Rapid early warning method and system for water quality of drinking water source

Abstract

The invention discloses a rapid early warning method and system for water quality of a drinking water source. The system integrates a multi-source monitoring module, a data fusion processing module, a water quality prediction analysis module, a dynamic early warning decision module and a digital twinborn simulation platform, and acquires multi-dimensional data such as physical and chemical parameters, biological behavior indexes and spectral characteristics; a self-adaptive weighted deep belief network (AW-DBN) model is used for pollutant identification, diffusion simulation and overproof time prediction, an early warning threshold value is dynamically adjusted by combining a Bayesian optimization algorithm so as to reduce the false alarm rate and the missing report rate, and visual simulation and emergency disposal scheme evaluation of the pollution diffusion process are realized based on the digital twinborn technology. Rapid and accurate early warning and scientific emergency decision making of the water quality risk are realized, and the safety guarantee capability of the drinking water source is remarkably improved.

Description

Technical Field

[0001] This invention relates to the field of water quality detection and early warning technology, specifically to a rapid early warning method and system for drinking water source quality. Background Technology

[0002] Drinking water sources are core infrastructure for safeguarding public health and social stability, and their water quality safety is directly related to residents' drinking water safety and the sustainable development of the ecological environment. With accelerated industrialization, increased agricultural non-point source pollution, and more frequent extreme weather events, drinking water sources face multiple risks, including heavy metal, organic matter, and microbial contamination, significantly increasing the probability of sudden pollution incidents. These incidents are characterized by their high degree of concealment, rapid spread, and wide impact; failure to detect and intervene in a timely manner could lead to regional water supply crises. Therefore, constructing a rapid water quality early warning system capable of real-time sensing, accurate prediction, dynamic early warning, and support for emergency decision-making has become a key requirement for preventing drinking water source pollution risks and improving water supply security.

[0003] Currently, drinking water source quality monitoring and early warning technologies mainly revolve around the processes of data collection, analysis and evaluation, and early warning response. Specifically, these include: in terms of monitoring technology, physicochemical sensors are the core, with sensors for parameters such as pH, dissolved oxygen, and turbidity deployed to achieve real-time monitoring of basic water quality indicators; some systems supplement this with manual sampling and laboratory analysis to detect pollutant components; in terms of data processing, simple data cleaning and statistical analysis methods are used to remove outliers and standardize monitoring data, and threshold comparisons are used to determine whether water quality exceeds standards; in terms of prediction and early warning, traditional statistical models or simple machine learning algorithms are used to make short-term predictions of water quality parameter change trends; early warning mechanisms mostly adopt a fixed threshold trigger mode, issuing an alarm when the monitored value exceeds a preset threshold; in terms of visualization, real-time monitoring data and exceedance information are displayed in chart form, and some systems combine geographic information systems to mark pollution points, but lack dynamic simulation capabilities.

[0004] Existing technologies rely excessively on physicochemical parameters, neglecting early pollution signals such as biological behavior and spectral characteristics, leading to delayed pollution warnings. Multi-source monitoring data exhibit heterogeneity, and existing technologies lack effective fusion and standardization methods, making it difficult to uncover correlations between data. Prediction accuracy and robustness are lacking; traditional models have limited ability to fit nonlinear, multi-factor coupled water quality change processes, making it difficult to accurately identify pollutant types, simulate diffusion paths, and predict exceedance times. Many warnings are triggered by fixed thresholds, failing to consider dynamic factors such as source hydrological conditions and seasonal variations, easily leading to false alarms, missed alarms, or delayed responses. Furthermore, the lack of visualization simulations of pollution diffusion processes and emergency response assessment tools makes it difficult to assist decision-makers in formulating scientific and efficient disposal strategies. Summary of the Invention

[0005] Purpose of the Invention: To overcome the shortcomings of existing technologies, this invention provides a rapid early warning method and system for drinking water source quality. By integrating multi-dimensional water quality data acquisition (including physicochemical parameters, biological behavior indicators, and spectral characteristics), intelligent fusion processing of multi-source heterogeneous data, accurate predictive analysis based on adaptive weighted deep belief networks, Bayesian optimized dynamic threshold early warning, and digital twin simulation technology, this invention solves the problems of single monitoring dimensions, weak data fusion capabilities, insufficient prediction accuracy, static early warning thresholds, and weak emergency support capabilities in existing technologies. It enables early identification, accurate prediction, dynamic early warning, and scientific evaluation of emergency response plans for drinking water source pollution, thereby improving the timeliness and accuracy of water quality safety assurance.

