Multi-source data fused water source safety evaluation method and system

Abstract

The invention relates to the technical field of water resource management, and discloses a water source safety evaluation method and system fusing multi-source data, and the method comprises the steps: obtaining the multi-source data of a water source, simulating the migration process of pollutants, and generating a preliminary migration path of the pollutants through the analysis of the multi-source data, integrating the geological medium characteristic data of the water source with the preliminary migration path, and calculating the interaction between the pollutant and the medium to obtain a concentration gradient calculation result; the concentration gradient calculation result is analyzed, the pollutant diffusion risk is judged, and a risk assessment result is generated; degradation parameters of the water source are obtained, the migration path of the pollutants is adjusted, particle trajectory simulation is carried out in combination with the correction coefficient and the multi-source data, the migration trajectory of the pollutants is optimized, concentration distribution prediction results of the pollutants in different time periods and spatial positions are obtained, the concentration distribution prediction results are compared with a preset risk assessment standard, and preferential intervention area distribution is determined. According to the invention, the prediction precision of pollutant migration can be improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of water resource management, in particular to a water source safety evaluation method and system fusing multi-source data, which is widely used in the fields of water resource protection, groundwater pollution prevention and control, and environmental risk assessment. BACKGROUND

[0002] With the increasingly serious problem of water source pollution, especially in complex geological environments such as soluble rock areas, traditional water source pollutant migration path prediction methods are difficult to cope with rapidly changing environmental factors, resulting in the inability to provide accurate pollution risk assessment. The existing technology usually evaluates based on single monitoring data, which cannot effectively fuse different types of real-time data such as water quality parameters, meteorological data and geological medium characteristics. The spatio-temporal variation of these data is not fully considered, resulting in low prediction accuracy of pollutant diffusion path. The dynamic changes of water quality monitoring, rainfall variation, groundwater flow and other factors are often ignored, which affects the formulation of water source safety management and pollution prevention and control strategies.

[0003] In addition, the pollutant diffusion model of the traditional method usually cannot respond to environmental changes in real time, especially in the case of sudden rainfall or other meteorological changes. The existing model lacks a dynamic adjustment mechanism, resulting in lag in predicting the risk of pollutant diffusion. Moreover, based on a single simulation path for risk assessment, the complex geological and hydrological characteristics of the water source are not fully reflected, especially in the presence of multiple pollution sources or complex groundwater flow channels (such as fractures, caves, etc.). The traditional model is difficult to accurately simulate the migration trajectory of pollutants. SUMMARY

[0004] To solve the above technical problems, the present application provides a water source safety evaluation method and system fusing multi-source data, which can effectively solve the problems of insufficient multi-source data fusion, insufficient response to sudden environmental events, etc. in water source safety evaluation, significantly improve the accuracy and prevention efficiency of evaluation, and thus ensure the long-term safety of the water source.

[0005] Solve the problems of multi-source data fusion difficulty, insufficient simulation accuracy of pollutant migration path, lag in dynamic risk response, and lack of scientific basis for intervention decision-making in traditional groundwater pollution prediction and evaluation.

[0006] In a first aspect, the present application provides a water source safety evaluation method fusing multi-source data, which comprises: Step S1, acquiring multi-source data of the water source, the multi-source data comprising real-time water quality parameters and environmental variable data sets; Step S2, simulating the migration process of pollutants by using a numerical simulation method, and generating a preliminary migration path of the pollutants in the water source by analyzing the multi-source data; Step S3, integrating the geological medium characteristic data of the water source site with the preliminary migration path, calculating the interaction between the pollutant and the medium through the adsorption isotherm method to obtain concentration gradient calculation results; Step S4: analyzing the concentration gradient calculation results, judging the pollutant diffusion risk, quantifying the interaction energy between the pollutant and the environmental variable, and generating a risk assessment result; Step S5, based on the risk assessment result, obtaining the degradation parameter of the water source site, adjusting the migration path of the pollutant, combining the correction coefficient and the multi-source data to simulate the particle trajectory, optimizing the migration trajectory of the pollutant, and obtaining the concentration distribution prediction result of the pollutant at different time periods and spatial positions; Step S6, comparing the concentration distribution prediction result with the preset risk assessment standard to determine the priority intervention area distribution.

