Comprehensive analysis system based on water environment monitoring information

Abstract

The invention discloses a comprehensive analysis system based on water environment monitoring information, and relates to the technical field of water environment monitoring. A pollutant diffusion model is established based on a standard data set C, and spatial distribution and time change of pollutants are calculated, so that a predicted concentration feature vector P is generated; key prediction information is provided for pollutant diffusion simulation, a diffusion feature vector D of pollutants is output, and data support is provided for further risk assessment. And through comparison with a preset water quality risk threshold value R, traversing and matching the diffusion feature vector D, generating pollution treatment suggestion schemes for different pollution degrees, and ensuring that proper measures are taken for precise treatment. And periodic optimization is carried out according to a feedback result, so that the effect and the accuracy of pollution treatment are continuously improved. The identification and treatment efficiency of the pollution source is improved, the scientificity and pertinence of the pollution treatment scheme are enhanced, and dynamic monitoring and continuous improvement of the water environment are ensured.

Description

Technical Field

[0001] The present invention relates to the technical field of water environment monitoring, and particularly to a comprehensive analysis system based on water environment monitoring information. Background Art

[0002] Water environment monitoring and management, as an important part of the environmental protection field, widely covers multiple aspects such as water quality, river basins, water resources, etc., and involves the monitoring and assessment of water body health. With the increasingly severe global environmental problems, water environment monitoring has become one of the key means to address water pollution and ensure water security. Among the numerous technical methods for water environment management, water quality monitoring occupies a core 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 mostly rely on static data collection and analysis. Usually, water quality parameters are regularly collected through water quality sensors or monitoring stations to generate environmental data at time points. Such systems have certain limitations because the changes in water quality are often affected by multiple factors such as time, space, and water flow dynamics, and traditional methods are difficult to accurately capture these dynamic changes. Especially in flowing water bodies such as rivers and streams, the laws of pollutant propagation and diffusion with the water flow have strong spatio-temporal differences, which makes single data points or simple statistical analysis unable to comprehensively predict and assess pollution risks. For example, in the case of sudden pollution source leakage, existing systems cannot accurately simulate the diffusion path of pollutants under different water flow velocities and water body structures in real time, resulting in ineffective pollutant control and often missing the best treatment opportunity. In addition, existing systems generally lack the ability to dynamically model complex water flow laws and are difficult to provide scientific decision-making basis for governance departments. Summary of the Invention

[0004] In view of the deficiencies of the prior art, the present 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 realized through the following technical solutions: A comprehensive analysis system based on water environment monitoring information includes a water environment data collection module, a water environment data processing module, a spatio-temporal 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 collection module collects water quality parameter data and terrain data of multiple monitoring points within a river section to form a water environment data set W;

[0007] The water environment data processing module preprocesses the water environment data set W to obtain a preprocessed water environment standard data group 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 variation of pollutants, and obtains the predicted concentration feature vector P;

[0009] The pollutant diffusion simulation module uses the fluid mechanics model to calculate the distribution of pollutants in different time and space based on the predicted concentration feature vector P, and outputs the pollutant diffusion feature vector D;

[0010] The risk assessment and decision support module uses the preset water quality risk threshold R to traverse and match the diffusion feature vector D to generate a pollution control proposal;

[0011] The iterative feedback module collects feedback results of the pollution control proposals after a fixed period according to the content of the pollution control proposals, and iteratively optimizes the generation of the 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 environment terrain acquisition unit;

[0013] 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 integrating multiple water quality sensors and water flow rate sensors at each monitoring point;

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

[0015] The water quality parameter data include water body pH, dissolved oxygen concentration DO, ammonia nitrogen concentration NH3-N, total phosphorus concentration TP, water body turbidity TU, water flow velocity V, water temperature T and electrical conductivity EC, and mark the position x and time t of the water quality parameter data to obtain the water body pH(x, t), dissolved oxygen concentration DO(x, t), ammonia nitrogen concentration NH3-N(x, t), total phosphorus concentration TP(x, t), water body turbidity TU(x, t), water flow velocity V(x, t), water temperature T(x, t) and electrical conductivity EC(x, t) at the position x and time t.

