutants and their evolution over time, accurately predicts the diffusion range and dynamic behavior of pollutants, and improves the accuracy and reliability of pollution diffusion analysis.

Description

Technical Field

[0001] The present invention relates to the technical field of water quality monitoring, and in particular to a method for tracing the source of a river sewage outlet based on grid-based water quality monitoring. Background Art

[0002] In water environment protection and water resources management, the identification and emission control of pollution sources have become the core tasks of water environment governance. Traditional monitoring methods usually adopt fixed monitoring points or periodic sampling methods. However, when pollution spreads rapidly and the river basin environment is complex, it is difficult to quickly obtain comprehensive and accurate pollutant diffusion data. This puts forward higher technical requirements for the accurate positioning of river pollution sources and the analysis of diffusion impacts.

[0003] Existing technologies have many shortcomings in river pollution monitoring and tracing. On the one hand, the traditional fixed monitoring points lack specificity and are difficult to dynamically adapt to changes in basin characteristics, resulting in waste of resources and monitoring blind spots. On the other hand, pollutant diffusion simulation based on static models cannot accurately reflect the dynamic migration process of pollutants in actual rivers affected by factors such as flow velocity, flow direction, and diffusion coefficient. In addition, existing tracing methods lack effective integration with actual monitoring data and model simulation results in the deduction of pollution source locations and emission intensity. The positioning accuracy is low, and it is difficult to meet the pollution prevention and control needs in complex basin environments.

[0004] The purpose of the present invention is to overcome the shortcomings of the prior art and provide a method for tracing the source of river sewage outlets based on grid water quality monitoring, which can meet the actual needs of pollution monitoring, tracing and control in complex river basin environments, and provide scientific basis and technical support for water environment protection. Summary of the invention

[0005] The present invention provides a method for tracing the source of a river sewage outlet based on grid-based water quality monitoring.

[0006] A method for tracing the source of a river sewage outlet based on grid-based water quality monitoring comprises the following steps: S1, Adaptive grid optimization based on watershed characteristics: Obtain environmental data of the river basin and use adaptive grid optimization technology to dynamically adjust the density and distribution of water quality monitoring grids, including: S11, obtaining river basin environmental data: using hydrological monitoring equipment, remote sensing technology and environmental sensors, real-time collection of river basin environmental data, including flow velocity, flow direction, water depth and meteorological conditions; S12, Adaptive grid optimization strategy design: Based on the collected environmental data, design adaptive grid optimization strategies, including density adjustment and grid layout optimization; S13, Implementation and adjustment of adaptive grid: Based on the designed adaptive grid optimization strategy, grid optimization is implemented and real-time adjustment is performed; S2, water quality parameter monitoring and collection: Based on adaptive grid optimization, water quality sensors are deployed to monitor water quality parameters in the water body in real time, including pollutant concentration, pH value, dissolved oxygen, and turbidity; S3, Establishment of pollutant diffusion model and source tracing analysis: Based on the real-time monitored water quality parameters and environmental data of the river basin, a pollutant diffusion model is established to simulate the propagation path, diffusion speed and concentration changes of pollutants in the river, and predict the potential location of the pollution source and the intensity of pollutant emissions; S4, Pollution source tracing and location: Based on the potential location of the pollution source and the predicted results of pollutant emission intensity, determine the location of the pollution source, emission volume and diffusion pattern of the sewage outlet.

[0007] Optionally, the acquiring of river basin environmental data in S11 includes: S111, flow velocity measurement: measuring the water flow velocity at different locations by using a flow meter; S112, flow direction monitoring: using a water flow direction sensor to obtain water flow direction information; S113, water depth measurement: using a water depth sensor to obtain the water depth of the river; S114, meteorological conditions: collect meteorological conditions provided by weather stations, including temperature, humidity, and precipitation.

