New pollutant treatment effect digital twin deduction method and system

Abstract

The present application relates to the technical field of environmental pollution control, in particular to a new pollutant treatment effect digital twin deduction method and system, comprising: obtaining hydrogeological parameters and pollutant characteristic data of a pollution site; constructing a three-dimensional digital twin of the pollution site; establishing a fluid-structure coupling calculation model of pollutant diffusion; collecting real-time monitoring data and inputting the digital twin; constructing a multi-agent environmental perception system to perceive pollution state and dynamic characteristics; generating a treatment scheme through deep reinforcement learning, including environmental state space construction, pollution situation reasoning and treatment strategy optimization; determining the technical combination and parameter configuration of pollutant treatment; implementing treatment; monitoring actual treatment effect and feeding back; calibrating the digital twin and the deep reinforcement learning model; through the fusion application of digital twin and multi-agent deep reinforcement learning, accurate prediction and optimal control are realized, which can reduce treatment cost by 30%-50% and shorten treatment cycle by 40%-60%.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of environmental pollution control, in particular to a new pollutant treatment effect digital twin deduction method and system, which is applied to the fields of industrial site remediation, groundwater pollution treatment, complex pollution site remediation, and emergency disposal of sudden pollution incidents. BACKGROUND

[0002] Traditional pollutant treatment methods mainly rely on experience-based judgment, lacking precise prediction capabilities and dynamic adjustment mechanisms, resulting in poor treatment effects, high costs, and long periods.

[0003] Existing pollutant treatment technologies typically use a single treatment scheme, which cannot be optimized and adjusted for complex and variable pollution scenarios. At the same time, due to the lack of effective simulation methods, it is difficult to accurately predict the actual effect of the treatment scheme, making it difficult to correct deviations in the implementation process in a timely manner. In addition, traditional treatment methods cannot effectively coordinate the combined application of multiple treatment technologies, failing to fully utilize the synergistic effects of each technology.

[0004] Digital twin technology, as a new technology that maps physical entities to virtual models, has shown great potential in industrial production, urban management, and other fields. However, applying digital twin technology to the field of new pollutant treatment still faces many challenges, including: how to accurately construct a digital twin of a pollution site; how to accurately simulate the diffusion process of pollutants; how to make intelligent decision optimization based on the digital twin; and how to continuously optimize the model and decision-making through a feedback mechanism. SUMMARY

[0005] The present application aims to provide a new pollutant treatment effect digital twin deduction method and system, aiming to solve the problems of low prediction accuracy, low treatment efficiency, and difficulty in cost control in the prior art.

[0006] The present application proposes a big data-driven dynamic pricing intelligent decision-making system, including:

[0007] Obtaining hydrogeological parameters and pollutant characteristic data of the pollution site;

[0008] Based on the hydrogeological parameters and pollutant characteristic data, a three-dimensional digital twin of the pollution site is constructed;

[0009] According to the three-dimensional digital twin of the pollution site, a fluid-structure coupling calculation model of pollutant diffusion is established;

[0010] Collecting real-time monitoring data of the pollution site, and inputting the real-time monitoring data into the three-dimensional digital twin of the pollution site;

[0011] construct a multi-agent environment perception system based on the three-dimensional digital twin of the contaminated site, the multi-agent environment perception system being configured to perceive the pollution state and dynamic characteristics of the contaminated site;

[0012] generate a governance scheme based on the pollution state and dynamic characteristics perceived by the multi-agent environment perception system, the governance scheme being generated by deep reinforcement learning including environment state space construction, pollution situation reasoning, and governance strategy optimization;

[0013] determine a combination of technologies and parameter configurations for pollution control based on the governance scheme;

[0014] implement the combination of technologies and parameter configurations to control pollution;

[0015] monitor the actual governance effect and feed the actual governance effect back to the three-dimensional digital twin of the contaminated site;

[0016] calibrate the three-dimensional digital twin of the contaminated site and the deep reinforcement learning model according to the difference between the actual governance effect and the expected governance effect.

[0017] Preferably, the construction of the three-dimensional digital twin of the contaminated site includes:

[0018] construct a multi-scale spatial representation including topography, groundwater structure, and pollutant distribution based on the hydrogeological parameters;

[0019] establish a spatiotemporal fusion model of static spatial structure and dynamic pollution process;

[0020] discretize the contaminated site in space to form a grid or finite element structure;

[0021] assign hydrogeological parameters and pollutant concentration attributes to the discrete units;

[0022] establish an association mapping between monitoring time series data and spatial units.

[0023] Preferably, the establishment of the fluid-solid coupling calculation model of pollutant diffusion includes:

[0024] construct a hydraulic field equation for groundwater flow to describe the water velocity field and pressure field;

[0025] construct an adsorption-desorption kinetics model of pollutants in soil;

[0026] construct a mass transfer process model of pollutants in water and solid phases;

[0027] establish an interaction mechanism of the hydraulic field, adsorption field, and mass transfer field;

[0028] Adaptive optimization of the computational grid is implemented, with increased grid density in areas of high pollution gradient and reduced grid density in areas of low pollution gradient.

[0029] As preferred, the environmental state space construction comprises:

[0030] Mapping the physical parameters of the contaminated site to a multi-dimensional state space forms a topological representation of the pollution state.

[0031] Establishing a multi-scale perception topology enables multi-level perception from macro-regions to micro-points.

[0032] Extracting dynamic characteristics of the contaminated site to construct a pollution dynamics model.

[0033] Nonlinear diffusion mapping of monitoring data generates a low-dimensional representation.

[0034] Based on the low-dimensional representation, key features and trends of the contaminated site are identified.

[0035] As preferred, the pollution state inference comprises:

[0036] Defining and calculating the environmental entropy of the contaminated site quantifies the uncertainty of pollution distribution.

