Emergency pollution diffusion model construction method

Abstract

The invention relates to the field of environmental protection, and discloses an emergency pollution diffusion model construction method, which comprises the following steps: constructing a pollutant diffusion basic model, and describing the diffusion process of pollutants in a water body along with time and space; dynamically optimizing control parameters of the pollution diffusion model, adjusting control variables in real time, and optimizing a pollution diffusion path through an adaptive optimization control method; based on the real-time monitoring data and the prediction result of the pollution diffusion model, performing state estimation and correction on the pollution diffusion path by using a Kalman filtering algorithm; in combination with real-time data from different monitoring sources, optimizing a prediction result of the pollution diffusion model by using a spatio-temporal data fusion technology; and according to the optimized pollution diffusion result, an emergency response scheme is provided for an environment management department through a decision support system. According to the method, a spatio-temporal data fusion technology is adopted, and the prediction result of the pollution diffusion model is optimized through weighted averaging of multi-source monitoring data.

Description

Technical Field

[0001] The present invention relates to the technical field of environmental protection, and specifically to a method for constructing an emergency pollution diffusion model. Background Art

[0002] With the continuous advancement of industrialization and urbanization, pollution problems, especially water pollution, have become one of the global environmental problems. The diffusion of pollutants not only affects water quality safety but may also pose a serious threat to the ecosystem and human health. Sudden pollution events, such as industrial emissions, agricultural runoff, and urban wastewater leakage, usually lead to the rapid diffusion of pollutants, and these events have put forward higher requirements for the emergency response of environmental management departments. However, there are still some significant deficiencies in the existing technologies for pollution diffusion prediction and emergency response, which cannot meet the needs of modern environmental governance.

[0003] Existing pollution diffusion models usually rely on static environmental data and fixed parameters, and these models lack dynamic adaptability to environmental changes. In traditional pollution diffusion models, parameters such as water flow velocity, pollution source intensity, and diffusion coefficient are often assumed to be constants, while in the actual environment, these factors will fluctuate with time and weather changes. Traditional models fail to fully consider the dynamic changes of these variables, resulting in the lag and inaccuracy of pollution diffusion prediction results. Especially in the process of emergency management, such prediction errors may cause catastrophic consequences.

[0004] In addition, there is a lack of an effective real-time data collection and feedback mechanism in the existing technologies, resulting in the inability of pollution diffusion models to be adjusted in a timely manner in the face of sudden pollution events. These traditional methods usually cannot provide sufficient accuracy and flexibility in the prediction of pollution sources and pollutant concentrations, resulting in a lack of a reliable basis for emergency response by management departments. Especially in the case of drastic changes in pollutant concentrations or multi-source pollution, the existing technologies cannot track the diffusion process of pollutants in real time, and thus cannot adjust emergency response measures in a timely manner.

[0005] In addition, most of the pollution diffusion predictions in the existing technologies rely on a single data source, such as ground water quality sensors or fixed monitoring stations, and cannot effectively integrate data from different sources. Pollution diffusion involves multiple variables and complex spatial distributions, and a single data source cannot comprehensively reflect the changes of pollutants in different regions and at different times. Therefore, the effective integration of multi-source data has become the key to solving the problem of pollution diffusion prediction accuracy. However, in the existing technologies, the integration of these multi-source data is often handled improperly, resulting in large errors in the estimated results of pollutant concentrations.

[0006] Therefore, in the prior art, the application of pollution diffusion models in emergency response has significant limitations. Especially when dealing with complex water environments and sudden pollution incidents, it cannot provide precise and reliable decision-making support for environmental management departments. The lack of an adaptive optimization and dynamic adjustment mechanism results in instability in predicting pollution diffusion paths. Traditional technologies have also failed to effectively integrate real-time data with pollution prediction, leading to delays and inefficiencies in emergency response measures.

[0007] Therefore, the present invention proposes a method for constructing an emergency pollution diffusion model to address the deficiencies of the prior art. Summary of the Invention

[0008] In view of the deficiencies of the prior art, the present invention provides a method for constructing an emergency pollution diffusion model, which solves the problems of the existing pollution diffusion model lacking dynamic adjustment, real-time data feedback, and insufficient multi-source data fusion in emergency response.

[0009] To achieve the above objectives, the present invention is realized through the following technical solutions: A method for constructing an emergency pollution diffusion model, comprising the following steps: Construct a basic model of pollutant diffusion to describe the diffusion process of pollutants in water over time and space; Dynamically optimize the control parameters of the pollution diffusion model, adjust the control variables in real time, and optimize the pollution diffusion path through an adaptive optimization control method; Based on real-time monitoring data and the prediction results of the pollution diffusion model, use the Kalman filter algorithm to perform state estimation and correction on the pollution diffusion path; Combine real-time data from different monitoring sources, use spatio-temporal data fusion technology to optimize the prediction results of the pollution diffusion model; According to the optimized pollution diffusion results, provide an emergency response plan for environmental management departments through a decision support system.

