Low-impact development facility optimization layout system based on multi-objective optimization and uncertainty analysis

Abstract

The invention belongs to the technical field of urban rainwater management, and particularly relates to a low-impact development facility optimization layout system based on multi-objective optimization and uncertainty analysis. The system comprises a data acquisition and preprocessing layer, an uncertainty modeling and analysis layer, a multi-target collaborative optimization layer, an intelligent decision support layer and a dynamic adaptation and optimization layer. The data acquisition and preprocessing layer serves as a basic support layer of the system and is responsible for acquisition, cleaning, standardization and integration of multi-source heterogeneous data; the uncertainty modeling and analysis layer serves as a core theory layer of the system and establishes a ternary integrated uncertainty processing framework fusing fuzzy logic, the probability theory and machine learning; the multi-objective collaborative optimization layer serves as an algorithm engine layer of the system and integrates a hybrid optimization technology of an adaptive evolutionary algorithm, machine learning and heuristic search; the intelligent decision support layer serves as an interactive interface layer of the system and provides personalized decision support service based on user preference learning; and the dynamic adaptation and optimization layer is used as a self-learning layer of the system and realizes online optimization and adaptive adjustment based on real-time monitoring data. The invention provides a low-impact development facility optimization layout system based on multi-objective optimization and uncertainty analysis.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of urban rainwater management, and particularly relates to a low-impact development facility optimized layout system based on multi-objective optimization and uncertainty analysis. BACKGROUND

[0002] With the accelerating process of global urbanization, urban rainwater management is facing unprecedented challenges. The traditional urban drainage system mainly relies on gray infrastructure such as pipes and pumping stations, which is not enough to deal with the increasingly frequent extreme rainfall events. The rapid increase of urban impervious area leads to a significant increase in surface runoff, which not only increases the risk of urban waterlogging, but also causes serious water environmental pollution problems. In this context, low-impact development (LID) facilities, as a kind of green rainwater infrastructure, provide a new solution for urban water management by simulating natural hydrological processes to manage rainwater runoff.

[0003] However, the current planning and layout of LID facilities still faces many technical challenges. First of all, the diversity and conflict of optimization objectives. The planning of LID facilities needs to consider multiple objectives such as runoff reduction effect, pollutant removal efficiency, construction cost, maintenance cost, and ecological service value, and these objectives often have a mutually restrictive relationship. For example, pursuing better runoff control effect usually requires increasing the size and number of facilities, thereby increasing the construction cost; in order to control the cost, part of the environmental benefits may be sacrificed. How to find the optimal balance point between multiple conflicting objectives is the core problem faced by current technology.

[0004] Secondly, the complex influence of uncertainty factors. The performance of LID facilities is affected by many uncertainty factors, including the randomness of rainfall patterns, the long-term trend of climate change, the dynamic change of land use, the spatial difference of soil characteristics, and the uncertainty of facility parameters. These uncertainty factors are coupled with each other, so that the actual effect of LID facilities often deviates from the design expectation. Traditional deterministic optimization methods cannot effectively handle these uncertainties, resulting in insufficient robustness of the design scheme, which is prone to performance fluctuations or even failure in actual operation.

[0005] Thirdly, the technical complexity of multi-scale optimization. The layout of LID facilities involves multiple spatial levels from plot scale to watershed scale, and the optimization objectives, constraints and decision variables at different scales are different. Small-scale optimization focuses on the technical performance of individual facilities, while large-scale optimization pays more attention to systematic benefits and cost efficiency. How to realize the organic connection of multi-scale optimization and ensure the coordination and unity of local optimum and global optimum is a major challenge in technology implementation.

[0006] Fourth, the imperfection of cost-benefit assessment. Existing LID facility optimization methods often only consider direct construction costs and operation and maintenance expenses, ignoring the quantification of indirect benefits such as ecological service value, social benefit, environmental externality, etc. This one-sided cost-benefit analysis cannot fully reflect the true value of LID facilities, leading to the incompleteness of decision support information. At the same time, the lack of dynamic cost-benefit assessment mechanism cannot adapt to the cost structure adjustment brought by technological progress and market changes.

[0007] Fifth, the restriction of algorithm efficiency and computational complexity. LID facility optimization involves a large number of design variables and constraint conditions. Traditional optimization algorithms often face low computational efficiency and are prone to local optimization when dealing with large-scale, high-dimensional optimization problems. Especially when considering uncertainty factors, a large number of scenario analysis and Monte Carlo simulation are needed, and the computational load increases dramatically. The existing algorithm framework cannot meet the timeliness requirements of practical engineering applications.

[0008] Sixth, the lack of real-time adaptability. The continuous change of urban environment and climate conditions requires LID facilities to have dynamic adaptability, which can adjust the layout strategy in real time according to the operation data and environmental changes. However, the existing optimization methods are mostly static design, lacking real-time feedback and self-adaptive adjustment mechanism, which is difficult to cope with the complex and changeable urban environment.

[0009] The existence of these technical problems seriously restricts the popularization and application of LID facilities in urban rainwater management. Solving these problems has important practical significance and strategic value for improving urban water security capacity, promoting the construction of sponge city, and realizing sustainable urban development. SUMMARY

[0010] In order to solve the above problems existing in the prior art, the purpose of the present application is to provide a low-impact development facility optimization layout system based on multi-objective optimization and uncertainty analysis.

[0011] The technical scheme adopted by the present application is: A low-impact development facility optimization layout system based on multi-objective optimization and uncertainty analysis, comprising a data acquisition and preprocessing layer, an uncertainty modeling and analysis layer, a multi-objective collaborative optimization layer, an intelligent decision support layer, and a dynamic adaptation and optimization layer; The data acquisition and preprocessing layer: as the basic support layer of the system, responsible for the collection, cleaning, standardization and integration of multi-source heterogeneous data; The uncertainty modeling and analysis layer: as the core theoretical layer of the system, establishes a three-in-one uncertainty processing framework integrating fuzzy logic, probability theory and machine learning; Multi-objective collaborative optimization layer: as the algorithm engine layer of the system, it integrates hybrid optimization techniques of adaptive evolutionary algorithm, machine learning and heuristic search; Intelligent decision support layer: as the interactive interface layer of the system, it provides personalized decision support services based on user preference learning; Dynamic adaptation and optimization layer: as the self-learning layer of the system, it realizes online optimization and adaptive adjustment based on real-time monitoring data.

[0012] As a preferred scheme of the present application, the data acquisition and preprocessing layer, the uncertainty modeling and analysis layer, the multi-objective collaborative optimization layer, the intelligent decision support layer and the dynamic adaptation and optimization layer are loosely coupled and integrated through RESTful API and message queue mechanism, supporting distributed deployment and cloud computing; the system adopts micro-service architecture, and each functional module can be independently deployed, upgraded and extended; The data interface supports GeoJSON, Shapefile, NetCDF and CSV, and is compatible with mainstream GIS software and hydrological models; the algorithm interface adopts plug-in design, supporting user-defined algorithms and integration of third-party algorithms.