[0007] In a second aspect, the present application provides a water source site safety evaluation system integrating multi-source data, which comprises: A data acquisition unit is configured to acquire multi-source data of a water source site, wherein the multi-source data comprises real-time water quality parameters and environmental variable data sets. A path simulation unit is configured to simulate the migration process of a pollutant by using a numerical simulation method, and generate a preliminary migration path of the pollutant in the water source site by analyzing the multi-source data. A concentration distribution unit is configured to integrate the geological medium characteristic data of the water source site with the preliminary migration path, calculate the interaction between the pollutant and the medium through the adsorption isotherm method, and obtain concentration gradient calculation results. A risk assessment unit is configured to analyze the concentration gradient calculation results, judge the pollutant diffusion risk, quantify the interaction energy between the pollutant and the environmental variable, and generate a risk assessment result. A path adjustment unit is configured to obtain the degradation parameter of the water source site based on the risk assessment result, adjust the migration path of the pollutant, combine the correction coefficient and the multi-source data to simulate the particle trajectory, optimize the migration trajectory of the pollutant, and obtain the concentration distribution prediction result of the pollutant at different time periods and spatial positions. A distribution prediction unit is configured to compare the concentration distribution prediction result with the preset risk assessment standard to determine the priority intervention area distribution.

[0008] Compared with the prior art, the present application has at least the following advantages: 1. This application can effectively improve the accuracy of pollutant migration path prediction in water sources, especially under complex geological conditions, such as the impact of groundwater flow channels in soluble rocks, such as fissures and caves, on pollutant diffusion. Through dynamic fusion of multi-source data, this application overcomes the problem of lagging response to environmental changes in traditional methods, and can adjust the prediction of migration paths in real time, thereby improving the response speed and prediction accuracy of pollutant diffusion risks.

[0009] 2. By acquiring water quality parameters and environmental variables in real time, and combining them with risk assessment standards and emergency event identification mechanisms, this application can respond promptly to sudden changes, such as rainfall and flow velocity changes, during the prediction of pollutant migration paths. This reduces the impact of sudden events on pollution diffusion and provides a timely and effective early warning system. This feature significantly improves the effectiveness of pollution prevention and control measures and the flexibility of emergency response.

[0010] 3. This application employs an uncertainty quantification method to consider spatial heterogeneity, further refining the prediction results and making the generated pollutant migration paths more consistent with the characteristics of actual groundwater flow. By optimizing the correction coefficients and dynamically adjusting the model parameters, this application can provide a more accurate pollutant distribution mapping in complex groundwater flow environments, ensuring that pollution prevention and control measures for water sources can be accurately formulated, improving prevention and control efficiency and risk response capabilities, thereby providing strong technical support for the sustainable management and protection of water sources. Attached Figure Description

[0011] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0012] Figure 1 This is a schematic diagram of an embodiment of the water source safety assessment method that integrates multi-source data in this application. Figure 2 This is a schematic diagram of the trajectory at the basic resolution (1 hour / 100 meters) in the embodiments of this application; Figure 3 This is a schematic diagram of a high-resolution (15 minutes / 50 meters) trajectory after rainfall in an embodiment of this application; Figure 4 This is a schematic diagram showing the distribution of priority intervention areas for water sources in the embodiments of this application; Figure 5 This is a schematic diagram of an embodiment of the water source safety assessment system that integrates multi-source data in this application. Detailed Implementation

[0013] This application provides a method and system for assessing the safety of water sources by integrating multi-source data. The terms "first," "second," "third," "fourth," etc. (if present) in the specification, claims, and accompanying drawings of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments described herein can be implemented in a sequence other than that illustrated or described herein. Furthermore, the terms "comprising" or "having" and any variations thereof are intended to cover a non-exclusive inclusion; for example, a process, method, system, product, or device that includes a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or devices.

[0014] Example 1: Existing methods for assessing the safety of water sources often fail to fully consider the spatiotemporal dynamics of environmental variables, especially when real-time access to multi-source data such as water quality and meteorological data is difficult. Traditional methods struggle to comprehensively reflect the risk factors of water sources. Furthermore, the geological characteristics of water sources, the distribution of pollution sources, and the nonlinear nature of water quality changes make it difficult for traditional assessment models to accurately predict potential pollution risks, particularly in the face of sudden environmental events. This results in significant errors in existing technologies for water source safety assessments, hindering the provision of scientifically effective early warning and prevention recommendations. Therefore, this application proposes a water source safety assessment method that integrates multi-source data. By acquiring real-time water quality parameters and environmental variable data, and employing numerical simulation methods to preliminarily predict pollutant migration paths in water sources, this method further optimizes the pollution risk assessment of water sources by combining factors such as geological media characteristics, pollutant-media interactions, and biodegradation processes. Simultaneously, by utilizing real-time data and risk assessment mechanisms, the safety assessment results of water sources can be updated promptly, and assessment parameters can be dynamically adjusted, ultimately providing a more accurate and scientific basis for the safety management of water sources.