[0010] Preferably, the model includes pollutant concentration, flow velocity, diffusion coefficient, and the influence of pollution sources.

[0011] Preferably, the basic model of pollutant diffusion is described by the advection-diffusion equation, where the pollutant concentration changes over time and space, and the water flow velocity, diffusion coefficient, and pollution source term are all input parameters.

[0012] Preferably, the advection-diffusion equation is: Where, C(x,t) is the pollutant concentration; v(x,t) is the water flow velocity; D(x,t) is the diffusion coefficient; is the spatial gradient of the pollutant concentration, representing the change rate of the pollutant concentration in space; The Laplacian operator of the pollutant concentration describes the diffusion behavior of the pollutant concentration in space; S(x,t) is the pollution source term, representing the pollutant discharge of the pollution source into the water body at position x and time t.

[0013] Preferably, the control variable adjustment includes the following steps: According to the objective function J in the pollution diffusion model, calculate the control variable u(t) through the optimal control algorithm to minimize the objective function: where J is the objective function, C(x,t) is the pollutant concentration, Ω is the computational domain, t0 and t f are the starting time and ending time of the simulation; according to the real-time monitoring data and prediction results, dynamically optimize the pollution diffusion path by adjusting the pollution source emission amount u(t) and the water flow rate, and update the control variable in real time to ensure the minimization of the pollutant concentration.

[0014] Preferably, the adaptive optimization control method includes the following steps: Determine the objective function J of the pollutant concentration, and optimize the pollution diffusion path according to control variables such as the pollution source intensity and the water flow rate. Through the feedback correction of the difference between the prediction of the pollutant diffusion path and the actual data, adjust control variables such as the pollution source intensity and the water flow rate. The correction step is: u k = u k-1 + Δu k where u k is the control variable at time k, representing the emission intensity of the pollution source or the water flow rate; u k-1 is the control variable at time k-1; Δu k is the adjustment amount of the control variable; Adjust the pollution diffusion path according to the real-time data to minimize the objective function and ensure the accuracy of the pollutant diffusion prediction.

[0015] Preferably, the Kalman filter algorithm includes the following steps: Prediction step, according to the previous state of the pollution diffusion model and the control input u k-1 , predict the state at the current moment where is the predicted value of the pollutant concentration at time k, indicating that at time k, based on the previous moment state and the control input u k-1The obtained pollutant concentration; A is the state transition matrix, representing the state transition process in the pollutant diffusion model; B is the control input matrix, describing the influence of control variables on the pollutant concentration, and this matrix maps the control variables to the change in pollutant concentration; u k-1 is the control variable at time k - 1; Update step, based on the actual measurement value y k and the predicted value to perform an update and calculate the Kalman gain.

[0016] Preferably, the spatio-temporal data fusion technology performs weighted averaging on data from different monitoring sources to optimize the spatio-temporal prediction results of pollution diffusion and real-time update the pollution diffusion path.

[0017] Preferably, the decision support system provides spatio-temporal visualization, concentration analysis, and emergency response measures for pollution diffusion to the environmental management department according to the pollution diffusion prediction results.

[0018] The present invention also provides an emergency pollution diffusion model construction system, including: A real-time data acquisition module for collecting water quality data, meteorological data, and pollution source data; A data processing module for performing multi-scale adaptive optimal control, extended Kalman filtering, and spatio-temporal data fusion processes in the emergency pollution diffusion model construction method; A decision support module for providing emergency response decision support to the environmental management department according to the optimized pollution diffusion results.

[0019] The present invention provides an emergency pollution diffusion model construction method. It has the following beneficial effects: 1. The present invention adopts the spatio-temporal data fusion technology. Through the weighted averaging of multi-source monitoring data, the prediction results of the pollution diffusion model are optimized. It achieves a more accurate prediction effect of pollutant concentration. Compared with the existing solutions that rely on a single data source, it solves the problems of incomplete data or large errors. This technology can effectively handle the heterogeneity of data sources in the environment and improve the prediction accuracy.

[0020] 2. The present invention introduces the Kalman filtering algorithm to perform real-time correction and calibration in the estimation of the pollution diffusion path. By combining real-time data and prediction results, it achieves a significant improvement in the prediction accuracy of the pollution diffusion path. Compared with the existing static models that cannot self-correct, the feedback correction mechanism of the present invention significantly reduces the prediction error, enabling the model to maintain high efficiency and accuracy in a dynamically changing environment.

[0021] 3. The emergency response system of the present invention provides an immediate emergency response plan for environmental management departments by combining real-time monitoring data with optimized pollution diffusion prediction results. Compared with the relatively lagging response mechanism in the prior art, the present invention greatly improves the timeliness of emergency response, enabling the management department to take effective countermeasures at the initial stage of pollution diffusion, thereby reducing the impact of environmental pollution.