[0013] The present application solves the key problems faced by the prior art by constructing an intelligent five-layer optimization framework. The overall technical architecture adopts a progressive design pattern of "data-model-algorithm-decision-adaptation", with high modularity and scalability.

[0014] The present application will provide more intelligent, efficient and reliable technical solutions for urban rainwater management, promote the engineering application and large-scale popularization of LID technology, and make important contributions to the construction of sponge cities and the realization of sustainable development.

[0015] As a preferred scheme of the present application, the data acquisition and preprocessing layer is realized through multi-source data fusion technology, spatial data processing technology innovation and intelligent hydrological data processing. Multi-source data fusion technology: semantic data fusion technology based on ontology is adopted to establish a unified data model covering three dimensions of space, time and attribute; Spatial data processing technology innovation: adaptive spatial interpolation algorithm: combining the advantages of Kriging interpolation and radial basis function, a hybrid interpolation method with adaptive weight is developed; multi-scale spatial clustering technology: based on density-based spatial clustering algorithm, combined with geographic weighted regression, spatial partition optimization under different scales is realized; Intelligent hydrological data processing: including rainfall data quality control unit, runoff data preprocessing optimization unit and evapotranspiration data estimation unit.

[0016] As a preferred scheme of the present application, the unified data model of the multi-source data fusion technology comprises an intelligent data quality control unit, a spatial data standardization processing unit, and a time data synchronization alignment unit; The intelligent data quality control unit: based on machine learning, it automatically identifies abnormal values, missing values and inconsistencies in the data; it uses the Isolation Forest algorithm to detect spatial anomalies, uses the LSTM neural network to identify time series anomalies, and verifies attribute consistency through the rule engine; The spatial data standardization processing unit: it establishes a spatial data standardization process including coordinate system conversion, projection transformation and scale normalization; The time data synchronization alignment unit: for time series data of different frequencies, it develops a multi-scale time alignment algorithm based on wavelet transform.

[0017] As a preferred scheme of the present application, the rainfall data quality control unit: it establishes a three-level quality control system including time continuity test, spatial consistency test and physical reasonableness test; the time continuity test uses continuous wavelet transform to identify data discontinuity, the spatial consistency test uses the Thiessen polygon method to verify the data of adjacent stations, and the physical reasonableness test determines abnormal values based on historical extreme value statistics; The runoff data preprocessing optimization unit: it develops a digital filtering-based base flow separation algorithm to decompose total runoff into surface runoff and groundwater runoff; The evapotranspiration data estimation unit: it integrates the Penman-Monteith equation, the Hargreaves equation and the Priestley-Taylor equation to establish a multi-model set evapotranspiration estimation method; it automatically selects the most suitable calculation method according to data availability.

[0018] As a preferred scheme of the present application, the uncertainty modeling and analysis layer is realized through multi-source uncertainty intelligent identification technology, improved fuzzy logic reasoning system, efficient Monte Carlo simulation engine and uncertainty propagation analysis framework; Multi-source uncertainty intelligent identification technology: it adopts a three-level uncertainty classification system to establish a systematic uncertainty classification framework; the first level is divided into random uncertainty, fuzzy uncertainty and cognitive uncertainty according to the essential characteristics of uncertainty; the second level is divided into parameter uncertainty, model uncertainty, scenario uncertainty and decision uncertainty according to the source of influencing factors; the third level is divided into input uncertainty, process uncertainty and output uncertainty according to the specific influencing mechanism; Improved fuzzy logic reasoning system: including adaptive membership function optimization module and fuzzy reasoning rule automatic generation module; adaptive membership function optimization module aims at the problem that membership function parameters are difficult to determine in traditional fuzzy logic, and develops an adaptive optimization method based on data driving: trapezoidal membership function parameter optimization, Gaussian membership function parameter optimization; the fuzzy reasoning rule automatic generation module generates and optimizes the fuzzy reasoning rule by using the improved genetic algorithm; Efficient Monte Carlo simulation engine: including hierarchical importance sampling module and variance reduction technology integration module; the hierarchical importance sampling module develops a hierarchical importance sampling algorithm for the problem of low efficiency of traditional Monte Carlo method; the variance reduction technology integration module includes dual variable technology, control variable technology, conditional expectation technology and importance sampling technology; Uncertainty propagation analysis framework: polynomial chaos expansion proxy modeling: establish an orthogonal polynomial-based proxy model to replace complex numerical simulation; global sensitivity analysis module: calculate Sobol sensitivity index based on PCE model.

[0019] As a preferred scheme of the present application, the hierarchical importance sampling module includes variable importance analysis unit, hierarchical sampling strategy unit and importance sampling implementation unit; Variable importance analysis unit: the Morris sensitivity analysis method is used to determine the variable importance ranking; Hierarchical sampling strategy unit: according to the variable importance, the input space is divided into k layers; Importance sampling implementation unit: different sampling densities are used for each layer.

[0020] As a preferred scheme of the present application, the multi-objective collaborative optimization layer is realized by adaptive NSGA-III algorithm design, reinforcement learning parameter adaptive mechanism, mixed search strategy integration, constraint processing and feasibility guarantee; Adaptive NSGA-III algorithm design: reference point adaptive adjustment mechanism: aiming at the limitation of fixed reference point in traditional NSGA-III algorithm, a reference point adaptive adjustment strategy based on solution distribution feedback is developed; initial reference point generation: uniform distribution reference points are generated by using Das-Dennis method; reference point quality evaluation: define reference point quality index; adaptive adjustment strategy; Reinforcement learning parameter adaptive mechanism: Q-Learning parameter tuning: algorithm parameter adjustment is modeled as Markov decision process; state space definition; action space definition; reward function design; Q value update strategy; Mixed search strategy integration: multi-operator collaborative evolution: integrate multiple evolutionary operators and adaptively select according to search state; operator selection strategy: use multi-armed bandit model; Constraint handling and feasibility guarantee: epsilon-constraint handling technique: for complex constraints in LID optimization, a constraint handling method with dynamic epsilon value is adopted; constraint violation degree calculation; repair operator design: develop special repair strategies for different types of constraint violations.

[0021] As a preferred scheme of the application, the intelligent decision support layer is realized through user preference learning and modeling, multi-attribute decision analysis engine, visual display and interactive interface, sensitivity and robustness analysis; User preference learning and modeling: interactive preference acquisition: develop a preference acquisition method based on progressive learning; fuzzy preference modeling: adopt trapezoidal fuzzy numbers to represent the inaccuracy of preference; Multi-attribute decision analysis engine: TOPSIS-GRA hybrid decision method: combining the distance concept of TOPSIS method and the similarity concept of grey correlation analysis, including decision matrix standardization, weighted standardized matrix, ideal solution and negative ideal solution determination, grey correlation degree calculation, comprehensive evaluation index; Visual display and interactive interface: multi-dimensional data visualization: develop a visualization method suitable for high-dimensional target space; parallel coordinate graph enhancement; improved RadViz visualization; 3D Pareto frontier visualization; Sensitivity and robustness analysis: global sensitivity analysis: Sobol index calculation based on variance decomposition; Monte Carlo robustness evaluation.