[0015] To make the objectives, technical solutions, and advantages of this invention clearer, the present application will be described in detail below with reference to the accompanying drawings and specific embodiments. Please refer to [the accompanying drawings]. Figure 1 The water source safety assessment method integrating multi-source data in this application embodiment includes: Step S1: Obtain multi-source data from the water source, including real-time water quality parameters and environmental variable datasets. Obtaining multi-source data from the water source includes: acquiring real-time water quality parameters and rainfall data from monitoring stations and meteorological sensors through a multi-source data acquisition system to obtain an initial dataset of pollutant concentration distribution and environmental variables; performing spatiotemporal grid partitioning on the initial dataset to generate a gridded data structure that supports particle trajectory simulation. The gridded data structure includes discretized representations of the time and spatial dimensions for subsequent numerical simulation of pollutant migration paths.

[0016] Specifically, in order to address the problems of insufficient integration of multi-source data and difficulty in accurately tracking real-time changes in environmental variables in traditional water source safety assessments, existing technologies often fail to effectively integrate data from different sources, especially real-time changes in environmental data such as water quality and meteorology, resulting in an inability to accurately assess the pollution risk and potential safety hazards of water sources. Therefore, this application constructs a dynamic and comprehensive water source safety assessment framework by effectively integrating water quality monitoring data with meteorological data, thereby achieving accurate assessment and prevention of water source safety.

[0017] Specifically, monitoring stations are deployed at groundwater monitoring points in the water source area to collect water quality parameters such as pH value, dissolved oxygen, and pollutant concentration in real time to comprehensively reflect changes in water quality in the water source area. Real-time meteorological data such as rainfall intensity, duration, temperature, and wind speed are also collected. Rainfall information is crucial for assessing the impact of precipitation on pollutant diffusion. Meanwhile, environmental variables such as temperature and wind speed provide necessary support for subsequent pollutant diffusion simulation and risk assessment. These data are integrated into an initial dataset using wireless transmission technology, further providing a data foundation for predicting pollutant migration paths and conducting safety assessments. Pollutant concentration distribution is represented in map form.

[0018] The initial dataset is divided into spatiotemporal grids to generate a gridded data structure that supports particle trajectory simulation. In the implementation process, real-time water quality parameters and environmental variables of the water source are first acquired through a multi-source data acquisition system, such as pollutant concentration, pH value, dissolved oxygen, rainfall intensity, and wind speed. Then, spatiotemporal grids are applied to these initial datasets, dividing the spatial area of ​​the water source into uniform grids, such as 100m x 100m grids, with each grid representing a specific region. The spatial resolution is optimized according to actual needs; for example, in areas with complex geological structures and soluble rock, the grid size is adjusted based on the distribution characteristics of caves and fissures to match the local conditions. Qualitative features, such as using Finer grids (e.g., 50m x 50m) in areas with dense karst caves, enhance simulation accuracy and ensure that the grid division reflects the true migration of pollutants in different areas. Furthermore, the time dimension is discretized, allowing for real-time fusion of new data to refine the resolution, ensuring that spatiotemporal grid data accurately reflects the migration process of pollutants at different times. For example, time can be divided into hourly intervals, or during sudden events such as rainfall, higher-frequency time discretization (e.g., every minute) can be used to capture the immediate impact of sudden rainfall on pollutant migration paths, supporting subsequent particle trajectory tracking from one grid to adjacent grids in simulations.

[0019] After the spatiotemporal grid is divided, the generated three-dimensional gridded data structure will store multi-dimensional data such as pollutant concentration values ​​and rainfall data, and serve as input for subsequent particle trajectory simulation. It supports particle trajectory simulation to calculate the migration path of pollutants in the water source area based on pollutant concentration and geological characteristics using the finite difference method, and simulate their diffusion process under different time and spatial conditions. In this process, the spatiotemporal gridded data structure provides high-precision and high-resolution data support for particle trajectory simulation by uniformly managing water quality data and environmental data. The simulation results can not only reflect the spatial distribution changes of pollutants in groundwater, but also provide dynamic information on temporal evolution, helping to predict the migration trend of pollutants in the future.