[0022] 4. The present invention optimizes the pollution diffusion path by dynamically adjusting the pollutant emission amount and water flow rate through a multi-scale adaptive optimal control method. Compared with the prior art solutions that only rely on fixed parameters, the control method of the present invention can flexibly respond to changes in different environments and pollution sources, achieving precise control of the pollution diffusion process, reducing the fluctuation of pollutant concentration, and ensuring a more stable environmental protection effect. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] Figure 1 is the method flowchart of the present invention; Figure 2 is the system architecture diagram of the present invention. DETAILED DESCRIPTION OF THE INVENTION

[0024] The technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0025] Please refer to Figure 1 , the embodiments of the present invention provide a method for constructing an emergency pollution diffusion model, including the following steps: S1. Construct a basic model of pollutant diffusion to describe the diffusion process of pollutants in water over time and space; S2. Dynamically optimize the control parameters of the pollution diffusion model, adjust the control variables in real time, and optimize the pollution diffusion path through an adaptive optimization control method; S3. Based on the real-time monitoring data and the prediction results of the pollution diffusion model, use the Kalman filter algorithm to perform state estimation and correction on the pollution diffusion path; S4. Combine the real-time data from different monitoring sources and use spatio-temporal data fusion technology to optimize the prediction results of the pollution diffusion model; S5. According to the optimized pollution diffusion results, provide an emergency response plan for environmental management departments through a decision support system.

[0026] For step S1 in this embodiment, the core of step S1 is to construct a basic mathematical model that can comprehensively describe the pollution diffusion behavior based on the actual physicochemical process of pollutant diffusion in water bodies. This model takes the diffusion-convection equation as the core, comprehensively considers key factors such as pollutant concentration, flow velocity, diffusion coefficient, and pollution sources, and can dynamically adjust parameters according to the actual environment. By constructing this model, a solid theoretical foundation is laid for subsequent optimization control, real-time data fusion, and emergency response.

[0027] Generally, the core mechanism of pollutant diffusion is mainly determined by two processes: one is the convective action of water flow, and the other is the diffusion action of pollutants. The movement of pollutants in water bodies is usually jointly affected by the velocity field of water flow, diffusion coefficient, and external pollution source emissions. Therefore, a mathematical model covering these elements needs to be established.

[0028] In this embodiment, the pollutant diffusion model is described by the following diffusion-convection equation: Where C(x,t) is the pollutant concentration (unit: mg / L), describing the concentration distribution of pollutants at spatial position x and time t; v(x,t) is the water flow velocity vector (unit: m / s), describing the direction and velocity distribution of water flow in the water body, which affects the migration behavior of pollutants; v(x,t) = (v x (x,t), v y (x,t), v z (x,t)) represents that in three-dimensional space, the three components of the water flow velocity are v x , v y and v z ; D(x,t) is the diffusion coefficient (unit: m2 / s), describing the diffusion ability of pollutants in water bodies, which usually depends on the temperature, viscosity of water bodies, and the properties of pollutants; is the spatial gradient of pollutant concentration, representing the change rate of pollutant concentration in space; is the Laplace operator (second-order spatial derivative) of pollutant concentration, describing the diffusion behavior of pollutant concentration in space; S(x,t) is the pollution source term (unit: mg / L-s), representing the pollutant emission of the pollution source to the water body at position x and time t.

[0029] For the change in pollutant concentration, the first term on the left side of the equation represents the change rate of pollutant concentration with time and is the main prediction target of the pollution diffusion model.

[0030] Convective action The second term represents the convective transport of pollutants by water flow, describing the migration path and rate of pollutants with water flow.

[0031] The water flow velocity v(x, t) can be calculated by a hydrodynamic model. For example, the two-dimensional shallow water equations are used to describe the velocity variation in the shallow water area: where ρ is the density of water (unit: kg / m3), P is the pressure field (unit: Pa), ν is the kinematic viscosity coefficient (unit: m2 / s), and F is the external force term (unit: N / m3).

[0032] Diffusion The first term on the right side describes the diffusion effect of pollutants in the water body. The magnitude of the diffusion coefficient D(x, t) depends on the properties of the pollutants, the temperature and salinity of the water body, etc.

[0033] The diffusion coefficient can be calculated by an empirical formula: D(x, t) = D0(1 + αT) where D0 is the reference diffusion coefficient (unit: m2 / s), α is the temperature coefficient (unit: 1 / °C), and T is the water temperature (unit: °C).

[0034] Pollution source term The second term S(x, t) is the input source term of the pollution diffusion model, representing the external input of pollutants, such as industrial emissions, agricultural runoff, or sudden pollution events.

[0035] In some embodiments, the pollution source term can be estimated by the following formula: where Q i (t) is the emission intensity of the i-th pollution source (unit: mg / s), x i is the location of the i-th pollution source, and δ(x - x i ) is the spatial distribution function of the location x and the pollution source location x i .

[0036] To improve the applicability of the model, in this embodiment, dynamic data such as water flow velocity, pollutant concentration, and diffusion coefficient are collected through a real-time monitoring system, and the input parameters of the model are updated at any time.