[0022] As a preferred scheme of the application, the dynamic adaptation and optimization layer is realized through real-time data integration and processing, performance prediction and early warning, online learning and model updating, and self-adaptive optimization adjustment mechanism; Real-time data integration and processing: the stream data processing architecture adopts Lambda architecture mode, supporting the mixed mode of batch processing and stream processing; Performance prediction and early warning: time series prediction model integration, integrating multiple prediction models to improve prediction accuracy; Online learning and model updating: incremental learning algorithm, adopting an incremental learning strategy combining online gradient descent and experience replay; Self-adaptive optimization adjustment mechanism: with a rolling horizon optimization strategy.

[0023] The application has the following beneficial effects: 1. The application constructs a comprehensive multi-objective optimization framework: integrates multi-dimensional objectives such as runoff control, pollutant removal, cost benefit, ecological service value, and social benefit, and establishes a hierarchical target system. Adopting a self-adaptive weight adjustment mechanism, dynamic preference expression and target balance are realized. Developing an interactive optimization algorithm based on decision maker preference learning improves the effectiveness of decision support.

[0024] 2. The invention establishes a multi-source uncertainty processing mechanism: comprehensive consideration of randomness, fuzziness, cognitive uncertainty and other types of uncertainty, to establish a unified uncertainty modeling framework. Develop uncertainty identification and quantification methods based on artificial intelligence, combining the advantages of fuzzy logic and probability theory, to realize effective fusion processing of multi-source uncertainty.

[0025] 3. The invention perfects the cost-benefit evaluation system: a full life cycle cost-benefit evaluation model is constructed, including direct cost, indirect cost, ecological benefit and social benefit. Develop ecological service value quantification methods based on contingent valuation method and choice experiment method, to realize the monetization of environmental benefits. Establish a dynamic cost-benefit analysis framework to adapt to changes in technology and market.

[0026] 4. The invention realizes multi-scale collaborative optimization: a multi-level optimization model from plot to watershed is established, and information transmission and coordinated optimization between different levels are realized through scale coupling mechanism. Develop large-scale optimization algorithm based on decomposition coordination theory, to improve computational efficiency while ensuring global optimality.

[0027] 5. The invention improves the intelligent level of the algorithm: artificial intelligence technology is integrated to develop adaptive hybrid optimization algorithm, combining the global search ability of evolutionary algorithm and the local refinement ability of machine learning. Adopt proxy model technology to reduce computational load, and improve model accuracy through online learning mechanism.

[0028] 6. The invention enhances the adaptability of the system: a dynamic optimization framework based on real-time monitoring data is established to realize online adjustment and performance optimization of LID facility layout. Develop predictive maintenance and adaptive design mechanisms to improve the robustness and sustainability of the system. BRIEF DESCRIPTION OF DRAWINGS

[0029] Figure 1 is a structural schematic diagram of the invention. DETAILED DESCRIPTION

[0030] To make the purpose, technical scheme and advantages of the embodiments of the invention clearer, the technical scheme in the embodiments of the invention will be described clearly and completely in conjunction with the drawings in the embodiments of the invention. Obviously, the described embodiments are part of the embodiments of the invention, not all embodiments. The components of the embodiments of the invention described and shown in the drawings herein can be arranged and designed in various different configurations.

[0031] Therefore, the following detailed description of the embodiments of the application provided in the drawings is not intended to limit the scope of the claimed application, but merely represents selected embodiments of the application. Based on the embodiments in the application, all other embodiments obtained by those of ordinary skill in the art without creative labor are within the scope of protection of the application. It should be noted that the embodiments in the application and the features in the embodiments can be combined with each other without conflict.

[0032] I. Overall technical architecture and system composition The application proposes a LID facility optimization layout method based on multi-objective optimization and uncertainty analysis. The method systematically solves the key problems faced by the prior art through the construction of an intelligent five-layer optimization framework. As shown in the figure, Figure 1 The overall technical architecture adopts a progressive design mode of "data-model-algorithm-decision-adaptation", and has high modularity and scalability.

[0033] 1. Five-layer architecture design: Data acquisition and preprocessing layer (L1): As the basic support layer of the system, it is responsible for the collection, cleaning, standardization and integration of multi-source heterogeneous data. This layer adopts the ETL (Extract-Transform-Load) data processing architecture, supports the parallel processing of real-time data flow and batch data, and the data processing capacity reaches more than 10 GB / hour.

[0034] Uncertainty modeling and analysis layer (L2): As the core theory layer of the system, it establishes a three-in-one uncertainty processing framework integrating fuzzy logic, probability theory and machine learning. This layer realizes automatic classification and quantification of uncertain factors through an intelligent recognition engine, and the processing accuracy reaches more than 95%.

[0035] Multi-objective collaborative optimization layer (L3): As the algorithm engine layer of the system, it integrates hybrid optimization technology of adaptive evolutionary algorithm, machine learning and heuristic search. This layer supports simultaneous optimization of 3-15 objective functions, and the convergence efficiency is improved by more than 60% compared with traditional algorithms.

[0036] Intelligent decision support layer (L4): As the interactive interface layer of the system, it provides personalized decision support services based on user preference learning. This layer adopts a human-computer collaborative decision-making mode, and the decision accuracy reaches more than 85% and the user satisfaction is more than 90%.

[0037] Dynamic adaptation and optimization layer (L5): As the self-learning layer of the system, it realizes online optimization and adaptive adjustment based on real-time monitoring data. This layer adopts an incremental learning mechanism, and the model update time is shortened to 30% of the original, and the prediction accuracy is maintained at more than 92%.

[0038] 2. System integration and interface design: The system is loosely coupled between layers through RESTful APIs and message queue mechanisms, supporting distributed deployment and cloud computing. The system adopts a microservices architecture, with each functional module being independently deployable, upgradable, and extensible, ensuring high availability and maintainability.

[0039] The data interface supports multiple standard formats, including GeoJSON, Shapefile, NetCDF, CSV, and is compatible with mainstream GIS software and hydrological models. The algorithm interface uses a plug-in design, supporting user-defined algorithms and the integration of third-party algorithms.

[0040] II. Technical implementation of the data preprocessing module 1. Multi-source data fusion technology: The data preprocessing module uses ontology-based semantic data fusion technology to establish a unified data model covering space, time, and attribute dimensions. The module has the following core functions: Intelligent data quality control unit: Based on machine learning, the anomaly detection algorithm can automatically identify outliers, missing values, and inconsistencies in the data. The Isolation Forest algorithm is used to detect spatial anomalies, the LSTM neural network is used to identify time series anomalies, and the rule engine is used to verify attribute consistency. The data quality control accuracy is above 95%, and the false positive rate is controlled below 3%.

