A comprehensive analysis system based on water environment monitoring information

Abstract

This invention discloses a comprehensive analysis system based on water environment monitoring information, belonging to the field of water environment monitoring technology. It establishes a pollutant diffusion model based on a standard data set C, calculates the spatial distribution and temporal changes of pollutants, and generates a predicted concentration feature vector P, providing crucial predictive information for pollutant diffusion simulation. It outputs a pollutant diffusion feature vector D, providing data support for further risk assessment. By comparing the system with a preset water quality risk threshold R, and iteratively matching the diffusion feature vector D, it generates pollution control recommendations for different pollution levels, ensuring appropriate and precise control measures are taken. Periodic optimization is performed based on feedback results to continuously improve the effectiveness and accuracy of pollution control. This enhances the efficiency of pollution source identification and control, strengthens the scientific rigor and relevance of pollution control solutions, and ensures dynamic monitoring and continuous improvement of the water environment.

Description

Technical Field

[0001] This invention relates to the field of water environment monitoring technology, specifically to a comprehensive analysis system based on water environment monitoring information. Background Technology

[0002] Water environment monitoring and management, as a crucial component of environmental protection, broadly encompasses multiple aspects such as water quality, watersheds, and water resources, involving the monitoring and assessment of water body health. With the increasing severity of global environmental problems, water environment monitoring has become a key means of addressing water pollution and ensuring water security. Among the numerous technical methods for water environment management, water quality monitoring occupies a central position, and the accuracy and timeliness of water quality monitoring directly affect the efficiency and quality of environmental governance and decision-making.

[0003] Currently, water quality monitoring systems largely rely on static data acquisition and analysis, typically using water quality sensors or monitoring stations to periodically collect water quality parameters and generate point-in-time environmental data. These systems have limitations because water quality changes are often influenced by multiple factors related to time, space, and water flow dynamics, which traditional methods struggle to accurately capture. Particularly in flowing water bodies, such as rivers and streams, the propagation and diffusion patterns of pollutants exhibit strong spatiotemporal variability, making it impossible to comprehensively predict and assess pollution risks using a single data point or simple statistical analysis. For example, in the event of a sudden pollution source leak, existing systems cannot accurately simulate the diffusion paths of pollutants under different water flow velocities and water body structures in real time, leading to ineffective pollutant control and often missing the optimal treatment window. Furthermore, existing systems generally lack the ability to dynamically model complex water flow patterns, making it difficult to provide governance departments with a scientific basis for decision-making. Summary of the Invention

[0004] To address the shortcomings of existing technologies, this invention provides a comprehensive analysis system based on water environment monitoring information, which solves the problems mentioned in the background art.

[0005] To achieve the above objectives, the present invention is implemented through the following technical solution: a comprehensive analysis system based on water environment monitoring information, comprising a water environment data acquisition module, a water environment data processing module, a spatiotemporal dynamic modeling module, a pollutant diffusion simulation module, a risk assessment and decision support module, and an iterative feedback module;

[0006] The water environment data acquisition module collects water quality parameter data and topographic data from multiple monitoring points within the river section, forming a water environment dataset W.

[0007] The water environment data processing module preprocesses the water environment dataset W to obtain the preprocessed water environment standard data set C.

[0008] The spatiotemporal dynamic modeling module establishes a pollutant diffusion model for the water environment standard data set C, calculates the spatial distribution and temporal changes of pollutants, and obtains the predicted concentration feature vector P.

[0009] The pollutant diffusion simulation module uses a fluid dynamics model to calculate the distribution of pollutants in different times and spaces based on the obtained predicted concentration feature vector P, and outputs the pollutant diffusion feature vector D.

[0010] The risk assessment and decision support module generates pollution control recommendations by matching the preset water quality risk threshold R with the diffusion feature vector D.

[0011] The iterative feedback module collects feedback results on the pollution control proposals at fixed intervals based on the content of the proposed solutions, and iteratively optimizes the generation of pollution control proposals based on the feedback results.

[0012] Preferably, the water environment data acquisition module includes a water quality data acquisition unit and an environmental topography acquisition unit;

[0013] The water quality data acquisition unit collects water quality parameter data in real time by deploying multiple monitoring points within a fixed river section and integrating multiple water quality sensors and water flow rate sensors at each monitoring point.

[0014] Water quality sensors include pH sensors, dissolved oxygen sensors, turbidity sensors, ammonia nitrogen sensors, total phosphorus sensors, water temperature sensors, and conductivity sensors;

[0015] Water quality parameter data include water pH, dissolved oxygen (DO), ammonia nitrogen (NH3-N), total phosphorus (TP), turbidity (TU), flow velocity (V), water temperature (T), and conductivity (EC). The location x and time t of the water quality parameter data are marked, and the water pH (x, t), dissolved oxygen (DO), ammonia nitrogen (NH3-N), total phosphorus (TP), turbidity (TU), flow velocity (V), water temperature (T), and conductivity (EC) are obtained at location x and time t.