[0037] In some embodiments, the diffusion coefficient D(x, t) and the pollution source term S(x, t) can be adjusted in real time based on the water quality monitoring data collected by sensors. For example: When the water flow velocity v(x, t) increases, the contribution of the convection effect is enhanced, and the weight of the convection term can be dynamically adjusted. When the emission intensity Q i (t) of the pollution source suddenly increases, the model will automatically correct the magnitude of the pollution source term S(x, t).

[0038] Generally speaking, the implementation of step S1 comprehensively considers the influence of water flow, diffusion, and pollution sources by constructing a basic model of pollutant diffusion. By using the diffusion-convection equation and related parameters, the model can accurately describe the dynamic change process of pollutants in water bodies, providing strong basic support for subsequent steps such as adaptive optimization control, Kalman filtering, and spatio-temporal data fusion. Through this step, the model can provide clear and accurate pollution diffusion predictions for subsequent optimization and decision-making support.

[0039] Regarding step S2, the present invention adopts a scheme for dynamically optimizing the control parameters of the pollution diffusion model. By the feedback of real-time monitoring data, key control variables such as the emission amount of pollution sources and the water flow rate are adjusted to optimize the pollution diffusion path. This step follows immediately after the construction of the basic model and is dynamically adjusted based on the preliminary results of the pollution diffusion model. Its core purpose is to real-time correct and optimize the model during the process of pollution diffusion so that it can effectively cope with various complex and sudden environmental changes.

[0040] In this embodiment, first, according to the pollution diffusion model constructed in step S1, the change law of pollutant concentration is determined. Then, according to the objective function of the pollution diffusion model, the control variables are calculated by an optimal control algorithm. The objective function is generally defined in the following form: where J is the objective function, C(x,t) is the pollutant concentration (unit: mg / L), representing the concentration of pollutants at a certain position x and time t in the water body; Ω is the computational domain, usually the polluted area of the water body; t0 and t f are the starting time and ending time of the simulation.

[0041] The role of the objective function is to optimize the pollution diffusion path by minimizing the weighted sum of squares of pollutant concentrations, making it achieve the minimum pollution diffusion effect. Specifically, the optimization of the pollution diffusion path not only needs to consider the pollution source intensity, but also needs to consider the influence of variables such as the water flow velocity and diffusion coefficient. Therefore, this objective function ensures the optimal combination of control variables such as the emission amount of pollution sources and the water flow rate.

[0042] As an option, the adjustment process of control variables is based on real-time monitoring data. When the monitoring data indicates that the pollutant concentration reaches a certain threshold, the system can relieve pollution diffusion by increasing the water flow rate or adjusting the emission amount of pollution sources, thereby reducing the pollutant concentration to an acceptable level.

[0043] Furthermore, this embodiment adopts a recursive feedback mechanism to dynamically adjust the pollution diffusion path according to the error between real-time monitoring data and model prediction results. The steps of real-time feedback correction are as follows: uk = u k-1 + Δu k where u k is the control variable at time k, representing the emission intensity of the pollution source or the water flow rate; u k-1 is the control variable at time k - 1; Δu k is the adjustment amount of the control variable.

[0044] Through this adjustment mechanism, the system can optimize the pollution diffusion model in real time and ensure the effectiveness of the control variable. Specifically, by making feedback corrections based on the difference between the predicted pollution diffusion path and the actual data, the pollution source intensity and water flow rate are adjusted, ultimately minimizing the pollutant concentration.

[0045] In some embodiments, to further improve the control accuracy, the feedback data of multiple sensors can be combined to ensure more accurate adjustment of the pollution source emission amount and water flow rate. For example, if the water flow rate is too low and the pollution source intensity is too high, the system can automatically adjust the water flow rate to increase the flow rate to accelerate the pollutant diffusion process, thereby carrying the pollutants to a farther area to reduce the pollution concentration in the local area.

[0046] Generally speaking, step S2 ensures the real-time adjustment and precise control of the pollution diffusion model by dynamically optimizing the control variable. By combining control variables such as the pollution source intensity and water flow rate with real-time data, the present invention can continuously optimize the pollution diffusion path in a complex and changeable environment and achieve precise control of the pollutant concentration.

[0047] In step S3, the present invention uses the Kalman filter algorithm to perform precise state estimation and correction on the pollution diffusion path to ensure the accuracy of the pollution diffusion prediction. This process follows the steps of constructing and optimizing the control of the pollution diffusion model. Through the two basic steps of the Kalman filter - prediction and update, the predicted value of the pollutant concentration is corrected to improve the model accuracy.

[0048] In this embodiment, the Kalman filter algorithm realizes precise control of the pollutant concentration prediction through real-time estimation and correction of the pollution diffusion path. This process includes two major steps. First, the change of the pollutant concentration is predicted through the pollution diffusion model, and then the model is corrected based on the real-time monitoring data, and finally the updated concentration estimate is obtained.