[0041] Spatial data standardization processing: A spatial data standardization process is established, including coordinate system conversion, projection transformation, and scale normalization. It supports more than 20 commonly used coordinate systems, including WGS84, Beijing 54, Xi'an 80, and CGCS2000. The projection accuracy is controlled within 1 meter, and the scale normalization preserves the spatial relationship of the original data.

[0042] Time data synchronization alignment: For time series data of different frequencies, a multi-scale time alignment algorithm based on wavelet transform is developed. It supports multi-time scale data fusion from minutes to years, with a time synchronization accuracy of seconds and a data interpolation error controlled within 5%.

[0043] 2. Spatial data processing technology innovation: Adaptive spatial interpolation algorithm: Combining the advantages of Kriging interpolation and radial basis functions, a hybrid interpolation method with adaptive weights is developed. The algorithm automatically selects the optimal interpolation method based on the spatial correlation and variability of the data, with an interpolation accuracy improvement of 20% to 30% compared to traditional methods.

[0044] Interpolation accuracy evaluation index: RMSE (Root Mean Square Error) ≤ 10% of the original data standard deviation; MAE (Mean Absolute Error) ≤ 5% of the mean of the original data; R 2 R-squared (Determination Coefficient) ≥ 0.85.

[0045] Multi-scale spatial clustering technique: Using the density-based spatial clustering algorithm (DBSCAN) combined with geographic weighted regression (GWR), we achieve spatial partition optimization at different scales. The clustering accuracy reaches 90%, supporting multi-scale analysis from 10m x 10m to 1km x 1km.

[0046] 3. Intelligent processing of hydrological data: Rainfall data quality control: A three-level quality control system is established, including time continuity test, spatial consistency test, and physical reasonableness test. Time continuity test uses continuous wavelet transform to identify data discontinuity, spatial consistency test uses Thiessen polygon method to verify adjacent station data, and physical reasonableness test is based on historical extreme value statistics for outlier determination.

[0047] Runoff data preprocessing optimization: A digital filtering-based base flow separation algorithm is developed, which can separate total runoff into surface runoff and groundwater runoff. The separation accuracy is more than 85%, providing more accurate input data for subsequent hydrological simulation.

[0048] Evapotranspiration data estimation: Integrating the Penman-Monteith equation, Hargreaves equation and Priestley-Taylor equation, a multi-model ensemble evapotranspiration estimation method is established. According to data availability, the most suitable calculation method is automatically selected, and the estimation error is controlled within 15%.

[0049] Three, the core innovation of uncertainty modeling module 1. Multi-source uncertainty intelligent identification technology: Three-level uncertainty classification system: A systematic uncertainty classification framework is established, including: First level: According to the nature of uncertainty, it is divided into three categories: random uncertainty, fuzzy uncertainty and cognitive uncertainty.

[0050] Second level: According to the source of influencing factors, it is divided into four subcategories: parameter uncertainty, model uncertainty, scenario uncertainty and decision uncertainty.

[0051] Third level: According to the specific influencing mechanism, it is divided into three levels: input uncertainty, process uncertainty and output uncertainty.

[0052] 2. Improved fuzzy logic reasoning system: Adaptive membership function optimization: To solve the problem of difficult determination of membership function parameters in traditional fuzzy logic, an adaptive optimization method based on data-driven is developed: L-trapezoidal membership function parameter optimization: Let the L-trapezoidal membership function be μ(x) = max(0, min((x-a) / (b-a), 1, (d-x) / (d-c))).

[0053] where the parameters (a, b, c, d) are determined by the following optimization model: Objective function: minΣ(μ(xi) - μ̂(xi)) 2 .

[0054] Constraint conditions: a ≤ b ≤ c ≤ d; 0 ≤ μ(x) ≤ 1; ∫μ(x)dx ≥ 0.1 (to avoid excessive concentration).

[0055] Gaussian membership function parameter optimization: Let the Gaussian membership function be μ(x) = exp(-(x-c 2 / (2σ 2 ))).

[0056] The parameters (c, σ) are determined by the combination of maximum likelihood estimation and cross-validation.

[0057] Automatic generation of fuzzy inference rules: An improved genetic algorithm is used to automatically generate and optimize fuzzy inference rules: Rule structure: IF (X1 is A1) AND (X2 is A2) THEN (Y is B) [CF = w].

[0058] Where: X1, X2 are input variables; A1, A2 are fuzzy sets; Y is the output variable; B is the output fuzzy set; CF is the confidence factor; w is the rule weight.

[0059] The rule generation algorithm uses the following encoding method: Gene length: input variable number × fuzzy set number + output variable number × fuzzy set number + 1; Fitness function: weighted combination of inference accuracy and rule simplicity; Selection strategy: tournament selection; Crossover operator: adaptive selection of single-point crossover and uniform crossover; Mutation operator: non-uniform mutation based on rule importance.

[0060] 3. Efficient Monte Carlo simulation engine: Hierarchical importance sampling technique: To solve the problem of low efficiency of traditional Monte Carlo method, a hierarchical importance sampling algorithm is developed: First step: Variable importance analysis determines the variable importance ranking using the Morris sensitivity analysis method: μ*=(1 / r)Σ|EE_i|; # Corrected mean value; σ=sqrt((1 / r)Σ(EE_i-μ) 2 );# Standard deviation.

[0061] Second step: The stratified sampling strategy divides the input space into k layers according to the variable importance: Layer_i={x|P_{i-1}≤Importance(x)<P_i}; Where, P_0=0, P_k=1.

[0062] Third step: Importance sampling implements different sampling densities for each layer: n_i=n×w_i; Where, w_i=Importance_i / ΣImportance_j.

[0063] Variance reduction techniques integration: Antithetic variables technique; Control variates technique; Conditional expectation technique; Importance sampling technique.

[0064] Through the integration of the above techniques, the simulation efficiency is improved by 50%~80%, while maintaining more than 95% calculation accuracy.

[0065] 4. Uncertainty propagation analysis framework: Polynomial chaos expansion (PCE) surrogate modeling: Establish a surrogate model based on orthogonal polynomials to replace complex numerical simulation: Y=Σα_iΨ_i(ξ); Where, Y is the output response; α_i is the PCE coefficient; Ψ_i is the orthogonal polynomial basis function; ξ is the standardized random variable.

[0066] PCE coefficients are solved by the following methods: Projection method: α_i=<Y, Ψ_i> / <Ψ_i, Ψ_i>; Regression method: α=(ΨTΨ)(-1)Ψ^TY; Sparse regression method: min||Ψα-Y|| 2 +λ||α||1.

[0067] Global sensitivity analysis: Sobol sensitivity indices are calculated based on PCE model: First-order sensitivity index: S_i=Var[E(Y|X_i)] / Var(Y); Total sensitivity index: ST_i=E[Var(Y|X_i)] / Var(Y); Where X_i represents all variables except X_i.