[0008] Optionally, the adaptive grid optimization strategy design in S12 includes: S121, grid density adjustment: Based on the collected environmental data, the density of the grid is dynamically adjusted by calculating the pollution diffusion potential of each area in the basin; S122, grid layout optimization: based on the determined grid density, the spatial layout of the grid is optimized through flow direction data.

[0009] Optionally, the implementation and adjustment of the adaptive grid in S13 includes: S131, implement grid optimization: based on the design results of the adaptive grid optimization strategy, deploy monitoring grids in the river basin according to the optimized grid density and layout requirements; S132, real-time adjustment of grid layout: real-time calculation is performed based on the collected watershed environmental data to dynamically adjust the density and distribution of the grid.

[0010] Optionally, the water quality parameter monitoring and collection in S2 includes: S21, water quality sensor layout: based on the results of adaptive grid optimization, water quality sensors are laid out in the optimized grid layout; S22, real-time monitoring of water quality parameters: using the deployed water quality sensors to collect real-time water quality parameters in the water body, including pollutant concentration, pH value, dissolved oxygen and turbidity.

[0011] Optionally, the pollutant diffusion model establishment and source tracing analysis in S3 includes: S31, data preprocessing: preprocessing the monitored water quality parameters and environmental data of the river basin, including denoising, interpolation and normalization; S32, establishment of pollutant diffusion model: based on the pre-treated water quality parameters and environmental data, a pollutant diffusion model is established to simulate the propagation path, diffusion speed and concentration changes of pollutants in the river; S33, Model simulation and emission intensity prediction: Based on the simulation results of the pollutant diffusion model, predict the potential location of pollution sources and pollutant emission intensity.

[0012] Optionally, the data preprocessing in S31 includes: S311, data denoising: Kalman filtering algorithm is used to denoise the monitored water quality parameters and environmental data of the river basin; S312, data interpolation: Use Kriging interpolation algorithm to interpolate missing or incomplete data in water quality parameters and environmental data; S313, data normalization: use the minimum-maximum normalization method to normalize water quality parameters and environmental data.

[0013] Optionally, the pollutant diffusion model in S32 adopts a convection diffusion equation, and the convection diffusion equation includes: S321, Establishment of pollutant diffusion equation: Based on river environment data and water quality parameters, establish the convection diffusion equation that describes the change of pollutant concentration to simulate the diffusion process and convection migration of pollutants; S322, acquisition of flow field characteristic parameters: based on actual river environment data and water quality parameters, key parameters in the convection-diffusion equation are acquired, including water flow velocity and diffusion coefficient; S323, numerical solution and boundary condition setting: use finite difference method to perform numerical solution and set boundary conditions; S324, Simulation of pollutant concentration distribution and time evolution: Analyze the concentration distribution and propagation characteristics of pollutants in the river using numerical solution results.

[0014] Optionally, the model simulation and emission intensity prediction in S33 include: S331, reverse deduction of the potential location of pollution sources: deduction of the location of pollution sources based on the spatial distribution of pollutant concentrations and the inverse problem solving method; S332, predict pollutant emission intensity: based on the location of the pollution source obtained by inversion and combined with the simulation results, calculate the emission intensity of the pollution source.

[0015] Optionally, the pollution source tracing and locating in S4 includes: S41, Pollution source location based on potential location: Determine the location of the pollution source based on the deduced pollution source location ; S42, calculating the emission amount of the pollution source: based on the calculated emission intensity of the pollution source, the emission amount of the pollution source is taken as the emission amount of the pollution source; S43, determine the diffusion pattern of the sewage outlet: calculate the diffusion radius according to the pollutant concentration gradient and diffusion coefficient through simulation results and actual environmental data and water quality parameters.

