Water pollution traceability analysis and ecological restoration decision support system

Abstract

The invention discloses a water pollution traceability analysis and ecological restoration decision support system. The system comprises a data acquisition layer, a data management and fusion layer, a pollution traceability analysis layer, an ecological restoration decision layer and a user interaction and visualization layer. The system collects multi-source water quality, hydrology and discharge data by deploying a distributed water quality sensing network, remote sensing and unmanned aerial vehicle monitoring equipment and a pollution discharge source online access module; through data cleaning, fusion and knowledge modeling, a pollution propagation model and a traceability inversion algorithm are constructed, and a pollution source and a contribution rate are identified; and simulating a multi-class repair measure response process in the digital twin water environment, and automatically recommending an optimal repair scheme in combination with a multi-objective optimization algorithm. The method has the advantages of accurate traceability, scientific decision, eco-friendliness, dynamic optimization and the like, and is suitable for pollution treatment and ecological restoration scenes of various water bodies such as drainage basins, lakes, urban rivers and the like.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of water environment protection and intelligent decision support, in particular to a water pollution traceability analysis and ecological restoration decision support system. BACKGROUND

[0002] With the development of industrialization and urbanization, water pollution incidents occur frequently, and problems such as water body eutrophication, heavy metal exceeding standard, and organic pollutant accumulation seriously affect drinking water safety and ecological environment. The traditional water pollution treatment has the following shortcomings:

[0003] It is difficult to trace the pollution source: the existing water quality monitoring relies on fixed-point sampling, and the time and space coverage is insufficient, which makes it difficult to accurately locate the pollution source.

[0004] Data island problem: hydrological monitoring, environmental monitoring, and pollution discharge enterprise data are scattered, and there is a lack of unified integration and real-time analysis platform.

[0005] Blind selection of repair measures: ecological restoration schemes rely on manual experience, and lack scientific simulation and multi-objective optimization decision support.

[0006] Insufficient dynamic feedback: after the implementation of the repair measures, there is a lack of real-time effect evaluation, and it is difficult to adjust the strategy in a timely manner.

[0007] Therefore, an integrated system that integrates multi-source data collection, pollution traceability analysis, ecological restoration simulation, and optimization decision is needed to improve the scientificity and intelligent level of water pollution treatment. SUMMARY

[0008] (I) Technical problems solved

[0009] In view of the shortcomings of the prior art, the present application provides a water pollution traceability analysis and ecological restoration decision support system, which solves the problems of inaccurate traceability, unscientific decision making, and insufficient dynamic feedback in the existing water pollution treatment.

[0010] (II) Technical scheme

[0011] In order to achieve the above purpose, the present application is realized by the following technical scheme: a water pollution traceability analysis and ecological restoration decision support system, comprising a data collection layer, a data management and fusion layer, a pollution traceability analysis layer, an ecological restoration decision layer, and a user interaction and visualization layer;

[0012] The data collection layer, as the basis for system operation, is responsible for comprehensive collection and real-time update of water environment related data;

[0013] The data management and fusion layer is responsible for data cleaning, storage, fusion and knowledge modeling, and ensures the reliability of the upper layer analysis;

[0014] The pollution source analysis layer, which is the core of the system, is responsible for tracking the source of pollution, simulating the spread of pollution, and evaluating uncertainty.

[0015] The ecological restoration decision-making layer, which combines a restoration measure library, digital twin technology, and multi-objective optimization, enables scientific decision-making.

[0016] The user interaction and visualization layer provides an intuitive interface and decision support functions, making it easy for management departments to use.

[0017] As a further preferred mode of the present application, the data acquisition layer includes a water quality sensing network that uses distributed wireless sensor nodes to detect pH, dissolved oxygen (DO), conductivity, turbidity, ammonia nitrogen, total phosphorus, COD, and heavy metal ions. The sensors have self-calibration and anti-pollution design, and the nodes communicate through LoRa / 5G networks for data transmission, improving long-distance transmission and low power consumption performance.

[0018] The remote sensing and unmanned aerial vehicle monitoring module uses remote sensing satellites to obtain information about large-scale water bodies, and unmanned aerial vehicles equipped with hyperspectral cameras and infrared sensors conduct detailed local patrols to fill in the gaps in ground monitoring.