[0037] Constructing a multi-level neural network extracts hierarchical feature representations of the contaminated site from perception data.

[0038] Based on the Bayesian probability framework, the cognitive uncertainty and accidental uncertainty of the prediction results are quantified.

[0039] Applying the principle of environmental entropy minimization, the optimal governance behavior sequence is inferred.

[0040] Constructing a prediction error feedback mechanism adjusts model parameters through historical prediction errors.

[0041] As preferred, the governance strategy optimization comprises:

[0042] Analyzing the chaotic characteristics of the pollution diffusion system identifies chaotic regions and deterministic regions in the system.

[0043] In the deterministic region, an accurate control strategy is adopted to directly optimize the governance parameters.

[0044] In the chaotic region, a stable periodic orbit method is used to guide the system to a controllable state.

[0045] Realize the adaptive adjustment of governance strategy in space and time, increase the control accuracy and frequency in high pollution area.

[0046] Constructing a feedback stability guarantee mechanism adjusts the control parameters by real-time monitoring of the system state.

[0047] As preferred, the combination of technologies and parameter configuration for determining the pollution remediation includes:

[0048] Analyzing the characteristics of remediation technologies such as electro-Fenton oxidation and biological stimulation and their synergistic effects;

[0049] Determining the combination mode of remediation technologies according to the type and concentration distribution of pollutants, including sequential combination, parallel combination or alternating combination;

[0050] Optimizing electro-Fenton oxidation parameters, including dosage, electrode current and reaction time;

[0051] Optimizing biological stimulation parameters, including strain selection, nutrient ratio and dosage frequency;

[0052] Generating a remediation implementation plan including time plan, space plan and resource plan.

[0053] As preferred, the monitoring of actual remediation effect includes:

[0054] Evaluating the absolute reduction of pollutant concentration, the relative reduction rate and the compliance with environmental standards;

[0055] Analyzing the area change of pollution group, spatial uniformity and hot spot area elimination;

[0056] Evaluating the response rate, effect durability and potential rebound trend at the beginning of remediation;

[0057] Monitoring changes in soil physical and chemical properties, effects on microbial communities and potential secondary pollution risks;

[0058] Generating a multi-dimensional remediation effect evaluation report.

[0059] As preferred, the calibration of the three-dimensional digital twin of the contaminated site and the deep reinforcement learning model includes:

[0060] Based on expert knowledge and historical data, determine the prior distribution of model parameters;

[0061] Integrate different types of monitoring data, standardize and assign weights;

[0062] Update the parameter distribution using Bayesian framework to evaluate parameter uncertainty;

[0063] Update model parameters and evaluate model performance through cross-validation;

[0064] Build a model knowledge base to accumulate remediation experience and apply it to new scenarios.

[0065] New pollutant remediation effect digital twin deduction system, including:

[0066] A data acquisition module is configured to acquire hydrogeological parameters, pollutant characteristic data, and real-time monitoring data of the contaminated site.

[0067] A digital twin construction module is configured to construct a three-dimensional digital twin of the contaminated site based on the hydrogeological parameters and the pollutant characteristic data.

[0068] A fluid-structure interaction calculation module is configured to establish a fluid-structure interaction calculation model of pollutant diffusion based on the three-dimensional digital twin of the contaminated site.

[0069] A multi-agent environment perception module is configured to perceive the pollution state and dynamic characteristics of the contaminated site based on the three-dimensional digital twin of the contaminated site.

[0070] A deep reinforcement learning module is configured to generate a treatment scheme based on the pollution state and dynamic characteristics perceived by the multi-agent environment perception module, and the deep reinforcement learning module includes an environment state space construction unit, a pollution situation reasoning unit, and a treatment strategy optimization unit.

[0071] A treatment scheme optimization module is configured to determine a combination of technologies and a configuration of parameters for pollutant treatment based on the treatment scheme.

[0072] A treatment execution module is configured to implement the combination of technologies and the configuration of parameters for pollutant treatment.

[0073] An effect evaluation module is configured to monitor the actual treatment effect and feed back the actual treatment effect to the three-dimensional digital twin of the contaminated site.

[0074] A model calibration module is configured to calibrate the three-dimensional digital twin of the contaminated site and the deep reinforcement learning model based on the difference between the actual treatment effect and the expected treatment effect.

[0075] The present application combines digital twin technology with multi-agent deep reinforcement learning method to construct a complete new pollutant treatment effect prediction and optimization system. The system can accurately construct a three-dimensional digital twin of the contaminated site, accurately simulate the pollutant diffusion process, realize intelligent decision optimization based on multi-agent environment perception and deep reinforcement learning, and continuously calibrate the model and optimize the decision through the feedback mechanism.

[0076] The present application has the following advantages:

[0077] 1. The prediction accuracy of pollutant treatment is improved. By constructing an accurate three-dimensional digital twin of the contaminated site and a fluid-structure interaction calculation model, the pollutant concentration prediction accuracy is improved by 45% to 60%, which provides a reliable basis for the development of treatment schemes.

[0078] 2. Reduced governance cost. The intelligent decision-making mechanism based on multi-agent deep reinforcement learning can optimize the governance technology combination and parameter configuration, reduce the amount of drug use and labor cost, and reduce the overall governance cost by 30%-50%.

[0079] 3. Shortened governance cycle. Through precise simulation and optimal control of the governance process, the governance cycle can be shortened by 40%-60%, accelerating the recovery and utilization of contaminated sites.

[0080] 4. Improved governance effect. The multi-technology collaborative optimization mechanism can fully exert the synergistic effect of various governance technologies, increase the removal rate of pollutants by 20%-30%, and improve the uniformity of residual pollutants by more than 50%.