[0016] Preferably, the environmental terrain acquisition 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;

[0017] The terrain data are obtained through 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 water flow slope change rate S and the water body 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 data set W, including denoising preprocessing, missing value filling preprocessing, and data standardization preprocessing, to obtain the preprocessed water environment standard data group C;

[0020] The denoising preprocessing performs denoising preprocessing on the water environment data set W by using a low-pass filter;

[0021] The missing value filling preprocessing fills the missing values in the water environment data set W after denoising preprocessing by using the linear interpolation method;

[0022] The data standardization preprocessing standardizes the water environment data set W after missing value filling preprocessing by using the Z-score standardization method.

[0023] Preferably, the spatio-temporal dynamic modeling module includes a pollutant diffusion modeling unit and a time change prediction unit;

[0024] The pollutant diffusion modeling unit establishes a pollutant diffusion model based on the water environment standard data group C, simulates the dynamic three-dimensional model of the river section through the pollutant diffusion model, and solves the pollutant diffusion equation by numerical solution to simulate the spatial distribution and the process of change over time of the concentration Cp of the p-th pollutant in the water environment standard data group C in the pollutant diffusion model;

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

[0026] Among them, the specific form of the pollutant diffusion equation is the structure of a partial differential equation:

[0027]

[0028] In the formula, represents the time change term, specifically representing the rate of change of the pollutant concentration Cp(x, t) with time at position x, and Cp(x, t) represents the pollutant concentration at position x and time t, represents the convection term, specifically representing the movement of the pollutant concentration Cp(x, t) at position x and time t to adjacent positions with the water flow, and v(x) represents the water flow velocity vector, specifically representing the flow direction and rate of the water flow at position x, represents the gradient of the pollutant concentration Cp(x, t) at position x and time t, specifically representing the rate of change of the pollutant concentration Cp(x, t) with spatial position at position x and time t, represents the diffusion term, specifically representing the natural diffusion process of the pollutant concentration Cp(x, t) at position x and time t in the water body. D represents the diffusion coefficient, specifically representing the diffusion rate of the pollutant in the water body. represents the Laplace operator of the pollutant concentration Cp(x, t) at position x and time t. Rp represents the source strength of the p-th pollutant concentration, which is a preset constant, and the specific value is set by the user. represents the partial derivative symbol.

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

[0030] where △t represents the time step, specifically representing the time interval from time t to time t + △t.

[0031] The pollutant concentration Cp(x, t + △t) at time t + △t is predicted by the following finite difference method 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 obtains the diffusion coefficient K(x, t) based on the hydrodynamic parameters, which include the water flow path K, the bottom bed water flow slope change rate S, the water body depth H, the water flow velocity V(x, t), and the water temperature T(x, t), reflecting the diffusion ability of different pollutants in the water body.

[0035] The diffusion coefficient K(x, t) is obtained by the following calculation 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 diffusion feature vector D of the pollutant reflects the propagation speed, range, peak value, and change trend of the pollutant.

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

[0040]

[0041] Wherein, 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 recommendation generation unit matches the feature vectors corresponding to each subscript in the diffusion feature vector D through a preset water quality risk threshold R, obtains the matching result of the d-th subscript, and generates a pollution control recommendation plan according to the matching result;

[0044] When the feature vector corresponding to the d-th subscript in the diffusion feature vector D is greater than or equal to twice the water quality risk threshold R, the obtained matching result is a warning result, a warning pollution control recommendation plan is generated, the feature vector corresponding to the d-th subscript is extracted, filled in the preset warning notice template, and sent to the relevant environmental protection department for attention. At the same time, it is filled in the preset pollution detection template and sent to the top of the processing task list of the relevant detection department for processing;

[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 obtained matching result is an unqualified result, a pollution control recommendation plan is generated, the feature vector corresponding to the d-th subscript is extracted, filled in the preset pollution detection template, and sent to the processing task list of the relevant detection department 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 obtained matching result is a qualified result, and no pollution control recommendation plan is generated.