[0016] Beneficial effects of the present invention: The present invention uses adaptive grid optimization technology based on watershed characteristics, combined with environmental data such as flow velocity, flow direction, water depth and meteorological conditions, to dynamically adjust the density and distribution of monitoring grids to ensure the monitoring accuracy and coverage of pollution sources in different watershed characteristic areas. By optimizing and adjusting the grid layout in real time, high-density grids are deployed in areas with fast flow rates and high pollution diffusion potential, while low-density grids are deployed in areas with low flow rates and stable conditions. Monitoring resources are reasonably allocated, which effectively improves the flexibility and efficiency of the monitoring system and provides precise spatial support for water quality parameter collection and pollution tracing.

[0017] The present invention adopts the convection-diffusion equation to comprehensively simulate the propagation path, diffusion speed and concentration change of pollutants in the river channel. Through data preprocessing, acquisition of flow field characteristic parameters and numerical solution methods, a pollutant diffusion model that conforms to the actual river channel characteristics is established to ensure the accuracy and completeness of the input data. The model simulation results can generate the concentration distribution of pollutants and their evolution over time, accurately predict the diffusion range and dynamic behavior of pollutants, and improve the accuracy and reliability of pollution diffusion analysis.

[0018] The present invention, through pollution source tracing and emission intensity prediction, accurately inverts the potential location of pollution sources, and quantifies their emissions and diffusion impact range. Combining actual monitoring data and model simulation results, through the least squares method and diffusion radius calculation method, not only the specific location and emission intensity of the pollution source are determined, but also the diffusion pattern of the sewage outlet and the impact range of pollutants in the environment are clarified. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] In order to more clearly illustrate the technical solutions in the present invention or the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings in the following description are only for the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0020] Figure 1A schematic diagram of a traceability method flow chart of an embodiment of the present invention; Figure 2 Schematic diagram of adaptive grid optimization based on watershed characteristics according to an embodiment of the present invention. DETAILED DESCRIPTION

[0021] The present invention is described in detail below in conjunction with the accompanying drawings and specific embodiments. At the same time, it is explained here that in order to make the embodiments more detailed, the following embodiments are the best and preferred embodiments, and those skilled in the art may also adopt other alternatives to implement some known technologies; and the accompanying drawings are only for more specific description of the embodiments, and are not intended to specifically limit the present invention.

[0022] It should be noted that the references to "one embodiment", "embodiment", "exemplary embodiments", "some embodiments" and the like in the specification indicate that the embodiments described may include specific features, structures or characteristics, but not every embodiment may include the specific features, structures or characteristics. In addition, when a specific feature, structure or characteristic is described in conjunction with an embodiment, it should be within the knowledge of a person skilled in the art to implement such feature, structure or characteristic in conjunction with other embodiments (whether or not explicitly described).

[0023] In general, a term can be understood, at least in part, from its use in context. For example, depending, at least in part, on the context, the term "one or more" as used herein can be used to describe any feature, structure, or characteristic in the singular sense, or can be used to describe a combination of features, structures, or characteristics in the plural sense. Additionally, the term "based on" can be understood as not necessarily intended to convey an exclusive set of factors, but can instead, depending, at least in part, on the context, allow for the presence of other factors that are not necessarily explicitly described.

