New pollutant treatment effect digital twinborn deduction method and system

Abstract

The invention relates to the technical field of environmental pollution abatement, in particular to a new pollutant abatement effect digital twinborn deduction method and system, and the method comprises the steps: obtaining hydrogeological parameters and pollutant characteristic data of a polluted site; constructing a three-dimensional digital twinborn body of the polluted site; establishing a fluid-solid coupling calculation model of pollutant diffusion; collecting real-time monitoring data and inputting the data into the digital twin; constructing a multi-agent environment sensing system to sense a pollution state and dynamic characteristics; a treatment scheme is generated through deep reinforcement learning, wherein the treatment scheme comprises environment state space construction, pollution situation reasoning and treatment strategy optimization; determining a technical combination and parameter configuration of pollutant treatment; implementing treatment; an actual treatment effect is monitored and fed back; the digital twinborn and deep reinforcement learning model is calibrated, through fusion application of digital twinborn and multi-agent deep reinforcement learning, accurate prediction and optimal control are achieved, the treatment cost can be reduced by 30%-50%, and the treatment period can be shortened by 40%-60%.

Description

Technical Field

[0001] This invention relates to the field of environmental pollution control technology, and in particular to a digital twin simulation method and system for the treatment effect of new pollutants, which can be applied to industrial site remediation, groundwater pollution control, remediation of complex polluted sites, and emergency response to sudden pollution incidents. Background Technology

[0002] Traditional pollution control methods rely mainly on experience-based judgments, lacking accurate predictive capabilities and dynamic adjustment mechanisms, resulting in problems such as poor treatment effects, excessively high costs, and excessively long cycles.

[0003] Existing pollution control technologies typically employ single treatment schemes, failing to optimize and adapt to complex and ever-changing pollution scenarios. Furthermore, the lack of effective simulation methods makes it difficult to accurately predict the actual effectiveness of treatment schemes, leading to difficulties in timely correction of deviations during implementation. In addition, traditional treatment methods struggle to effectively coordinate the combined application of multiple treatment technologies, failing to fully leverage their synergistic effects.

[0004] Digital twin technology, as an emerging technology that maps physical entities to virtual models, has shown great potential in fields such as industrial production and urban management. However, applying digital twin technology to the treatment of new pollutants still faces many challenges, including: how to accurately construct digital twins of contaminated sites; how to accurately simulate the pollutant diffusion process; how to perform intelligent decision optimization based on digital twins; and how to continuously optimize models and decisions through feedback mechanisms. Summary of the Invention

[0005] The purpose of this invention is to provide a digital twin simulation method and system for the treatment effect of new pollutants, aiming to solve the problems of low prediction accuracy, low treatment efficiency, and difficulty in cost control in the existing technology.

[0006] This invention proposes a big data-driven dynamic pricing intelligent decision-making system, comprising: Obtain hydrogeological parameters and pollutant characteristics data of contaminated sites; Based on the aforementioned hydrogeological parameters and pollutant characteristic data, a three-dimensional digital twin of the contaminated site is constructed; Based on the three-dimensional digital twin of the contaminated site, a fluid-structure interaction calculation model for pollutant diffusion is established; Collect real-time monitoring data of the contaminated site and input the real-time monitoring data into the three-dimensional digital twin of the contaminated site; Based on the three-dimensional digital twin of the contaminated site, a multi-agent environmental perception system is constructed. The multi-agent environmental perception system is used to perceive the pollution status and dynamic characteristics of the contaminated site. Based on the pollution state and dynamic characteristics perceived by the multi-agent environmental perception system, a governance scheme is generated through deep reinforcement learning. The deep reinforcement learning includes environmental state space construction, pollution situation reasoning, and governance strategy optimization. Based on the aforementioned treatment plan, the combination of technologies and parameter configurations for pollutant treatment are determined; The aforementioned combination of technologies and parameter configurations are used for pollutant treatment; Monitor the actual remediation effect and feed it back to the three-dimensional digital twin of the contaminated site; Based on the difference between the actual and expected remediation effects, the three-dimensional digital twin of the contaminated site and the deep reinforcement learning model are calibrated.

[0007] Preferably, the construction of the three-dimensional digital twin of the contaminated site includes: Based on the aforementioned hydrogeological parameters, a multi-scale spatial representation including topography, groundwater structure, and pollutant distribution is constructed. Establish a spatiotemporal fusion model of static spatial structure and dynamic pollution process; The contaminated site is spatially discretized to form a grid or finite element structure; Assign hydrogeological parameters and pollutant concentration attributes to discrete units; Establish a mapping relationship between monitoring time series data and spatial units.

[0008] Preferably, the fluid-structure interaction calculation model for pollutant diffusion includes: Construct the hydraulic field equations for groundwater flow to describe the velocity and pressure fields of the water flow; Construct an adsorption-desorption kinetic model for pollutants in soil; Construct mass transfer process models for pollutants in aqueous and solid phases; Establish the interaction mechanism of hydraulic field, adsorption field and mass transfer field; To achieve adaptive optimization of the computational grid, increase the grid density in regions with high contamination gradients and decrease the grid density in regions with low contamination gradients.

[0009] Preferably, the construction of the environmental space includes: The physical parameters of the contaminated site are mapped to a multidimensional state space to form a topological representation of the contamination state. Establish a multi-scale sensing topology to achieve multi-level sensing from macro-regions to micro-viewpoints; Extract dynamic characteristics of the pollution field and construct a pollution dynamics model; Nonlinear diffusion mapping is applied to the monitoring data to generate a low-dimensional representation; Based on the aforementioned low-dimensional characterization, key features and trends of pollution fields are identified.

[0010] Preferably, the pollution situation reasoning includes: Define and calculate the environmental entropy of contaminated sites to quantify the uncertainty of pollution distribution; Construct a multi-level neural network to extract hierarchical feature representations of the pollution field from the sensing data; Based on the Bayesian probability framework, the cognitive uncertainty and accidental uncertainty of the prediction results are quantified; Applying the principle of minimizing environmental entropy, we deduce the optimal sequence of governance behaviors; Construct a prediction error feedback mechanism to adjust model parameters based on historical prediction errors.