[0019] The online access module for pollution sources connects with the online monitoring platform at the enterprise's pollution discharge outlet to obtain discharge flow and pollutant concentration. Flow meters and electromagnetic water quality monitors are installed on the river pollution pipe network to monitor illegal discharges in real time.

[0020] The meteorological and hydrological data interface accesses data from meteorological bureaus and hydrological stations, including rainfall, evaporation, wind speed, water level, and river flow, which are used for pollutant transport modeling and source tracing calculations.

[0021] As a further preferred mode of the present application, the data management and fusion layer includes a data preprocessing module that performs outlier rejection, missing value filling, and sensor time series alignment and spatial interpolation. A multi-source heterogeneous data fusion module uses spatio-temporal data fusion algorithms to combine sensor point data with remote sensing large-scale images to generate high-resolution water quality distribution maps, and integrates pollution source data and hydrological conditions to provide complete inputs for source tracing. A database and knowledge base establishes a pollution event database that stores historical pollution source tracing cases and pollutant diffusion model parameters, and an ecological restoration case knowledge base that records the application scenarios, costs, effects, and applicable conditions of domestic and foreign restoration measures. A big data storage architecture uses a distributed database to support PB-level data processing, and combines a cloud computing platform to achieve elastic computing and high-concurrency access.

[0022] As a further preferred mode of the present application, the pollution source tracing analysis layer comprises a pollution propagation model module, which is based on a water dynamics model, a two-dimensional shallow water equation and a pollution transport model, a convection-diffusion equation, and supports simulation of point source, area source and non-point source pollution;

[0023] A source inversion module introduces machine learning methods, such as random forest, Bayesian inversion and neural network, to fit and invert the monitoring data, identify potential pollution sources, and calculate the emission intensity and contribution rate of the pollution sources using a spatio-temporal inversion algorithm; an uncertainty evaluation module performs perturbation on the input parameters through Monte Carlo simulation, evaluates the confidence of the results, and gradually corrects the model prediction and enhances the credibility by combining the Bayesian updating method.

[0024] As a further preferred mode of the present application, the ecological restoration decision-making layer comprises a restoration measure library, including physical restoration: aeration oxygenation, interception dam, sediment dredging, chemical restoration: precipitant addition, modified adsorbent, ecological restoration: constructed wetland, ecological floating bed, microbial restoration, and aquatic plant community construction; a digital twin water environment module establishes a virtual water environment simulation platform, dynamically simulates the response process of water quality to different restoration measures, and provides multi-time scale prediction; a multi-objective optimization decision-making module has a target function: maximum pollution reduction rate, minimum treatment cost, shortest restoration period, and minimum ecological impact; an algorithm: NSGA-II and MOEA / D evolutionary algorithm are used to generate an optimal solution set; and an intelligent recommendation module matches the optimization results with a case knowledge base, and recommends the most suitable treatment scheme based on user preferences.

[0025] As a further preferred mode of the present application, the user interaction and visualization layer comprises a GIS visualization platform, which displays pollution source distribution, water quality thermal map and flow field simulation results, supports multi-layer map overlay, including remote sensing image, pipe network distribution, monitoring point location, and decision support interface; provides a scheme comparison and analysis function, intuitively views the differences in cost, time and effect of different restoration measures, supports drag-and-drop operation, and users can combine measures and instantly view the prediction results; an early warning and feedback module automatically alarms when the water quality exceeds the preset threshold, and pushes emergency disposal suggestions; after restoration implementation, real-time monitoring results are fed back to the model, and subsequent schemes are automatically optimized, forming a closed-loop management; multi-terminal access supports PC terminal, mobile terminal and large screen display, and meets the needs of management departments, researchers and the public at different levels.

[0026] (Three) beneficial effects

[0027] The present application provides a water pollution source tracing analysis and ecological restoration decision support system, which has the following beneficial effects: high accuracy of source tracing: through water quality sensor network, remote sensing and unmanned aerial vehicle monitoring, online access to pollution sources and access to meteorological and hydrological data, multi-source heterogeneous data fusion is realized, and the spatial coverage and time accuracy of pollution source tracing are greatly improved.