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

[0048] The feedback verification module collects the feedback results of the pollution control recommendation plan after a fixed period according to the content of the pollution control recommendation plan, including the verification results of the feature vectors corresponding to the d-th subscript. When there is an abnormality in the feature vector corresponding to the d-th subscript, the on-site feedback result index Fs = 1 is marked. When there is no abnormality in the feature vector corresponding to the d-th subscript, the on-site feedback result index Fs = 0 is marked, and the generation of the iterative optimization pollution control recommendation plan is triggered according to the status of the on-site feedback result index Fs;

[0049] The generation of the iterative optimization pollution control recommendation plan is obtained through the following status comparison method:

[0050] When the on-site feedback result index Fs = 1, no iterative optimization pollution control recommendation plan is triggered;

[0051] When the on-site feedback result index Fs = 0, an iterative optimization pollution control recommendation plan is triggered, including iterative optimization of the pollutant diffusion model and the diffusion coefficient K(x, t).

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

[0053] (1) By establishing a pollutant diffusion model based on the standard data set C, calculating the spatial distribution and temporal variation of pollutants, a predicted concentration feature vector P is generated, which provides key prediction information for pollutant diffusion simulation, and the diffusion feature vector D of pollutants is output, providing data support for further risk assessment. By comparing with the preset water quality risk threshold R, traversing and matching the diffusion feature vector D, pollution control suggestion schemes for different pollution degrees are generated to ensure that appropriate measures are taken for precise treatment. Periodic optimization is carried out according to the feedback results to continuously improve the effect and accuracy of pollution control. It improves the identification and treatment efficiency of pollution sources, and also strengthens the scientificity and pertinence of pollution control schemes, ensuring the dynamic monitoring and continuous improvement of the water environment.

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

[0055] (3) Through the matching results of the water quality risk threshold R and the concentration of each pollutant in the diffusion feature vector D, a pollution control suggestion scheme is automatically generated and sent to the detection department for further processing. When the pollutant concentration is lower than the risk threshold, no treatment suggestions are generated, thus optimizing the use of resources. By periodically collecting the feedback of pollution control suggestion schemes, the change of pollutant concentration is verified in real time, further improving the pollutant diffusion prediction and treatment effect. Through this mechanism, the system can not only adjust the treatment scheme according to real-time data, but also continuously optimize the prediction accuracy according to the feedback results, ensuring that the pollution control scheme is always in the optimal state. Brief Description of the Drawings

[0056] Figure 1 It is a schematic block diagram of a comprehensive analysis system based on water environment monitoring information of the present invention;

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

[0058] Figure 3 Schematic diagram of predicted pollutant concentration for predicted concentration feature vector P;

[0059] Figure 4 The figure is a schematic diagram of the data transmission process of a comprehensive analysis system based on water environment monitoring information. DETAILED DESCRIPTION

[0060] The technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0061] Example 1

[0062] The present invention provides a comprehensive analysis system based on water environment monitoring information. Figure 1 , including water environment data acquisition module, water environment data processing module, spatiotemporal dynamic modeling module, pollutant diffusion simulation module, risk assessment and decision support module and iterative feedback module;

[0063] 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;

[0064] The water environment data processing module preprocesses the water environment data set 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 variation of pollutants, and obtains the predicted concentration feature vector P;

[0066] The pollutant diffusion simulation module uses the fluid mechanics model to calculate the distribution of pollutants in different time and space based on the predicted concentration feature vector P, and outputs the pollutant diffusion feature vector D;

[0067] The risk assessment and decision support module uses the preset water quality risk threshold R to traverse and match the diffusion feature vector D to generate a pollution control proposal;

[0068] The iterative feedback module collects feedback results of the pollution control proposals after a fixed period according to the content of the pollution control proposals, and iteratively optimizes the generation of the pollution control proposals based on the feedback results.