[0006] A rapid early warning method and system for drinking water source quality includes: a multi-source monitoring module for collecting multi-dimensional water quality data from drinking water sources, specifically including physicochemical parameters, biological behavior indicators, and spectral characteristic data; a data fusion processing module connected to the multi-source monitoring module for cleaning, fusion, and standardization of the multi-source heterogeneous data; a water quality prediction and analysis module connected to the data fusion processing module, comprising a prediction model constructed based on an adaptive weighted deep belief network, for outputting pollutant type identification results, diffusion simulation prediction results, and exceedance time prediction results; a dynamic early warning decision module connected to the water quality prediction and analysis module for generating early warning information based on the above prediction results and dynamic thresholds; and a digital twin simulation platform connected to both the water quality prediction and analysis module and the dynamic early warning decision module for realizing visualized simulation of pollution diffusion processes and evaluation of emergency response plans; wherein, the multi-source monitoring module captures image data of microorganisms in the water in real time through its included microscopic imaging unit, and extracts the quantity, type, and movement behavior characteristics of microorganisms through a convolutional neural network, using them as biological early warning indicators.

[0007] The system described in this invention includes a multi-source monitoring module comprising a physical sensor array consisting of multiple submersible water quality sensors for monitoring parameters such as pH, dissolved oxygen, turbidity, conductivity, temperature, ammonia nitrogen, COD, and ORP.

[0008] The biosensing unit, comprising an underwater high-definition camera and an infrared light source, is used to continuously monitor the swimming behavior of fish schools and extract their activity pattern characteristics; the spectral detection unit, employing an ultraviolet-visible full-spectrum scanner, is used to detect organic pollution indicators in the water; the miniature microscopic imaging unit, comprising an autofocus microscope and a CCD camera, is used to capture image data of microorganisms in the water in real time; each unit of the multi-source monitoring module performs preliminary data fusion and preprocessing through an IoT edge computing gateway.

[0009] The adaptive weighted deep belief network (AW-DBN) model in the water quality prediction and analysis module of the system described in this invention is constructed using the following algorithm: The energy function of the adaptive weighted deep belief network (AW-DBN) model is defined as:

[0010]

[0011] Where ωi is the adaptive weight coefficient, calculated using the following formula:

[0012]

[0013] In the formula, MI(vi,y) represents the mutual information between the input feature vi and the warning target y, γ is the adjustment parameter, D is the dimension of the input feature, and F is the number of hidden layer nodes.

[0014] The system described in this invention further employs a dual time series prediction algorithm in its water quality prediction and analysis module, comprising: a short-term mutation detection sub-model, which extracts local mutation features of water quality parameters based on a one-dimensional convolutional neural network; a long-term trend prediction sub-model, which learns the long-term dependencies of water quality parameters based on a gated recurrent unit (GRU) network; and a feature fusion layer, which fuses short-term and long-term features and inputs them to the prediction layer after weighting by an attention mechanism.

[0015] The system described in this invention uses a Bayesian optimization algorithm in its dynamic early warning decision module to dynamically adjust the early warning threshold. Specifically, the method involves defining a threshold optimization objective function:

[0016] f(θ)=α·FPR(θ)+β·FNR(θ)+γ·ResponseTime

[0017] Where θ is the threshold parameter, FPR is the false alarm rate, FNR is the false negative rate, ResponseTime is the system response time, and α, β, and γ are weighting coefficients; f(θ) is modeled using Gaussian process regression and the acquisition function is calculated, selecting the next evaluation point until the optimal solution θ is found. * .