[0028] Strong decision-making: combined with pollution transmission model, machine learning inversion algorithm and uncertainty evaluation module, it can accurately identify pollution sources and their contribution rate, and provide result confidence analysis to assist managers in scientific decision-making.

[0029] Intelligent repair measures: built-in multi-class repair measure library, and automatically generate optimal repair scheme through digital twin water environment simulation platform and multi-objective optimization algorithm, taking into account pollution reduction rate, treatment cost, implementation cycle and ecological impact, etc.

[0030] Feedback closed-loop management: the system can automatically obtain real-time monitoring results after repair implementation and feedback to the model, continuously optimize subsequent schemes, and form a closed-loop management mechanism of "monitoring-tracing-decision-repair-feedback".

[0031] Strong visualization and operability: using GIS visualization platform and decision support interface, providing multi-scheme comparison analysis, drag-and-drop operation and real-time prediction, supporting PC, mobile and large screen access, and convenient for various users to use. BRIEF DESCRIPTION OF DRAWINGS

[0032] Figure 1 is a schematic diagram of the system principle framework of the present application;

[0033] Figure 2 is a schematic diagram of the data acquisition layer principle framework of the present application;

[0034] Figure 3 is a schematic diagram of the data management and fusion layer principle framework of the present application;

[0035] Figure 4 is a schematic diagram of the pollution tracing analysis layer principle framework of the present application;

[0036] Figure 5 is a schematic diagram of the ecological restoration decision-making layer principle framework of the present application;

[0037] Figure 6 is a schematic diagram of the user interaction and visualization layer principle framework of the present application;

[0038] Figure 7 is a code schematic diagram of the tracing inversion module of the present application. DETAILED DESCRIPTION

[0039] The technical solutions in the embodiments of the present application will be described clearly and completely below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative labor fall within the scope of the present application.

[0040] Referring to Figures 1-7 The embodiment of the present application provides a technical scheme: a water pollution tracing analysis and ecological restoration decision support system, comprising a data acquisition layer, a data management and fusion layer, a pollution tracing analysis layer, an ecological restoration decision layer and a user interaction and visualization layer;

[0041] The data acquisition layer, as the basis for system operation, is responsible for comprehensive collection and real-time update of water environment related data;

[0042] The data management and fusion layer is responsible for data cleaning, storage, fusion and knowledge modeling, ensuring the reliability of the upper layer analysis;

[0043] The pollution tracing analysis layer is the core of the system, responsible for tracing the source of pollution, simulating the spread of pollution, and evaluating uncertainty;

[0044] The ecological restoration decision layer combines the restoration measure library, digital twin technology and multi-objective optimization to realize scientific decision-making;

[0045] The user interaction and visualization layer provides an intuitive interface and auxiliary decision-making function for the management department.

[0046] The data acquisition layer includes a water quality sensor network that uses distributed wireless sensor nodes to detect pH, dissolved oxygen (DO), conductivity, turbidity, ammonia nitrogen, total phosphorus, COD, and heavy metal ions. The sensors have self-calibration and anti-pollution design, and the nodes are connected through LoRa / 5G network for data backhaul, improving long-distance transmission and low power consumption performance;

[0047] The remote sensing and unmanned aerial vehicle monitoring module uses remote sensing satellites to obtain large-scale water body information, and unmanned aerial vehicles equipped with hyperspectral cameras and infrared sensors for fine local patrol, filling the blind area of ground monitoring.

[0048] The online access module of the pollution source is connected with the online monitoring platform of the enterprise pollution outlet to obtain the discharge flow and pollutant concentration. Flow meters and electromagnetic water quality monitors are installed on the river pollution pipe network to monitor illegal discharges in real time.

[0049] The meteorological and hydrological data interface accesses the data of the meteorological bureau and the hydrological station, including rainfall, evaporation, wind speed, water level and river flow, which are used for pollution transport modeling and tracing calculation. The data acquisition layer: real-time acquisition of pH, dissolved oxygen, conductivity, turbidity, ammonia nitrogen, total phosphorus, COD, heavy metal ions and other data of water body through distributed wireless water quality sensor nodes; remote sensing and unmanned aerial vehicle monitoring module obtains large-scale and local high-resolution images; the online access module of the pollution source obtains real-time enterprise and river discharge data; the meteorological and hydrological data interface provides rainfall, wind speed, water level and other hydrological conditions.