[0024] like Figure 1-Figure 2 As shown, a method for tracing the source of a river sewage outlet based on grid-based water quality monitoring includes the following steps: S1, Adaptive grid optimization based on watershed characteristics: Obtain environmental data of the river basin and use adaptive grid optimization technology to dynamically adjust the density and distribution of water quality monitoring grids to ensure that pollution sources in different watershed characteristic areas are fully monitored, including: S11, obtaining river basin environmental data: using hydrological monitoring equipment, remote sensing technology and environmental sensors, real-time collection of river basin environmental data, including flow velocity, flow direction, water depth and meteorological conditions; S12, Adaptive grid optimization strategy design: Based on the collected environmental data, design adaptive grid optimization strategies, including density adjustment and grid layout optimization; S13, Implementation and adjustment of adaptive grid: Based on the designed adaptive grid optimization strategy, grid optimization is implemented and real-time adjustment is performed; S2, water quality parameter monitoring and collection: Based on adaptive grid optimization, water quality sensors are deployed to monitor water quality parameters in the water body in real time, including pollutant concentration, pH value, dissolved oxygen, and turbidity; S3, Establishment of pollutant diffusion model and source tracing analysis: Based on the real-time monitored water quality parameters and environmental data of the river basin, a pollutant diffusion model is established to simulate the propagation path, diffusion speed and concentration changes of pollutants in the river, and predict the potential location of the pollution source and the intensity of pollutant emissions; S4, Pollution source tracing and location: Based on the potential location of the pollution source and the predicted results of pollutant emission intensity, determine the location of the pollution source, emission volume and diffusion pattern of the sewage outlet; Through the above content, the density and distribution of water quality monitoring grids can be dynamically adjusted to effectively respond to changes in environmental conditions in different river basins, and efficient pollution source positioning and tracing can be achieved. It not only improves the accuracy and coverage of water quality monitoring, but also can reflect the diffusion of pollutants in real time, thereby providing accurate data support and decision-making basis for river pollution prevention and control.

[0025] The acquisition of river basin environmental data in S11 includes: S111, flow velocity measurement: measuring the water flow velocity at different locations by using a flow meter; S112, flow direction monitoring: using a water flow direction sensor to obtain water flow direction information; S113, water depth measurement: using a water depth sensor to obtain the water depth of the river; S114, meteorological conditions: collect meteorological conditions provided by weather stations, including temperature, humidity, and precipitation; Through the above content, we can fully understand the dynamic environmental characteristics of the river basin. Flow velocity measurement and flow direction monitoring provide key data for water flow behavior analysis and help predict the diffusion path of pollutants. Water depth measurement provides an accurate basis for river flow changes and water capacity assessment. The collection of meteorological conditions helps to consider the impact of meteorological factors on water quality and ensure that monitoring data remains accurate and reliable under complex weather changes.

[0026] The adaptive grid optimization strategy design in S12 includes: S121, grid density adjustment: Based on the collected environmental data, the density of the grid is dynamically adjusted by calculating the pollution diffusion potential of each area in the basin. In areas with faster flow, the grid density should be higher to more accurately capture the diffusion process of pollutants, while in areas with slower flow or shallower water depth, the grid density is reduced accordingly, expressed as: ; in, Indicates location The mesh density at represents the flow velocity at that location, Indicates the water depth at that location. is the adjustment factor; S122, grid layout optimization: Based on the determination of grid density, the spatial layout of the grid is optimized through flow direction data. It is used to guide the grid layout, ensure that the grid is consistent with the main flow direction of the water flow, and maximize the effective coverage of the grid. The goal of grid layout optimization is to improve monitoring efficiency, reduce redundant areas, and avoid gaps between grids. The objective function of the optimized layout is: ; in, is the weight coefficient, is the optimal value for the expected flow direction, is the optimal grid density obtained from environmental data, is the total number of grid cells, that is, the number of grids distributed in the entire monitoring area, is the grid position The direction of water flow, is the grid position The mesh density at ; Through the above content, dynamic adjustment of the density and layout of the grid can effectively adapt to different watershed characteristics and pollution diffusion conditions. By minimizing the deviation between the grid flow direction and the target flow direction and the difference between the grid density and the optimal density, this strategy ensures efficient coverage and accurate monitoring of the monitoring area, reduces resource waste in redundant areas, and significantly improves the accuracy and efficiency of pollution source positioning and traceability analysis.

[0027] The implementation and adjustment of adaptive grid in S13 include: S131, implement grid optimization: based on the design results of the adaptive grid optimization strategy, deploy monitoring grids in the river basin according to the optimized grid density and layout requirements; S132, real-time adjustment of grid layout: real-time calculation is performed based on the collected watershed environmental data to dynamically adjust the density and distribution of the grid; Through the above content, it can be ensured that the monitoring grid is always consistent with the changes in the basin environment, high-density grids can be deployed in areas with fast flow velocities and complex flow directions, the monitoring accuracy of key areas can be improved, the grid density can be reduced in stable areas, and resource allocation can be optimized. This not only improves the flexibility and efficiency of the monitoring system, but also ensures the comprehensiveness and real-time nature of data coverage, providing precise spatial support for subsequent pollution source tracing.