[0016] Preferably, the environmental topography acquisition unit is used to collect topographic data of the river section in the monitoring point area and integrate it with water quality parameter data to form a water environment dataset W;

[0017] Topographic data were obtained by using satellite remote sensing and ground laser scanning of the river section in the monitoring point area to acquire the water flow path K, the rate of change of bottom water flow slope S, and the water depth H of the river section in the monitoring point area.

[0018] Preferably, the water environment data processing module includes a preprocessing unit;

[0019] The preprocessing unit preprocesses the water environment dataset W, including noise reduction preprocessing, missing value imputation preprocessing, and data standardization preprocessing, to obtain the preprocessed water environment standard dataset C.

[0020] Denoising preprocessing is performed on the water environment dataset W using a low-pass filter.

[0021] Missing value imputation preprocessing is performed by using linear interpolation to impute missing values ​​in the denoised water environment dataset W.

[0022] Data standardization preprocessing was performed on the water environment dataset W after missing value imputation using the Z-score standardization method.

[0023] Preferably, the spatiotemporal dynamic modeling module includes a pollutant diffusion modeling unit and a time-varying prediction unit;

[0024] The pollutant diffusion modeling unit establishes a pollutant diffusion model based on the water environment standard data set C. The model simulates the dynamic three-dimensional model of the river section and solves the pollutant diffusion equation using numerical methods. It simulates the spatial distribution and time-varying process of the concentration Cp of the p-th pollutant in the water environment standard data set C.

[0025] Among them, the pollutant concentration Cp includes water pH (x,t), dissolved oxygen concentration DO (x,t), ammonia nitrogen concentration NH3-N (x,t), total phosphorus concentration TP (x,t), water turbidity TU (x,t) and electrical conductivity EC (x,t);

[0026] The pollutant diffusion equation is specifically structured as a partial differential equation:

[0027]

[0028] In the formula, This represents the time-varying term, specifically the rate at which the pollutant concentration Cp(x,t) at location x at time t changes with time. Cp(x,t) represents the pollutant concentration at location x at time t. Let represent the convection term, specifically the pollutant concentration Cp(x, t) at position x at time t carried to adjacent positions by the water flow; v(x) represents the velocity vector of the water flow, specifically the direction and speed of the water flow at position x. This represents the gradient of pollutant concentration Cp(x,t) at location x and time t, specifically indicating the rate of change of pollutant concentration Cp(x,t) at location x and time t with respect to spatial location. Let represent the diffusion term, specifically the natural diffusion process of pollutant concentration Cp(x,t) at location x and time t in the water body. D represents the diffusion coefficient, specifically the diffusion rate of the pollutant in the water body. Let Rp be the Laplace operator representing the pollutant concentration Cp(x,t) at location x and time t, where Rp represents the source intensity of the p-th pollutant concentration, which is a preset constant whose specific value is set by the user. The symbol represents the partial derivative.

[0029] Preferably, the time change prediction unit calculates the time change of pollutant concentration Cp based on the pollutant diffusion model, predicts the concentration change of the p-th pollutant Cp at different time points in the future, and obtains the predicted concentration feature vector P by integrating the pollutant concentrations Cp(x, t+Δt) of different pollutants in the pollutant concentration Cp at time t+Δt.

[0030] Where △t represents the time step, specifically the time interval from time t to time t+△t;

[0031] The pollutant concentration Cp(x, t+Δt) at time t+Δt is predicted using the following finite difference formula:

[0032]

[0033] Preferably, the pollutant diffusion simulation module includes a diffusion initialization unit and a diffusion process calculation unit;

[0034] The diffusion initialization unit is based on fluid dynamic parameters, including the water flow path K, the rate of change of the bottom water flow slope S, the water depth H, the water flow velocity V(x,t) and the water temperature T(x,t), to obtain the diffusion coefficient K(x,t), which reflects the diffusion capacity of different pollutants in the water body;

[0035] The diffusion coefficient K(x,t) is obtained by the following formula:

[0036]

[0037] In the formula, K0 represents the basic diffusion coefficient.

[0038] Preferably, the diffusion process calculation unit combines the obtained diffusion coefficient K(x,t) with the predicted concentration feature vector P, and the pollutant diffusion feature vector D reflects the speed and range of pollutant propagation, as well as the peak value and trend of change.

[0039] The diffusion feature vector D is obtained through the following calculation formula:

[0040]

[0041] In the formula, n represents the length of the predicted concentration feature vector P, and i represents the i-th subscript in the predicted concentration feature vector P.

[0042] Preferably, the risk assessment and decision support module includes a pollution recommendation generation unit;

[0043] The pollution suggestion generation unit matches the feature vector corresponding to each subscript in the diffusion feature vector D with the preset water quality risk threshold R to obtain the matching result of the d-th subscript, and generates a pollution control suggestion scheme based on the matching result.