[0050] The data management and fusion layer includes a data preprocessing module for outlier elimination, missing value filling, sensor time series alignment and spatial interpolation; a multi-source heterogeneous data fusion module for combining sensor point data and remote sensing large-scale images using a spatio-temporal data fusion algorithm to generate high-resolution water quality distribution maps, and for fusing pollution source data and hydrological conditions to provide complete input for source tracing; a database and knowledge base for establishing a pollution event database to store historical pollution source tracing cases and pollutant diffusion model parameters, and for establishing an ecological restoration case knowledge base to record application scenarios, costs, effects and applicable conditions of restoration measures at home and abroad; and a big data storage architecture using a distributed database to support PB-level data processing, combined with a cloud computing platform to realize elastic computing and high-concurrency access. The data management and fusion layer: uses the data preprocessing module to eliminate outliers and fill in missing values; generates high-precision water quality distribution maps by combining sensor data and remote sensing images using the multi-source heterogeneous data fusion module; stores pollution source tracing cases and restoration measure cases in the database and knowledge base, respectively; and the big data storage architecture supports PB-level data processing and cloud computing.

[0051] The pollution source analysis layer includes a pollution propagation model module based on a hydrodynamic model, a two-dimensional shallow water equation and a pollutant transport model, a convection-diffusion equation, supporting simulation of point source, area source and non-point source pollution;

[0052] A source inversion module introduces machine learning methods such as random forest, Bayesian inversion and neural network to fit and invert monitoring data, identify potential pollution sources, and calculate pollution source emission intensity and contribution rate using a spatio-temporal inversion algorithm; and an uncertainty evaluation module performs perturbation on input parameters through Monte Carlo simulation to evaluate the confidence of the results, and gradually corrects the model prediction using a Bayesian updating method to enhance the credibility. The pollution source analysis layer: includes a pollution propagation model module for simulating pollutant diffusion based on a two-dimensional shallow water equation and a convection-diffusion equation; a source inversion module for identifying pollution sources and calculating contribution rates using machine learning methods; and an uncertainty evaluation module for improving source tracing credibility based on Monte Carlo simulation and Bayesian updating.

[0053] The ecological restoration decision-making layer includes: a restoration measure library (physical restoration: aeration, interception dams, sediment dredging; chemical restoration: flocculant addition, modified adsorbents; ecological restoration: constructed wetlands, ecological floating beds, microbial remediation, aquatic plant community construction); a digital twin water environment module (establishing a virtual water environment simulation platform to dynamically simulate the response process of water quality to different restoration measures and provide multi-timescale predictions); and a multi-objective optimization decision-making module (objective functions: maximizing pollution reduction rate, minimizing treatment cost, shortening restoration cycle, and minimizing ecological impact). Algorithms: NSGA-II and MOEA / D evolutionary algorithms are used to generate the optimal solution set; and an intelligent recommendation module matches the optimization results with a case knowledge base and recommends the most suitable treatment plan based on user preferences. The ecological restoration decision-making layer includes: a restoration measure library containing various physical, chemical, and ecological measures; a digital twin module establishing a virtual water environment simulation system to predict restoration measures at multiple timescales; a multi-objective optimization module generating an optimal solution set based on objectives such as pollution reduction rate, cost, time, and ecological impact; and an intelligent recommendation module combining a case knowledge base and user preferences to recommend feasible treatment plans.