[0081] 5. Enhanced adaptability. The system can adapt to changes in hydrological conditions, unexpected pollution incidents, and other unpredictable situations, automatically adjust the governance strategy, and has strong environmental adaptability and anti-interference ability. BRIEF DESCRIPTION OF DRAWINGS

[0082] Figure 1 is a flowchart of the new pollutant governance effect digital twin deduction method of the present application;

[0083] Figure 2 is a structural schematic diagram of the pollution site three-dimensional digital twin body construction module of the present application;

[0084] Figure 3 is a schematic diagram of the fluid-solid coupling calculation model of the present application;

[0085] Figure 4 is an architectural diagram of the multi-agent environment perception system of the present application;

[0086] Figure 5 is a structural diagram of the deep reinforcement learning module of the present application;

[0087] Figure 6 is a whole architectural diagram of the new pollutant governance effect digital twin deduction system of the present application. DETAILED DESCRIPTION

[0088] Please refer to Figures 1-6 , the specific embodiments of the present application will be described in detail below with reference to the accompanying drawings.

[0089] Referring to Figure 1 , the new pollutant governance effect digital twin deduction method provided by the present application includes the following steps:

[0090] Step S1: Obtain hydrogeological parameters and pollutant characteristic data of the contaminated site.

[0091] In an embodiment of the present application, the hydrogeological parameters include groundwater flow direction, groundwater flow velocity, groundwater depth, groundwater flow time, and soil permeability coefficient, etc. Preferably, the groundwater flow direction can be east-west, north-south, or both; the groundwater flow velocity is usually in the range of 0.01-10 m / d; the groundwater depth is generally between 0.5-50 m; and the soil permeability coefficient is typically between 10 -8 -10 -3 m / s, depending on the soil quality. The pollutant characteristic data include 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 usually , and the adsorption coefficient is generally between 0.5-5 L / kg, depending on the soil organic matter content.

[0092] In addition, historical monitoring data and treatment experience data also need to be obtained to provide basic data support for subsequent model construction and optimization. Data acquisition can be carried out through various ways such as field sampling, laboratory analysis, historical record query, and expert knowledge integration, etc.

[0093] Step S2: Based on the hydrogeological parameters and pollutant characteristic data, a three-dimensional digital twin of the contaminated site is constructed.

[0094] Referring to Figure 2 , the process of constructing the three-dimensional digital twin of the contaminated site includes two key links: multi-scale spatial representation and spatio-temporal fusion modeling. Multi-scale spatial representation realizes comprehensive modeling from macro-terrain (resolution 1-5 m) to micro-pollution distribution (accuracy up to centimeter level). Spatio-temporal fusion modeling realizes seamless fusion of static structure and dynamic process through steps such as spatial discretization, parameter assignment, time series association, spatio-temporal interpolation, and spatio-temporal consistency verification.

[0095] Specifically, spatial discretization can use methods such as finite difference, finite element, or finite volume to convert continuous space into discrete grids. For example, for a site of 100 m x 100 m x 10 m, a grid division of 1 m x 1 m x 0.5 m can be used to form 2 x 10 5

[0096] Spatio-temporal interpolation uses Kriging interpolation method to fill in the data gaps between monitoring points. The mathematical expression of Kriging interpolation is:

[0097] ,

[0098] ​wherein, is the estimated value of the position , is the observation value of the th known point, is the weight coefficient, and n is the number of known points. The position and are three-dimensional space coordinates, representing the specific sampling point position.

[0099] The weight coefficient is obtained by solving the following equation set:

[0100] ,

[0101] ,

[0102] wherein, is the semi-variogram value between the position and , is the Lagrange multiplier, used to ensure the constraint condition that the weight sum is 1. The semi-variogram usually adopts a spherical model, an exponential model or a Gaussian model, and the most suitable model is selected according to the actual data characteristics. For example, the semi-variogram expression of the spherical model is:

[0103] ,

[0104] wherein, is the distance between two points, is the nugget value (representing small-scale variability and measurement error), is the sill value (representing a measure of structural variability), is the range (representing the range of spatial correlation).

[0105] Step S3: According to the three-dimensional digital twin of the contaminated site, a fluid-structure coupling calculation model of pollutant diffusion is established.

[0106] Referring to Figure 3 , the fluid-structure coupling calculation model is based on the principle of multi-physical field coupling, and a groundwater-soil-pollutant three-phase coupling model is constructed. 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.

[0107] The hydraulic field of groundwater flow can be described by Darcy's law and continuity equation:

[0108] ,

[0109] wherein, is the permeability tensor (unit: m / s), representing the water conductivity of the porous medium; is the water head (unit: m), representing the mechanical energy possessed by unit weight of fluid; is the specific water storage rate (unit: ), representing the change in water storage caused by unit change in water head; is the source-sink term (unit: ), representing the injection or extraction rate per unit volume; and are the spatial coordinate components (unit: m); t is time (unit: s). Subscripts and represent the components of spatial coordinates, taking values of 1, 2, and 3 in three-dimensional space, corresponding to the x, y, and z directions, respectively.

[0110] The mass transfer process of pollutants in porous media can be described by the convection-dispersion equation:

[0111] ,

[0112] where n is the porosity (dimensionless), representing the ratio of pore volume to total volume in the porous medium; is the pollutant concentration in the liquid phase (unit: ); is the medium bulk density (unit: ); S is the pollutant concentration in the solid phase (unit: kg / kg); is the dispersion coefficient tensor (unit: ), describing the dispersion process of pollutants in the porous medium; is the pore water velocity vector (unit: m / s); is the source-sink term flux (unit: ); is the source-sink term concentration (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, and the three terms on the right side represent the contributions of dispersion, convection, and source-sink terms to the pollutant concentration.