[0020] This application also incorporates geological media data into the gridded data structure to ensure that the grid structure can reflect the complexity of soluble rock groundwater flow. For example, in areas with well-developed fissures or dense karst caves, the grid density can be appropriately increased, such as by adjusting to smaller grid units of 20 meters x 20 meters, thereby improving the accuracy of pollutant migration path prediction in these areas. This method effectively quantifies the pollution risk of water sources and scientifically predicts future pollutant diffusion. Furthermore, the gridded data structure plays a crucial role in the discretization of the time dimension; for example, during peak rainfall periods, the high-frequency updates of real-time data can adjust pollutant migration paths in a timely manner through particle trajectory simulation, reflecting the accelerated risk of pollutant diffusion. Adjusting the model using degradation parameter correction coefficients from historical databases allows for full consideration of the impact of biodegradation and chemical degradation on pollutant concentrations, further optimizing the accuracy of migration path prediction, which will be explained below. Through the above technical means, this application can improve the response efficiency and accuracy of water source pollution prevention and control.

[0021] Step S2: Simulate the migration process of pollutants using numerical simulation methods, and generate preliminary migration paths of pollutants within the water source area by analyzing multi-source data; Step S2 further includes: simulating convection and dispersion processes using the finite difference method based on real-time water quality parameters and environmental variable datasets to generate preliminary migration paths of pollutants in the groundwater of the water source area; updating the spatiotemporal resolution of the preliminary migration paths by fusing real-time data to obtain high-resolution pollutant migration trajectories; wherein, the high-resolution pollutant migration trajectory includes the spatial location and temporal evolution information of pollutants in the groundwater.

[0022] Specifically, in order to address the problem of low accuracy in predicting the migration paths of pollutants in water sources in existing technologies, particularly the failure to effectively combine environmental variables and water quality data, which leads to the inability to accurately predict the migration trajectories of pollutants and their impact on water source safety; and because traditional methods often fail to fully utilize real-time water quality and environmental data, resulting in inaccurate prediction results, this application proposes a technical solution to generate pollutant migration paths by combining real-time water quality parameters and environmental variable datasets with numerical simulation methods, thereby improving the accuracy of water source safety assessment.

[0023] In practice, real-time water quality parameters and environmental variable datasets are first acquired and collected in real time through a multi-source data acquisition system to form an initial dataset, which serves as the input for subsequent numerical simulation methods. Based on this, the numerical simulation method employs the finite difference method to simulate the convection and dispersion processes of pollutants. Specifically, the finite difference method discretizes partial differential equations, transforming the continuous water flow and pollutant diffusion process into a numerical solution at grid points, thereby simulating the movement of pollutants with water flow and the diffusion process caused by the influence of geological media. Geological media refers to the collection of rocks, soil, minerals, sediments, and other materials on and beneath the Earth's surface, directly affecting the flow, storage, and migration of groundwater and pollutants. Through this method… This simulation method can accurately describe the migration characteristics of pollutants in groundwater at water sources, especially in complex terrains. Considering the influence of geological structures such as karst caves and fissures on pollutant migration paths, it can effectively simulate the diffusion and migration trajectories of pollutants in different geological units. For example, when using the finite difference method for gridded calculations, setting a grid spacing of 0.5 meters simulates pollutants spreading 10 meters from the pollution source within 24 hours, providing early warning for pollution control and identifying potential pollution areas early. This simulation process can solve the problem of traditional methods failing to accurately predict pollutant migration in complex groundwater systems, thereby improving the accuracy of pollutant migration path prediction and providing precise evidence for pollution risk assessment and control measures at water sources. Figure 2 This paper presents an example of pollutant migration trajectories at a basic resolution (1 hour / 100 meters), where the X and Y coordinates represent the spatial location (in meters) of the water source. This trajectory map visually displays the migration path of pollutants in the initial simulation stage. It is generated based on spatiotemporal grid partitioning and the finite difference method, reflecting the spatial distribution and temporal evolution trend of pollutants at the basic resolution. For example, the grid points in the trajectory represent the positional changes of pollutants at different time intervals (e.g., per hour) and spatial scales (per 100 meters), providing a basic visualization reference for subsequent fusion of real-time data to generate high-resolution trajectories. Figure 3 This displays high-resolution (15-minute / 50-meter) pollutant migration trajectories updated using data assimilation techniques after incorporating real-time data such as sudden rainfall events; compared to the baseline resolution trajectory. Figure 2 In comparison, this trajectory refines the time dimension from 1 hour to 15 minutes and the spatial dimension from 100 meters to 50 meters, significantly enhancing the accuracy and timeliness of the path simulation. The trajectory in the figure clearly reflects the accelerated diffusion process of pollutants under the influence of rainfall and more refined spatial location changes. For example, the dynamic process of pollutants rapidly migrating to 300 meters downstream within 12 hours under heavy rainfall conditions is accurately captured. This high-resolution trajectory verifies the effectiveness of updating the initial migration path by integrating real-time data, providing a more reliable dynamic basis for subsequent risk assessment and intervention measures.