[0018] The system described in this invention includes a digital twin simulation platform comprising: a hydrodynamic and water quality coupling model, constructed based on an improved three-dimensional Navier-Stokes equation and pollutant transport equation; an augmented reality visualization interface, used to display the pollution diffusion path and impact range via AR glasses or mobile devices; and an emergency response plan evaluation module, used to simulate the implementation effects and costs of different response measures (such as adding chemicals or activating backup water sources).

[0019] A rapid early warning method for drinking water source quality, applied to the above system, includes the following steps: Step S101: Collect physicochemical parameters, biological behavior indicators and spectral characteristic data of drinking water source through a multi-source monitoring module;

[0020] Step S102: Clean, fuse, and standardize the multi-source heterogeneous data, and extract feature vectors;

[0021] Step S103: Input the feature vector into the adaptive weighted deep belief network prediction model, and output the pollutant type identification result, diffusion simulation prediction result, and exceedance time prediction result;

[0022] Step S104: Generate early warning information based on the prediction results and dynamic thresholds, and adaptively update the early warning thresholds using a Bayesian optimization algorithm;

[0023] The method of the present invention, in step S103, the training of the adaptive weighted deep belief network includes the following steps:

[0024] Step S201: Pre-train the weights of each layer using an unsupervised layer-by-layer greedy algorithm;

[0025] Step S202: Fine-tune the network parameters using the backpropagation algorithm;

[0026] Step S203: Calculate the mutual information between the input features and the warning target, and dynamically adjust the adaptive weight coefficients;

[0027] Step S204: Verify the model accuracy using historical pollution event data, and adjust the network structure based on the verification results.

[0028] The method described in this invention, in step S104, employs a Bayesian optimization algorithm for dynamic threshold updating, specifically including:

[0029] Step S301: Initialize threshold parameters and optimize objective function;

[0030] Step S302: Model the probability distribution of the objective function using Gaussian process regression;

[0031] Step S303: Calculate the acquisition function (e.g., the desired improvement in EI) and determine the next evaluation point;

[0032] Step S304: Evaluate the objective function value and update the Gaussian process model;

[0033] Step S305: Repeat steps S303-S304 until the maximum number of iterations is reached or convergence is achieved.

[0034] The method of the present invention, in step S105, includes the digital twin simulation process as follows:

[0035] Step S401: Based on real-time hydrodynamic conditions and water quality data, drive the hydrodynamic-water quality coupled model to simulate the pollution diffusion process;

[0036] Step S402: Overlay the pollution diffusion path and impact range onto the real scene using augmented reality technology;

[0037] Step S403: Simulate the effects and costs of different emergency response plans and recommend the optimal response plan;

[0038] Step S404: Update the parameters of the digital twin model based on the actual treatment effect feedback.

[0039] As can be seen from the above technical solution, the present invention has the following beneficial effects:

[0040] This invention discloses a rapid early warning system for drinking water source quality. By constructing a comprehensive early warning system integrating multi-source monitoring, data fusion, intelligent prediction, dynamic decision-making, and digital twin simulation, it achieves rapid, accurate, and visualized early warning of water quality risks. The system utilizes multi-dimensional data (physicochemical, biological behavior, and spectral characteristics) fusion analysis, combined with an adaptive weighted deep belief network (AW-DBN) and a dual time-series prediction algorithm, significantly improving the accuracy and robustness of pollutant identification and prediction. Through Bayesian optimization and dynamic adjustment of the early warning threshold, it effectively balances the false alarm rate and the false negative rate, improving the sensitivity and reliability of the early warning. By leveraging digital twin technology, it achieves visualized simulation of the pollution diffusion process and evaluation of emergency response plans, providing decision-makers with intuitive and scientific emergency support, and comprehensively enhancing the water quality safety management and emergency response capabilities of drinking water sources.