[0054] The user interaction and visualization layer includes a GIS visualization platform that displays pollution source distribution, water quality heat maps, and flow field simulation results. It supports multi-layer map overlay, including remote sensing imagery, pipeline distribution, and monitoring point locations, as well as a decision support interface. It provides a scheme comparison and analysis function, allowing users to intuitively view the differences in cost, time, and effectiveness of different remediation measures. It supports drag-and-drop operations, allowing users to combine measures and view prediction results instantly. The early warning and feedback module automatically alarms and pushes emergency response suggestions when water quality exceeds preset thresholds. After remediation implementation, real-time monitoring results are fed back to the model, automatically optimizing subsequent schemes to form a closed-loop governance system. Multi-terminal access is supported, including PC, mobile, and large-screen displays, meeting the needs of management departments, researchers, and the public. The user interaction and visualization layer includes: a GIS platform displaying pollution sources, water quality heat maps, and simulation results; a decision support interface providing multi-scheme comparison and real-time predictions; an early warning module that alarms and provides emergency response suggestions when pollution exceeds standards; a feedback module that updates remediation implementation effects in real time; and multi-terminal access supporting PC, mobile, and large-screen interaction.

[0055] Example 1

[0056] Data acquisition layer implementation

[0057] New data acquisition layer implemented

[0058] 210 wireless water quality sensor nodes were deployed in the Taihu Lake basin:

[0059] Monitoring indicators: pH, dissolved oxygen (DO), conductivity, turbidity, ammonia nitrogen, total phosphorus (TP), chemical oxygen demand (COD), and heavy metal ions (lead, cadmium, mercury).

[0060] Monitoring results (partial data):

[0061] Average pH: 7.3 ± 0.4

[0062] Dissolved oxygen: 3.2 mg / L (lower than Class III water body standard ≥ 5 mg / L)

[0063] Total phosphorus: 0.18 mg / L (exceeding the standard by approximately 2 times; the Class III standard is 0.05 mg / L)

[0064] Ammonia nitrogen: 1.6 mg / L (exceeding the standard by approximately 1.6 times; the Class III standard is 1.0 mg / L)

[0065] COD: 35 mg / L (exceeding the standard by approximately 1.17 times; the Class III standard is 30 mg / L)

[0066] Drone hyperspectral imagery shows a high-density algal bloom zone within a 30 km² area in the lake's center, with a significantly increased NDVI value.

[0067] Upon connecting to the online monitoring platform for sewage discharge outlets, it was found that the total discharge of three companies was approximately 2,500 m³ / d, with ammonia nitrogen concentration of approximately 15 mg / L and total phosphorus concentration of approximately 2.5 mg / L.

[0068] Meteorological and hydrological interface data show:

[0069] Recent rainfall intensity: 20 mm / day

[0070] River flow rate: approximately 300 m³ / s

[0071] Data Management and Integration Layer Implementation

[0072] After data preprocessing, outliers of approximately 1.2% (mainly caused by sensor drift) were removed, and the missing data imputation rate was 100%.

[0073] After spatiotemporal fusion, a water quality heat map with a resolution of 1 km² was generated, clearly showing that the northwestern waters of Taihu Lake are the main pollution concentration area.

[0074] The pollution incident database retrieved 52 cases of algal blooms in Taihu Lake from 2015 to 2022 as a comparison sample.

[0075] Pollution source tracing analysis layer implementation

[0076] A two-dimensional shallow water equation + convection-diffusion model was constructed to simulate the diffusion process of pollutants over 7 days.

[0077] Source tracing and inversion results:

[0078] Pollution source A (enterprise discharge outlet 1): Contribution rate 45%, mainly contributing ammonia nitrogen and total phosphorus;

[0079] Pollution source B (urban domestic sewage discharge outlet): Contribution rate 35%, mainly contributing COD and ammonia nitrogen;

[0080] Pollution source C (agricultural non-point source runoff): Contribution rate 20%, mainly contributing total phosphorus and suspended particulate matter.

[0081] After 500 iterations of Monte Carlo simulation, uncertainty assessment showed that the confidence interval for the source tracing results was ±8%.

[0082] Ecological restoration decision-making level implementation

[0083] The remediation measures library was accessed, and the following were selected: constructed wetland (300 mu), aeration and oxygenation devices (20 sets), and flocculant addition (ferric chloride 0.5 mg / L).

[0084] Digital twin simulation prediction:

[0085] Three months later, dissolved oxygen increased to 5.8 mg / L;

[0086] Total phosphorus decreased to 0.09 mg / L;

[0087] Ammonia nitrogen decreased to 0.8 mg / L;

[0088] COD decreased to 25 mg / L.