[0069] In this embodiment, by collecting water quality parameter data and topographic data from multiple monitoring points in the river section, a water environment data set W is formed, which provides detailed basic data for subsequent analysis. The water environment data processing module preprocesses the data to ensure that the obtained water environment standard data set C is accurate and meets the analysis requirements. The spatiotemporal dynamic modeling module establishes a pollutant diffusion model based on the standard data set C, calculates the spatial distribution and temporal changes of pollutants, and thus generates a predicted concentration characteristic vector P, which provides key prediction information for pollutant diffusion simulation. The pollutant diffusion simulation module uses a fluid mechanics model to infer the distribution of pollutants in different times and spaces, and outputs the diffusion characteristic vector D of pollutants. This result can clearly show the dynamic process of pollutant diffusion and provide data support for further risk assessment. The risk assessment and decision support module compares with the preset water quality risk threshold R, traverses the matching diffusion characteristic vector D, generates pollution control suggestions for different pollution levels, and ensures that appropriate measures are taken for precise governance. Finally, the iterative feedback module performs periodic optimization based on the feedback results to continuously improve the effect and accuracy of pollution control. Through the effective coordination of this system, not only the efficiency of pollution source identification and control has been greatly improved, but also the scientific nature and pertinence of pollution control plans have been strengthened, ensuring dynamic monitoring and continuous improvement of the water environment.

[0070] Example 2

[0071] This embodiment is explained in Example 1, please refer to Figure 1 ,Specifically: the water environment data acquisition module includes a water quality data acquisition unit and an ,environmental terrain acquisition unit;

[0072] 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 integrating multiple water quality sensors and water flow rate sensors at each monitoring point;

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

[0074] The water quality parameter data include water body pH, dissolved oxygen concentration DO, ammonia nitrogen concentration NH3-N, total phosphorus concentration TP, water body turbidity TU, water flow velocity V, water temperature T and electrical conductivity EC, and mark the position x and time t of the water quality parameter data to obtain the water body pH(x, t), dissolved oxygen concentration DO(x, t), ammonia nitrogen concentration NH3-N(x, t), total phosphorus concentration TP(x, t), water body turbidity TU(x, t), water flow velocity V(x, t), water temperature T(x, t) and electrical conductivity EC(x, t) at the position x and time t.

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

[0076] The terrain data monitors the river section in the monitoring point area through satellite remote sensing and ground laser scanning, and obtains the water flow path K, the change rate S of the bottom bed water flow slope, 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 data set W, including denoising preprocessing, missing value filling preprocessing, and data standardization preprocessing, to obtain the preprocessed water environment standard data group C;

[0079] The denoising preprocessing performs denoising preprocessing on the water environment data set W by using a low-pass filter;

[0080] The missing value filling preprocessing fills the missing values in the water environment data set W after denoising preprocessing by using the linear interpolation method;

[0081] The data standardization preprocessing standardizes the water environment data set W after missing value filling preprocessing by 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, the water quality parameter data can be collected in real time and accurately, and the corresponding spatial position x and time point t can be accurately marked, ensuring the spatio-temporal accuracy and integrity of the monitoring data. At the same time, the environmental terrain acquisition unit obtains the water flow path K, the change rate S of the bottom bed water flow slope, and the water depth H of the river section through satellite remote sensing and ground laser scanning, providing high-precision terrain information for subsequent water quality assessment and pollutant diffusion models. The water environment data processing module further converts the original data into a standardized water environment data group C through processing methods such as denoising, missing value filling, and data standardization, ensuring the quality and consistency of the data. The denoising preprocessing effectively removes the noise in the water quality data by using a low-pass filter, the missing value filling preprocessing fills the missing data by using the linear interpolation method, and the data standardization preprocessing ensures the consistency of the data, making it suitable for subsequent modeling and analysis.

[0083] Embodiment 3

[0084] This embodiment is an explanatory description based on Embodiment 2. Please refer to Figure 1 , specifically: The spatio-temporal dynamic modeling module includes a pollutant diffusion modeling unit and a time change prediction unit;

[0085] The pollutant diffusion modeling unit establishes a pollutant diffusion model based on the water environment standard data set C, simulates the dynamic three-dimensional model of the river section through the pollutant diffusion model, and solves the pollutant diffusion equation by numerical methods to simulate the spatial distribution and the process of change over time of the concentration Cp of the p-th pollutant in the water environment standard data set C in the pollutant diffusion model;

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

[0087] Among them, the specific form of the pollutant diffusion equation is the structure of a partial differential equation:

[0088]

[0089] In the formula, represents the time variation term, specifically representing the rate of change of the pollutant concentration Cp(x, t) with time at position x. Cp(x, t) represents the pollutant concentration at position x and time t. represents the convection term, specifically representing the movement of the pollutant concentration Cp(x, t) at position x and time t to adjacent positions by the flow of water. v(x) represents the velocity vector of the water flow, specifically representing the flow direction and rate of the water flow at position x. represents the gradient of the pollutant concentration Cp(x, t) at position x and time t, specifically representing the rate of change of the pollutant concentration Cp(x, t) with spatial position at position x and time t. represents the diffusion term, specifically representing the natural diffusion process of the pollutant concentration Cp(x, t) at position x and time t in the water body. D represents the diffusion coefficient, specifically representing the diffusion rate of the pollutant in the water body. represents the Laplace operator of the pollutant concentration Cp(x, t) at position x and time t. Rp represents the source intensity of the p-th pollutant concentration, which is a preset constant, and the specific value is set by the user. represents the partial derivative symbol;

[0090] Among them, the two terms on the left side of the structure of the partial differential equation and the terms on the right side represent different physical processes, describing the change of the pollutant concentration Cp(x, t) over time and space;

[0091] The two terms on the left side together describe how the pollutant concentration evolves in space and time. The two terms on the right side represent the diffusion process and the source term, which may be a pollution source or a reaction, that is, describing how the pollutant diffuses or affects the concentration change through an external source;

[0092] Meanwhile, both sides of the "=" in the structure of the partial differential equation are balanced, indicating that the rate of change of pollutant concentration is jointly determined by diffusion, source terms, and convection, which are interdependent and interact with each other, and are also used to describe the equilibrium relationship of the change in pollutant concentration;

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

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

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

[0096]

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

[0098] The diffusion initialization unit obtains the diffusion coefficient K(x, t) based on the hydrodynamic parameters, which include the water flow path K, the change rate of the bottom bed water flow slope S, the water depth H, the water flow velocity V(x, t), and the water temperature T(x, t), reflecting the diffusion ability of different pollutants in the water body;

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

[0100]

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

[0102] Among them, The ratio of reflects the intensity of the water flow on the pollutant propagation. The stronger the water flow, the stronger the diffusion ability of the pollutants. When the water body is shallower, the vertical diffusion of the pollutants is stronger;

[0103] The ratio of reflects that the change rate of the bottom bed water flow slope S affects the turbulence intensity of the water flow. The larger the slope, the stronger the turbulence, and the faster the pollutant diffusion. The water temperature T(x, t) affects the viscosity of the water body. When the water temperature is high, the water body viscosity decreases and the molecular diffusion increases. Therefore, the ratio reflects the comprehensive influence of the slope and water temperature on diffusion;

[0104] It reflects the impact of the complexity of the water flow path on pollutant diffusion. Complex paths (such as multiple bends and variability of the water flow path) usually cause pollutants to diffuse faster. Therefore, this item will increase the diffusion coefficient. If the water flow path is simple and the water flow path K is small, the diffusion coefficient will be low.

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

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

[0107]

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

[0109] In this embodiment, by calculating and predicting the concentration changes of the p-th pollutant at different future time points, the predicted concentration eigenvector P is generated. This vector not only contains the concentration prediction data of different pollutants at future times, but also provides detailed pollutant concentration distribution information in space and time for subsequent analysis. The diffusion initialization unit calculates the diffusion coefficient K(x, t) using hydrodynamic parameters, thereby reflecting the diffusion ability of different pollutants in the water body. The diffusion process calculation unit then combines this diffusion coefficient K(x, t) with the predicted concentration eigenvector P to generate the diffusion eigenvector D of pollutants, further revealing the propagation speed, range, peak value and change 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 the future concentration changes of pollutants, and provide an important basis for water quality management and decision-making.