[0028] The monitoring and collection of water quality parameters in S2 include: S21, water quality sensor layout: based on the results of adaptive grid optimization, water quality sensors are laid out in the optimized grid layout; S22, real-time monitoring of water quality parameters: using the deployed water quality sensors to collect water quality parameters in the water body in real time, including pollutant concentration, pH value, dissolved oxygen and turbidity; Through the above content, efficient coverage of key areas is ensured. Sensors collect water quality parameters in real time, including pollutant concentration, pH value, dissolved oxygen and turbidity, forming a continuous data set in time and space, which effectively improves the accuracy and response speed of the monitoring system. At the same time, it optimizes resource allocation, makes the monitoring system more flexible and adaptable, and provides a scientific basis for pollution source tracing and water quality management.

[0029] The pollutant diffusion model establishment and source tracing analysis in S3 include: S31, data preprocessing: preprocessing the monitored water quality parameters and environmental data of the river basin, including denoising, interpolation and normalization; S32, establishment of pollutant diffusion model: based on the pre-treated water quality parameters and environmental data, a pollutant diffusion model is established to simulate the propagation path, diffusion speed and concentration changes of pollutants in the river; S33, Model simulation and emission intensity prediction: Based on the simulation results of the pollutant diffusion model, predict the potential location of pollution sources and pollutant emission intensity; Through the above content, the accuracy and completeness of the input data are ensured, a pollutant diffusion model that conforms to the actual river basin characteristics is established, the propagation path, diffusion speed and concentration changes of pollutants are accurately simulated, and the location and emission intensity of pollution sources are predicted based on the model simulation results, providing a scientific basis for the precise positioning of pollution sources. This not only improves the accuracy and efficiency of source tracing analysis, but also provides important decision-making support for water pollution control and risk assessment.

[0030] The data preprocessing in S31 includes: S311, data denoising: Kalman filtering algorithm is used to denoise the monitored water quality parameters and environmental data of the river basin to improve the accuracy and reliability of the data, expressed as: ; ; in, is the estimated value of the current state, is the estimated value of the previous state, is the current measured value, is the observation matrix, is the Kalman gain, is the state covariance, is the measurement noise covariance; S312, data interpolation: Kriging interpolation algorithm is used to interpolate missing or incomplete data in water quality parameters and environmental data to ensure the continuity and integrity of the data, expressed as: ; in, The position to be estimated The data value of For known location The data value of is the interpolation weight, satisfying , is the number of known data points (or monitoring points); S313, data normalization: The minimum-maximum normalization method is used to normalize water quality parameters and environmental data to eliminate the dimensional differences between different parameters and ensure the consistency of the data, expressed as: ; in, is the original data value, is the minimum value of the data set, is the maximum value of the data set, is the normalized data value, ranging from ; Through the above contents, the accuracy, completeness and consistency of the monitoring data have been comprehensively improved. The denoising process has effectively eliminated the noise introduced by equipment or environmental interference in dynamic monitoring, ensuring the reliability of the data. The interpolation operation has made up for the missing monitoring data and ensured the temporal and spatial continuity of the data. Normalization has eliminated the dimensional differences between different parameters, providing balanced and consistent input for subsequent model calculations.