[0049] Prediction step In the prediction step, the state prediction of the pollutant concentration is based on the state at the previous moment and the influence of the control variable. This prediction is carried out through the following formula: where The predicted value of the pollutant concentration at time k, which represents the pollutant concentration at time k based on the previous state and the control input u k-1 obtained; A is the state transition matrix, representing the state transition process in the pollutant diffusion model, usually calculated according to the diffusion law of pollutants in water bodies. This matrix takes into account the spatial and temporal variations of pollutants; B is the control input matrix, describing the influence of control variables (such as water flow rate, pollutant source emission, etc.) on the pollutant concentration. This matrix maps the control variables to the change in pollutant concentration; u k-1 is the control variable at time k - 1, representing parameters such as pollutant source intensity or water flow rate. The changes of these control variables within a certain time directly affect the pollutant diffusion process.

[0050] In the prediction step, the predicted value of the pollutant concentration is calculated based on the combination of the predicted concentration value at the previous time and the control variable u k-1 , thereby providing a preliminary estimate of the pollutant diffusion path.

[0051] Update step The update step is a key part of the Kalman filter, mainly by correcting the predicted value of the pollutant concentration through the difference between the real-time observation data and the prediction result of the pollution diffusion model. The update step is carried out according to the following formula: K k = P k|k-1 H T (HP k|k-1 H T + R) -1 P k|k = (I - K k H)P k|k-1 where K k is the Kalman gain, representing the weighted ratio of the prediction result and the observation result. The Kalman gain is calculated through the covariance matrix and the observation matrix, controlling the amplitude of model correction; P k|k-1 is the prediction covariance matrix, representing the error of pollutant concentration prediction. It measures the uncertainty of the predicted concentration. The smaller the covariance, the more accurate the prediction; H is the observation matrix, representing the relationship between the observed value of the pollutant concentration and the state variable. The observation matrix maps the actually measured pollutant concentration data to the state space predicted by the model; R is the observation noise covariance, representing the noise or error of the observation data. This parameter is used to adjust the trust degree of the observation data. A larger R value indicates a larger error in the observation data; y k is the actual observation value at time k, representing the pollutant concentration collected by sensors or monitoring systems; is the predicted pollutant concentration at time k; is the updated pollutant concentration at time k, representing the pollutant concentration corrected according to real-time observation data; P k|k is the updated covariance matrix, representing the uncertainty of the updated concentration prediction.

[0052] The Kalman gain K k calculates the weight ratio between the predicted value and the observed value, ensuring that the prediction results with larger errors are corrected by more actual observation data. Through this update step, the pollution diffusion model can self-correct according to real-time observation data, correct the estimation of pollutant concentration, and reduce errors.

[0053] As an extended implementation, the Kalman filter algorithm can also be combined with other data fusion techniques, such as the Extended Kalman Filter (EKF) or the Unscented Kalman Filter (UKF). These methods can provide better estimation accuracy in non-linear pollution diffusion models. In non-linear pollution diffusion problems, EKF and UKF can further improve the adaptability of the model when dealing with non-linear state transitions and observations.

[0054] Through the detailed description of step S3 above, the Kalman filter algorithm not only provides real-time state estimation for the optimization of the pollution diffusion path, but also effectively corrects the errors in the model prediction.

[0055] For step S4, the present invention introduces a spatio-temporal data fusion technique to optimize the prediction results of the pollution diffusion model by combining the real-time data from multiple monitoring sources. Through the method of weighted average, the pollutant concentration data from different monitoring sources are integrated to make up for the possible deficiencies of a single monitoring source and improve the accuracy and reliability of pollution diffusion prediction. Through this technical means, the spatial distribution of pollutant concentration is estimated more accurately, providing a more scientific and accurate basis for pollution control and emergency response.

[0056] In this embodiment, the basic idea of spatio-temporal data fusion is to perform weighted average on the pollutant concentrations from different data sources such as different sensors, monitoring stations, remote sensing devices, etc., so as to obtain a more accurate estimation of pollutant concentration. The key to this process is how to assign appropriate weights w i (t) to each monitoring source. The magnitude of the weight depends on the reliability, accuracy, and timeliness of the data source. The goal of spatio-temporal data fusion is to perform weighted average on the concentration data C i (x,t) from multiple monitoring sources to obtain a fused pollutant concentration The specific calculation method is as follows: where, is the optimized pollutant concentration at time t and location x, representing the final estimated pollution concentration after spatio-temporal data fusion (unit: mg / L); C i (x, t) is the pollutant concentration data of the i-th monitoring source (unit: mg / L), representing the pollution concentration from different sensors or data sources; w i (t) is the weight coefficient of the i-th monitoring source, representing the contribution degree of each monitoring source in data fusion. The weight coefficient w i (t) is dynamically adjusted according to the reliability, accuracy and timeliness of the monitoring source; N is the number of monitoring sources, representing the total number of monitoring sources participating in spatio-temporal data fusion.