[0041] This invention discloses a rapid early warning method for drinking water source quality. Through a collaborative process encompassing multi-source data acquisition, fusion processing, intelligent prediction, dynamic threshold optimization, and digital twin simulation, it achieves closed-loop management from data to decision-making. This method utilizes an adaptive weighted deep belief network to dynamically extract key features and accurately predict pollution trends. Combined with a Bayesian optimization algorithm, it achieves adaptive threshold updates, significantly improving the accuracy and timeliness of early warnings. Furthermore, the digital twin platform enables visualization of the pollution process and pre-assessment of solutions, supporting efficient and scientific emergency decision-making, and demonstrating high practicality. Attached Figure Description

[0042] Figure 1 This is a diagram illustrating the architecture of a rapid early warning system for drinking water source quality according to the present invention.

[0043] Figure 2 This is a flowchart of a rapid early warning method for drinking water source quality according to the present invention. Detailed Implementation

[0044] Example 1

[0045] This embodiment uses a large reservoir serving as a drinking water source as an application scenario. Located downstream of a densely populated industrial area, the reservoir faces constant risks of heavy metal pollution, organic matter leaks, and excessive microbial levels. The water source covers an area of ​​approximately 50 square kilometers, with a depth of 5-20 meters, and its flow dynamics are significantly affected by seasonal rainfall. To simulate a sudden pollution event, it is assumed that after a heavy rainfall, a benzene-based organic compound leak occurs in the upstream industrial area. The initial concentration of the pollutant is low (0.5 mg / L), but it spreads rapidly. The system aims to achieve early pollution identification, accurate diffusion simulation, and scientific emergency response through multi-dimensional data collection, intelligent prediction, and dynamic early warning.

[0046] In this embodiment, the system is implemented based on the patent document architecture and includes a multi-source monitoring module, a data fusion processing module, a water quality prediction and analysis module, a dynamic early warning decision-making module, and a digital twin simulation platform. All modules are deployed in a cloud-edge hybrid architecture through an IoT edge computing gateway to ensure real-time data processing (response time < 5 seconds).

[0047] Multi-source monitoring module:

[0048] Physical sensor array: Ten submersible sensors are deployed to monitor in real time pH (range 6.5-8.5), dissolved oxygen (DO, target >5 mg / L), turbidity (<5 NTU), conductivity, temperature, ammonia nitrogen (<0.5 mg / L), COD (<20 mg / L), and ORP (oxidation-reduction potential) parameters. The sensors are deployed in a grid layout (500-meter spacing) and transmit data via the LoRaWAN protocol.

[0049] Biosensor Unit: Utilizes an underwater high-definition camera paired with an infrared light source to continuously monitor fish schooling behavior. A convolutional neural network (CNN) model (pre-trained on ImageNet) is employed to extract behavioral features, such as abnormal fish school aggregation (normal schooling index >0.7, pollution index <0.3).

[0050] Spectroscopic detection unit: Integrated ultraviolet-visible full-spectrum scanner, scans water every 30 minutes (wavelength 200-700nm) to detect characteristic peaks of organic matter (such as the absorption peak of benzene series compounds at 254nm).

[0051] Miniature microscopic imaging unit: Equipped with an autofocus microscope and CCD camera, it captures microbial images in real time (resolution 1920×1080). Microbial features, such as a sudden increase in E. coli count (threshold >500 CFU / mL) or abnormal movement behavior (normal speed 0.1 mm / s, reduced to 0.05 mm / s during contamination), are extracted using a customized CNN model.

[0052] All unit data undergoes initial fusion (weighted averaging) and anomaly detection (based on the 3σ principle) by the edge gateway.

[0053] The data fusion processing module uses the Apache Spark framework to process multi-source heterogeneous data. Cleaning rules include: removing sensor drift values ​​(moving window filtering), filling missing data (KNN interpolation), and standardization (Z-score normalization). After fusion, a feature vector (32 dimensions) is generated, including trends in physicochemical parameters, biological behavior indices, and spectral features (such as absorbance at 254 nm).

[0054] Water quality prediction and analysis module: The core is an Adaptive Weighted Deep Belief Network (AW-DBN) model, with a 4-layer structure (32 nodes in the input layer, 64 nodes in each of the two hidden layers, and 3 nodes in the output layer). Training data comes from a historical pollution event database (1000+ samples). The energy function and weight coefficients are calculated strictly according to the formulas in the documentation.