[0031] The pollutant diffusion model in S32 uses the convection diffusion equation, which includes: S321, Establishment of pollutant diffusion equation: Based on river environment data and water quality parameters, establish the convection diffusion equation that describes the change of pollutant concentration to simulate the diffusion process and convection migration of pollutants, expressed as: ; in, is the pollutant concentration, For time, and is the spatial coordinate, and The water flow and Flow rate in direction, and The pollutants are and The diffusion coefficient in the direction, is the degradation or reaction rate of the pollutant; S322, acquisition of flow field characteristic parameters: based on actual river environment data and water quality parameters, key parameters in the convection-diffusion equation are obtained, including water flow velocity and diffusion coefficient, including: Water flow rate: Get the water flow rate through the flow meter ; Diffusion coefficient: The diffusion coefficient of pollutants is estimated by an empirical formula, expressed as: ; ; in, is an empirical constant, between, is the characteristic length (the diffusion range of the pollution source); S323, numerical solution and boundary condition setting: use finite difference method to perform numerical solution and set boundary conditions, including: Numerical discretization: The convection-diffusion equation is discretized in time and space, expressed as: ; in, For the next time step Time space grid point The pollutant concentration at For the Time grid point The pollutant concentration at is the time step, is the spatial step length, For the Time grid point Pollutant degradation or reaction items at the site; Boundary conditions: Set boundary conditions according to actual river conditions, including inlet and outlet boundaries; The inlet boundary is the specified inlet pollutant concentration , expressed as: ; The outlet boundary is set to a non-diffusion boundary, expressed as: (There is no concentration gradient in the outlet direction); S324, Simulation of pollutant concentration distribution and time evolution: Analyze the concentration distribution and propagation characteristics of pollutants in the river using numerical solution results, including: Concentration distribution analysis: Calculate the concentration of pollutants at different locations in the river, expressed as: Numerical solution results; Pollutant propagation path: determine the main diffusion direction of pollutants based on concentration changes; Pollutant diffusion rate: determined by the rate of change of the concentration front over time; Pollutant attenuation characteristics: combined degradation terms , analyze the reduction rules of pollutants; Through the above content, the propagation path, diffusion speed and concentration changes of pollutants in the river can be accurately simulated, and dynamic factors such as water convection, pollutant diffusion and degradation are comprehensively considered. By discretizing and solving the equation, the spatiotemporal distribution data of pollutant concentration can be generated based on parameters such as the actual river flow velocity, diffusion coefficient and reaction rate. It can not only reflect the migration law of pollutants in complex river environments, but also provide a scientific basis for subsequent pollution source tracing and emission intensity prediction, improve the reliability of pollution diffusion analysis, and provide strong technical support for river pollution control.

[0032] Model simulations and emission intensity forecasts in S33 include: S331, reverse deduction of the potential location of pollution sources: The location of pollution sources is deduced based on the spatial distribution of pollutant concentrations and the inverse problem solving method. The least squares method is used to construct an objective function to fit the monitoring point data and the model inversion results, which is expressed as: ; in, is the total number of monitoring points, For the The actual pollutant concentration at each monitoring point is For When the pollution source location is assumed, The predicted concentration at each monitoring point, For the assumed pollution source location, through optimization solution, determine the minimum objective function , i.e. the potential location of pollution sources; S332, predicting pollutant emission intensity: based on the pollution source location obtained by inversion and combined with the simulation results, calculate the emission intensity of the pollution source, expressed as: ; in, is the emission intensity of the pollution source, is the pollutant concentration at the pollution source location, is the river flow velocity, The depth of the river. is the pollutant degradation coefficient, is the propagation distance from the pollution source to the monitoring point; Through the above content, the potential location of the pollution source is accurately inverted, and the emission intensity of pollutants is calculated through concentration distribution and hydrological characteristics. The convection diffusion of pollutants, river basin characteristics and degradation laws are comprehensively considered. It has high accuracy and reliability, and can dynamically adapt to the pollution diffusion laws in complex river environments. It not only provides a scientific basis for the precise positioning of pollution sources, but also provides an effective means for the quantitative evaluation of emission intensity, and provides data support for the formulation of pollution control and prevention strategies.