[0011] Preferably, the governance strategy optimization includes: Analyze the chaotic characteristics of the pollution diffusion system and identify the chaotic and deterministic regions in the system; In deterministic regions, precise control strategies are employed to directly optimize governance parameters; In the chaotic region, a stable periodic orbit method is used to guide the system to a controllable state; To achieve spatiotemporal adaptive adjustment of governance strategies, thereby improving control precision and frequency in highly polluted areas; A feedback stability assurance mechanism is established, and control parameters are adjusted by monitoring the system status in real time.

[0012] Preferably, the determination of the technology combination and parameter configuration for pollutant treatment includes: Analyze the characteristics and synergistic effects of treatment technologies such as electro-Fenton oxidation and biostimulation; Based on the type and concentration distribution of pollutants, determine the combination of treatment technologies, including sequential combination, parallel combination, or alternating combination; Optimize the parameters of electro-Fenton oxidation, including dosage, electrode current and reaction time; Optimize biostimulation parameters, including strain selection, nutrient formulation, and dosing frequency; Generate a governance implementation plan that includes time, space, and resource plans.

[0013] Preferably, the monitoring of actual treatment effects includes: Assess the absolute reduction in pollutant concentration, the relative reduction rate, and the compliance with environmental standards; Analyze the changes in the area of ​​the pollution plume, its spatial uniformity, and the elimination of hotspot areas; Assess the initial response rate, duration of effectiveness, and potential rebound trends in the treatment process; Monitor changes in soil physicochemical properties, their impact on microbial communities, and potential risks of secondary pollution; Generate multi-dimensional governance effectiveness evaluation reports.

[0014] Preferably, calibrating the three-dimensional digital twin of the contaminated site and the deep reinforcement learning model includes: Based on expert knowledge and historical data, determine the prior distribution of model parameters; Integrate different types of monitoring data, standardize the data, and assign weights. A Bayesian framework is used to update the parameter distribution and assess parameter uncertainty. Update the model parameters and evaluate the model performance using cross-validation; Build a model knowledge base, accumulate governance experience, and apply it to new scenarios.

[0015] The digital twin simulation system for the effectiveness of new pollutant treatment includes: The data acquisition module is used to acquire hydrogeological parameters, pollutant characteristic data, and real-time monitoring data of the contaminated site; A digital twin construction module is used to construct a three-dimensional digital twin of the contaminated site based on the hydrogeological parameters and pollutant characteristic data; The fluid-structure interaction calculation module is used to establish a fluid-structure interaction calculation model for pollutant diffusion based on the three-dimensional digital twin of the contaminated site. A multi-agent environmental perception module is used to perceive the pollution status and dynamic characteristics of the polluted site based on the three-dimensional digital twin of the polluted site. The deep reinforcement learning module is used to generate a governance plan based on the pollution state and dynamic characteristics perceived by the multi-agent environmental perception module. The deep reinforcement learning module includes an environmental state space construction unit, a pollution situation reasoning unit, and a governance strategy optimization unit. The governance scheme optimization module is used to determine the combination of technologies and parameter configurations for pollutant treatment based on the governance scheme. The treatment execution module is used to implement the aforementioned technology combination and parameter configuration for pollutant treatment; The effect evaluation module is used to monitor the actual treatment effect and feed the actual treatment effect back to the three-dimensional digital twin of the contaminated site; The model calibration module is used to calibrate the three-dimensional digital twin of the contaminated site and the deep reinforcement learning model based on the difference between the actual and expected remediation effects.

[0016] This invention combines digital twin technology with multi-agent deep reinforcement learning to construct a complete new system for predicting and optimizing the effectiveness of pollutant control. This system can accurately construct a three-dimensional digital twin of a contaminated site, precisely simulate the pollutant diffusion process, achieve intelligent decision optimization based on multi-agent environmental perception and deep reinforcement learning, and continuously calibrate the model and optimize decisions through a feedback mechanism.

[0017] The present invention has the following beneficial effects: 1. Improved the accuracy of pollutant treatment prediction. By constructing accurate three-dimensional digital twins and fluid-structure interaction calculation models of contaminated sites, the accuracy of pollutant concentration prediction was improved by 45% to 60%, providing a reliable basis for the formulation of treatment plans.

[0018] 2. Reduced governance costs. The intelligent decision-making mechanism based on multi-agent deep reinforcement learning can optimize the combination of governance technologies and parameter configuration, reduce the amount of reagents used and labor costs, and reduce the overall governance cost by 30% to 50%.

[0019] 3. Shortened remediation cycle. Through precise simulation and optimized control of the remediation process, the remediation cycle can be shortened by 40% to 60%, accelerating the restoration and utilization of contaminated sites.

[0020] 4. Improved treatment effectiveness. The multi-technology synergistic optimization mechanism can fully leverage the synergistic effect of various treatment technologies, increasing pollutant removal rate by 20%–30% and improving the uniformity of residual pollutants by more than 50%.

[0021] 5. Enhanced adaptability. The system can adapt to unforeseen circumstances such as changes in hydrological conditions and sudden pollution events, automatically adjusting its treatment strategies and demonstrating strong environmental adaptability and anti-interference capabilities. Attached Figure Description

[0022] Figure 1 This is a flowchart of the new pollutant treatment effect digital twin simulation method of the present invention; Figure 2 This is a schematic diagram of the structure of the three-dimensional digital twin construction module for contaminated sites according to the present invention; Figure 3 This is a schematic diagram of the fluid-structure interaction calculation model of the present invention; Figure 4 This is an architecture diagram of the multi-agent environmental perception system of the present invention; Figure 5 This is a structural diagram of the deep reinforcement learning module of the present invention; Figure 6 This is the overall architecture diagram of the new pollutant treatment effect digital twin simulation system of the present invention. Detailed Implementation

[0023] Please refer to Figures 1-6 The specific embodiments of the present invention will now be described in detail with reference to the accompanying drawings.

[0024] Reference Figure 1 The new digital twin simulation method for pollutant treatment effects provided by this invention includes the following steps: Step S1: Obtain hydrogeological parameters and pollutant characteristics data of the contaminated site.