[0113] The adsorption process of pollutants between solid and liquid phases can be described by the linear isothermal adsorption model, Freundlich model, or Langmuir model. Taking the Freundlich model as an example:

[0114] ,

[0115] where is the pollutant concentration in the solid phase (unit: kg / kg); is the Freundlich coefficient (unit varies according to the value of , when , the unit is L / kg). Cp is the concentration of pollutants in liquid phase (unit: ); Cp is the concentration of pollutants in liquid phase (unit: Cp is the concentration of pollutants in liquid phase (unit:

[0116] To improve the calculation efficiency and accuracy, the adaptive grid optimization mechanism is adopted. This mechanism increases the grid density in high gradient areas and reduces the grid density in low gradient areas by calculating the pollutant concentration gradient and numerical solution error estimate. Practice shows that compared with uniform grid, adaptive grid can reduce the calculation time by 40%-60% while maintaining the same accuracy.

[0117] 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.

[0118] In an embodiment of the present application, the real-time monitoring data includes parameters such as groundwater level, pollutant concentration, pH value, oxidation-reduction potential, etc. The monitoring equipment can include groundwater monitoring wells, soil gas monitoring probes, online water quality analyzers, etc. The monitoring frequency is dynamically adjusted according to the pollutant diffusion rate and the treatment process, usually using high-frequency monitoring (such as once an hour) in the initial and key stages, and reducing the frequency (such as once a day) in the stable stage.

[0119] After preprocessing such as outlier detection, missing value completion, and noise filtering, the monitoring data is input into the three-dimensional digital twin of the contaminated site through the data interface, to update the state and parameters of the digital twin. The data input frequency should match the model calculation time step to ensure that the digital twin can timely reflect the changes of the actual site.

[0120] Step S5: Based on the three-dimensional digital twin of the contaminated site, a multi-agent environment perception system is constructed, which is used to perceive the pollution state and dynamic characteristics of the contaminated site.

[0121] Referring to Figure 4 , the multi-agent environment perception system adopts a distributed architecture, and the environment perception task is completed by multiple agents. Each agent is responsible for a specific area or task, and the overall environment is comprehensively perceived through information sharing and collaborative decision-making.

[0122] The multi-agent environmental perception system realizes the mapping from the physical pollution field to the agent perception space, which includes a multi-dimensional state space mapping mechanism. The mechanism first preprocesses the monitoring data, including outlier detection and processing, missing data completion, and time series smoothing. Then multi-dimensional feature extraction is performed, including trend, periodicity, and rate of change features from the time dimension, concentration gradient and pollution cluster morphology features from the spatial dimension, and chemical composition and phase distribution features from the material dimension.

[0123] In the dynamic state construction link, 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. Multi-scale representation integration integrates the features of microscale (molecular diffusion and reaction kinetics), mesoscale (convective diffusion process in porous media), and macroscale (regional pollutant migration and transformation process) to form a complete environmental state awareness.

[0124] Step S6: According to the pollution state and dynamic characteristics perceived by the multi-agent environmental perception system, a governance scheme is generated through deep reinforcement learning, which includes environmental state space construction, pollution state reasoning, and governance strategy optimization.

[0125] Reference Figure 5 The deep reinforcement learning module is one of the core innovations of the present application, including three key links of environmental state space construction, pollution state reasoning, and governance strategy optimization.

[0126] Environmental state space construction maps the pollution field physical parameters to a multi-dimensional state space, forming a topological representation of the pollution state. Specifically, the pollution field is regarded as an n-dimensional manifold, and each manifold point represents the pollution characteristics at a specific location. The state space construction process can be represented as:

[0127] ,

[0128] Where M is the state space, representing the abstract representation of the pollution state; P is the set of physical parameters, including pollutant concentration, hydrological parameters, and actual measurement values; is the mapping function that maps the physical parameter space to the abstract state space. The mapping function can be realized by manifold learning methods such as Isometric Mapping (Isomap) or Local Linear Embedding (LLE).

[0129] The multi-scale perception topology adopts a hierarchical design, forming a multi-resolution environmental perception network from macro regional perception to micro point perception. Dynamic feature extraction extracts the time-varying characteristics of the pollution field through time series analysis and constructs a pollution dynamics model.

[0130] Pollution state inference is based on the deep learning inference mechanism of environmental entropy, which optimizes governance decisions by reducing the uncertainty of pollution systems. The definition of environmental entropy refers to the concept of information entropy:

[0131] ,

[0132] where, is the environmental entropy, representing the degree of uncertainty of pollution distribution; is the probability of event , representing the possibility of a certain pollution state; is the possible pollution state event; n is 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 nite accordingly. In the pollution scene, environmental entropy can represent the uncertainty of pollution distribution, and the higher the entropy value, the more uncertain the pollution distribution.

[0133] Deep representation learning constructs a multi-level neural network architecture to extract hierarchical feature representations of pollution scenes from perception data. Typical network structures include convolutional layers, pooling layers, fully connected layers, etc. The network depth is usually 5-10 layers, which can be dynamically adjusted according to the complexity of the data.

[0134] Uncertainty quantification is based on the Bayesian probability framework, which distinguishes between cognitive uncertainty and accidental uncertainty. The parameter distribution of the Bayesian neural network can be represented as:

[0135] ,

[0136] where, is the posterior distribution of parameter given data D; is the likelihood function, representing the probability of observing data D given parameter ; is the prior distribution of parameter , representing the initial cognition of the parameter before observing the data; is the evidence or marginal likelihood, representing the overall probability of observing data D, mathematically .

[0137] Environmental entropy reduction strategy inference follows the principle of minimizing the increment of environmental entropy, and infers the optimal governance behavior sequence. This process can be formalized as:

[0138] ,

[0139] where, is the optimal behavior, representing the best governance action to be taken in the current state; is the environmental entropy increment after executing behavior , representing the action the impact of system uncertainty; E is the environment state. The operator represents finding the argument that minimizes the function.