[0057] To ensure the more accurate prediction results of the pollution diffusion model, in the present invention, the weight coefficient w i (t) calculation takes into account multiple factors, such as data source error, monitoring frequency, spatial distribution, etc. Specifically, the weight coefficient of the monitoring source is proportional to the accuracy and timeliness of its data. Monitoring sources with smaller data errors or higher update frequencies will be given larger weights.

[0058] Generally, the weight coefficient w i (t) can be calculated by the following formula: where is the measurement error variance of the i-th monitoring source, representing the noise level of this monitoring source. A smaller error variance indicates that this data source is more reliable and should be given a larger weight; N is the number of monitoring sources, representing all monitoring sources participating in spatio-temporal data fusion.

[0059] The weight coefficient is calculated based on the error variance of each monitoring source. The smaller the error variance, the more reliable the data of this monitoring source and the larger the weight coefficient. Therefore, more accurate monitoring sources will contribute more to the estimated result of the final pollution concentration.

[0060] In practical applications, the pollutant concentration data not only depends on the data of ground monitoring stations, but also includes multiple sources such as remote sensing data, meteorological data and historical monitoring data. Spatio-temporal data fusion can generate a more accurate pollutant concentration distribution map in real time through weighted calculation of these data sources. Specifically, the system can perform weighted processing on the data according to the timeliness and spatial distribution of different monitoring sources to ensure the more accurate prediction results of the pollution diffusion model.

[0061] For example, some monitoring sources may have a high spatial resolution but a low data update frequency; while others have a high timeliness but a low spatial resolution. Through data fusion, the information of these sources can be comprehensively utilized to make up for the deficiencies of each monitoring source, thereby improving the overall prediction ability of the pollution diffusion model.

[0062] In addition, during the process of pollutant diffusion, environmental factors (such as meteorological changes, terrain, etc.) may affect the prediction of the pollution diffusion model. Therefore, spatio-temporal data fusion not only considers pollutant concentration, but also combines various information such as meteorological data and historical pollution data to obtain a more comprehensive pollution prediction result.

[0063] As a possible expansion method, spatio-temporal data fusion technology can also be optimized by combining more advanced algorithms, such as weighted algorithms or adaptive filtering methods based on machine learning. In these expansion methods, the system not only considers the spatial distribution of pollutant concentration, but also combines multi-dimensional information such as meteorological factors and historical pollution data for data fusion to further improve the prediction accuracy.

[0064] For example, using deep learning methods to train historical data can make the data fusion process more intelligent, automatically adjust the weight coefficients of each data source, and thus achieve more efficient pollution diffusion prediction.

[0065] The spatio-temporal data fusion technology in the present invention combines real-time data from multiple monitoring sources and optimizes the prediction result of the pollution diffusion model through the weighted average method.

[0066] The core of step S5 is to convert the prediction results of the optimized pollution diffusion model obtained in the previous steps into specific emergency response measures. The role of the decision support system is crucial in this process. By combining pollution diffusion prediction, real-time monitoring data, and environmental changes, the decision support system can provide clear and operable emergency response plans for environmental management departments. Compared with traditional pollution response systems, the present invention can not only improve the response speed, but also significantly enhance the accuracy of pollution diffusion prediction, ensuring that all emergency measures are timely and effective.

[0067] In this embodiment, the decision support system generates a spatio-temporal distribution map of pollution diffusion based on the spatio-temporal data fusion result of step S4 and the Kalman filter correction data of step S3. This graphical output result helps the management department quickly identify the polluted areas, pollutant concentration hotspots, and potential paths of pollution diffusion. The spatio-temporal distribution map of pollutant concentration not only considers the current state of pollutants, but also predicts the pollution development trend in the next few hours or days.

[0068] Generally, the visualization map of pollution diffusion can display the concentration changes of pollutants in different regions through time series. This graphical result provides the spatial layout of pollutant diffusion, the impact of pollution sources, and the impact of external factors (such as water flow, wind speed, etc.), enabling the decision support system to provide accurate emergency response suggestions.

[0069] For example, in some embodiments, the decision support system automatically calculates the time and area when the pollutant concentration reaches a preset threshold based on the pollution diffusion prediction results. This information is used to guide the environmental management department to deploy emergency resources (such as cleaning teams, isolation measures, etc.) and provide suggestions for evacuation areas. In addition, the system will also update the prediction results in real time according to factors such as meteorological changes and watershed characteristics, so as to flexibly adjust the emergency response measures.

[0070] In a possible implementation, the optimized pollution diffusion results are converted into an emergency response plan through the spatio-temporal analysis model in the system. By continuously monitoring the location of the pollution source, the diffusion path, and the pollutant concentration, the system generates a specific area map of pollution diffusion and gives the predicted time window of impact. This plan not only helps the management department quickly identify the affected areas but also provides data support for subsequent emergency decision-making.

[0071] Specifically, when the prediction results show that the pollution concentration in a certain area is about to exceed the safety standard, the system will automatically send an alarm to the decision-maker, indicating that preventive measures need to be taken. For example, the system can suggest increasing the water flow rate or taking blocking measures to slow down the pollution diffusion, or arranging the evacuation of personnel according to the diffusion path of the pollutant.