[0110] Embodiment 4

[0111] This embodiment is an explanatory description based on Embodiment 3. Please refer to Figure 1 and Figure 2 , specifically: The risk assessment and decision support module includes a pollution recommendation generation unit;

[0112] The pollution recommendation generation unit matches the preset water quality risk threshold R with the eigenvectors corresponding to each subscript in the diffusion eigenvector D to obtain the matching result of the d-th subscript, and generates a pollution control recommendation plan according to the matching result. A specific example is shown in Table 1;

[0113] When the eigenvector corresponding to the d-th subscript in the diffusion eigenvector D is greater than or equal to twice the water quality risk threshold R, the matching result is obtained as an early warning result, an early warning pollution control suggestion plan is generated, the eigenvector corresponding to the d-th subscript is extracted, filled into the preset early warning notice template, and sent to the relevant environmental protection departments for attention, and at the same time filled into the preset pollution detection template and sent to the top of the processing task list of the relevant detection departments for processing;

[0114] When the eigenvector corresponding to the d-th subscript in the diffusion eigenvector D is greater than or equal to the water quality risk threshold R, the matching result is obtained as an unqualified result, a pollution control suggestion plan is generated, the eigenvector corresponding to the d-th subscript is extracted, filled into the preset pollution detection template, and sent to the processing task list of the relevant detection departments for processing;

[0115] When the eigenvector corresponding to the d-th subscript in the diffusion eigenvector D is less than the water quality risk threshold R, the matching result is obtained as a qualified result, and no pollution control suggestion plan is generated;

[0116] Table 1:

[0117]

[0118]

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

[0120] The feedback verification module collects the feedback results of the pollution control suggestion plan after a fixed period according to the content of the pollution control suggestion plan, including the verification results of the eigenvector corresponding to the d-th subscript. When the eigenvector corresponding to the d-th subscript is abnormal, the on-site feedback result index Fs = 1 is marked. When the eigenvector corresponding to the d-th subscript is not abnormal, the on-site feedback result index Fs = 0 is marked, and the generation of the iterative optimization pollution control suggestion plan is triggered according to the status of the on-site feedback result index Fs;

[0121] The generation of the iterative optimization pollution control suggestion plan is obtained through the following status comparison method:

[0122] When the on-site feedback result index Fs = 1, the generation of the iterative optimization pollution control suggestion plan is not triggered;

[0123] When the on-site feedback result index Fs = 0, the generation of the iterative optimization pollution control suggestion plan is triggered, including the iterative optimization of the pollutant diffusion model and the diffusion coefficient K(x, t).

[0124] In this embodiment, a pollution control suggestion plan is automatically generated based on the matching results between the water quality risk threshold R and the concentrations of each pollutant in the diffusion feature vector D. When the pollutant concentration exceeds twice the water quality risk threshold, the system will generate a warning pollution control suggestion and immediately notify the relevant environmental protection departments and detection departments to ensure a quick response. When the pollutant concentration exceeds the risk threshold but does not reach the warning level, the system will automatically generate a pollution control suggestion and send it to the detection department for further processing. When the pollutant concentration is lower than the risk threshold, no control suggestion will be generated, thus optimizing the use of resources. The iterative feedback module periodically collects the feedback of the pollution control suggestion plan, 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 there is an abnormality in the pollutant concentration, the system will stop optimization; otherwise, if the feedback shows that the concentration is normal, the system will start the iterative optimization of the diffusion model and the diffusion coefficient K(x, t) to further improve the prediction and treatment effect of pollutant diffusion. Through this mechanism, the system can not only adjust the treatment 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 the embodiments of the present invention have been shown and described, it will be understood by those of ordinary skill in the art that various changes, modifications, substitutions and variations can be made in these embodiments without departing from the principles and spirit of the present invention, and the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. An integrated analysis system based on water environment monitoring information, characterized in that: It includes water environment data acquisition module, water environment data processing module, spatiotemporal dynamic modeling module, pollutant diffusion simulation module, risk assessment and decision support module and 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 preprocesses the water environment data set W to obtain the preprocessed water environment standard data set C; The spatiotemporal dynamic modeling module establishes a pollutant diffusion model for the water environment standard data set C, calculates the spatial distribution and temporal variation of pollutants, and obtains the predicted concentration feature vector P; The pollutant diffusion simulation module uses the fluid mechanics model to calculate the distribution of pollutants in different time and space based on the predicted concentration feature vector P, and outputs the pollutant diffusion feature vector D; The risk assessment and decision support module uses the preset water quality risk threshold R to traverse and match the diffusion feature vector D to generate a pollution control proposal; The iterative feedback module collects feedback results of the pollution control proposals after a fixed period according to the content of the pollution control proposals, and iteratively optimizes the generation of the pollution control proposals based on the feedback results.