[0140] The governance strategy optimization adopts a chaotic-deterministic dual regulation mechanism, which realizes precise control of the chaotic characteristics of complex pollution systems. First, the chaotic characteristics of the system are identified through Lyapunov exponent analysis:

[0141]

[0142] where, is the Lyapunov exponent, which represents a measure of the sensitivity of the system to initial conditions; is the distance between two initial phase space trajectories at time t; is the distance between the two trajectories at the initial time; t is the time. When , it indicates that the system has chaotic characteristics, and the distance between the trajectories increases exponentially; when , it indicates that the system is stable, and the distance between the trajectories decreases exponentially; when , it indicates that the system is in a critical state.

[0143] Phase space reconstruction uses the time delay embedding method to reconstruct the pollution dynamics system in a high-dimensional phase space:

[0144]

[0145] where, is the reconstructed phase space vector, which is an m-dimensional vector representing the state of the system at time t; is the time series observation value, representing the value of a certain variable of the system 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. The time delay is usually determined by the mutual information method, and the embedding dimension m is determined by the false nearest neighbor method. In pollution systems, may be the concentration of pollutants at a monitoring point, is usually 1-10 times the sampling interval, and m is usually 2-10.

[0146] The dual regulation mechanism adopts a deterministic control strategy at the micro level and utilizes chaos 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 a complex and variable environment.

[0147] Step S7: Based on the governance scheme, determine the technical combination and parameter configuration for pollution control.

[0148] ​​The governance scheme optimization module realizes multi-technology collaborative optimization and multi-objective balanced decision-making. The multi-technology collaborative optimization mechanism analyzes the characteristics and synergistic effects of technologies such as electro-Fenton oxidation and biological stimulation, and determines the optimal technology combination mode according to the type and concentration distribution of pollutants.

[0149] In the technology combination strategy, sequential combination is suitable for scenarios where high-concentration pollution is treated first and low-concentration residues are treated later; parallel combination is suitable for scenarios where different types of mixed pollution are treated simultaneously; and alternating combination is suitable for scenarios where the balance between treatment effect and cost needs to be achieved. For example, for a trichloroethylene contaminated site, when the concentration is higher than 1000 μg / L, electro-Fenton oxidation is preferred; when the concentration drops to 100-1000 μg / L, parallel combination of electro-Fenton oxidation and biological stimulation can be used; and when the concentration is lower than 100 μg / L, biological stimulation technology is mainly relied on.

[0150] Electro-Fenton oxidation parameter optimization includes dosage, electrode current, and reaction time, etc. Typical parameter configurations are: hydrogen peroxide concentration 5%-15%, iron ion concentration 1-5 mM, electrode current density 10-50 , reaction time 1-8 hours. Biological stimulation parameter optimization includes strain selection, nutrient agent ratio, and dosing frequency, etc. Common strains include Pseudomonas and Bacillus, and nutrient agents usually contain nitrogen, phosphorus, and potassium elements, with a ratio of C:N:P=100:10:1. The dosing frequency is determined according to the growth cycle of microorganisms, usually once every 7-14 days.

[0151] The multi-objective balanced decision-making mechanism considers multiple objectives such as treatment cost, treatment effect, treatment time, and environmental impact. The objective function is constructed using weighted summation or multiplication:

[0152] ,

[0153] or

[0154] ,

[0155] where F is the comprehensive objective function, representing the comprehensive evaluation index of multi-objective optimization; is the th single objective function, representing the evaluation of the th optimization objective; is the weight coefficient, representing the relative importance of the th objective, satisfying and ; m is the number of objectives; x is the decision variable vector, containing all parameters that need to be optimized. The first formula uses linear weighting, suitable for cases where objectives are relatively independent; the second formula uses geometric weighting, suitable for cases where objectives interact with each other.

[0156] weighting factor The determination needs to consider factors such as site characteristics, time requirements, budget constraints, and risk acceptance. For example, for a contaminated site located in a sensitive area, the weights of treatment effect and environmental impact should be higher; and for a time-urgent project, the weight of treatment time should be correspondingly increased.

[0157] Step S8: Implementing the technology combination and parameter configuration for pollution treatment.

[0158] In an embodiment of the present application, the treatment execution process includes device deployment, parameter setting, operation monitoring, and emergency response. The treatment device is deployed according to the requirements of the technology combination, such as electro-Fenton oxidation equipment (including electrodes, power supply, and reagent injection system) and biological stimulation equipment (including nutrient injection system and microbial culture equipment).

[0159] Parameter setting is strictly in accordance with the optimized parameter configuration, and the key parameter changes in the treatment process are tracked in real time through the monitoring system. The operation monitoring system collects and analyzes operation data, including current, voltage, reagent consumption, pH value, and oxidation-reduction potential, to ensure that the treatment process proceeds as expected. The emergency response mechanism formulates corresponding plans for equipment failure, parameter abnormalities, and other situations to ensure the safety and stability of the treatment process.

[0160] Step S9: Monitoring the actual treatment effect and feeding back the actual treatment effect to the three-dimensional digital twin of the contaminated site.

[0161] Referring to Figure 6 The effect evaluation module evaluates the treatment effect from multiple dimensions, including concentration reduction evaluation, spatial distribution evaluation, time response evaluation, and environmental impact evaluation. The concentration reduction evaluation analyzes the absolute reduction amount, relative reduction rate, and compliance with environmental standards of the pollutant concentration. The spatial distribution evaluation focuses on the area change of the pollution group, spatial uniformity, and hot spot elimination.

[0162] The time response evaluation investigates the initial response rate, effect durability, and potential rebound trend. The environmental impact evaluation monitors changes in soil physical and chemical properties, the impact on microbial communities, and potential secondary pollution risks. These evaluation results are fed back to the three-dimensional digital twin of the contaminated site through a unified data interface to update the state and parameters of the digital twin.