[0089] The multi-objective optimization algorithm (NSGA-II) generates five schemes. The system recommends the combination of "constructed wetland + aeration and oxygenation", which has lower treatment costs and minimal ecological impact.

[0090] User interaction and visualization layer implementation

[0091] The GIS platform displays the location distribution of pollution sources, water quality heat maps, and diffusion simulation animations.

[0092] The decision-making interface compared five treatment options, and the managers chose the combination of "artificial wetland + aeration and oxygenation".

[0093] Early warning module: When the total phosphorus concentration exceeds 0.15 mg / L, an alarm will be automatically triggered and it will be recommended to start the emergency precipitant dosing.

[0094] After the repair is implemented, real-time monitoring and feedback are sent to the model to dynamically optimize subsequent measures.

[0095] Results Comparison (Before Implementation vs. 3 Months After Implementation)

[0096] Dissolved oxygen (DO): 3.2 → 5.8 mg / L (an increase of 81%)

[0097] Total phosphorus (TP): 0.18 → 0.09 mg / L (reduction of 50%)

[0098] Ammonia nitrogen (NH3-N): 1.6 → 0.8 mg / L (reduction of 50%)

[0099] COD: 35 → 25 mg / L (reduction of 28.6%)

[0100] Area affected by cyanobacteria blooms: 30 km² → 12 km² (a 60% reduction).

[0101] Example 2 (Application Case of Urban Waterways in Suzhou City)

[0102] Data acquisition layer implementation

[0103] Fifty sensor nodes were deployed in the main waterways of the urban area to monitor the following indicators: pH, dissolved oxygen (DO), ammonia nitrogen (NH3-N), total phosphorus (TP), and COD.

[0104] Monitoring data results:

[0105] Average pH: 7.1

[0106] Dissolved oxygen: 2.8 mg / L (Class III standard ≥ 5 mg / L, seriously low)

[0107] Ammonia nitrogen: 3.2 mg / L (exceeding the standard by approximately 3.2 times; Class III standard ≤ 1.0 mg / L)

[0108] Total phosphorus: 0.16 mg / L (exceeding the standard by approximately 3.2 times; Class III standard ≤ 0.05 mg / L)

[0109] COD: 45 mg / L (exceeds the standard by approximately 1.5 times; Class III standard ≤ 30 mg / L)

[0110] Drone patrols acquired thermal infrared images, showing an abnormal increase in water surface temperature of 2-3°C in a 1.5 km stretch of river, corresponding to the sewage outlet area.

[0111] Data Management and Integration Layer Implementation

[0112] Using the data preprocessing module, 1.5% of abnormal data were removed, and missing values ​​were filled in (mainly sensor signal loss at night).

[0113] By integrating monitoring results from 50 locations with UAV thermal infrared imagery, a 10 m resolution water quality distribution map was generated, revealing a distinct high-value zone of pollutant concentration in the middle and lower reaches of the river.

[0114] Historical databases were consulted for feature comparison of a pollution discharge case from a chemical plant in southern Jiangsu in 2018, confirming that the pollutants were mainly ammonia nitrogen and COD.

[0115] Pollution source tracing analysis layer implementation

[0116] Based on two-dimensional shallow water equations and a convection-diffusion model, the trajectory of pollutant diffusion within a river channel over 48 hours was simulated.

[0117] Source tracing and inversion results:

[0118] Pollution source A (industrial discharge outlet): Contribution rate 52%, mainly contributing COD and ammonia nitrogen;

[0119] Pollution source B (municipal sewage overflow outlet): Contribution rate 30%, mainly contributing ammonia nitrogen and total phosphorus;

[0120] Pollution source C (rainwater runoff): Contribution rate 18%, mainly contributing to total phosphorus.

[0121] The Monte Carlo simulation was performed for 300 iterations with a confidence interval of ±10%, indicating that the source tracing results are highly reliable.