2. The integrated analysis system based on water environment monitoring information according to claim 1, wherein: The water environment data acquisition module includes a water quality data acquisition unit and an environmental terrain acquisition 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 integrating multiple water quality sensors and water flow rate sensors at each monitoring point; Water quality sensors include pH sensor, dissolved oxygen sensor, turbidity sensor, ammonia nitrogen sensor, total phosphorus sensor, water temperature sensor and conductivity sensor; The water quality parameter data include water body pH, dissolved oxygen concentration DO, ammonia nitrogen concentration NH3-N, total phosphorus concentration TP, water body turbidity TU, water flow velocity V, water temperature T and electrical conductivity EC, and mark the position x and time t of the water quality parameter data to obtain the water body pH(x, t), dissolved oxygen concentration DO(x, t), ammonia nitrogen concentration NH3-N(x, t), total phosphorus concentration TP(x, t), water body turbidity TU(x, t), water flow velocity V(x, t), water temperature T(x, t) and electrical conductivity EC(x, t) at the position x and time t.

3. The integrated analysis system based on water environment monitoring information according to claim 2, characterized in that: The environmental terrain acquisition unit is used to collect the 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 are obtained through 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 water flow slope change rate S and the water body depth H of the river section in the monitoring point area.

4. An integrated analysis system based on water environment monitoring information according to claim 3, characterized in that: The water environment data processing module includes a pre-processing 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; De-noising preprocessing is performed on the water environment data set W by using a low-pass filter; Missing value filling preprocessing is to fill missing values in the water environment data set W after denoising preprocessing by using linear interpolation method; Data standardization preprocessing performs data standardization on the water environment data set W after missing value filling preprocessing by using the Z-score standardization method.

5. The integrated analysis system based on water environment monitoring information according to claim 4, wherein: The spatio-temporal dynamic modeling module includes a pollutant diffusion modeling unit and a time change prediction unit; The pollutant diffusion modeling unit establishes a pollutant diffusion model based on the water environment standard data set C, simulates the dynamic three-dimensional model of the river section through the pollutant diffusion model, and solves the pollutant diffusion equation by numerical solution method to simulate the spatial distribution and the process of change over time of the concentration Cp of the p-th pollutant in the water environment standard data set C in the pollutant diffusion model; Among them, the pollutant concentration Cp includes the water body acidity and alkalinity pH(x, t), dissolved oxygen concentration DO(x, t), ammonia nitrogen concentration NH3-N(x, t), total phosphorus concentration TP(x, t), water body turbidity TU(x, t), and conductivity EC(x, t); Among them, the specific form of the pollutant diffusion equation is the structure of a partial differential equation: In the formula, represents the time-varying term, specifically representing the rate of change of the pollutant concentration Cp(x, t) with respect to time at position x. Cp(x, t) represents the pollutant concentration at position x and time t. v(x)*▽Cp(x,t) represents the convection term, specifically representing the movement of the pollutant concentration Cp(x, t) at position x and time t to adjacent positions by the water flow. v(x) represents the velocity vector of the water flow, specifically representing the flow direction and rate of the water flow at position x. ▽Cp(x, t) represents the gradient of the pollutant concentration Cp(x, t) at position x and time t, specifically representing the rate of change of the pollutant concentration Cp(x, t) with respect to the spatial position at position x and time t. D▽ 2 Cp(x,t) represents the diffusion term, specifically representing the natural diffusion process of the pollutant concentration Cp(x, t) in the water body at position x and time t. D represents the diffusion coefficient, specifically representing the diffusion rate of the pollutant in the water body. ▽ 2 Cp(x,t) represents the Laplace operator of the pollutant concentration Cp(x, t) at position x and time t. Rp represents the source strength of the p-th pollutant concentration, which is a preset constant, and the specific value is set by the user. represents the partial derivative symbol.