[0025] In one embodiment of the present invention, the hydrogeological parameters include groundwater flow direction, groundwater flow velocity, groundwater depth, groundwater flow time, and soil permeability coefficient. Preferably, the groundwater flow direction can be east-west, north-south, or a combination of both; the groundwater flow velocity is typically 0.01–10 m / d; the groundwater depth is generally between 0.5 and 50 m; and the soil permeability coefficient varies depending on the soil type, with a typical value around 10. -8 -10 -3 Between m / s. The pollutant characteristic data includes pollutant type, concentration distribution, diffusion coefficient, adsorption coefficient, etc. For example, for a typical organic pollutant such as trichloroethylene, its diffusion coefficient in porous media is typically between [value missing]. The adsorption coefficient varies from 0.5 to 5 L / kg depending on the soil organic matter content.

[0026] In addition, historical monitoring data and governance experience data need to be acquired to provide basic data support for subsequent model construction and optimization. Data acquisition can be carried out through various methods such as on-site sampling, laboratory analysis, historical record querying, and integration of expert knowledge.

[0027] Step S2: Based on the hydrogeological parameters and pollutant characteristic data, construct a three-dimensional digital twin of the contaminated site.

[0028] Reference Figure 2 The process of constructing a three-dimensional digital twin of a contaminated site includes two key steps: multi-scale spatial representation and spatiotemporal fusion modeling. Multi-scale spatial representation enables comprehensive modeling from macroscopic topography (resolution 1–5 m) to microscopic pollution distribution (accuracy down to the centimeter level). Spatiotemporal fusion modeling, through steps such as spatial discretization, parameter assignment, time series correlation, spatiotemporal interpolation, and spatiotemporal consistency verification, achieves seamless integration of static structure and dynamic processes.

[0029] Specifically, spatial discretization can employ methods such as finite difference, finite element method, or finite volume method to transform continuous space into a discrete mesh. For example, for a 100m × 100m × 10m site, a 1m × 1m × 0.5m mesh can be used to create a 2 × 10n mesh. 5 Each grid cell is a discrete unit. During parameter assignment, corresponding hydrogeological parameters and initial pollutant concentration values ​​are assigned to each grid cell. Time series correlation establishes a correspondence between the monitoring time series data and the spatial units, achieving a unified expression of spatial and temporal dimensions.

[0030] Spatiotemporal interpolation uses the Kriging interpolation method to fill data gaps between monitoring points. The mathematical expression for Kriging interpolation is: , in, For position The estimated value at that location, For the first Observations at known points, Here, n represents the weighting coefficient, and n is the number of known points. Location and These are three-dimensional spatial coordinates, representing the specific location of the sampling point.

[0031] Weighting coefficient The following system of equations was obtained: , , in, For position and The semivariogram values ​​between These are Lagrange multipliers used to ensure the weight sum is 1 as a constraint. The semivariogram is typically modeled using a spherical, exponential, or Gaussian model, with the most suitable model chosen based on the characteristics of the actual data. For example, the semivariogram expression for a spherical model is: , in, The distance between two points The nugget value (representing small-scale variability and measurement error), The sill value (a measure of structural variability), For range (representing the range of spatial correlation).

[0032] Step S3: Based on the three-dimensional digital twin of the contaminated site, establish a fluid-structure interaction calculation model for pollutant diffusion.

[0033] Reference Figure 3 The fluid-structure interaction computational model is based on the principle of multiphysics coupling and constructs a three-phase coupling model of groundwater-soil-pollutants. This model comprehensively considers the interaction of hydraulic field, adsorption field and mass transfer field, and can accurately simulate the migration and transformation process of pollutants in porous media.

[0034] The hydraulic field of groundwater flow can be described by Darcy's law and the continuity equation: , in, The permeability tensor (unit: m / s) represents the water conductivity of porous media. Head (unit: m) represents the mechanical energy per unit weight of fluid. Specific water storage ratio (unit: ), which represents the change in water storage caused by a unit change in water head; Source and sink items (unit: ), representing the injection or extraction rate per unit volume; and t represents spatial coordinate components (unit: m); t represents time (unit: s). Subscript and The components representing spatial coordinates take values ​​of 1, 2, and 3 in three-dimensional space, corresponding to the x, y, and z directions, respectively.

[0035] The mass transfer process of pollutants in porous media can be described by the convection-dispersion equation: , Where n is porosity (dimensionless), representing the ratio of pore volume to total volume in a porous medium; Concentration of pollutants in the liquid phase (unit: ); Medium bulk density (unit: S represents the concentration of pollutants in the solid phase (unit: kg / kg). For the dispersion coefficient tensor (unit: This describes the dispersion process of pollutants in porous media. This is the pore water velocity vector (unit: m / s); Source and sink flow (unit: ); Source and sink concentrations (unit: The two terms on the left side of the equation represent the rate of change of pollutant mass in the liquid and solid phases over time, respectively, while the three terms on the right side represent the contributions of dispersion, convection, and source-sink terms to the pollutant concentration, respectively.

[0036] The adsorption process of pollutants between solid and liquid phases can be described using linear isothermal adsorption models, Freundlich models, or Langmuir models. Taking the Freundlich model as an example: , in, The concentration of pollutants in the solid phase (unit: kg / kg); Freundlich coefficient (units according to...) Value changes, when (The unit is L / kg). Concentration of pollutants in the liquid phase (unit: ); The Freundlich exponent (dimensionless) typically takes a value between 0.7 and 1.0. This model describes the nonlinear adsorption behavior, when... It degenerates into a linear adsorption model.

[0037] To improve computational efficiency and accuracy, this invention employs an adaptive grid optimization mechanism. This mechanism increases grid density in high-gradient regions and decreases it in low-gradient regions by calculating pollutant concentration gradients and estimating numerical solution errors. Practical experience shows that, compared to a uniform grid, the adaptive grid can reduce computation time by 40%–60% while maintaining the same accuracy.

[0038] Step S4: Collect real-time monitoring data of the contaminated site and input the real-time monitoring data into the three-dimensional digital twin of the contaminated site.