[0122] Ecological restoration decision-making level implementation

[0123] Call the restoration measures library and select the appropriate measures for urban river conditions:

[0124] Temporary aeration devices (10 sets)

[0125] Pipeline network scheduling (intercepting part of the overflow of domestic sewage)

[0126] Precipitating agent addition (polyaluminum chloride, dosage 0.3 mg / L)

[0127] Digital twin simulation prediction:

[0128] Within 2 weeks, dissolved oxygen can increase from 2.8 mg / L to 4.9 mg / L;

[0129] Ammonia nitrogen levels decreased from 3.2 mg / L to 1.9 mg / L.

[0130] COD can be reduced from 45 mg / L to 34 mg / L;

[0131] Total phosphorus can be reduced from 0.16 mg / L to 0.09 mg / L.

[0132] The recommended solution based on the multi-objective optimization results is "aeration + pipeline scheduling" because it has lower treatment costs (reduced chemical dosage) and significant short-term effects.

[0133] User interaction and visualization layer implementation

[0134] The GIS platform displays real-time pollution heat maps and flow field diffusion simulations, and pushes early warnings to the large screen of the municipal emergency command center.

[0135] Early warning module: When the ammonia nitrogen concentration is > 2.5 mg / L, a red warning is triggered, and the system automatically suggests starting aeration and adding chemicals.

[0136] The decision-making interface compared the three governance plans, and the management personnel adopted the "aeration + pipeline scheduling" measure.

[0137] After implementation, the sensor monitors data in real time and feeds it back to the model, automatically optimizing the aeration runtime.

[0138] Results and Benefits (Before Implementation vs. 2 Weeks After Implementation)

[0139] Dissolved oxygen (DO): 2.8 → 4.9 mg / L (75% increase)

[0140] Ammonia nitrogen (NH3-N): 3.2 → 1.9 mg / L (a decrease of 40.6%)

[0141] Total phosphorus (TP): 0.16 → 0.09 mg / L (a decrease of 43.8%)

[0142] COD: 45 → 34 mg / L (a decrease of 24.4%)

[0143] Frequency of alerts: reduced from 4 times per day to once per week (a reduction of approximately 80%).

[0144] Management efficiency: The visual interface reduces manual judgment time by 60%, and the decision response time is shortened from 2 hours to 30 minutes.

[0145] The foregoing has shown and described the basic principles, main features, and advantages of the present invention. It will be apparent to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above, and that the invention can be implemented in other specific forms without departing from its spirit or essential characteristics. Therefore, the embodiments should be considered illustrative and non-limiting in all respects, and the scope of the invention is defined by the appended claims rather than the foregoing description. Thus, all variations falling within the meaning and scope of equivalents of the claims are intended to be included within the present invention. No reference numerals in the claims should be construed as limiting the scope of the claims.

[0146] Furthermore, it should be understood that although this specification describes embodiments, not every embodiment contains only one independent technical solution. This narrative style is merely for clarity. Those skilled in the art should consider the specification as a whole, and the technical solutions in each embodiment can also be appropriately combined to form other embodiments that can be understood by those skilled in the art.

Claims

1. A water pollution source tracing analysis and ecological restoration decision support system, characterized in that: It includes a data acquisition layer, a data management and fusion layer, a pollution source tracing and analysis layer, an ecological restoration decision-making layer, and a user interaction and visualization layer; The data acquisition layer, as the foundation of the system operation, is responsible for comprehensively collecting and updating water environment-related data in real time. The data management and fusion layer is responsible for data cleaning, storage, fusion, and knowledge modeling, ensuring the reliability of the upper-layer analysis. The pollution source tracing and analysis layer is the core of the system, responsible for tracking pollution sources, simulating pollution spread, and assessing uncertainties. The ecological restoration decision-making layer combines a restoration measures library, digital twin technology, and multi-objective optimization to achieve scientific decision-making. The user interaction and visualization layer provides an intuitive interface and decision support functions, making it easy for management departments to use.