[0044] When the feature vector corresponding to the d-th subscript in the diffusion feature vector D is ≥ twice the water quality risk threshold R, the matching result is obtained as the early warning result, an early warning pollution control suggestion plan is generated, the feature vector corresponding to the d-th subscript is extracted, the preset early warning notification template is filled in, and it is sent to the relevant environmental protection departments for attention. Simultaneously, the preset pollution detection template is filled in, and it is sent to the top of the relevant detection department's processing task list for waiting to be processed.

[0045] When the feature vector corresponding to the d-th subscript in the diffusion feature vector D is greater than or equal to the water quality risk threshold R, the matching result is unqualified, a pollution control suggestion is generated, the feature vector corresponding to the d-th subscript is extracted, the preset pollution detection template is filled, and the sample is sent to the relevant detection department's processing task list for processing.

[0046] When the feature vector corresponding to the d-th subscript in the diffusion feature vector D is less than the water quality risk threshold R, the matching result is considered qualified, and no pollution control suggestion is generated.

[0047] Preferably, the iterative feedback module includes a feedback verification module;

[0048] The feedback verification module collects feedback results of the pollution control proposals at fixed intervals based on the content of the pollution control proposals. This includes the verification results of the feature vector corresponding to the d-th subscript. When there is an anomaly in the feature vector corresponding to the d-th subscript, the field feedback result index Fs is marked as 1. When there is no anomaly in the feature vector corresponding to the d-th subscript, the field feedback result index Fs is marked as 0. The module then triggers iterative optimization of the generation of the pollution control proposal based on the state of the field feedback result index Fs.

[0049] The generation of pollution control recommendations is triggered through the following state comparison method:

[0050] When the on-site feedback result index Fs = 1, obtain a pollution control suggestion that does not trigger iterative optimization.

[0051] When the on-site feedback result index Fs = 0, the proposed pollution control scheme is obtained to trigger iterative optimization, including iterative optimization of the pollutant diffusion model and diffusion coefficient K(x,t).

[0052] This invention provides a comprehensive analysis system based on water environment monitoring information, which has the following beneficial effects:

[0053] (1) A pollutant diffusion model was established based on standard data set C to calculate the spatial distribution and temporal changes of pollutants, thereby generating a predicted concentration feature vector P, which provides key predictive information for pollutant diffusion simulation. The pollutant diffusion feature vector D is output, providing data support for further risk assessment. By comparing with the preset water quality risk threshold R, the diffusion feature vector D is matched to generate pollution control suggestions for different pollution levels, ensuring that appropriate measures are taken for precise control. Periodic optimization is performed based on feedback results to continuously improve the effectiveness and accuracy of pollution control. This improves the efficiency of pollution source identification and control, strengthens the scientific nature and pertinence of pollution control plans, and ensures dynamic monitoring and continuous improvement of the water environment.

[0054] (2) By calculating and predicting the concentration changes of pollutant p at different future time points, a predicted concentration feature vector P is generated. This vector not only contains the predicted concentration data of different pollutants at future time, but also provides detailed spatial and temporal information on pollutant concentration distribution for subsequent analysis. Using hydrodynamic parameters, the diffusion coefficient K(x,t) is calculated, reflecting the diffusion capacity of different pollutants in the water body. Based on this diffusion coefficient K(x,t) and the predicted concentration feature vector P, a pollutant diffusion feature vector D is generated, further revealing the propagation speed, range, peak value, and trend of pollutants in the water body. Through this series of predictions and simulations, the system can track the diffusion dynamics of pollutants in real time, accurately predict future concentration changes of pollutants, and provide important basis for water quality management and decision-making.

[0055] (3) By matching the water quality risk threshold R with the concentration of each pollutant in the diffusion feature vector D, a pollution control proposal is automatically generated and sent to the testing department for further processing. When the pollutant concentration is below the risk threshold, no control proposal is generated, thus optimizing resource utilization. By periodically collecting feedback on the pollution control proposal, the changes in pollutant concentration are verified in real time, further improving the prediction and control effectiveness of pollutant diffusion. Through this mechanism, the system can not only adjust the control plan based on real-time data, but also continuously optimize the prediction accuracy based on feedback results, ensuring that the pollution control plan is always in the optimal state. Attached Figure Description

[0056] Figure 1 This is a schematic diagram of a comprehensive analysis system based on water environment monitoring information according to the present invention.

[0057] Figure 2This is a schematic diagram of the data processing flow and notification flow of the present invention;

[0058] Figure 3 This is a schematic diagram illustrating the predicted pollutant concentration for the predicted concentration feature vector P.

[0059] Figure 4 This is a schematic diagram of the data transmission process of a comprehensive analysis system based on water environment monitoring information. Detailed Implementation

[0060] The technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative effort are within the scope of protection of the present invention.