[0055]

[0056] Where γ = 0.5, the mutual information MI calculation is based on KL divergence. The output includes pollutant type (e.g., identified as "benzene series organic compounds" in this scenario), diffusion simulation results (pollution plume direction), and predicted exceedance time (e.g., COD exceedance time t = 2 hours).

[0057] It is supplemented by a dual time series prediction algorithm: a short-term mutation detection sub-model (1D-CNN, kernel size 5) captures local anomalies; a long-term trend sub-model (GRU network, hidden units 32) learns seasonal changes; and the feature fusion layer is weighted by an attention mechanism (weight learning rate 0.01).

[0058] Dynamic early warning decision-making module:

[0059] The warning threshold is dynamically adjusted using a Bayesian optimization algorithm. Objective function:

[0060] f(θ)=α·FPR(θ)+β·FNR(θ)+γ·ResponseTime

[0061] Let α = 0.4, β = 0.4, γ = 0.2 (false positive rate (FPR) and false negative rate (FNR) have higher priority). Gaussian process regression (with a Matern 5 / 2 kernel) is used for modeling, and the acquisition function is the expected improvement (EI). The optimized threshold θ... * Achieve FPR < 5%, FNR < 3%, and response time < 10 seconds;

[0062] Digital twin simulation platform: Built on the Unity3D engine, integrating a hydrodynamic and water quality coupling model (improved Navier-Stokes equations) and an augmented reality (AR) interface. The platform runs on a cloud server and supports mobile devices and AR glasses.

[0063] like Figure 1 As shown, the system architecture diagram illustrates the data flow and interaction relationships between modules.

[0064] like Figure 2 The diagram shows the method steps, which are further refined based on the above scenario. The method flow is as follows:

[0065] 1. Step S101: Data Acquisition. In the pollution event simulation, the multi-source monitoring module is activated: the physical sensor detects a sudden increase in turbidity to 8 NTU (60% exceeding the standard); the spectral unit shows an increase in absorbance to 0.8 at 254 nm (reference value 0.3); the microscopic imaging unit captures an increase in E. coli count to 800 CFU / mL (exceeding the threshold); and the biosensing unit shows a decrease in fish aggregation to 0.2. Data sampling frequency is 1 minute to ensure real-time performance.

[0066] 2. Step S102: Data fusion processing. The edge gateway performs cleaning (removing outliers <5%), fusion (weighted fusion of spectral and biological data with a weight of 0.6), and standardization. The output feature vector includes: physicochemical parameters (e.g., turbidity trend slope = 0.5), biological behavior index (group anomaly degree 0.8), and spectral features (254nm absorbance). Processing time < 2 seconds.

[0067] 3. Step S103: AW-DBN prediction analysis, inputting feature vectors into the AW-DBN model. The training process is as follows:

[0068] S201: Unsupervised pre-training (layer-by-layer greedy algorithm, 1000 iterations);

[0069] S202: Backpropagation fine-tuning (learning rate 0.001, batch size 32);

[0070] S203: Dynamic weight adjustment, calculate ω_i weight after MI (spectral feature weight is the highest at 0.35);

[0071] S204: Verify accuracy (test set F1-score > 92%).

[0072] Output: The pollutant type is "benzene series organic compounds" (confidence level 95%). The diffusion simulation shows that the pollution spreads southeast (velocity 0.2 m / s). The predicted time for exceeding the standard is that COD will exceed the standard after 1.8 hours.

[0073] 4. Step S104: Dynamic early warning decision-making, based on prediction results, Bayesian optimization of threshold update:

[0074] S301: Initialize θ (FPR threshold 0.05, FNR threshold 0.03);

[0075] S302: Gaussian process modeling (mean function is a constant);

[0076] S303: Calculate the EI acquisition function and select a new evaluation point θ_new;

[0077] S304: Evaluate f(θ_new) (simulated false alarm rate reduced to 4.5%);

[0078] S305: After 5 iterations, converge to θ* and generate a first-level early warning message (pushed to the management platform).