[0163] The feedback frequency of the evaluation results should match the actual monitoring frequency to ensure that the digital twin can timely reflect the changes in the actual treatment effect. For rapidly changing parameters (such as oxidation-reduction potential), the feedback frequency can be once an hour; and for slowly changing parameters (such as microbial community structure), the feedback frequency can be once a week or once a month.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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).

[0168] The posterior distribution was calculated using the Markov Chain Monte Carlo (MCMC) method:

[0169] ,

[0170] 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.

[0171] 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.

[0172] After the model calibration is completed, the updated model is applied to subsequent governance processes to form a closed-loop optimization control. In addition, a model knowledge base is established to accumulate governance experience and apply it to new scenarios, realizing the accumulation and migration of knowledge.

[0173] Referring Figure 6 The new pollutant governance effect digital twin deduction system provided by the application comprises a data acquisition module 1, a digital twin construction module 2, a fluid-solid coupling calculation module 3, a multi-agent environment perception module 4, a deep reinforcement learning module 5, a governance scheme optimization module 6, a governance execution module 7, an effect evaluation module 8 and a model calibration module 9.

[0174] The data acquisition module 1 is used for acquiring hydrogeological parameters, pollutant characteristic data and real-time monitoring data of a contaminated site. The module comprises 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 the collection of groundwater and soil samples; the laboratory analysis unit completes the physicochemical analysis of the samples; the historical data query unit retrieves and arranges historical monitoring records; and the data preprocessing unit performs processing such as outlier detection, missing value completion and noise filtering on the collected data.

[0175] The digital twin construction module 2 is used for constructing a three-dimensional digital twin of the contaminated site based on the hydrogeological parameters and the pollutant characteristic data. The module comprises a multi-scale space representation unit 21 and a space-time fusion modeling unit 22. The multi-scale space representation unit 21 realizes comprehensive modeling from macro-topography to micro-pollution distribution; and the space-time fusion modeling unit 22 realizes the fusion of static structure and dynamic process through steps such as spatial discretization, parameter assignment and time series association.

[0176] The fluid-solid coupling calculation module 3 is used for establishing a fluid-solid coupling calculation model of pollutant diffusion based on the three-dimensional digital twin of the contaminated site. The module comprises a multi-physical field coupling unit 31 and an adaptive grid optimization unit 32. The multi-physical field coupling unit 31 constructs a three-phase coupling model of groundwater-soil-pollutant; and the adaptive grid optimization unit 32 increases the grid density in the high-pollution gradient area and reduces the grid density in the low-pollution gradient area, thereby improving the calculation efficiency and accuracy.

[0177] The multi-agent environment perception module 4 is used for perceiving the pollution state and dynamic characteristics of the contaminated site based on the three-dimensional digital twin of the contaminated site. The module comprises a multi-dimensional state space mapping unit 41, a dynamic situation construction unit 42 and a multi-scale representation integration unit 43. The multi-dimensional state space mapping unit 41 realizes the mapping of the physical pollution field to the agent perception space; the dynamic situation construction unit 42 constructs the pollutant concentration field and its time derivative; and the multi-scale representation integration unit 43 integrates the characteristics of micro-, meso- and macro-scales.

[0178] The deep reinforcement learning module 5 is used to generate a treatment scheme according to the pollution state and dynamic characteristics perceived by the multi-agent environment perception module. The module includes an environmental state space construction unit 51, a pollution situation reasoning unit 52, and a treatment strategy optimization unit 53. The environmental state space construction unit 51 maps the physical parameters of the pollution site to a multi-dimensional state space; the pollution situation reasoning unit 52 infers the optimal treatment behavior sequence based on the principle of environmental entropy minimization; and the treatment strategy optimization unit 53 uses a chaotic-deterministic dual regulation mechanism to achieve accurate control of complex pollution systems.

[0179] The treatment scheme optimization module 6 is used to determine the technical combination and parameter configuration for pollution treatment based on the treatment scheme. The module includes a multi-technology collaborative optimization unit and a multi-objective balance decision unit. The multi-technology collaborative optimization unit analyzes the characteristics of different treatment technologies and their synergistic effects to determine the best combination method; and the multi-objective balance decision unit considers multiple objectives such as treatment cost, treatment effect, treatment time, and environmental impact to achieve multi-objective balance.

[0180] The treatment execution module 7 is used to implement the technical combination and parameter configuration for pollution treatment. The module includes a device deployment unit, a parameter setting unit, a running monitoring unit, and an emergency response unit. The device deployment unit deploys treatment equipment according to the requirements of the technical combination; the parameter setting unit sets the equipment parameters according to the optimized parameter configuration; the running monitoring unit tracks the changes of key parameters in real time; and the emergency response unit starts the corresponding plan for abnormal situations.

[0181] The effect evaluation module 8 is used to monitor the actual treatment effect and feed back the actual treatment effect to the pollution site three-dimensional digital twin. The module includes a concentration reduction evaluation unit, a spatial distribution evaluation unit, a time response evaluation unit, and an environmental impact evaluation unit. These units evaluate the treatment effect from different dimensions and feed back the evaluation results to the digital twin through a unified data interface.

[0182] The model calibration module 9 is used to calibrate the pollution site three-dimensional digital twin and the deep reinforcement learning model according to the difference between the actual treatment effect and the expected treatment effect. The module includes a parameter prior distribution unit, an observation data integration unit, a posterior distribution calculation unit, and a model update verification unit. These units realize continuous calibration and optimization of the model based on the Bayesian framework to ensure the accuracy and reliability of the model.

[0183] The following will further illustrate the embodiments of the present application with specific examples.

[0184]

Example 1

[0185] The benzene series pollutants in the groundwater of a petrochemical plant area were caused by long-term production activities. The main pollutants were benzene, toluene and xylene, with the highest concentrations of 2500 μg / L, 1800 μg / L and 1200 μg / L, respectively, which were several to dozens of times higher than the Class IV standard of groundwater. The pollution range was about 200 m x 150 m, with a depth of 2-8 m below the ground surface.