[0061] Example 1

[0062] This invention provides a comprehensive analysis system based on water environment monitoring information. Please refer to [link / reference]. Figure 1 It includes a water environment data acquisition module, a water environment data processing module, a spatiotemporal dynamic modeling module, a pollutant diffusion simulation module, a risk assessment and decision support module, and an iterative feedback module;

[0063] The water environment data acquisition module collects water quality parameter data and topographic data from multiple monitoring points within the river section, forming a water environment dataset W.

[0064] The water environment data processing module preprocesses the water environment dataset W to obtain the preprocessed water environment standard data set C.

[0065] The spatiotemporal dynamic modeling module establishes a pollutant diffusion model for the water environment standard data set C, calculates the spatial distribution and temporal changes of pollutants, and obtains the predicted concentration feature vector P.

[0066] The pollutant diffusion simulation module uses a fluid dynamics model to calculate the distribution of pollutants in different times and spaces based on the obtained predicted concentration feature vector P, and outputs the pollutant diffusion feature vector D.

[0067] The risk assessment and decision support module generates pollution control recommendations by traversing and matching the preset water quality risk threshold R with the diffusion feature vector D.

[0068] The iterative feedback module collects feedback results on the pollution control proposals at fixed intervals based on the content of the proposed solutions, and iteratively optimizes the generation of pollution control proposals based on the feedback results.

[0069] In this embodiment, water quality parameter data and topographic data from multiple monitoring points within the river section are collected to form a water environment dataset W, providing detailed foundational data for subsequent analysis. The water environment data processing module preprocesses the data to ensure the accuracy of the obtained water environment standard data set C and its compliance with analytical requirements. The spatiotemporal dynamic modeling module establishes a pollutant diffusion model based on the standard data set C, calculating the spatial distribution and temporal changes of pollutants, thereby generating a predicted concentration feature vector P, providing crucial predictive information for pollutant diffusion simulation. The pollutant diffusion simulation module uses a fluid dynamics model to calculate the distribution of pollutants in different times and spaces, outputting a pollutant diffusion feature vector D. This result clearly demonstrates the dynamic process of pollutant diffusion and provides data support for further risk assessment. The risk assessment and decision support module compares the pollutant diffusion with a preset water quality risk threshold R, iterates through and matches the diffusion feature vector D, and generates pollution control recommendations for different pollution levels, ensuring appropriate measures are taken for precise control. Finally, the iterative feedback module performs periodic optimization based on the feedback results to continuously improve the effectiveness and accuracy of pollution control. Through the effective coordination of this system, not only has the efficiency of pollution source identification and treatment been greatly improved, but the scientific nature and pertinence of pollution control solutions have also been enhanced, ensuring dynamic monitoring and continuous improvement of the water environment.

[0070] Example 2

[0071] This embodiment is an explanation based on Embodiment 1. Please refer to it. Figure 1 Specifically: the water environment data acquisition module includes a water quality data acquisition unit and an environmental topography acquisition unit;

[0072] The water quality data acquisition unit collects water quality parameter data in real time by deploying multiple monitoring points within a fixed river section and integrating multiple water quality sensors and water flow rate sensors at each monitoring point.

[0073] Water quality sensors include pH sensors, dissolved oxygen sensors, turbidity sensors, ammonia nitrogen sensors, total phosphorus sensors, water temperature sensors, and conductivity sensors;

[0074] Water quality parameter data include water pH, dissolved oxygen (DO), ammonia nitrogen (NH3-N), total phosphorus (TP), turbidity (TU), flow velocity (V), water temperature (T), and conductivity (EC). The location x and time t of the water quality parameter data are marked, and the water pH (x, t), dissolved oxygen (DO), ammonia nitrogen (NH3-N), total phosphorus (TP), turbidity (TU), flow velocity (V), water temperature (T), and conductivity (EC) are obtained at location x and time t.

[0075] The environmental topography acquisition unit is used to collect topographic data of the river section in the monitoring point area and integrate it with water quality parameter data to form a water environment dataset W;

[0076] Topographic data were obtained by using satellite remote sensing and ground laser scanning of the river section in the monitoring point area to acquire the water flow path K, the rate of change of bottom water flow slope S, and the water depth H of the river section in the monitoring point area.

[0077] The water environment data processing module includes a preprocessing unit;

[0078] The preprocessing unit preprocesses the water environment dataset W, including denoising preprocessing, missing value imputation preprocessing, and data standardization preprocessing, to obtain the preprocessed water environment standard dataset C.

[0079] Denoising preprocessing is performed on the water environment dataset W using a low-pass filter.

[0080] Missing value imputation preprocessing is performed by using linear interpolation to impute missing values ​​in the denoised water environment dataset W.

[0081] Data standardization preprocessing was performed on the water environment dataset W after missing value imputation using the Z-score standardization method.