[0079] 5. Step S105: Digital twin simulation. The digital twin platform performs the following steps:

[0080] S401: Drives a hydrodynamic model (based on real-time wind speed and water flow) to simulate pollution diffusion (pollutant concentration distribution map);

[0081] S402: The AR interface overlays the pollution path onto the real scene (affected area of ​​3 square kilometers);

[0082] S403: Evaluate treatment options, such as adding activated carbon (cost ¥50,000, reduces contaminants by 70%) or activating a backup water source (cost ¥20,000, delays by 2 hours). The optimal option is "adding activated carbon";

[0083] S404: Update model parameters (diffusion coefficient calibration) based on actual treatment feedback (pollutant concentration reduced to 0.2 mg / L).

[0084] Digital Twin Simulation and Result Analysis

[0085] In AR simulation, the pollution diffusion process was visualized, showing the pollution plume covering the intake area within 30 minutes. Emergency response plan evaluation results: the activated carbon dosing scheme reduced the pollutant concentration to a safe level (<0.1 mg / L) in the simulation, demonstrating the best cost-effectiveness. The system response time for the entire process was <8 minutes (from data acquisition to early warning), a 50% speedup compared to traditional systems. The measured false alarm rate was 4.2%, and the false alarm rate was 2.8%, validating the effectiveness of Bayesian optimization.

[0086] The above embodiments are exemplary and are intended to illustrate the technical concept and features of the present invention, so that those skilled in the art can understand the content of the present invention and implement it accordingly. They should not be construed as limiting the scope of protection of the present invention. All changes or modifications made according to the spirit and essence of the present invention should be covered within the scope of protection of the present invention.

Claims

1. A rapid early warning system for drinking water source quality, characterized in that, include: The multi-source monitoring module is used to collect multi-dimensional water quality data from drinking water sources, specifically including physicochemical parameters, biological behavior indicators, and spectral characteristic data. The data fusion processing module is connected to the multi-source monitoring module and is used to perform cleaning, fusion and standardization processing on multi-source heterogeneous data. The water quality prediction and analysis module, connected to the data fusion and processing module, includes a prediction model built based on an adaptive weighted deep belief network, used to output pollutant type identification results, diffusion simulation prediction results, and exceedance time prediction results. A dynamic early warning decision module is connected to the water quality prediction and analysis module and is used to generate early warning information based on the above prediction results and dynamic thresholds. The digital twin simulation platform is connected to the water quality prediction and analysis module and the dynamic early warning decision-making module, respectively, to realize the visualization simulation of the pollution diffusion process and the evaluation of emergency response plans; The multi-source monitoring module captures image data of microorganisms in the water in real time through its included microscopic imaging unit, and extracts the quantity, type and movement behavior characteristics of microorganisms through a convolutional neural network, using them as biological early warning indicators.

2. The rapid early warning system for drinking water source quality according to claim 1, characterized in that, The multi-source monitoring module includes: The physical sensor array consists of multiple submersible water quality sensors used to monitor parameters such as pH, dissolved oxygen, turbidity, conductivity, temperature, ammonia nitrogen, COD, and ORP. The biosensing unit, which includes an underwater high-definition camera and an infrared light source, is used to continuously monitor the swimming behavior of fish schools and extract their activity pattern characteristics. The spectral detection unit uses an ultraviolet-visible full-spectrum scanner to detect organic pollution indicators in water bodies; The miniature microscopic imaging unit, which includes an autofocusing microscope and a CCD camera, is used to capture image data of microorganisms in water in real time. Each unit of the multi-source monitoring module performs preliminary data fusion and preprocessing through an IoT edge computing gateway.

3. The rapid early warning system for drinking water source quality according to claim 1, characterized in that, The adaptive weighted depth belief network model in the water quality prediction and analysis module is constructed using the following algorithm: The energy function of the adaptive weighted deep belief network model is defined as: Where ωi is the adaptive weight coefficient, calculated using the following formula: In the formula, MI(vi,y) represents the mutual information between the input feature vi and the warning target y, γ is the adjustment parameter, D is the dimension of the input feature, and F is the number of hidden layer nodes.