[0033] Pollution source tracing and location in S4 include: S41, Pollution source location based on potential location: Determine the location of the pollution source based on the deduced pollution source location ; S42, calculating the emission amount of the pollution source: based on the calculated emission intensity of the pollution source, the emission amount of the pollution source is taken as the emission amount of the pollution source; S43, determine the diffusion mode of the sewage outlet: through the simulation results and actual environmental data and water quality parameters, the diffusion radius is calculated according to the pollutant concentration gradient and diffusion coefficient, expressed as: ; in, is the diffusion radius, is the diffusion coefficient, is the diffusion time; Through the above content, the location of the pollution source, the emission amount and the diffusion range of pollutants are accurately determined. By combining model simulation and actual data, the scientificity and accuracy of source tracing are improved. By calculating the emission intensity, the actual emission amount of the pollution source is quantified, providing data support for pollution control. By calculating the diffusion radius, the impact range of pollutants in the environment is clarified, which facilitates the formulation of more targeted pollution prevention and control measures.

[0034] The present invention covers any substitution, modification, equivalent method and scheme made on the essence and scope of the present invention. In order to make the public have a thorough understanding of the present invention, specific details are described in detail in the following preferred embodiments of the present invention, but those skilled in the art can fully understand the present invention without the description of these details. In addition, in order to avoid unnecessary confusion about the essence of the present invention, well-known methods, processes, procedures, components and circuits are not described in detail.

[0035] The above is only a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principle of the present invention. These improvements and modifications should also be regarded as the scope of protection of the present invention.

Claims

1. A method for tracing the source of river sewage outlets based on grid-based water quality monitoring, characterized in that: The following steps are involved: S1, Adaptive grid optimization based on watershed characteristics: Obtain environmental data of the river basin and use adaptive grid optimization technology to dynamically adjust the density and distribution of water quality monitoring grids, including: S11, obtaining river basin environmental data: using hydrological monitoring equipment, remote sensing technology and environmental sensors, real-time collection of river basin environmental data, including flow velocity, flow direction, water depth and meteorological conditions; S12, Adaptive grid optimization strategy design: Based on the collected environmental data, design adaptive grid optimization strategies, including density adjustment and grid layout optimization; S13, Implementation and adjustment of adaptive grid: Based on the designed adaptive grid optimization strategy, grid optimization is implemented and real-time adjustment is performed; S2, water quality parameter monitoring and collection: Based on adaptive grid optimization, water quality sensors are deployed to monitor water quality parameters in the water body in real time, including pollutant concentration, pH value, dissolved oxygen, and turbidity; S3, Establishment of pollutant diffusion model and source tracing analysis: Based on the real-time monitored water quality parameters and environmental data of the river basin, a pollutant diffusion model is established to simulate the propagation path, diffusion speed and concentration changes of pollutants in the river, and predict the potential location of the pollution source and the intensity of pollutant emissions; S4, Pollution source tracing and location: Based on the potential location of the pollution source and the predicted results of pollutant emission intensity, determine the location of the pollution source, emission volume and diffusion pattern of the sewage outlet.

2. A method for tracing the source of river sewage outlets based on grid-based water quality monitoring according to claim 1, characterized in that: The acquisition of river basin environmental data in S11 includes: S111, flow velocity measurement: measuring the water flow velocity at different locations by using a flow meter; S112, flow direction monitoring: using a water flow direction sensor to obtain water flow direction information; S113, water depth measurement: using a water depth sensor to obtain the water depth of the river; S114, meteorological conditions: collect meteorological conditions provided by weather stations, including temperature, humidity, and precipitation.

3. A method for tracing the source of river sewage outlets based on grid-based water quality monitoring according to claim 2, characterized in that: The adaptive grid optimization strategy design in S12 includes: S121, grid density adjustment: Based on the collected environmental data, the density of the grid is dynamically adjusted by calculating the pollution diffusion potential of each area in the basin; S122, grid layout optimization: based on the determined grid density, the spatial layout of the grid is optimized through flow direction data.