[0082] In this embodiment, by deploying multiple monitoring points and combining water quality sensors and water flow rate sensors, water quality parameter data can be collected in real time and accurately. The corresponding spatial location x and time point t can be precisely marked, ensuring the spatiotemporal accuracy and completeness of the monitoring data. Simultaneously, the environmental topography acquisition unit obtains the river flow path K, the rate of change of bottom slope S, and the water depth H through satellite remote sensing and ground laser scanning, providing high-precision topographic information for subsequent water quality assessment and pollutant diffusion models. The water environment data processing module further transforms the raw data into standardized water environment data sets C through denoising, missing value imputation, and data standardization, ensuring data quality and consistency. Denoising preprocessing effectively removes noise from the water quality data using a low-pass filter, missing value imputation preprocessing fills in missing data using linear interpolation, and data standardization preprocessing ensures data consistency, making it suitable for subsequent modeling and analysis.

[0083] Example 3

[0084] This embodiment is an explanation based on Embodiment 2. Please refer to it. Figure 1 Specifically: the spatiotemporal dynamic modeling module includes a pollutant diffusion modeling unit and a time-varying prediction unit;

[0085] The pollutant diffusion modeling unit establishes a pollutant diffusion model based on the water environment standard data set C. The model simulates the dynamic three-dimensional model of the river section and solves the pollutant diffusion equation using numerical methods. It simulates the spatial distribution and time-varying process of the concentration Cp of the p-th pollutant in the water environment standard data set C.

[0086] Among them, the pollutant concentration Cp includes water pH (x,t), dissolved oxygen concentration DO (x,t), ammonia nitrogen concentration NH3-N (x,t), total phosphorus concentration TP (x,t), water turbidity TU (x,t) and electrical conductivity EC (x,t);

[0087] The pollutant diffusion equation is specifically structured as a partial differential equation:

[0088]

[0089] In the formula, This represents the time-varying term, specifically the rate at which the pollutant concentration Cp(x,t) at location x at time t changes with time. Cp(x,t) represents the pollutant concentration at location x at time t. Let represent the convection term, specifically the pollutant concentration Cp(x, t) at position x at time t carried to adjacent positions by the water flow; v(x) represents the velocity vector of the water flow, specifically the direction and speed of the water flow at position x. This represents the gradient of pollutant concentration Cp(x,t) at location x and time t, specifically indicating the rate of change of pollutant concentration Cp(x,t) at location x and time t with respect to spatial location. Let represent the diffusion term, specifically the natural diffusion process of pollutant concentration Cp(x,t) at location x and time t in the water body. Let D represent the diffusion coefficient, specifically the diffusion rate of the pollutant in the water body. Let Rp be the Laplace operator representing the pollutant concentration Cp(x,t) at location x and time t, where Rp represents the source intensity of the p-th pollutant concentration, which is a preset constant whose specific value is set by the user. Indicates the sign of partial derivatives;

[0090] In this partial differential equation, the two terms on the left and the terms on the right represent different physical processes, describing the change of pollutant concentration Cp(x,t) with time and space.

[0091] The two items on the left together describe how pollutant concentrations evolve in space and time, while the two items on the right represent diffusion processes and source terms, which may be pollution sources or reactions, that is, describing how pollutants diffuse or affect concentration changes through external sources.

[0092] Meanwhile, the two sides of the "=" in the structure of the partial differential equation are in equilibrium, indicating that the rate of change of pollutant concentration is determined by diffusion, source term and convection. They are interdependent and mutually influential, and are also used to describe the equilibrium relationship of pollutant concentration changes.

[0093] The time-varying prediction unit calculates the time-varying changes of pollutant concentration Cp based on the pollutant diffusion model, predicts the concentration changes of the p-th pollutant Cp at different time points in the future, and obtains the predicted concentration feature vector P by integrating the pollutant concentrations Cp(x, t+Δt) of different pollutants at time t+Δt.

[0094] Where △t represents the time step, specifically the time interval from time t to time t+△t;

[0095] The pollutant concentration Cp(x, t+Δt) at time t+Δt is predicted using the following finite difference formula:

[0096]

[0097] The pollutant diffusion simulation module includes a diffusion initialization unit and a diffusion process calculation unit;

[0098] The diffusion initialization unit is based on fluid dynamic parameters, including the water flow path K, the rate of change of the bottom water flow slope S, the water depth H, the water flow velocity V(x,t) and the water temperature T(x,t), to obtain the diffusion coefficient K(x,t), which reflects the diffusion capacity of different pollutants in the water body;

[0099] The diffusion coefficient K(x,t) is obtained by the following formula:

[0100]

[0101] In the formula, K0 represents the basic diffusion coefficient, which specifically represents the diffusion capacity of pollutants under standard conditions;

[0102] in, The ratio reflects the intensity of pollutant propagation by water flow. The stronger the water flow, the stronger the ability of pollutants to diffuse. When the water body is shallow, the vertical diffusion of pollutants is stronger.