[0039] In one embodiment of the present invention, real-time monitoring data includes parameters such as groundwater level, pollutant concentration, pH value, and redox potential. Monitoring equipment may include groundwater monitoring wells, soil gas monitoring probes, and online water quality analyzers. The monitoring frequency is dynamically adjusted according to the pollutant diffusion rate and the treatment process. High-frequency monitoring (e.g., once per hour) is typically used in the initial and critical stages, while the frequency can be reduced (e.g., once per day) in the stable stage.

[0040] After preprocessing including outlier detection, missing value completion, and noise filtering, the monitoring data is input into the 3D digital twin of the contaminated site via a data interface to update the digital twin's status and parameters. The data input frequency should match the model's calculation time step to ensure that the digital twin can reflect changes in the actual site in a timely manner.

[0041] Step S5: Based on the three-dimensional digital twin of the contaminated site, construct a multi-agent environmental perception system. The multi-agent environmental perception system is used to perceive the pollution status and dynamic characteristics of the contaminated site.

[0042] Reference Figure 4 The multi-agent environmental perception system adopts a distributed architecture, with multiple agents working together to complete the environmental perception task. Each agent is responsible for a specific area or a specific task, achieving comprehensive perception of the overall environment through information sharing and collaborative decision-making.

[0043] The multi-agent environmental perception system realizes the mapping from the physical pollution field to the agent's perception space. This process includes a multi-dimensional state space mapping mechanism. This mechanism first preprocesses the monitoring data, including outlier detection and handling, missing data completion, and time series smoothing. Then, it extracts multi-dimensional features: trend, periodicity, and rate of change features from the time dimension; concentration gradient and pollutant morphology features from the spatial dimension; and chemical composition and phase distribution features from the material dimension.

[0044] In the dynamic situation construction phase, the system constructs the pollutant concentration field and its time derivative, calculates the pollutant diffusion flux vector field, and constructs the phase space representation of key features. The multi-scale characterization integration integrates features at the microscopic level (molecular diffusion and reaction kinetics), mesoscopic level (convective diffusion processes in porous media), and macroscopic level (regional pollutant migration and transformation processes) to form a complete understanding of the environmental situation.

[0045] Step S6: Based on the pollution state and dynamic characteristics perceived by the multi-agent environmental perception system, a governance scheme is generated through deep reinforcement learning. The deep reinforcement learning includes environmental state space construction, pollution situation reasoning, and governance strategy optimization.

[0046] Reference Figure 5 The deep reinforcement learning module is one of the core innovations of this invention, which includes three key steps: environmental state space construction, pollution situation reasoning, and governance strategy optimization.

[0047] Environmental state space construction maps the physical parameters of a contaminated site to a multidimensional state space, forming a topological representation of the contamination state. Specifically, the contaminated site is viewed as an n-dimensional manifold, where each manifold point represents the contamination characteristics at a specific location. The state space construction process can be represented as follows: , Where M is the state space, representing an abstract representation of the pollution state; P is the set of physical parameters, including actual measured values ​​such as pollutant concentration and hydrological parameters. This is a mapping function that maps the physical parameter space to the abstract state space. This can be achieved through manifold learning methods such as isomap or local linear embedding (LLE).

[0048] The multi-scale sensing topology adopts a hierarchical design, forming a multi-resolution environmental sensing network from macro-regional sensing to micro-level location sensing. Dynamic feature extraction utilizes time-series analysis to extract the time-varying characteristics of the pollution field and construct a pollution dynamics model.

[0049] Pollution situation reasoning is based on a deep learning reasoning mechanism using environmental entropy, which optimizes governance decisions by reducing the uncertainty of the pollution system. The definition of environmental entropy is similar to that of information entropy. , in, Environmental entropy represents the degree of uncertainty in the distribution of pollution. For the event The probability represents the likelihood of a certain pollution state occurring; Let be the possible pollution state events; n be the total number of possible events. The base of the logarithm is usually 2 or the natural logarithm e, and the unit of the logarithm is bit or nit, respectively. In a pollution scenario, environmental entropy can be represented as the uncertainty of the pollution distribution; the higher the entropy value, the more uncertain the pollution distribution.

[0050] Deep representation learning constructs multi-layered neural network architectures to extract hierarchical feature representations of contamination fields from perceptual data. Typical network structures include convolutional layers, pooling layers, and fully connected layers, with network depths typically ranging from 5 to 10 layers, dynamically adjusted according to data complexity.

[0051] Uncertainty quantification, based on a Bayesian probabilistic framework, distinguishes between cognitive uncertainty and accidental uncertainty. The parameter distribution of a Bayesian neural network can be represented as: , in, Parameters under given data D The posterior distribution of; Let be the likelihood function, representing the likelihood given parameters. The probability of observing data D; For parameters The prior distribution represents the initial understanding of the parameters before the observation data; The marginal likelihood, or evidence, represents the overall probability of observing data D, mathematically expressed as... .

[0052] The reasoning behind environmental entropy reduction strategies follows the principle of minimizing the increase in environmental entropy, deriving the optimal sequence of governance actions. This process can be formalized as follows: , in, The optimal behavior represents the best governance action that should be taken in the current state. For the execution of actions The subsequent increase in environmental entropy represents the action. Impact on system uncertainty; For possible actions, E is a predefined set of actions; E is the environment state. The operator indicates the identification of the independent variable that minimizes the function.

[0053] The governance strategy optimization employs a dual control mechanism of chaos and determinism to achieve precise control over the chaotic characteristics of complex pollution systems. Firstly, the chaotic characteristics of the system are identified through Lyapunov exponent analysis: , in, Lyapunov exponent, representing a measure of the system's sensitivity to initial conditions; Let be the distance between two initially close orbits in phase space at time t; is the initial distance between the two orbits; t is time. When When the system exhibits chaotic characteristics, the distance between orbits increases exponentially; when... When the system is stable, the distance between orbits decreases exponentially; when... When the time is 0, it indicates that the system is in a critical state.

[0054] Phase space reconstruction employs a time-delay embedding method to reconstruct the pollution dynamics system in a high-dimensional phase space. , in, The reconstructed phase space vector is an m-dimensional vector representing the state of the system at time t. These are time series observations, representing the value of a system variable measured at time t. is the time delay, representing the time interval between adjacent embedded components; m is the embedding dimension, representing the dimension of the reconstructed phase space. Time Delay The embedding dimension *m* is typically determined using mutual information, and the false nearest neighbor method is used. In contaminated systems, It could be the pollutant concentration at a certain monitoring point. Typically, it is 1-10 times the sampling interval, and m is typically 2-10.