6. The integrated analysis system based on water environment monitoring information according to claim 5, characterized in that: The time change prediction unit calculates the time change of the pollutant concentration Cp based on the pollutant diffusion model, predicts the concentration change of the p-th pollutant concentration Cp at different future time points, and obtains the predicted concentration feature vector P by integrating the pollutant concentrations Cp(x, t + △t) of different pollutants in the pollutant concentration Cp at time t + △t; Among them, △t represents the time step, specifically representing 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 method formula:

7. An integrated analysis system based on water environment monitoring information according to claim 6, characterized in that: The pollutant diffusion simulation module includes a diffusion initialization unit and a diffusion process calculation unit; The diffusion initialization unit obtains the diffusion coefficient K(x, t) based on the hydrodynamic parameters, and the hydrodynamic parameters include the water flow path K, the change rate S of the bottom bed water flow slope, the water body depth H, the water flow velocity V(x, t), and the water temperature T(x, t), which reflects the diffusion ability of different pollutants in the water body; The diffusion coefficient K(x, t) is obtained by the following calculation formula: In the formula, K0 represents the basic diffusion coefficient.

8. An integrated analysis system based on water environment monitoring information according to claim 7, characterized in that: The diffusion process calculation unit combines the obtained diffusion coefficient K(x, t) with the predicted concentration feature vector P to obtain the diffusion feature vector D of the pollutant, which reflects the propagation speed, range, peak value, and change trend of the pollutant; The diffusion feature vector D is obtained by the following calculation formula: 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.

9. An integrated analysis system based on water environment monitoring information according to claim 8, characterized in that: The risk assessment and decision support module includes a pollution suggestion generation unit; The pollution suggestion generation unit matches the preset water quality risk threshold R with the feature vectors corresponding to each subscript in the diffusion feature vector D to obtain the matching result of the d-th subscript, and generates a pollution control suggestion plan according to the matching result; When the eigenvector corresponding to the d-th subscript in the diffusion eigenvector D is greater than or equal to twice the water quality risk threshold R, the matching result is obtained as an early warning result, an early warning pollution control suggestion plan is generated, the eigenvector corresponding to the d-th subscript is extracted, filled into the preset early warning notice template, and sent to the relevant environmental protection departments for attention. At the same time, it is filled into the preset pollution detection template and sent to the top of the processing task list of the relevant detection departments for processing; When the eigenvector corresponding to the d-th subscript in the diffusion eigenvector D is greater than or equal to the water quality risk threshold R, the matching result is obtained as an unqualified result, a pollution control suggestion plan is generated, the eigenvector corresponding to the d-th subscript is extracted, filled into the preset pollution detection template, and sent to the processing task list of the relevant detection departments for processing; When the eigenvector corresponding to the d-th subscript in the diffusion eigenvector D is less than the water quality risk threshold R, the matching result is obtained as a qualified result, and no pollution control suggestion plan is generated.

10. The comprehensive analysis system based on water environment monitoring information according to claim 9, wherein: The iterative feedback module includes a feedback verification module; The feedback verification module collects the feedback results of the pollution control suggestion plan after a fixed period according to the content of the pollution control suggestion plan, including the verification results of the eigenvector corresponding to the d-th subscript. When the eigenvector corresponding to the d-th subscript is abnormal, the on-site feedback result index Fs = 1 is marked. When the eigenvector corresponding to the d-th subscript is not abnormal, the on-site feedback result index Fs = 0 is marked, and the generation of the iterative optimization pollution control suggestion plan is triggered according to the status of the on-site feedback result index Fs; The generation of the iterative optimization pollution control suggestion plan is obtained through the following status comparison method: When the on-site feedback result index Fs = 1, it is obtained that the iterative optimization pollution control suggestion plan is not triggered; When the on-site feedback result index Fs = 0, it is obtained that the iterative optimization pollution control suggestion plan is triggered, including the iterative optimization of the pollutant diffusion model and the diffusion coefficient K(x, t).

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