[0103] The ratio reflects the influence of the rate of change of the bottom bed slope S on the turbulence intensity of the water flow. The greater the slope, the stronger the turbulence and the faster the pollutants diffuse. The water temperature T(x,t) affects the viscosity of the water. When the water temperature is high, the water viscosity decreases and molecular diffusion is enhanced. Therefore, the ratio reflects the combined influence of slope and water temperature on diffusion.

[0104] This reflects the impact of the complexity of the water flow path on pollutant diffusion. Complex paths (such as multiple bends or variability in the flow path) generally cause pollutants to diffuse faster, thus increasing the diffusion coefficient. Conversely, if the water flow path is simple and the flow path value K is small, the diffusion coefficient will be lower.

[0105] The diffusion process calculation unit combines the obtained diffusion coefficient K(x,t) with the predicted concentration feature vector P, and the pollutant diffusion feature vector D reflects the speed and range of pollutant propagation, as well as the peak value and trend of change.

[0106] The diffusion feature vector D is obtained through the following calculation formula:

[0107]

[0108] In the formula, n represents the length of the predicted concentration feature vector P, and i represents the i-th subscript in the predicted concentration feature vector P, specifically corresponding to the pollutant concentration Cp(x, t+Δt) at the time t+Δt of the i-th subscript.

[0109] In this embodiment, the concentration changes of the p-th pollutant at different future time points are calculated and predicted, thereby generating a predicted concentration feature vector P. This vector not only contains the predicted concentration data of different pollutants at future times but also provides detailed spatial and temporal information on pollutant concentration distribution for subsequent analysis. The diffusion initialization unit uses hydrodynamic parameters to calculate the diffusion coefficient K(x,t), which reflects the diffusion capacity of different pollutants in the water body. The diffusion process calculation unit then combines the diffusion coefficient K(x,t) with the predicted concentration feature vector P to generate a pollutant diffusion feature vector D, further revealing the propagation speed, range, peak value, and trend of pollutants in the water body. Through this series of predictions and simulations, the system can track the diffusion dynamics of pollutants in real time and accurately predict future concentration changes, providing important basis for water quality management and decision-making.

[0110] Example 4

[0111] This embodiment is an explanation based on Embodiment 3. Please refer to it. Figure 1 and Figure 2 Specifically: the risk assessment and decision support module includes a pollution suggestion generation unit;

[0112] The pollution suggestion generation unit matches the feature vector corresponding to each subscript in the diffusion feature vector D with the preset water quality risk threshold R to obtain the matching result of the d-th subscript, and generates a pollution control suggestion scheme based on the matching result. Specific examples are shown in Table 1.

[0113] When the feature vector corresponding to the d-th subscript in the diffusion feature vector D is ≥ twice the water quality risk threshold R, the matching result is obtained as the early warning result, an early warning pollution control suggestion plan is generated, the feature vector corresponding to the d-th subscript is extracted, the preset early warning notification template is filled in, and it is sent to the relevant environmental protection departments for attention. Simultaneously, the preset pollution detection template is filled in, and it is sent to the top of the relevant detection department's processing task list for waiting to be processed.

[0114] When the feature vector corresponding to the d-th subscript in the diffusion feature vector D is greater than or equal to the water quality risk threshold R, the matching result is unqualified, a pollution control suggestion is generated, the feature vector corresponding to the d-th subscript is extracted, the preset pollution detection template is filled, and the sample is sent to the relevant detection department's processing task list for processing.

[0115] When the feature vector corresponding to the d-th subscript in the diffusion feature vector D is less than the water quality risk threshold R, the matching result is qualified and no pollution control suggestion is generated.

[0116] Table 1:

[0117]

[0118]

[0119] The iterative feedback module includes a feedback verification module;

[0120] The feedback verification module collects feedback results of the pollution control proposals at fixed intervals based on the content of the pollution control proposals. This includes the verification results of the feature vector corresponding to the d-th subscript. When there is an anomaly in the feature vector corresponding to the d-th subscript, the field feedback result index Fs is marked as 1. When there is no anomaly in the feature vector corresponding to the d-th subscript, the field feedback result index Fs is marked as 0. The module then triggers iterative optimization of the generation of the pollution control proposal based on the state of the field feedback result index Fs.

[0121] The generation of pollution control recommendations is triggered through the following state comparison method:

[0122] When the on-site feedback result index Fs = 1, obtain a pollution control suggestion that does not trigger iterative optimization.

[0123] When the on-site feedback result index Fs = 0, the proposed pollution control scheme is obtained to trigger iterative optimization, including iterative optimization of the pollutant diffusion model and diffusion coefficient K(x,t).