[0055] The dual regulation mechanism employs a deterministic control strategy at the micro level and utilizes chaotic control theory at the macro level to guide the long-term behavior of the system. This mechanism can achieve stable and reliable pollution control in complex and ever-changing environments.

[0056] Step S7: Based on the treatment plan, determine the combination of technologies and parameter configurations for pollutant treatment.

[0057] The governance scheme optimization module realizes multi-technology synergistic optimization and multi-objective balance decision-making. The multi-technology synergistic optimization mechanism analyzes the characteristics and synergistic effects of governance technologies such as electro-Fenton oxidation and bio-stimulation, and determines the optimal technology combination based on pollutant type and concentration distribution.

[0058] In technology combination strategies, sequential combination is suitable for scenarios where high-concentration pollution is treated first, followed by low-concentration residue; parallel combination is suitable for scenarios where different types of mixed pollution are treated simultaneously; and alternating combination is suitable for scenarios where a balance between remediation effectiveness and cost is required. For example, for trichloroethylene-contaminated sites, when the concentration is higher than 1000 μg / L, electro-Fenton oxidation is preferred; when the concentration drops to 100-1000 μg / L, a parallel combination of electro-Fenton oxidation and biostimulation can be used; and when the concentration is lower than 100 μg / L, biostimulation technology is mainly relied upon.

[0059] Optimization of electro-Fenton oxidation parameters includes dosage, electrode current, and reaction time. Typical parameter configurations are: hydrogen peroxide concentration 5%–15%, iron ion concentration 1–5 mM, and electrode current density 10–50. The reaction time is 1–8 hours. Optimization of biostimulation parameters includes strain selection, nutrient ratio, and dosing frequency. Commonly used strains include Pseudomonas and Bacillus. Nutrients typically contain nitrogen, phosphorus, and potassium in a ratio of C:N:P = 100:10:1. The dosing frequency is determined based on the microbial growth cycle, generally once every 7–14 days.

[0060] The multi-objective equilibrium decision-making mechanism comprehensively considers multiple objectives, including governance costs, governance effectiveness, governance time, and environmental impact. The objective function is constructed using a weighted summation or product form: , or , Where F is the comprehensive objective function, representing the comprehensive evaluation index of multi-objective optimization; For the first The nth single-objective function represents the expression for the nth... Evaluation of each optimization objective; Let be the weighting coefficient, representing the th The relative importance of each objective, satisfying and ; m represents the number of objectives; x represents the decision variable vector, containing all parameters that need to be optimized. The first formula uses a linear weighting method, suitable for situations where the objectives are relatively independent; the second formula uses a geometric weighting method, suitable for situations where the objectives influence each other.

[0061] Weighting coefficient The determination of remediation effectiveness and environmental impact should take into account factors such as site characteristics, time requirements, budget constraints, and risk tolerance. For example, for contaminated sites located in sensitive areas, the weight of remediation effectiveness and environmental impact should be higher; while for projects with tight deadlines, the weight of remediation time should be increased accordingly.

[0062] Step S8: Implement the aforementioned technology combination and parameter configuration for pollutant treatment.

[0063] In one embodiment of the present invention, the treatment process includes stages such as treatment equipment deployment, parameter setting, operation monitoring, and emergency response. The treatment equipment is deployed according to the technical combination requirements, such as electro-Fenton oxidation equipment (including electrodes, power supply, reagent injection system, etc.) and biostimulation equipment (including nutrient injection system, microbial culture equipment, etc.).

[0064] Parameter settings are strictly in accordance with the optimized configuration, and key parameter changes during the treatment process are tracked in real time through a monitoring system. The monitoring system collects and analyzes operational data, including current, voltage, reagent consumption, pH value, and redox potential, to ensure the treatment process proceeds as expected. An emergency response mechanism is in place to address equipment failures and abnormal parameters, ensuring the safety and stability of the treatment process.

[0065] Step S9: Monitor the actual treatment effect and feed it back to the three-dimensional digital twin of the contaminated site.

[0066] Reference Figure 6 The effectiveness assessment module evaluates the treatment effect from multiple dimensions, including concentration reduction assessment, spatial distribution assessment, time response assessment, and environmental impact assessment. Concentration reduction assessment analyzes the absolute reduction in pollutant concentration, the relative reduction rate, and compliance with environmental standards. Spatial distribution assessment focuses on changes in the area of ​​the pollution plume, spatial uniformity, and the elimination of hotspots.

[0067] Time-based response assessment examines the initial response rate, duration of effectiveness, and potential rebound trends in remediation. Environmental impact assessment monitors changes in soil physicochemical properties, impacts on microbial communities, and potential risks of secondary pollution. These assessment results are fed back to a 3D digital twin of the contaminated site through a unified data interface to update the digital twin's status and parameters.

[0068] The feedback frequency of the assessment results should match the actual monitoring frequency to ensure that the digital twin can reflect changes in the actual treatment effect in a timely manner. For rapidly changing parameters (such as redox potential), the feedback frequency can be as high as once per hour; while for parameters that change more slowly (such as microbial community structure), the feedback frequency can be once per week or once per month.

[0069] Step S10: Based on the difference between the actual treatment effect and the expected treatment effect, calibrate the three-dimensional digital twin of the contaminated site and the deep reinforcement learning model.

[0070] Reference Figure 5 The model calibration module uses a Bayesian framework for model calibration, including the determination of prior parameter distributions, integration of observation data, calculation of posterior distributions, and model updates and validation.

[0071] The prior distribution of parameters is determined based on expert knowledge and historical data. For example, for the permeability coefficient, it can be assumed that it follows a log-normal distribution, with the mean and standard deviation determined according to geological conditions; for the adsorption coefficient, it can be assumed that it follows a normal or uniform distribution, with the parameter range determined according to experimental data.