[0068] Four, algorithm innovation of multi-objective optimization module 1. Adaptive NSGA-III algorithm design: Reference point adaptive adjustment mechanism: In view of the limitations of fixed reference points in traditional NSGA-III algorithm, a reference point adaptive adjustment strategy based on solution distribution feedback is developed: Initial reference point generation: Das-Dennis method is used to generate uniformly distributed reference points: H objectives, each objective is divided into p equal parts, the number of reference points is: N_ref=C(H+p-1,p)=(H+p-1)! / (p!(H-1)!).

[0069] Reference point quality evaluation: define reference point quality index: Quality(r_i)=α×Coverage(r_i)+β×Uniformity(r_i)+γ×Diversity(r_i).

[0070] Where Coverage(r_i)=|{x∈P|closest_ref(x)=r_i}| / |P|; Uniformity(r_i)=1 / (1+std_dev(dist_to_neighbors)); Diversity(r_i)=min_j≠i||r_i-r_j||.

[0071] Adaptive adjustment strategy: When Coverage(r_i)<threshold_low, delete reference point r_i; When the solution density in a certain area is too high, add reference points in that area; Adjustment frequency: reference point evaluation and adjustment are performed every 20 generations.

[0072] 2. Reinforcement learning parameter adaptive mechanism: Q-Learning parameter tuning: model the algorithm parameter adjustment as a Markov decision process: State space definition: State = (generation, convergence_rate, diversity_metric, constraint_violation_rate, population_size).

[0073] Action space definition: Action = (crossover_prob_adjustment, mutation_prob_adjustment, selection_pressure_adjustment).

[0074] Reward function design: Reward = w1 x ΔHypervolume + w2 x ΔSpread + w3 x Δconvergence.

[0075] Where ΔHypervolume is the improvement in hypervolume; ΔSpread is the improvement in spread of solution set; ΔConvergence is the improvement in convergence.

[0076] Q-value update strategy: Q(s, a) ← Q(s, a) + α[r + γmax_a'Q(s', a') - Q(s, a)].

[0077] Where α is the learning rate (0.1-0.3); γ is the discount factor (0.8-0.95).

[0078] 3. Integration of hybrid search strategies: Multi-operator cooperative evolution: integrate multiple evolutionary operators and adaptively select based on search state: Crossing operator library: Simulated Binary Crossover (SBX): suitable for continuous variables; Uniform Crossover (UX): suitable for discrete variables; Arithmetic Crossover (AX): suitable for bounded variables; Heuristic Crossover (HX): suitable for constraint optimization.

[0079] Mutation operator library: Polynomial Mutation (PM): suitable for continuous variables; Bit Flip Mutation (BFM): suitable for binary variables; Gaussian Mutation (GM): suitable for real number coding; Adaptive Mutation (AM): adjusted according to individual fitness.

[0080] Operator selection strategy: use Multi-Armed Bandit model: UCB selection strategy: UCB_i = μ_i + C√(ln(t) / n_i).

[0081] Where: μ_i is the average reward of operator i; C is the exploration parameter; t is the total selection number; n_i is the selection number of operator i.

[0082] 4. Constraint handling and feasibility guarantee: ε-constraint handling technique: for complex constraints in LID optimization, a dynamic ε value constraint handling method is adopted: ε(t) = ε(0) × (1-t / T)^cp.

[0083] Where: ε(0) is the initial tolerance; t is the current generation number; T is the maximum generation number; cp is the convergence parameter.

[0084] Constraint violation degree calculation: CV(x) = Σmax(0, g_i(x)) + Σ|h_j(x)|.

[0085] Where: g_i(x) ≤ 0 is an inequality constraint; h_j(x) = 0 is an equality constraint.

[0086] Repair operator design: develop special repair strategies for different types of constraint violations: Boundary constraint violation: projection repair; Linear constraint violation: simplex repair; Nonlinear constraint violation: gradient repair; Integer constraint violation: nearest integer repair.

[0087] Five, the realization of intelligent decision support module 1. User preference learning and modeling: Interactive preference acquisition: developed a preference acquisition method based on progressive learning: Preference information type: Ranking preference: scheme A is better than scheme B; Weight preference: the importance of target i is wi; Threshold preference: the minimum acceptable value of target i is ti; Interval preference: the satisfaction interval of target i is [li, ui].

[0088] Fuzzy preference modeling: use trapezoidal fuzzy numbers to represent the inaccuracy of preference: Preference intensity = (a, b, c, d).

[0089] Where: [a, d] is the support set; [b, c] is the core set; the membership function is trapezoidal distribution; 2. Multi-attribute decision analysis engine: TOPSIS-GRA hybrid decision-making method: combines the distance concept of TOPSIS method and the similarity concept of grey relational analysis: Step 1: Standardization of decision matrix r_ij=x_ij / √(Σx_ij 2 ) # Vector normalization, or, r_ij=(x_ij-min_jx_ij) / (max_jx_ij-min_jx_ij) # Linear standardization.

[0090] Step 2: Weighted normalized matrix v_ij=w_i×r_ij; where w_i is the target weight, satisfying Σw_i=1.

[0091] Step 3: Ideal solution and negative ideal solution determination A⁺={max_iv_ij|j∈J⁺;min_iv_ij|j∈J⁻}; A⁻={min_iv_ij|j∈J⁺;max_iv_ij|j∈J⁻}; where J⁺ is the benefit type index, and J⁻ is the cost type index.

[0092] Step 4: Grey correlation degree calculation γ_i=(1 / n)Σζ_ij; where ζ_ij=(min_all_imin_all_j|A⁺_j-v_ij|+ρmax_all_imax_all_j|A⁺_j-v_ij|) / (|A⁺_j-v_ij|+ρmax_all_imax_all_j|A⁺_j-v_ij|)。

[0093] Step 5: Comprehensive evaluation index C_i=α×C_TOPSIS+β×γ_i; where α+β=1, usually α=β=0.5.

[0094] 3. Visualization display and interactive interface: Multi-dimensional data visualization: developed visualization methods suitable for high-dimensional target space: Parallel coordinate graph enhancement: Axis ordering optimization: intelligent ordering based on target correlation; Cluster coloring: color coding based on k-means clustering; Interactive filtering: supports interval brushing and logical filtering; Density display: display the density distribution of solutions.

[0095] 4. Sensitivity and robustness analysis: Global sensitivity analysis: Sobol index computation based on variance decomposition.

[0096] VI. Implementation mechanism of dynamic adaptation module 1. Real-time data integration and processing: Stream data processing architecture: Adopting Lambda architecture mode, supporting mixed mode of batch processing and stream processing: Speed Layer, Batch Layer, real-time monitoring of data quality.

[0097] 2. Performance prediction and early warning: Time series prediction model integration: Integrate multiple prediction models to improve prediction accuracy: LSTM neural network prediction, ARIMA-GARCH model, integrated prediction and uncertainty quantification.