[0124] In this embodiment, pollution control recommendations are automatically generated based on the matching results of the water quality risk threshold R and the concentration of each pollutant in the diffusion feature vector D. When the pollutant concentration exceeds twice the water quality risk threshold, the system generates an early warning pollution control recommendation and immediately notifies relevant environmental protection departments and testing departments to ensure rapid response. When the pollutant concentration exceeds the risk threshold but does not reach the early warning level, the system automatically generates a pollution control recommendation and sends it to the testing department for further processing. When the pollutant concentration is below the risk threshold, no control recommendation is generated, thereby optimizing resource utilization. The iterative feedback module periodically collects feedback on the pollution control recommendations, verifies the changes in pollutant concentration in real time, and uses the on-site feedback result index Fs to iteratively optimize the pollution control plan. If the feedback shows that the pollutant concentration is abnormal, the system will stop optimization; conversely, if the feedback shows that the concentration is normal, the system will start iterative optimization of the diffusion model and diffusion coefficient K(x,t) to further improve the pollutant diffusion prediction and control effect. Through this mechanism, the system can not only adjust the control plan according to real-time data, but also continuously optimize the prediction accuracy according to the feedback results, ensuring that the pollution control plan is always in the optimal state.

[0125] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and variations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A comprehensive analysis system based on water environment monitoring information, characterized by: The system comprises a water environment data collection module, a water environment data processing module, a space-time dynamic modeling module, a pollutant diffusion simulation module, a risk assessment and decision support module, and an iterative feedback module. The water environment data collection module collects water quality parameter data and terrain data of multiple monitoring points in the river section to form a water environment data set W. The water environment data processing module pre-processes the water environment data set W to obtain a pre-processed water environment standard data set C. The space-time dynamic modeling module establishes a pollutant diffusion model based on the water environment standard data set C, calculates the spatial distribution and time variation of the pollutant, and obtains a predicted concentration feature vector P. The space-time dynamic modeling module comprises a pollutant diffusion modeling unit and a time variation prediction unit. The pollutant diffusion modeling unit establishes a pollutant diffusion model based on the water environment standard data set C, simulates a dynamic three-dimensional model of the river section through the pollutant diffusion model, and solves the pollutant diffusion equation through a numerical solution method to simulate the spatial distribution and time variation of the concentration Cp of the pth pollutant in the pollutant diffusion model. The pollutant concentration Cp includes water pH (x, t), dissolved oxygen concentration DO (x, t), ammonia nitrogen concentration NH3-N (x, t), total phosphorus concentration TP (x, t), water turbidity TU (x, t), and conductivity EC (x, t). The pollutant diffusion equation is a partial differential equation. ； In the formula, represents a time variation term, and specifically represents a speed of variation of the pollutant concentration Cp(x, t) at position x and time t with time, Cp(x, t) representing the pollutant concentration at position x and time t, represents a convection term, and specifically represents a movement of the pollutant concentration Cp(x, t) at position x and time t to an adjacent position with the movement of the water flow, v(x) representing a speed vector of the water flow, specifically representing a flow direction and speed of the water flow at position x, and ▽Cp(x, t) representing a gradient of the pollutant concentration Cp(x, t) at position x and time t, specifically representing a speed of variation of the pollutant concentration Cp(x, t) at position x and time t with a spatial position, represents a diffusion term, and specifically represents a natural diffusion process of the pollutant concentration Cp(x, t) at position x and time t in the water body, D0 representing a diffusion coefficient, specifically representing a diffusion speed of the pollutant in the water body, represents a Laplace operator of the pollutant concentration Cp(x, t) at position x and time t, Rp representing a pollution source intensity of the pth pollutant concentration, specifically a preset constant, and a specific value being set by a user, and ∂ representing a partial derivative symbol. The time variation prediction unit calculates the time variation of the pollutant concentration Cp based on the pollutant diffusion model, predicts the concentration variation of the pth pollutant concentration Cp at different time points in the future, and obtains a predicted concentration feature vector P by integrating the pollutant concentration Cp of different pollutants at time t+△t. △t represents the time step, specifically the time interval from time t to time t+△t. The pollutant concentration Cp(x, t+△t) at time t+△t is predicted by the following finite difference formula: ； The pollutant diffusion simulation module uses a fluid mechanics model to calculate the distribution of the pollutant in different times and spaces based on the obtained predicted concentration feature vector P, and outputs a diffusion feature vector D of the pollutant. The pollutant diffusion simulation module comprises a diffusion initialization unit and a diffusion process calculation unit. The diffusion initialization unit obtains a diffusion coefficient K(x, t) based on fluid mechanics parameters, including water flow path K, bottom water flow slope change rate S, water depth H, water flow velocity V(x, t), and water temperature T(x, t). The diffusion process calculation unit combines the obtained diffusion coefficient K(x, t) with the predicted concentration feature vector P to obtain a diffusion feature vector D of the pollutant, which reflects the speed and range of the spread of the pollutant as well as the peak value and trend. The diffusion feature vector D is obtained by the following calculation formula: ； where n represents the length of the predicted concentration feature vector P, and i represents the ith subscript in the predicted concentration feature vector P. The risk assessment and decision support module generates a pollution control recommendation scheme by traversing and matching the diffusion feature vector D with a pre-set water quality risk threshold R. The iterative feedback module collects feedback results of the pollution treatment recommendation scheme after a fixed period according to the content of the pollution treatment recommendation scheme, and iteratively optimizes the generation of the pollution treatment recommendation scheme according to the feedback results.