[0072] The decision support system can adjust the emergency response plan in a timely manner by continuously receiving real-time monitoring data and model prediction results. In some embodiments, the system can adaptively adjust the emergency strategy according to different pollution sources, pollutant types, and environmental factors. For example, if the pollution source changes or a new pollution source appears, the system will immediately update the pollution diffusion model and adjust the emergency response measures.

[0073] In the system, a feedback mechanism is used to ensure the timeliness and accuracy of the emergency response. The system continuously optimizes the pollution diffusion prediction based on real-time data and automatically updates the decision-making suggestions. For pollution sources that change rapidly (such as sudden industrial emissions), the system will continuously adjust the pollution diffusion model to ensure that the emergency response plan is updated in a timely manner to cope with emergencies.

[0074] As an extension, the decision support system of this embodiment can also be integrated with other intelligent systems (such as an automatic regulation and control system) to further improve the automation level of the emergency response. For example, the system can automatically adjust control parameters such as the water flow rate and the pollutant emission amount according to the pollution diffusion situation to automatically respond to pollution incidents. In addition, combined with emerging technologies such as drones and robots, the system can also provide a more comprehensive emergency response plan.

[0075] Through the above description, step S5 ensures that the decision support system can generate an emergency response plan in real time according to the prediction results of the optimized pollution diffusion model. This step details how to use the pollution diffusion results to guide emergency decision-making and ensures the combination of real-time data and prediction results, improving the accuracy and timeliness of emergency response.

[0076] Please refer to Figure 2 , the present invention also provides an emergency pollution diffusion model construction system, including: A real-time data acquisition module for collecting water quality data, meteorological data, and pollution source data; A data processing module for performing multi-scale adaptive optimal control, extended Kalman filtering, and spatio-temporal data fusion processes in the emergency pollution diffusion model construction method; A decision support module for providing emergency response decision support for environmental management departments based on the optimized pollution diffusion results.

[0077] For the real-time data acquisition module, the main function of this module is to collect various types of real-time data related to pollution diffusion, including water quality data, meteorological data, and pollution source data. Water quality data includes basic water quality parameters such as pollutant concentration, pH value, dissolved oxygen, and temperature, which are the basis of the pollution diffusion model. Meteorological data includes factors such as wind speed, wind direction, precipitation, and temperature, which help to understand the impact of environmental factors on the pollutant diffusion path. And pollution source data includes the real-time emission intensity and emission location from various pollution sources (such as industrial emissions, agricultural runoff, etc.).

[0078] To ensure the real-time and accurate data, this module collects data through various means such as multiple sensor networks, satellite remote sensing, and meteorological stations. To reduce data errors, the module also ensures the reliability of the collected data through a data verification and error correction mechanism. The module can also perform data preprocessing, such as noise filtering, data interpolation, and time series compensation, to ensure that the data meets the expected quality standards when entering the next processing link.

[0079] For the data processing module, the data processing module is the core of the entire system and is used to perform various calculations and optimization processes of the pollution diffusion model. First, the module updates the pollution diffusion model according to the real-time collected data to ensure its dynamic adaptation to the real-time changing environmental conditions. The main tasks of this module include the following aspects: Multi-scale adaptive optimal control: Adjust control variables such as pollution source emissions and water flow rates according to the spatio-temporal distribution of pollutant concentrations. Through the optimal control algorithm, ensure the minimization of pollutant concentration and optimize the pollution diffusion path.

[0080] Extended Kalman Filter (EKF): In pollution diffusion prediction, the Extended Kalman Filter is used to handle nonlinear problems. The system continuously adjusts the estimation of pollutant concentration according to the prediction and update steps of the Kalman Filter to improve the accuracy of the pollution diffusion path.

[0081] Spatio-temporal data fusion: By performing weighted averaging on data from multiple monitoring sources, the system can provide more accurate pollution diffusion predictions. Spatio-temporal data fusion ensures that pollutant concentration predictions can comprehensively consider the differences between different environmental factors and data sources, improving the stability and adaptability of the system.

[0082] For the decision support module, the decision support module is the core that provides decision support for emergency response to environmental management departments based on the optimized pollution diffusion results. This module combines real-time monitoring data, pollution diffusion predictions, and the results of spatio-temporal data fusion to generate a clear pollutant concentration distribution map and provides the following functions according to the prediction results: Pollution diffusion prediction: Provide the spatio-temporal distribution of pollution diffusion to management departments to help decision-makers quickly identify pollution hotspots and affected areas.

[0083] Emergency response plan generation: According to the spatio-temporal changes and diffusion trends of pollutant concentrations, the system automatically generates an emergency response plan. The plan includes content such as control of pollution sources, isolation of pollutants, and evacuation areas for personnel to effectively reduce the harm caused by pollution diffusion.