[0024] The preliminary migration path described above is a pollutant migration trajectory obtained through numerical simulation based on initial water quality parameters and environmental variable datasets. At this stage, a relatively coarse spatiotemporal resolution is used to simulate pollutant migration, primarily to provide the initial direction and approximate path of pollutant migration. Preliminary paths are typically estimates based on historical data or long-term statistical models, providing the migration trend of pollutants under specific conditions, but their spatiotemporal resolution is relatively low, thus failing to accurately capture the dynamic changes of pollutants at different temporal and spatial scales. After establishing the preliminary migration path, real-time monitoring data, such as real-time water quality, rainfall, and flow velocity, are then integrated to update the path. This real-time data provides key factors that change constantly during pollutant migration, such as the impact of water flow rate and environmental variables (rainfall, temperature changes, etc.) on pollutant diffusion, thereby significantly improving the accuracy and timeliness of the simulation. By integrating this real-time data, the simulated path is further refined, and the spatiotemporal resolution is improved, reflecting the migration of pollutants at shorter time intervals and finer spatial scales.

[0025] Obtaining high-resolution pollutant migration trajectories involves acquiring the latest rainfall and water quality readings from monitoring stations, overlaying them with preliminary migration path data, and adjusting simulation parameters using data assimilation techniques. By shortening the time step and increasing spatial resolution, the spatiotemporal resolution can be significantly improved. For example, shortening the time step from 1 hour to 15 minutes and increasing the spatial resolution from 1 meter to 0.2 meters allows the updated trajectory to more accurately reflect the movement trend of pollutants in groundwater. The trajectory information includes the spatial location of the pollutants in a three-dimensional coordinate system and their concentration values ​​changing over time, thereby achieving dynamic visualization of the pollutant migration process. In one embodiment, for nitrate pollutants, the trajectory generated after fusing monitoring data and simulation results shows that its concentration is 10 mg / L at the inlet point and increases to 5 mg / L downstream after 6 hours. Peak diffusion occurs at 0 meters, and the trajectory temporal evolution characteristics clearly reflect the time and location of the pollutant peak. In another embodiment, for organic pollutants, under heavy rainfall conditions, the rainfall intensity provided by the meteorological sensor is 10 mm / h. After data fusion, the updated trajectory shows that the pollutants rapidly migrate to 300 meters downstream within 12 hours, and the trajectory spatial location expands from (0, 0, 0) to (300, 100, -100). Moreover, the temporal evolution process reveals the diffusion acceleration stage caused by rainfall. This method can effectively solve the problem that traditional static simulation methods cannot timely reflect the deviation of pollutant migration paths caused by sudden environmental changes, improve the accuracy and dynamic adaptability of path prediction, reduce simulation uncertainty, and provide real-time and reliable support for the assessment of pollution diffusion risks in water sources and the determination of priority intervention areas.

[0026] The specific implementation process of obtaining the latest rainfall data and water quality readings from monitoring stations, overlaying them with preliminary migration path data, and adjusting simulation parameters using data assimilation technology is as follows: Real-time data, such as rainfall and pollutant concentrations, are compared with the simulation results of the preliminary migration path. If the real-time data is inconsistent with the prediction of the preliminary migration path, the data assimilation technology will adjust relevant parameters in the model, such as water flow velocity, pollutant diffusion coefficient, and degradation rate, based on the real-time observation data, so that the migration path output by the model is more consistent with the actual situation. Through data assimilation, the migration path of pollutants is updated in real time, and the model will correct the preliminary migration path, reflecting the true migration trend of pollutants under the current environmental conditions. This adjustment not only corrects the parameters of water flow and pollutant diffusion processes, but may also optimize the path at finer spatial and temporal scales to cope with sudden events such as short-term rainfall or changes in flow velocity. Data assimilation is a technique that combines real-time data with the prediction results of a numerical model, i.e., the preliminary migration path, with the aim of adjusting model parameters so that the model output is closer to the actual observation results. Its basic principle is to use a mathematical algorithm to combine observation data and model prediction data to optimize the model results.