2. The comprehensive analysis system based on water environment monitoring information according to claim 1, characterized in that: The water environment data collection module includes a water quality data collection unit and an environmental terrain collection unit; The water quality data collection unit collects water quality parameter data in real time by deploying multiple monitoring points in a fixed river section, and through multiple water quality sensors and water flow rate sensors integrated at each monitoring point; The water quality sensors include a pH sensor, a dissolved oxygen sensor, a turbidity sensor, an ammonia nitrogen sensor, a total phosphorus sensor, a water temperature sensor, and a conductivity sensor; The water quality parameter data includes water pH, dissolved oxygen concentration, ammonia nitrogen concentration, total phosphorus concentration, water turbidity, water flow velocity, water temperature, and conductivity, and the position x and time t of the water quality parameter data are marked to obtain water pH (x, t), dissolved oxygen concentration (x, t), ammonia nitrogen concentration (x, t), total phosphorus concentration (x, t), water turbidity (x, t), water flow velocity (x, t), water temperature (x, t), and conductivity (x, t) at position x and time t.

3. The comprehensive analysis system based on water environment monitoring information according to claim 2, characterized in that: The environmental terrain collection unit is used to collect terrain data of the river section in the monitoring point area and integrate it with the water quality parameter data to form a water environment data set W; The terrain data is obtained by satellite remote sensing and ground laser scanning of the river section in the monitoring point area to obtain the water flow path K, the bottom flow slope change rate S, and the water depth H of the river section in the monitoring point area.

4. The comprehensive analysis system based on water environment monitoring information according to claim 3, characterized in that: The water environment data processing module includes a preprocessing unit; The preprocessing unit preprocesses the water environment data set W, including denoising preprocessing, missing value filling preprocessing, and data standardization preprocessing, to obtain a preprocessed water environment standard data set C; The denoising preprocessing is performed on the water environment data set W using a low-pass filter; The missing value filling preprocessing is performed on the denoised water environment data set W using a linear interpolation method; The data standardization preprocessing is performed on the missing value filled water environment data set W using a Z-score standardization method.

5. The comprehensive analysis system based on water environment monitoring information according to claim 4, characterized in that: The risk assessment and decision support module includes a pollution suggestion generation unit; The pollution suggestion generation unit matches each subscript corresponding feature vector in the diffusion feature vector D with a pre-set water quality risk threshold R to obtain a matching result of the dth subscript, and generates a pollution treatment recommendation scheme according to the matching result; When the feature vector corresponding to the dth subscript in the diffusion feature vector D is greater than twice the water quality risk threshold R, the matching result is a warning result, an early warning pollution treatment recommendation scheme is generated, the feature vector corresponding to the dth subscript is extracted, and a pre-set early warning notification template is filled in and sent to the relevant environmental protection department for prompt attention, and a pre-set pollution detection template is filled in and sent to the relevant detection department for processing task list. When the feature vector corresponding to the dth subscript in the diffusion feature vector D is greater than or equal to the water quality risk threshold R, a matching result is an unqualified result, a pollution control recommendation scheme is generated, the feature vector corresponding to the dth subscript is extracted, a preset pollution detection template is filled in, and a related detection department is sent to handle a task list waiting for processing; When the feature vector corresponding to the dth subscript in the diffusion feature vector D is less than the water quality risk threshold R, a matching result is a qualified result, and no pollution control recommendation scheme is generated.

6. The comprehensive analysis system based on water environment monitoring information according to claim 5, characterized in that: The iterative feedback module includes a feedback verification module; The feedback verification module collects feedback results of the pollution control recommendation scheme after a fixed period according to contents of the pollution control recommendation scheme, including verification results of the feature vector corresponding to the dth subscript. When the feature vector corresponding to the dth subscript is abnormal, a field feedback result index Fs is marked as 1. When the feature vector corresponding to the dth subscript is not abnormal, the field feedback result index Fs is marked as 0. The generation of the iterative optimization pollution control recommendation scheme is triggered according to a state of the field feedback result index Fs; The generation of the iterative optimization pollution control recommendation scheme is triggered by the following state comparison mode: When the field feedback result index Fs is 1, the generation of the iterative optimization pollution control recommendation scheme is not triggered. When the field feedback result index Fs is 0, the generation of the iterative optimization pollution control recommendation scheme is triggered, including iterative optimization of a pollutant diffusion model and a diffusion coefficient K(x, t).

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