4. The rapid early warning system for drinking water source quality according to claim 3, characterized in that, The water quality prediction and analysis module also employs a dual time series prediction algorithm, including: A short-term mutation detection sub-model is developed based on a one-dimensional convolutional neural network to extract local mutation features of water quality parameters. The long-term trend prediction sub-model learns the long-term dependencies of water quality parameters based on a gated cyclic unit network; The feature fusion layer is used to fuse short-term and long-term features, which are then weighted by an attention mechanism and input into the prediction layer.

5. The rapid early warning system for drinking water source quality according to claim 1, characterized in that, The dynamic early warning decision module uses a Bayesian optimization algorithm to dynamically adjust the early warning threshold. The specific method is as follows: Define the threshold optimization objective function: f(θ)=α·FPR(θ)+β·FNR(θ)+γ·ResponseTime Where θ is the threshold parameter, FPR is the false alarm rate, FNR is the false negative rate, ResponseTime is the system response time, and α, β, and γ are weighting coefficients; f(θ) is modeled using Gaussian process regression and the acquisition function is calculated, selecting the next evaluation point until the optimal solution θ is found. * .

6. The rapid early warning system for drinking water source quality according to claim 1, characterized in that, The digital twin simulation platform includes: A hydrodynamic-water quality coupled model was constructed based on the improved three-dimensional Navier-Stokes equations and pollutant transport equations. Augmented reality visualization interface, used to display the pollution spread path and impact range through AR glasses or mobile devices; The emergency response plan evaluation module is used to simulate the implementation effects and costs of different response measures.

7. A rapid early warning method for drinking water source quality, applied to the system described in any one of claims 1-6, characterized in that, Includes the following steps: Step S101: Collect physicochemical parameters, biological behavior indicators, and spectral characteristic data of drinking water sources through a multi-source monitoring module; Step S102: Clean, fuse, and standardize the multi-source heterogeneous data, and extract feature vectors; Step S103: Input the feature vector into the adaptive weighted deep belief network prediction model, and output the pollutant type identification result, diffusion simulation prediction result, and exceedance time prediction result; Step S104: Generate early warning information based on the prediction results and dynamic thresholds, and adaptively update the early warning thresholds using a Bayesian optimization algorithm; Step S105: Simulate the pollution diffusion process and emergency response plan in the digital twin simulation platform, and visualize the early warning results through augmented reality technology.

8. The rapid early warning method for drinking water source quality according to claim 7, characterized in that, In step S103, the training of the adaptive weighted deep belief network includes the following steps: Step S201: Pre-train the weights of each layer using an unsupervised layer-by-layer greedy algorithm; Step S202: Fine-tune the network parameters using the backpropagation algorithm; Step S203: Calculate the mutual information between the input features and the warning target, and dynamically adjust the adaptive weight coefficients; Step S204: Verify the model accuracy using historical pollution event data, and adjust the network structure based on the verification results.

9. The rapid early warning method for drinking water source quality according to claim 7, characterized in that, In step S104, the dynamic threshold update employs a Bayesian optimization algorithm, specifically including: Step S301: Initialize threshold parameters and optimize objective function; Step S302: Model the probability distribution of the objective function using Gaussian process regression; Step S303: Calculate the acquisition function and determine the next evaluation point; Step S304: Evaluate the objective function value and update the Gaussian process model; Step S305: Repeat steps S303-S304 until the maximum number of iterations is reached or convergence is achieved.

10. The method for rapid early warning of drinking water source quality according to claim 7, characterized in that, In step S105, the digital twin simulation process includes: Step S401: Based on real-time hydrodynamic conditions and water quality data, drive the hydrodynamic-water quality coupled model to simulate the pollution diffusion process; Step S402: Overlay the pollution diffusion path and impact range onto the real scene using augmented reality technology; Step S403: Simulate the effects and costs of different emergency response plans and recommend the optimal response plan; Step S404: Update the parameters of the digital twin model based on the feedback of the actual treatment effect.

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