4. A method for tracing the source of river sewage outlets based on grid-based water quality monitoring according to claim 3, characterized in that: The implementation and adjustment of the adaptive grid in S13 includes: S131, implement grid optimization: based on the design results of the adaptive grid optimization strategy, deploy monitoring grids in the river basin according to the optimized grid density and layout requirements; S132, real-time adjustment of grid layout: real-time calculation is performed based on the collected watershed environmental data to dynamically adjust the density and distribution of the grid.

5. A method for tracing the source of river sewage outlets based on grid-based water quality monitoring according to claim 1, characterized in that: The water quality parameter monitoring and collection in S2 includes: S21, water quality sensor layout: based on the results of adaptive grid optimization, water quality sensors are laid out in the optimized grid layout; S22, real-time monitoring of water quality parameters: using the deployed water quality sensors to collect real-time water quality parameters in the water body, including pollutant concentration, pH value, dissolved oxygen and turbidity.

6. A method for tracing the source of river sewage outlets based on grid-based water quality monitoring according to claim 1, characterized in that: The pollutant diffusion model establishment and source tracing analysis in S3 include: S31, data preprocessing: preprocessing the monitored water quality parameters and environmental data of the river basin, including denoising, interpolation and normalization; S32, establishment of pollutant diffusion model: based on the pre-treated water quality parameters and environmental data, a pollutant diffusion model is established to simulate the propagation path, diffusion speed and concentration changes of pollutants in the river; S33, Model simulation and emission intensity prediction: Based on the simulation results of the pollutant diffusion model, predict the potential location of pollution sources and pollutant emission intensity.

7. A method for tracing the source of river sewage outlets based on grid-based water quality monitoring according to claim 6, characterized in that: The data preprocessing in S31 includes: S311, data denoising: Kalman filtering algorithm is used to denoise the monitored water quality parameters and environmental data of the river basin; S312, data interpolation: Use Kriging interpolation algorithm to interpolate missing or incomplete data in water quality parameters and environmental data; S313, data normalization: use the minimum-maximum normalization method to normalize water quality parameters and environmental data.

8. A method for tracing the source of river sewage outlets based on grid-based water quality monitoring according to claim 7, characterized in that: The pollutant diffusion model in S32 adopts the convection diffusion equation, which includes: S321, Establishment of pollutant diffusion equation: Based on river environment data and water quality parameters, establish the convection diffusion equation that describes the change of pollutant concentration to simulate the diffusion process and convection migration of pollutants; S322, acquisition of flow field characteristic parameters: based on actual river environment data and water quality parameters, key parameters in the convection-diffusion equation are acquired, including water flow velocity and diffusion coefficient; S323, numerical solution and boundary condition setting: use finite difference method to perform numerical solution and set boundary conditions; S324, Simulation of pollutant concentration distribution and time evolution: Analyze the concentration distribution and propagation characteristics of pollutants in the river using numerical solution results.

9. A method for tracing the source of river sewage outlets based on grid-based water quality monitoring according to claim 8, characterized in that: The model simulation and emission intensity prediction in S33 include: S331, reverse deduction of the potential location of pollution sources: deduction of the location of pollution sources based on the spatial distribution of pollutant concentrations and the inverse problem solving method; S332, predict pollutant emission intensity: based on the location of the pollution source obtained by inversion and combined with the simulation results, calculate the emission intensity of the pollution source.

10. A method for tracing the source of river sewage outlets based on grid-based water quality monitoring according to claim 9, characterized in that: The pollution source tracing and location in S4 include: S41, Pollution source location based on potential location: Determine the location of the pollution source based on the deduced pollution source location ; S42, calculating the emission amount of the pollution source: based on the calculated emission intensity of the pollution source, the emission amount of the pollution source is taken as the emission amount of the pollution source; S43, determine the diffusion pattern of the sewage outlet: calculate the diffusion radius according to the pollutant concentration gradient and diffusion coefficient through simulation results and actual environmental data and water quality parameters.

Images