[0098] 3. Online learning and model updating: Incremental learning algorithm: Adopting incremental learning strategy combining online gradient descent and experience replay.

[0099] Model performance monitoring and degradation detection.

[0100] VII. Integration of key technology processes and algorithms 1. Main algorithm flow design: The main algorithm flow of the invention adopts a six-stage progressive design, each stage has a clear input and output and quality control mechanism: Stage 1: Initialization and data preparation; Stage 2: Uncertainty identification and modeling; Stage 3: Multi-objective optimization execution; Stage 4: Scheme evaluation and decision support; Stage 5: Dynamic monitoring and prediction; Stage 6: Adaptive adjustment and optimization.

[0101] 2. Algorithm complexity analysis and optimization: Time complexity analysis: Data preprocessing: O(nlogn), where n is the number of data points; Uncertainty modeling: O(m×k 2 ), where m is the number of uncertainty factors and k is the number of sampling points; Multi-objective optimization: O(g×p×(d+f)), where g is the evolution generation, p is the population size, d is the decision variable dimension, and f is the number of objective functions; Decision support: O(s×f 2), where s is the number of solutions, and f is the number of objective functions; Dynamic adaptation: O(txl), where t is the number of time steps, and l is the number of learning samples.

[0102] Space complexity analysis: Memory complexity: O(pxd + sxf + kxm); Memory optimization strategies: Incremental processing and data compression techniques are employed.

[0103] 3. Quality control and verification mechanisms: Algorithm convergence verification.

[0104] Eight, the perfection of specific embodiments 1. Complete project case study: Project basic information: Project name: LID facility optimization layout for sponge city construction in a certain high-tech development zone; Planning area: 50.2 square kilometers; Topography: Plain area, average elevation 15-25 meters; Climate characteristics: Subtropical monsoon climate, annual rainfall 1100mm; Soil type: Light loam and medium loam are the main types, with a permeability coefficient of 2.5-8.5mm / h.

[0105] Detailed data preparation process: Spatial data collection and processing: 1. DEM data: Resolution 5m x 5m, accuracy ±0.5m, data volume about 40GB; 2. Land use data: Resolution 2m x 2m, containing 15 land use types; 3. Soil distribution data: 1:10000 scale, containing 12 attributes such as soil type, permeability coefficient, and water content; 4. Pipe network layout data: Including rainwater pipe network, sewage pipe network, combined pipe network, total length about 1200km; 5. Building data: Including building outline, height, use, etc. Information, a total of about 50000 buildings; 6. Road network data: Including roads of all levels, total length about 800km.

[0106] Hydrological data collection and processing: 1. Rainfall data: 30 years of hourly rainfall records from 5 weather stations; 2. Evaporation data: 20 years of daily evaporation records; 3. Groundwater level data: 15 years of monthly groundwater level records from 20 monitoring wells; 4. Runoff data: 10-year hourly runoff records from 3 hydrological stations 5. Water quality data: 5-year water quality monitoring records, including 8 indicators such as COD, SS, TN, TP, etc.

[0107] Engineering data collection and processing: 1. LID facility technical parameters: Collect technical specifications of 32 types of LID facilities in 8 categories; 2. Construction process data: Including construction methods, quality standards, acceptance specifications; 3. Material cost data: Collect material price fluctuation data for nearly 5 years; 4. Equipment parameter data: Including technical parameters of pump stations, pipelines, valves and other equipment.

[0108] 2. Uncertainty analysis detailed process: Step 1: Identify uncertainty factors Through expert interviews, literature research and data analysis, 18 main uncertainty factors affecting LID facility performance are identified: Random uncertainty factors (8): 1. Rainfall intensity: Lognormal distribution, μ = 2.1, σ = 0.8; 2. Rainfall duration: Gamma distribution, α = 1.5, β = 2.3; 3. Soil permeability coefficient: Lognormal distribution, μ = 1.2, σ = 0.6; 4. Evapotranspiration rate: Normal distribution, μ = 1200 mm / year, σ = 180 mm / year; 5. Groundwater level: Normal distribution, μ = 2.5 m, σ = 0.8 m; 6. Runoff coefficient: Beta distribution, α = 2.1, β = 3.2; 7. Initial pollutant flushing: Exponential distribution, λ = 0.8; 8. Facility attenuation coefficient: Weibull distribution, k = 2.1, λ = 0.15.

[0109] Fuzzy uncertainty factors (6): 1. Soil type boundary: Trapezoidal fuzzy number (0.2, 0.4, 0.6, 0.8); 2. Vegetation coverage: Triangular fuzzy number (0.3, 0.5, 0.7); 3. Facility maintenance level: Trapezoidal fuzzy number (0.1, 0.3, 0.7, 0.9); 4. Land use compatibility: Triangular fuzzy number (0.4, 0.6, 0.8); 5. Landscape coordination: Trapezoidal fuzzy number (0.2, 0.4, 0.6, 0.8); 6. Social acceptance: triangular fuzzy number (0.5, 0.7, 0.9).

[0110] Cognitive uncertainty factors (4): 1. Model parameter error: normal distribution, σ relative error = 15%; 2. Climate change impact: scenario analysis, 3 climate scenarios; 3. Urban development pattern: scenario analysis, 4 development scenarios; 4. Technological progress impact: expert judgment, uncertainty ±20%.

[0111] Step 2: Uncertainty quantification modeling Monte Carlo simulation settings.

[0112] 3. Multi-objective optimization detailed implementation: Optimization problem mathematical modeling: Decision variable definition: Facility location variable: x_ij∈{0,1}, indicates whether to build facility type i at location j; Facility scale variable: s_ij∈[s_min, s_max], indicates the size of the facility; Operating parameter variable: p_ijk∈[p_min, p_max], indicates the operating parameters of the facility.

[0113] Total number of decision variables: 8 (facility types) × 625 (candidate locations) × 3 (parameters) = 15000.

[0114] Objective function definition: f1(x, s, p) = minimize total construction cost = Σᵢⱼx_ij×(C_fixed_ij+C_variable_ij×s_ij).

[0115] f2(x, s, p) = minimize annual operation and maintenance cost = Σᵢⱼx_ij×s_ij×C_operation_ij.

[0116] f3(x, s, p) = maximize runoff reduction rate = (Q_original-Q_after_LID) / Q_original×100%.

[0117] f4(x, s, p) = maximize pollutant removal rate = Σ k w_k×(P_original_k-P_after_LID_k) / P_original_k×100%.

[0118] f5(x, s, p) = maximize ecosystem service value =∑ᵢⱼx_ij×s_ij×V_ecosystem_ij.

[0119] f6(x, s, p) = maximize social benefit index =∑ᵢⱼx_ij×(I_landscape_ij+I_recreation_ij+I_education_ij).

[0120] f7(x, s, p) = maximize system robustness = minimize CV(Performance) = σ(Performance) / μ(Performance).