[0072] During the data integration process, different types of monitoring data are standardized and weighted. Standardization can be performed using Z-score or Min-Max methods, and weight allocation is determined based on data quality and relevance. For example, directly measured pollutant concentration data can be assigned higher weights (e.g., 0.6-0.8), while indirectly derived parameters can be assigned lower weights (e.g., 0.2-0.4).

[0073] The posterior distribution was calculated using the Markov Chain Monte Carlo (MCMC) method: , in, For parameters The posterior distribution of; It is the likelihood function; It is the prior distribution; This indicates a direct proportionality, meaning the two sides of the equation differ by a constant factor. MCMC sampling uses the Metropolis-Hastings algorithm or Gibbs sampling, typically performing 10⁴ to 10⁶ sampling iterations to ensure sufficient convergence of the posterior distribution.

[0074] In the model update and validation phase, the model parameters are first updated using the mean or mode of the posterior distribution, and then the model performance is evaluated through cross-validation. Cross-validation uses the k-fold method (usually k=5 or 10), and the evaluation metrics include root mean square error (RMSE) and coefficient of determination (R²). If the validation results do not meet the preset criteria (e.g., RMSE < 10%, R² > 0.8), the model structure needs to be readjusted or more data needs to be obtained.

[0075] After model calibration, the updated model is applied to subsequent governance processes, forming a closed-loop optimization control. Furthermore, a model knowledge base is established to accumulate governance experience and apply it to new scenarios, achieving knowledge accumulation and transfer.

[0076] Reference Figure 6 The new pollutant treatment effect digital twin simulation system provided by the present invention includes a data acquisition module 1, a digital twin construction module 2, a fluid-structure interaction calculation module 3, a multi-agent environmental perception module 4, a deep reinforcement learning module 5, a treatment scheme optimization module 6, a treatment execution module 7, an effect evaluation module 8, and a model calibration module 9.

[0077] Data acquisition module 1 is used to acquire hydrogeological parameters, pollutant characteristic data, and real-time monitoring data of the contaminated site. This module includes a field sampling unit, a laboratory analysis unit, a historical data query unit, and a data preprocessing unit. The field sampling unit is responsible for collecting groundwater and soil samples; the laboratory analysis unit completes the physicochemical analysis of the samples; the historical data query unit retrieves and organizes historical monitoring records; and the data preprocessing unit performs outlier detection, missing value completion, and noise filtering on the acquired data.

[0078] Digital twin construction module 2 is used to construct a three-dimensional digital twin of a contaminated site based on hydrogeological parameters and pollutant characteristic data. This module includes a multi-scale spatial representation unit 21 and a spatiotemporal fusion modeling unit 22. The multi-scale spatial representation unit 21 realizes comprehensive modeling from macroscopic topography to microscopic pollution distribution; the spatiotemporal fusion modeling unit 22 realizes the fusion of static structure and dynamic process through steps such as spatial discretization, parameter assignment, and time series correlation.

[0079] The fluid-structure interaction (FSI) calculation module 3 is used to establish a FSI calculation model for pollutant diffusion based on a three-dimensional digital twin of the contaminated site. This module includes a multiphysics coupling unit 31 and an adaptive mesh optimization unit 32. The multiphysics coupling unit 31 constructs a three-phase coupling model of groundwater, soil, and pollutants; the adaptive mesh optimization unit 32 increases the mesh density in high pollution gradient regions and decreases the mesh density in low pollution gradient regions, thereby improving computational efficiency and accuracy.

[0080] The multi-agent environmental perception module 4 is used to perceive the pollution status and dynamic characteristics of a contaminated site based on a three-dimensional digital twin of the site. This module includes a multi-dimensional state space mapping unit 41, a dynamic situation construction unit 42, and a multi-scale characterization integration unit 43. The multi-dimensional state space mapping unit 41 maps the physical pollution field to the agent's perception space; the dynamic situation construction unit 42 constructs the pollutant concentration field and its time derivative; and the multi-scale characterization integration unit 43 integrates features at micro, meso, and macro scales.

[0081] The deep reinforcement learning module 5 is used to generate remediation plans based on the pollution state and dynamic characteristics perceived by the multi-agent environmental perception module. This module includes an environmental state space construction unit 51, a pollution situation inference unit 52, and a remediation strategy optimization unit 53. The environmental state space construction unit 51 maps the physical parameters of the contaminated site to a multi-dimensional state space; the pollution situation inference unit 52 infers the optimal remediation behavior sequence based on the principle of minimizing environmental entropy; and the remediation strategy optimization unit 53 adopts a chaotic-deterministic dual control mechanism to achieve precise control of complex pollution systems.

[0082] The pollution control scheme optimization module 6 is used to determine the combination of technologies and parameter configurations for pollution control based on the pollution control scheme. This module includes a multi-technology synergistic optimization unit and a multi-objective balance decision-making unit. The multi-technology synergistic optimization unit analyzes the characteristics and synergistic effects of different control technologies to determine the optimal combination; the multi-objective balance decision-making unit comprehensively considers multiple objectives such as control cost, control effect, control time, and environmental impact to achieve a balance among multiple objectives.

[0083] The treatment execution module 7 is used to implement technology combinations and parameter configurations for pollutant treatment. This module includes an equipment deployment unit, a parameter setting unit, an operation monitoring unit, and an emergency response unit. The equipment deployment unit deploys treatment equipment according to the technology combination requirements; the parameter setting unit sets equipment parameters according to the optimized parameter configuration; the operation monitoring unit tracks changes in key parameters in real time; and the emergency response unit activates corresponding contingency plans for abnormal situations.

[0084] The effectiveness evaluation module 8 is used to monitor the actual remediation effect and feed it back to the three-dimensional digital twin of the contaminated site. This module includes a concentration reduction assessment unit, a spatial distribution assessment unit, a time response assessment unit, and an environmental impact assessment unit. These units evaluate the remediation effect from different dimensions and feed the assessment results back to the digital twin through a unified data interface.

[0085] Model calibration module 9 is used to calibrate the 3D digital twin and deep reinforcement learning model of the contaminated site based on the difference between the actual and expected remediation effects. This module includes a parameter prior distribution unit, an observation data integration unit, a posterior distribution calculation unit, and a model update and verification unit. These units, based on a Bayesian framework, enable continuous calibration and optimization of the model, ensuring its accuracy and reliability.