2. The water pollution source tracing analysis and ecological restoration decision support system according to claim 1, characterized in that: The data acquisition layer includes a water quality sensing network, which uses distributed wireless sensor nodes to detect pH, dissolved oxygen (DO), conductivity, turbidity, ammonia nitrogen, total phosphorus, COD, and heavy metal ions. The sensors have self-calibration and anti-fouling designs, and the nodes transmit data back to each other through a LoRa / 5G network, improving long-distance transmission and low power consumption performance. The remote sensing and drone monitoring module uses remote sensing satellites to acquire information on large areas of water bodies, and drones equipped with hyperspectral cameras and infrared sensors to conduct detailed local surveys, making up for blind spots in ground monitoring. The online access module for sewage sources connects to the online monitoring platform of enterprise sewage outlets to obtain discharge flow and pollutant concentration. Flow meters and electromagnetic water quality monitoring instruments are installed on river sewage pipe networks to monitor illegal discharge in real time. Meteorological and hydrological data interface, access to data from meteorological bureaus and hydrological stations, including rainfall, evaporation, wind speed, water level and river flow, for pollutant transport modeling and source tracing calculations.

3. The water pollution source tracing analysis and ecological restoration decision support system according to claim 1, characterized in that: The data management and fusion layer includes: a data preprocessing module for outlier removal and missing value imputation, achieving sensor time series alignment and spatial interpolation; a multi-source heterogeneous data fusion module that uses spatiotemporal data fusion algorithms to combine sensor point data with large-scale remote sensing images to generate high-resolution water quality distribution maps, fusing pollution source data and hydrological conditions to provide complete input for source tracing; and a database and knowledge base, establishing a pollution event database to store historical pollution source tracing cases and pollutant diffusion model parameters, and establishing an ecological restoration case knowledge base to include application scenarios, costs, effects, and applicable conditions of domestic and international restoration measures. The big data storage architecture uses a distributed database to support petabyte-level data processing and combines it with a cloud computing platform to achieve elastic computing and high-concurrency access.

4. The water pollution source tracing analysis and ecological restoration decision support system according to claim 1, characterized in that: The pollution source tracing analysis layer includes a pollution propagation model module, which is based on a hydrodynamic model, a two-dimensional shallow water equation, a pollutant transport model, and a convection-diffusion equation, and supports the simulation of point source, area source and non-point source pollution. The source tracing and inversion module introduces machine learning methods, such as random forest, Bayesian inversion, and neural networks, to fit and invert monitoring data, identify potential pollution sources, and use spatiotemporal inversion algorithms to calculate the emission intensity and contribution rate of pollution sources. The uncertainty assessment module perturbs the input parameters through Monte Carlo simulation, assesses the confidence of the results, and combines Bayesian update methods to gradually correct model predictions, thereby enhancing credibility.

5. The water pollution source tracing analysis and ecological restoration decision support system according to claim 1, characterized in that: The ecological restoration decision-making layer includes a restoration measures library, physical restoration (aeration and oxygenation, interception dams, sediment dredging), chemical restoration (precipitant addition, modified adsorbents), and ecological restoration (constructed wetlands, ecological floating beds, microbial restoration, aquatic plant community construction); and a digital twin water environment module, which establishes a virtual water environment simulation platform to dynamically simulate the response process of water quality to different restoration measures and provide multi-timescale predictions. The multi-objective optimization decision-making module has the following objective functions: maximizing pollution reduction rate, minimizing governance cost, shortening remediation cycle, and minimizing ecological impact. The algorithm uses NSGA-II and MOEA / D evolutionary algorithms to generate the optimal solution set. The intelligent recommendation module matches the optimization results with the case knowledge base and recommends the most suitable governance solution based on user preferences.

6. The water pollution source tracing analysis and ecological restoration decision support system according to claim 1, characterized in that: The user interaction and visualization layer includes a GIS visualization platform that displays the distribution of pollution sources, water quality heat maps, and flow field simulation results. It supports multi-layer map overlay, including remote sensing images, pipeline distribution, and monitoring point locations. The decision support interface provides a scheme comparison and analysis function, allowing users to intuitively view the differences in cost, time, and effectiveness of different remediation measures. It also supports drag-and-drop operations, allowing users to combine measures and view prediction results in real time. The early warning and feedback module automatically alarms when water quality exceeds a preset threshold and pushes emergency response suggestions. After the repair is implemented, the real-time monitoring results are fed back to the model, automatically optimizing subsequent plans to form a closed-loop governance system. It supports multi-terminal access, including PC, mobile, and large-screen display, meeting the needs of management departments, researchers, and the public.

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