[0121] Constraint definitions: 1. Budget constraint:∑ᵢⱼx_ij×C_total_ij≤Budget_max; 2. Land use constraint:∑ᵢx_ij×A_ij≤Area_available_j, ∀j; 3. Technology constraint: s_min_ij≤s_ij≤s_max_ij, ∀i, j; 4. Capacity constraint:∑ⱼs_ij×Q_capacity_ij≥Q_demand_i, ∀i; 5. Compatibility constraint: x_ij+x_kj≤1, ∀(i, k)∈Incompatible, ∀j; 6. Connection constraint: Ensure hydraulic connection feasibility between facilities.

[0122] Optimization result analysis: After 500 generations of evolution, the algorithm converges, obtaining a Pareto frontier containing 280 non-dominated solutions: Representative solutions: Solution A (cost-oriented): Total investment: 230 million yuan; Annual operation and maintenance cost: 3.2 million yuan; Runoff reduction rate: 52%; Pollutant removal rate: 61%; Ecosystem service value: 180 million yuan; Social benefit index: 0.72; System robustness: 0.15.

[0123] Solution B (balanced solution): Total investment: 280 million yuan; Annual operation and maintenance cost: 4.5 million yuan; Runoff reduction rate: 65%; Pollutant removal rate: 73%; Ecosystem service value: 240 million yuan; Social benefit index: 0.85; System robustness: 0.12.

[0124] Scenario C (effect-oriented): Total investment: 350 million yuan; annual operation and maintenance cost: 6.8 million yuan; runoff reduction rate: 78%; pollutant removal rate: 84%; ecological service value: 310 million yuan; social benefit index: 0.93; system robustness: 0.09.

[0125] 4. Implementation effect verification: Verification method design.

[0126] Actual operation effect comparison.

[0127] Verification period: 2019-2022 (4 years).

[0128] Monitoring frequency: hourly rainfall runoff monitoring, monthly water quality monitoring.

[0129] Table 1: Comparison of predicted values and actual values:

[0130] Overall prediction accuracy: 95.2%.

[0131] Nine, technical effect and advantage analysis 1. Technical performance improvement: Computing efficiency improvement: Compared with traditional methods, the invention has significantly improved computing efficiency. Uncertainty processing efficiency is improved by 60%-80%; multi-objective optimization convergence speed is improved by 50%-70%; overall computing time is shortened to 30%-40% of traditional methods.

[0132] Optimization quality improvement: The diversity of Pareto solution set is improved by 40%; the convergence of solution is improved by 35%; the robustness index is improved by 50%.

[0133] Adaptability enhancement: Support simultaneous optimization of 3-15 objectives; ability to handle 18 types of uncertainty factors; adapt to different scales of 50-50000 square kilometers.

[0134] 2. Economic benefit analysis: Direct economic benefit: LID facility effect is improved by 15%-25% under the same investment; investment cost is reduced by 10%-20% under the same effect; operation and maintenance cost is optimized by 10%-15%.

[0135] Indirect economic benefit: Ecological service value is improved by 20%-30%; social benefit index is improved by 25%-35%; flood loss value is improved by 30%-50%.

[0136] 3. Social and environmental benefits: Environmental benefits: Annual runoff control rate is improved to 70%-85%; annual runoff pollutant removal rate reaches 65%-80%; biodiversity index is improved by 15%-25%.

[0137] Social benefits: urban livability index is improved by 20% to 30%; public environmental awareness index is improved by 25%; decision-making scientificity and transparency are significantly improved.

[0138] The application provides an intelligent, efficient and accurate technical solution for urban rainwater management through systematic innovation design and strict engineering verification, and has significant technical advantages and broad application prospect.

[0139] The application is not limited to the optional implementation manner described above, and anyone can derive other various forms of products under the inspiration of the application, but regardless of any change in shape or structure, any technical solution falling within the scope defined by the claims of the application falls within the protection scope of the application.

Claims

1. A low-impact development facility optimization layout system based on multi-objective optimization and uncertainty analysis, characterized in that: It includes a data acquisition and preprocessing layer, an uncertainty modeling and analysis layer, a multi-objective collaborative optimization layer, an intelligent decision support layer, and a dynamic adaptation and optimization layer; Data Acquisition and Preprocessing Layer: As the basic support layer of the system, it is responsible for the collection, cleaning, standardization and integration of multi-source heterogeneous data; Uncertainty Modeling and Analysis Layer: As the core theoretical layer of the system, it establishes a three-dimensional uncertainty processing framework that integrates fuzzy logic, probability theory, and machine learning; Multi-objective collaborative optimization layer: As the algorithm engine layer of the system, it integrates hybrid optimization techniques of adaptive evolutionary algorithm, machine learning and heuristic search; Intelligent Decision Support Layer: As the system's interactive interface layer, it provides personalized decision support services based on user preference learning; Dynamic Adaptation and Optimization Layer: As the system's self-learning layer, it enables online optimization and adaptive adjustment based on real-time monitoring data.

2. The low-impact development facility optimization layout system based on multi-objective optimization and uncertainty analysis according to claim 1, characterized in that: The data acquisition and preprocessing layer, uncertainty modeling and analysis layer, multi-objective collaborative optimization layer, intelligent decision support layer, and dynamic adaptation and optimization layer are loosely coupled and integrated through RESTful APIs and message queue mechanisms, supporting distributed deployment and cloud computing; the system adopts a microservice architecture, and each functional module can be deployed, upgraded, and expanded independently; The data interface supports GeoJSON, Shapefile, NetCDF, and CSV, and is compatible with mainstream GIS software and hydrological models; the algorithm interface adopts a plug-in design, supporting the integration of user-defined algorithms and third-party algorithms.

3. The low-impact development facility optimization layout system based on multi-objective optimization and uncertainty analysis according to claim 1, characterized in that: The data acquisition and preprocessing layer is achieved through multi-source data fusion technology, spatial data processing technology innovation, and intelligent hydrological data processing. Multi-source data fusion technology: Employing ontology-based semantic data fusion technology to establish a unified data model covering three dimensions: space, time, and attributes; Spatial data processing technology innovation: Adaptive spatial interpolation algorithm: Combining the advantages of Kriging interpolation and radial basis function, a hybrid interpolation method with adaptive weights is developed; Multi-scale spatial clustering technology: A density-based spatial clustering algorithm is adopted, combined with geographic weighted regression, to achieve spatial partitioning optimization at different scales; Intelligent hydrological data processing includes a rainfall data quality control unit, a runoff data preprocessing and optimization unit, and an evapotranspiration data estimation unit.

4. The low-impact development facility optimization layout system based on multi-objective optimization and uncertainty analysis according to claim 3, characterized in that: The unified data model of multi-source data fusion technology includes an intelligent data quality control unit, a spatial data standardization processing unit, and a temporal data synchronization and alignment unit. Intelligent data quality control unit: Based on machine learning anomaly detection algorithms, it automatically identifies outliers, missing values, and inconsistencies in the data; it uses the isolated forest algorithm to detect spatial anomalies, the LSTM neural network to identify time series anomalies, and the rule engine to verify attribute consistency. Spatial data standardization processing unit: Establish a spatial data standardization process that includes coordinate system transformation, projection transformation, and scale normalization; Time data synchronization and alignment unit: Develop a multi-scale time alignment algorithm based on wavelet transform for time series data of different frequencies.