[0086] The embodiments of the present invention will be further illustrated below with specific examples.

[0087] [Example 1] Treatment of benzene series pollution in groundwater of a petrochemical plant area A petrochemical plant area has experienced groundwater contamination due to long-term production activities. The main pollutants are benzene, toluene, and xylene, with peak concentrations of 2500 μg / L, 1800 μg / L, and 1200 μg / L, respectively, exceeding the Class IV groundwater standard by several to tens of times. The contaminated area is approximately 200m × 150m, with a depth of 2–8m below the surface.

[0088] Applying the novel digital twin simulation method for pollutant treatment effects of this invention, the site's hydrogeological parameters are first obtained, including groundwater flow direction (southwest-northeast), groundwater flow velocity (0.15 m / d), groundwater depth (1.5–2.5 m), and soil permeability coefficient (5 × 10⁻⁶). -5(m / s), etc. Simultaneously, pollutant characteristic data were acquired, including the diffusion coefficient of benzene series compounds (9×10). -10 m² / s), adsorption coefficient (benzene: 0.83 L / kg, toluene: 2.1 L / kg, xylene: 3.5 L / kg), etc.

[0089] Based on the above parameters, a three-dimensional digital twin of the site was constructed, using a 1m×1m×0.5m grid to form a 6×10 4 A discrete unit was established. A fluid-structure interaction computational model was constructed to simulate the diffusion process of pollutants in groundwater. Forty-one monitoring points were set up to form a multi-agent environmental perception system to sense the pollution status and dynamic characteristics.

[0090] A remediation plan was generated using deep reinforcement learning. Considering the high concentration and uneven distribution of pollutants, a sequential combination strategy of electro-Fenton oxidation and biostimulation was adopted. The electro-Fenton oxidation parameters were set as follows: hydrogen peroxide concentration 10%, iron ion concentration 3mM, electrode current density 30mA / cm², and reaction time 4 hours. The biostimulation parameters were set as follows: Pseudomonas aeruginosa was selected as the bacterial strain, the nutrient solution C:N:P ratio was 100:10:1, and the addition frequency was once every 10 days.

[0091] During the remediation process, monitoring data is collected daily and fed back to the digital twin. Remediation parameters are continuously optimized through model calibration, such as adjusting the electrode current density to 25 mA / cm², shortening the electro-Fenton oxidation reaction time to 3 hours, and increasing the phosphorus ratio in the biostimulating nutrient.

[0092] After six months of remediation, the concentrations of benzene, toluene, and xylene in the site's groundwater decreased to 80 μg / L, 70 μg / L, and 60 μg / L, respectively, meeting the Class IV groundwater standard requirements. Compared with traditional remediation methods, the remediation period was shortened by approximately 50%, the total cost was reduced by approximately 40%, and the risk of secondary pollution was avoided.

[0093] [Example 2] Treatment of organochlorine pesticide pollution at a pesticide factory's abandoned site A pesticide factory's abandoned site was contaminated with organochlorine pesticides, the main pollutants being hexachlorocyclohexane (HCH) and dichlorodiphenyltrichloroethane (DDT), with maximum concentrations of 8.5 mg / kg and 12.3 mg / kg, respectively. The contaminated area was approximately 150 m × 120 m, with a depth of 0-3 m below the surface.

[0094] Using the method of this invention, a three-dimensional digital twin of the site was constructed, a fluid-structure interaction calculation model was established, and 32 monitoring points were set up to form a multi-agent environmental perception system. Through deep reinforcement learning analysis, it was found that the pollutants in this site have strong adsorption properties and are difficult to degrade, making it difficult for traditional single technologies to achieve the expected results.

[0095] Based on multi-technology synergistic optimization, an alternating combination strategy of electro-Fenton oxidation and biostimulation was adopted. First, a 3-month electro-Fenton oxidation treatment was performed to reduce high-concentration contamination, with parameters set as follows: hydrogen peroxide concentration 15%, iron ion concentration 4mM, and electrode current density 40mA / cm². Then, a 6-month biostimulation treatment was performed to cultivate specific degrading bacterial communities, with parameters set as follows: a composite bacterial strain (Bacillus + Pseudomonas), with nutrient supplements fortified with C and N sources, and an addition frequency of once every 7 days. Finally, a 2-month electro-Fenton oxidation treatment was performed to remove residual contamination.

[0096] During the remediation process, remediation parameters and strategies were dynamically adjusted through real-time monitoring and model calibration. After 11 months of remediation, the concentrations of hexachlorocyclohexane (HCH) and dichlorodiphenyltrichloroethane (DDT) in the site soil decreased to 0.3 mg / kg and 0.5 mg / kg, respectively, meeting the relevant land use standards. Compared with traditional methods, the remediation effect was improved by approximately 30%, avoiding the problem of residual pollution that is difficult to remove using conventional methods.

[0097] The above embodiments demonstrate that the novel digital twin simulation method and system for pollutant treatment effects of the present invention can accurately predict treatment effects, optimize treatment plans, improve treatment efficiency, and reduce treatment costs, and has significant advantages for the treatment of complex contaminated sites.

[0098] The above description is merely a preferred embodiment of the present invention and is not intended to limit the invention. Various modifications and variations can be made to the present invention by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principle of the present invention should be included within the scope of protection of the present invention.

Claims

1. A digital twin simulation method for the treatment effect of new pollutants, characterized in that, include: Obtain hydrogeological parameters and pollutant characteristics data of contaminated sites; Based on the aforementioned hydrogeological parameters and pollutant characteristic data, a three-dimensional digital twin of the contaminated site is constructed; Based on the three-dimensional digital twin of the contaminated site, a fluid-structure interaction calculation model for pollutant diffusion is established; Collect real-time monitoring data of the contaminated site and input the real-time monitoring data into the three-dimensional digital twin of the contaminated site; Based on the three-dimensional digital twin of the contaminated site, a multi-agent environmental perception system is constructed. The multi-agent environmental perception system is used to perceive the pollution status and dynamic characteristics of the contaminated site. Based on the pollution state and dynamic characteristics perceived by the multi-agent environmental perception system, a governance scheme is generated through deep reinforcement learning. The deep reinforcement learning includes environmental state space construction, pollution situation reasoning, and governance strategy optimization. Based on the aforementioned treatment plan, the combination of technologies and parameter configurations for pollutant treatment are determined; The aforementioned combination of technologies and parameter configurations are used for pollutant treatment; Monitor the actual remediation effect and feed it back to the three-dimensional digital twin of the contaminated site; Based on the difference between the actual and expected remediation effects, the three-dimensional digital twin of the contaminated site and the deep reinforcement learning model are calibrated.