[0084] Real-time decision adjustment: During the pollution diffusion process, environmental conditions and pollution source situations may change. The system can adjust the emergency response strategy according to new real-time data. Through a feedback mechanism, the decision support module can timely correct the previous response plan to ensure the timeliness and accuracy of emergency measures.

[0085] 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 to 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. A method for constructing an emergency pollution diffusion model, characterized in that: The following steps are involved: Construct a basic model of pollutant diffusion to describe the diffusion process of pollutants in water bodies over time and space; Dynamically optimize the control parameters of the pollution diffusion model, adjust the control variables in real time, and optimize the pollution diffusion path through an adaptive optimization control method; Based on real-time monitoring data and the prediction results of the pollution diffusion model, the Kalman filter algorithm is used to estimate and correct the pollution diffusion path; Combine real-time data from different monitoring sources and use spatiotemporal data fusion technology to optimize the prediction results of pollution diffusion models; According to the optimized pollution diffusion results, emergency response plans are provided to environmental management departments through the decision support system.

2. The method for constructing an emergency pollution diffusion model according to claim 1, characterized in that: The model includes the effects of pollutant concentration, flow velocity, diffusion coefficient, and pollution source.

3. The method for constructing an emergency pollution diffusion model according to claim 1, characterized in that: The basic model of pollutant diffusion is described by the diffusion-convection equation, the pollutant concentration varies with time and space, and the water flow velocity, diffusion coefficient and pollution source term are all input parameters.

4. The method for constructing an emergency pollution diffusion model according to claim 3, characterized in that: The diffusion-convection equation is: Where, C(x,t) is the pollutant concentration; v(x,t) is the water velocity; D(x,t) is the diffusion coefficient; is the spatial gradient of pollutant concentration, indicating the rate of change of pollutant concentration in space; is the Laplace operator of pollutant concentration, which describes the diffusion behavior of pollutant concentration in space; S(x,t) is the pollution source term, which represents the pollutant emission from the pollution source to the water body at position x and time t.

5. The method for constructing an emergency pollution diffusion model according to claim 1, characterized in that: The control variable adjustment comprises the following steps: According to the objective function J in the pollution diffusion model, the control variable u(t) is calculated by the optimal control algorithm to minimize the objective function: Where J is the objective function, C(x, t) is the pollutant concentration, Ω is the computational domain, t0 and t f is the start time and end time of the simulation; according to the real-time monitoring data and prediction results, by adjusting the pollution source emission u(t) and water flow rate, the pollution diffusion path is dynamically optimized, and the control variables are updated in real time to ensure that the pollutant concentration is minimized.

6. The method for constructing an emergency pollution diffusion model according to claim 1, characterized in that: The adaptive optimization control method comprises the following steps: Determine the objective function J of pollutant concentration, and optimize the pollution diffusion path according to the control variables such as pollution source intensity and water flow rate. By feedback correction of the difference between the prediction and actual data of the pollutant diffusion path, adjust the control variables such as pollution source intensity and water flow rate. The correction steps are as follows: u k =u k-1 +Δu k Among them, u k is the control variable at time k, indicating the emission intensity or water flow rate of the pollution source; u k-1 is the control variable at time k-1; Δu k is the adjustment amount of the control variable; The pollution diffusion path is adjusted according to real-time data to minimize the objective function and ensure the accuracy of pollutant diffusion prediction.

7. The method for constructing an emergency pollution diffusion model according to claim 1, characterized in that: The Kalman filter algorithm comprises the following steps: Prediction step, based on the previous state of the pollution diffusion model and control input u k-1 , predict the current state in, is the predicted value of pollutant concentration at time k, indicating that at time k, based on the state at the previous moment and control input u k-1 The obtained pollutant concentration; A is the state transfer matrix, which represents the state transfer process in the pollutant diffusion model; B is the control input matrix, which describes the influence of the control variable on the pollutant concentration. The matrix maps the control variable to the change of the pollutant concentration; u k-1 is the control variable at time k-1; Update step, based on the actual measured value y k and predicted values Perform an update and calculate the Kalman gain.

8. The method for constructing an emergency pollution diffusion model according to claim 1, characterized in that: The spatiotemporal data fusion technology performs weighted averaging of data from different monitoring sources to optimize the spatiotemporal prediction results of pollution diffusion and update the pollution diffusion path in real time.

9. The method for constructing an emergency pollution diffusion model according to claim 1, characterized in that: The decision support system provides the environmental management department with spatial and temporal visualization of pollution diffusion, concentration analysis and emergency response measures based on the pollution diffusion prediction results.

10. An emergency pollution diffusion model construction system, applied to an emergency pollution diffusion model construction method according to any one of claims 1 to 9, characterized in that: include: Real-time data collection module, used to collect water quality data, meteorological data and pollution source data; A data processing module for executing the multi-scale adaptive optimal control, extended Kalman filtering and spatiotemporal data fusion processes in the emergency pollution diffusion model construction method; The decision support module is used to provide emergency response decision support for the environmental management department based on the optimized pollution diffusion results.

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