5. The low-impact development facility optimization layout system based on multi-objective optimization and uncertainty analysis according to claim 3, characterized in that: Rainfall data quality control unit: A three-level quality control system is established, including temporal continuity test, spatial consistency test, and physical rationality test; the temporal continuity test uses continuous wavelet transform to identify data discontinuities, the spatial consistency test uses the Thiessen polygon method to verify data from neighboring stations, and the physical rationality test is based on historical extreme value statistics to determine outliers. Runoff data preprocessing optimization unit: Develop a baseflow separation algorithm based on digital filtering to decompose total runoff into two components: surface runoff and groundwater runoff; Evapotranspiration data estimation unit: Integrates Penman-Monteith equation, Hargreaves equation and Priestley-Taylor equation to establish a multi-model ensemble evapotranspiration estimation method; automatically selects the most suitable calculation method based on data availability.

6. The low-impact development facility optimization layout system based on multi-objective optimization and uncertainty analysis according to claim 1, characterized in that: The uncertainty modeling and analysis layer is implemented through multi-source uncertainty intelligent identification technology, improved fuzzy logic reasoning system, high-efficiency Monte Carlo simulation engine, and uncertainty propagation analysis framework. Multi-source uncertainty intelligent identification technology: It adopts a three-level uncertainty classification system to establish a systematic uncertainty classification framework; the first level is divided into random uncertainty, fuzzy uncertainty and cognitive uncertainty according to the essential characteristics of uncertainty; the second level is divided into parameter uncertainty, model uncertainty, scenario uncertainty and decision uncertainty according to the source of influencing factors; the third level is divided into input uncertainty, process uncertainty and output uncertainty according to the specific influencing mechanism. Improved fuzzy logic reasoning system: including an adaptive membership function optimization module and a fuzzy reasoning rule automatic generation module; The adaptive membership function optimization module addresses the difficulty in determining membership function parameters in traditional fuzzy logic by developing data-driven adaptive optimization methods: trapezoidal membership function parameter optimization and Gaussian membership function parameter optimization; the fuzzy inference rule automatic generation module uses an improved genetic algorithm to automatically generate and optimize fuzzy inference rules. High-efficiency Monte Carlo simulation engine: including a hierarchical importance sampling module and a variance reduction technology integration module; The hierarchical importance sampling module addresses the inefficiency of traditional Monte Carlo methods by developing a hierarchical importance sampling algorithm; the variance reduction technique integration module includes dual variable techniques, control variable techniques, conditional expectation techniques, and importance sampling techniques. Uncertainty Propagation Analysis Framework: Polynomial Chaotic Expansion Agent Modeling: Establishing an agent model based on orthogonal polynomials to replace complex numerical simulations; Global Sensitivity Analysis Module: Calculates the Sobol sensitivity index based on the PCE model.

7. The low-impact development facility optimization layout system based on multi-objective optimization and uncertainty analysis according to claim 6, characterized in that: The hierarchical importance sampling module includes a variable importance analysis unit, a hierarchical sampling strategy unit, and an importance sampling implementation unit; Variable importance analysis unit: Morris sensitivity analysis method is used to determine the ranking of variable importance; Hierarchical sampling strategy unit: Divides the input space into k layers based on the importance of variables; Importance sampling implementation unit: different sampling densities are used for each layer.

8. The low-impact development facility optimization layout system based on multi-objective optimization and uncertainty analysis according to claim 1, characterized in that: The multi-objective collaborative optimization layer is achieved through adaptive NSGA-III algorithm design, reinforcement learning parameter adaptive mechanism, hybrid search strategy integration, constraint handling and feasibility guarantee. Adaptive NSGA-III Algorithm Design: Reference Point Adaptive Adjustment Mechanism: To address the limitation of fixed reference points in the traditional NSGA-III algorithm, an adaptive reference point adjustment strategy based on solution distribution feedback is developed; Initial Reference Point Generation: Uniformly distributed reference points are generated using the Das-Dennis method; Reference Point Quality Evaluation: Reference point quality indices are defined; Adaptive Adjustment Strategy; Reinforcement learning parameter adaptation mechanism: Q-Learning parameter tuning: adjusting algorithm parameters and modeling it as a Markov decision process; state space definition; Action space definition; reward function design; Q-value update strategy; Hybrid search strategy integration: Multi-operator co-evolution: Integrating multiple evolutionary operators and adaptively selecting them based on the search state; Operator selection strategy: Employing a multi-armed slot machine model; Constraint handling and feasibility assurance: ε-constraint handling technology: For complex constraints in LID optimization, a constraint handling method with dynamic ε values ​​is adopted; constraint violation degree calculation; repair operator design: special repair strategies are developed for different types of constraint violations.

9. The low-impact development facility optimization layout system based on multi-objective optimization and uncertainty analysis according to claim 1, characterized in that: The intelligent decision support layer is achieved through user preference learning and modeling, a multi-attribute decision analysis engine, visualization and interactive interface, and sensitivity and robustness analysis. User Preference Learning and Modeling: Interactive Preference Acquisition: Developing a preference acquisition method based on progressive learning; Fuzzy Preference Modeling: Using trapezoidal fuzzy numbers to represent the imprecision of preferences; Multi-attribute decision analysis engine: TOPSIS-GRA hybrid decision method: combines the distance concept of TOPSIS method and the similarity concept of grey relational analysis, including decision matrix standardization, weighted standardized matrix, determination of ideal solution and negative ideal solution, grey relational degree calculation, and comprehensive evaluation index; Visualization and Interactive Interface: Multidimensional Data Visualization: Developing visualization methods suitable for high-dimensional target spaces; Parallel coordinate plot enhancement; RadViz improves visualization; 3D Pareto frontier visualization; Sensitivity and robustness analysis: Global sensitivity analysis: Sobol index calculation based on variance decomposition; Monte Carlo robustness assessment.

10. The low-impact development facility optimization layout system based on multi-objective optimization and uncertainty analysis according to claim 1, characterized in that: The dynamic adaptation and optimization layer is achieved through real-time data integration and processing, performance prediction and early warning, online learning and model updating, and adaptive optimization and adjustment mechanisms. Real-time data integration and processing: The streaming data processing architecture adopts the Lambda architecture pattern, which supports a hybrid mode of batch processing and streaming processing; Performance Prediction and Early Warning: Time Series Prediction Model Integration, integrating multiple prediction models to improve prediction accuracy; Online learning and model update: Incremental learning algorithm, which adopts an incremental learning strategy that combines online gradient descent and experience replay; Adaptive optimization adjustment mechanism: It has a rolling time-domain optimization strategy.

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