[0186] The new pollutant treatment effect digital twin deduction method of the application first obtains hydrogeological parameters of the site, including groundwater flow direction (southwest-northeast direction), groundwater flow rate (0.15 m / d), groundwater depth (1.5-2.5 m) and soil permeability coefficient (5 x 10 -5 m / s) and the like. At the same time, pollutant characteristic data are obtained, including benzene series diffusion coefficient (9 x 10 -10 m² / s), adsorption coefficient (benzene: 0.83 L / kg, toluene: 2.1 L / kg, xylene: 3.5 L / kg) and the like.

[0187] Based on the above parameters, a three-dimensional digital twin of the site is constructed, which is divided into a grid of 1 m x 1 m x 0.5 m, forming 6 x 10 4 discrete units. A fluid-structure coupling calculation model is established to simulate the diffusion process of the pollutants in the groundwater. 41 monitoring points are set up to form a multi-agent environment perception system to perceive the pollution state and dynamic characteristics.

[0188] A treatment scheme is generated through deep reinforcement learning. Considering the high concentration and uneven distribution of the pollutants, a sequential combination strategy of electro-Fenton oxidation and biological stimulation is adopted. The electro-Fenton oxidation parameters are set as follows: hydrogen peroxide concentration 10%, iron ion concentration 3 mM, electrode current density 30 mA / cm², and reaction time 4 hours. The biological stimulation parameters are set as follows: the strain is Pseudomonas, the nutrient agent C:N:P ratio is 100:10:1, and the dosing frequency is 10 days.

[0189] During the implementation of the treatment process, monitoring data are collected every day and fed back to the digital twin. The treatment 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 proportion of phosphorus in the biological stimulation nutrient agent.

[0190] After 6 months of treatment, the concentrations of benzene, toluene and xylene in the groundwater of the site were reduced to 80 μg / L, 70 μg / L and 60 μg / L, respectively, meeting the Class IV standard of groundwater. Compared with the traditional treatment method, the treatment cycle is shortened by about 50%, the total cost is reduced by about 40%, and the risk of secondary pollution is avoided.

[0191]

Example 2

[0192] A pesticide factory abandoned land is polluted by organochlorine pesticides, the main pollutants are BHC and DDT, the maximum concentrations are 8.5 mg / kg and 12.3 mg / kg respectively, the pollution range is about 150 m x 120 m, and the depth is 0-3 m below the ground surface.

[0193] By applying the method, a three-dimensional digital twin of the site is constructed, a fluid-structure coupling calculation model is established, and 32 monitoring points are set to form a multi-agent environment perception system. Through deep reinforcement learning analysis, it is found that the site has strong adsorption of pollutants and difficult degradation, and traditional single technology is difficult to achieve the expected effect.

[0194] Based on multi-technology collaborative optimization, an alternating combination strategy of electro-Fenton oxidation and biological stimulation is adopted. First, electro-Fenton oxidation treatment is carried out for 3 months to reduce high concentration pollution, and the parameter settings are: hydrogen peroxide concentration 15%, iron ion concentration 4mM, and electrode current density 40mA / cm²; then biological stimulation treatment is carried out for 6 months to cultivate specific degrading bacteria, and the parameter settings are: composite bacteria (Bacillus + Pseudomonas), nutrient agent C source and N source are strengthened, and the dosing frequency is 7 days once; finally, electro-Fenton oxidation treatment is carried out for 2 months to remove residual pollution.

[0195] During the treatment process, the treatment parameters and strategies are dynamically adjusted through real-time monitoring and model calibration. After 11 months of treatment, the concentrations of BHC and DDT in the soil of the site are reduced to 0.3 mg / kg and 0.5 mg / kg respectively, meeting the requirements of the relevant site standards. Compared with traditional methods, the treatment effect is improved by about 30%, and the residual pollution problem that is difficult to remove by conventional methods is avoided.

[0196] The above examples show that the new pollutant treatment effect digital twin deduction method and system can accurately predict the treatment effect, optimize the treatment scheme, improve the treatment efficiency, and reduce the treatment cost, which has significant advantages for the treatment of complex contaminated sites.

[0197] The above description is only a preferred embodiment of the present application and is not intended to limit the present application. For those skilled in the art, the present application can have various modifications and changes. Any modification, equivalent replacement, improvement, etc. within the spirit and principles of the present application shall be included in the protection scope of the present application.

Claims

1. A method for new pollutant treatment effect digital twin inference, characterized in that, The method comprises the following steps: acquiring hydrogeological parameters and pollutant characteristic data of a contaminated site; constructing a three-dimensional digital twin of the contaminated site based on the hydrogeological parameters and pollutant characteristic data; establishing a fluid-structure interaction calculation model of pollutant diffusion according to the three-dimensional digital twin of the contaminated site; collecting real-time monitoring data of the contaminated site and inputting the real-time monitoring data into the three-dimensional digital twin of the contaminated site; constructing a multi-agent environment perception system based on the three-dimensional digital twin of the contaminated site, the multi-agent environment perception system being used for perceiving the pollution state and dynamic characteristics of the contaminated site; generating a treatment scheme through deep reinforcement learning according to the pollution state and dynamic characteristics perceived by the multi-agent environment perception system, the deep reinforcement learning including environment state space construction, pollution situation reasoning and treatment strategy optimization; determining a technical combination and parameter configuration for pollutant treatment based on the treatment scheme; implementing the technical combination and parameter configuration to treat the pollutants; monitoring the actual treatment effect and feeding back the actual treatment effect to the three-dimensional digital twin of the contaminated site; calibrating the three-dimensional digital twin of the contaminated site and the deep reinforcement learning model according to the difference between the actual treatment effect and an expected treatment effect. The fluid-structure interaction calculation model is based on the principle of multi-physical field coupling and constructs a groundwater-soil-pollutant three-phase coupling model, which comprehensively considers the interaction of the hydraulic field, the adsorption field and the mass transfer field and is used for simulating the migration and transformation process of pollutants in porous media. The establishment of the fluid-structure interaction calculation model of pollutant diffusion comprises the following steps: constructing a hydraulic field equation of groundwater flow to describe the water flow velocity field and the pressure field; constructing an adsorption-desorption kinetics model of pollutants in soil; constructing a mass transfer process model of pollutants in water and solid phases; establishing an interaction mechanism of the hydraulic field, the adsorption field and the mass transfer field; realizing adaptive optimization of the calculation grid, increasing the grid density in the high pollution gradient area and reducing the grid density in the low pollution gradient area.