2. The method according to claim 1, characterized in that, The construction of the three-dimensional digital twin of the contaminated site includes: Based on the aforementioned hydrogeological parameters, a multi-scale spatial representation including topography, groundwater structure, and pollutant distribution is constructed. Establish a spatiotemporal fusion model of static spatial structure and dynamic pollution process; The contaminated site is spatially discretized to form a grid or finite element structure; Assign hydrogeological parameters and pollutant concentration attributes to discrete units; Establish a mapping relationship between monitoring time series data and spatial units.

3. The method according to claim 1, characterized in that, The fluid-structure interaction calculation model for pollutant diffusion includes: Construct the hydraulic field equations for groundwater flow to describe the velocity and pressure fields of the water flow; Construct an adsorption-desorption kinetic model for pollutants in soil; Construct mass transfer process models for pollutants in aqueous and solid phases; Establish the interaction mechanism of hydraulic field, adsorption field and mass transfer field; To achieve adaptive optimization of the computational grid, increase the grid density in regions with high contamination gradients and decrease the grid density in regions with low contamination gradients.

4. The method according to claim 1, characterized in that, The construction of the environmental space includes: The physical parameters of the contaminated site are mapped to a multidimensional state space to form a topological representation of the contamination state. Establish a multi-scale sensing topology to achieve multi-level sensing from macro-regions to micro-viewpoints; Extract dynamic characteristics of the pollution field and construct a pollution dynamics model; Nonlinear diffusion mapping is applied to the monitoring data to generate a low-dimensional representation; Based on the aforementioned low-dimensional characterization, key features and trends of pollution fields are identified.

5. The method according to claim 1, characterized in that, The pollution situation reasoning includes: Define and calculate the environmental entropy of contaminated sites to quantify the uncertainty of pollution distribution; Construct a multi-level neural network to extract hierarchical feature representations of the pollution field from the sensing data; Based on the Bayesian probability framework, the cognitive uncertainty and accidental uncertainty of the prediction results are quantified; Applying the principle of minimizing environmental entropy, we deduce the optimal sequence of governance behaviors; Construct a prediction error feedback mechanism to adjust model parameters based on historical prediction errors.

6. The method according to claim 1, characterized in that, The optimization of the governance strategy includes: Analyze the chaotic characteristics of the pollution diffusion system and identify the chaotic and deterministic regions in the system; In deterministic regions, precise control strategies are employed to directly optimize governance parameters; In the chaotic region, a stable periodic orbit method is used to guide the system to a controllable state; To achieve spatiotemporal adaptive adjustment of governance strategies, thereby improving control precision and frequency in highly polluted areas; A feedback stability assurance mechanism is established, and control parameters are adjusted by monitoring the system status in real time.

7. The method according to claim 1, characterized in that, The determination of the technology combination and parameter configuration for pollutant treatment includes: Analyze the characteristics and synergistic effects of treatment technologies such as electro-Fenton oxidation and biostimulation; Based on the type and concentration distribution of pollutants, determine the combination of treatment technologies, including sequential combination, parallel combination, or alternating combination; Optimize the parameters of electro-Fenton oxidation, including dosage, electrode current and reaction time; Optimize biostimulation parameters, including strain selection, nutrient formulation, and dosing frequency; Generate a governance implementation plan that includes time, space, and resource plans.

8. The method according to claim 1, characterized in that, The actual governance effects monitored include: Assess the absolute reduction in pollutant concentration, the relative reduction rate, and the compliance with environmental standards; Analyze the changes in the area of ​​the pollution plume, its spatial uniformity, and the elimination of hotspot areas; Assess the initial response rate, duration of effectiveness, and potential rebound trends in the treatment process; Monitor changes in soil physicochemical properties, their impact on microbial communities, and potential risks of secondary pollution; Generate multi-dimensional governance effectiveness evaluation reports.

9. The method according to claim 1, characterized in that, The calibration of the three-dimensional digital twin of the contaminated site and the deep reinforcement learning model includes: Based on expert knowledge and historical data, determine the prior distribution of model parameters; Integrate different types of monitoring data, standardize the data, and assign weights. A Bayesian framework is used to update the parameter distribution and assess parameter uncertainty. Update the model parameters and evaluate the model performance using cross-validation; Build a model knowledge base, accumulate governance experience, and apply it to new scenarios.

10. A digital twin simulation system for the treatment effect of new pollutants, characterized in that: include: The data acquisition module is used to acquire hydrogeological parameters, pollutant characteristic data, and real-time monitoring data of the contaminated site; A digital twin construction module is used to construct a three-dimensional digital twin of the contaminated site based on the hydrogeological parameters and pollutant characteristic data; The fluid-structure interaction calculation module is used to establish a fluid-structure interaction calculation model for pollutant diffusion based on the three-dimensional digital twin of the contaminated site. A multi-agent environmental perception module is used to perceive the pollution status and dynamic characteristics of the polluted site based on the three-dimensional digital twin of the polluted site. The deep reinforcement learning module is used to generate a governance plan based on the pollution state and dynamic characteristics perceived by the multi-agent environmental perception module. The deep reinforcement learning module includes an environmental state space construction unit, a pollution situation reasoning unit, and a governance strategy optimization unit. The governance scheme optimization module is used to determine the combination of technologies and parameter configurations for pollutant treatment based on the governance scheme. The treatment execution module is used to implement the aforementioned technology combination and parameter configuration for pollutant treatment; The effect evaluation module is used to monitor the actual treatment effect and feed the actual treatment effect back to the three-dimensional digital twin of the contaminated site; The model calibration module is used to calibrate the three-dimensional digital twin of the contaminated site and the deep reinforcement learning model based on the difference between the actual and expected remediation effects.

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