2. The method of claim 1, wherein, The construction of the three-dimensional digital twin of the contaminated site comprises the following steps: constructing a multi-scale space representation including topography, groundwater structure and pollutant distribution based on the hydrogeological parameters; establishing a spatio-temporal fusion model of static space structure and dynamic pollution process; performing spatial discretization processing on the contaminated site to form a grid or finite element structure; assigning hydrogeological parameters and pollutant concentration attributes to the discrete units; establishing an association mapping between monitoring time series data and spatial units.

3. The method of claim 1, wherein, The environment state space construction comprises the following steps: mapping the physical parameters of the contaminated site to a multi-dimensional state space to form a topological representation of the pollution state; establishing a multi-scale perception topological structure to realize multi-level perception from a macro area to a micro point; extracting dynamic characteristics of the contaminated site to construct a pollution dynamics model; performing nonlinear diffusion mapping on the monitoring data to generate a low-dimensional representation; identifying key features and change trends of the contaminated site based on the low-dimensional representation.

4. The method of claim 1, wherein, The pollution situation reasoning comprises the following steps: defining and calculating the environmental entropy of the contaminated site to quantify the uncertainty of the pollution distribution; constructing a multi-level neural network to extract hierarchical feature representations of the contaminated site from the perception data; Quantify the cognitive uncertainty and accidental uncertainty of prediction results based on Bayesian probability framework; Apply the principle of environmental entropy minimization to infer the optimal governance behavior sequence; Construct a prediction error feedback mechanism to adjust model parameters through historical prediction errors.

5. The method of claim 1, wherein, The governance strategy optimization includes: Analyze the chaotic characteristics of the pollution diffusion system to identify chaotic regions and deterministic regions in the system; In the deterministic region, use the precise control strategy to directly optimize the governance parameters; In the chaotic region, use the stable periodic orbit method to guide the system to a controllable state; Achieve the adaptive adjustment of the governance strategy in space and time, and improve the control accuracy and frequency in high pollution areas; Construct a feedback stability guarantee mechanism to adjust the control parameters by monitoring the system state in real time.

6. The method of claim 1, wherein, The technical combination and parameter configuration of pollution control include: Analyze the characteristics of electro-Fenton oxidation and biological stimulation and their synergistic effects; According to the type and concentration distribution of pollutants, determine the combination mode of the control technology, including sequential combination, parallel combination or alternating combination; Optimize the electro-Fenton oxidation parameters, including dosage, electrode current and reaction time; Optimize the biological stimulation parameters, including strain selection, nutrient ratio and dosage frequency; Generate a governance implementation plan including time plan, space plan and resource plan.

7. The method of claim 1, wherein, The monitoring of the actual governance effect includes: Evaluate the absolute reduction, relative reduction rate and compliance with environmental standards of pollutant concentration; Analyze the area change, spatial uniformity and hot spot elimination of pollution groups; Evaluate the response rate, effect durability and potential rebound trend in the early stage of governance; Monitor changes in soil physical and chemical properties, effects on microbial communities and potential secondary pollution risks; Generate a multi-dimensional governance effect evaluation report.

8. The method of claim 1, wherein, The calibration of the pollution site three-dimensional digital twin and the deep reinforcement learning model includes: Determine the prior distribution of model parameters based on expert knowledge and historical data; Integrate different types of monitoring data, standardize and assign weights; Update the parameter distribution using the Bayesian framework to evaluate parameter uncertainty; Update model parameters and evaluate model performance through cross-validation; Build a model knowledge base to accumulate governance experience and apply it to new scenarios.

9. A new pollutant treatment effect digital twin inference system for implementing the method of any one of claims 1-8, characterized in that, It includes: A data acquisition module for obtaining hydrogeological parameters, pollutant characteristic data and real-time monitoring data of a contaminated site; A digital twin construction module for constructing a three-dimensional digital twin of a contaminated site based on the hydrogeological parameters and pollutant characteristic data; A fluid-structure coupling calculation module for establishing a fluid-structure coupling calculation model of pollutant diffusion based on the three-dimensional digital twin of the contaminated site; A multi-agent environment perception module for perceiving the pollution state and dynamic characteristics of the contaminated site based on the three-dimensional digital twin of the contaminated site; A deep reinforcement learning module for generating a governance scheme based on the pollution state and dynamic characteristics perceived by the multi-agent environment perception module, the deep reinforcement learning module including an environment state space construction unit, a pollution situation inference unit and a governance strategy optimization unit; A governance scheme optimization module for determining the technical combination and parameter configuration of pollution control based on the governance scheme. a governance execution module configured to implement the technology combination and parameter configuration for pollution governance; an effect evaluation module configured to monitor an actual governance effect and feed back the actual governance effect to the pollution site three-dimensional digital twin; a model calibration module configured to calibrate the pollution site three-dimensional digital twin and the deep reinforcement learning model according to a difference between the actual governance effect and an expected governance effect.

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