[0027] Step S3: Integrate the geological medium characteristic data of the water source with the preliminary migration path, and calculate the interaction between the pollutant and the medium using the adsorption isotherm method to obtain the concentration gradient calculation result; wherein, step S3 further includes: obtaining the geological medium characteristic data of the water source, integrating the preliminary migration path with the geological medium characteristic data, and using the adsorption isotherm fitting method to calculate the surface affinity between the pollutant and the medium to obtain the coupled concentration gradient calculation result, wherein the concentration gradient calculation result reflects the spatial distribution change trend of pollutants in groundwater.

[0028] Specifically, traditional water source safety assessment methods often ignore the heterogeneity of geological media and cannot accurately capture the migration patterns of pollutants under different geological conditions. Therefore, a technical solution that can integrate the characteristics of geological media with real-time pollutant migration path data is needed. Through the above technical means, this application can more accurately predict the migration and diffusion of pollutants in water sources, providing a more scientific basis for water source pollution prevention and control.

[0029] In the specific implementation process, the geological medium characteristics data of the water source area are first obtained, including parameters such as rock porosity, permeability coefficient, and mineral composition. For example, in typical limestone areas, the porosity is 0.15 to 0.35, and the permeability coefficient is 0.001 m / s to 0.01 m / s. Especially in soluble rock environments, the characteristics of the geological medium, such as fracture density and cave distribution, will directly affect groundwater flow and pollutant migration paths. Next, the preliminary migration paths are integrated with these geological medium characteristic data. In this process, spatiotemporal grid mapping technology is used to match the preliminary migration paths of pollutants with the spatial distribution of the geological medium, forming a unified coupled dataset. This dataset reflects the migration paths of pollutants in the groundwater of the water source area and takes into account the local characteristics of the geological medium, such as the influence of fractures and caves, to ensure the accuracy of path prediction.

[0030] After obtaining the coupled dataset, the adsorption isotherm fitting method was further used to calculate the interaction between pollutants and the geological medium. The Freundlich adsorption isotherm model was selected, which describes the nonlinear relationship between pollutant concentration and adsorption amount. In this process, the constants in the model, such as K and n, were used to fit the existing data to calculate the affinity between pollutant molecules and the medium surface. Specifically, the affinity calculation involves the energy calculation formula E=-RTln(K), where R is the gas constant, T is the temperature, and K is the fitting constant. The obtained affinity value reflects the strength of the binding between the pollutant and the medium surface, thereby quantifying the impact of the adsorption process on pollutant migration.

[0031] By incorporating the calculated surface affinity results into the finite difference simulation, the concentration distribution equation is updated, yielding the concentration gradient of pollutants in groundwater. This concentration gradient reflects the trend of pollutant diffusion from high-concentration areas to low-concentration areas. Furthermore, based on the characteristics of the geological medium, the migration process of pollutants under different geological conditions is simulated. During this process, the model is refined by adjusting simulation parameters, resulting in highly accurate predictions of pollutant migration trajectories and concentration distributions. Through this technical solution, the preliminary migration path is updated and refined in real time. The pollutant migration path and concentration gradient can fully consider the geological medium characteristics of the water source, making pollutant migration predictions more accurate. Especially in complex environments such as soluble rocks, it can better reflect the actual migration process of pollutants, ultimately providing reliable technical support for the safety assessment of water sources.

[0032] Step S4: Analyze the concentration gradient calculation results to determine the pollutant diffusion risk, quantify the interaction energy between pollutants and environmental variables, and generate risk assessment results. Specifically, the coupled concentration gradient calculation results are compared with a preset threshold. If the concentration gradient calculation results exceed the preset threshold, a threshold monitoring mechanism is triggered to identify sudden variables, including sudden rainfall or changes in geological fissures. Based on the sudden variables, the interaction energy between pollutants and environmental variables is quantified to generate risk assessment results. These risk assessment results are used to update the adjustment parameters of the pollutant migration path.

[0033] Specifically, traditional methods for assessing pollutant migration pathways in water sources often overlook unforeseen events such as rainfall and geological changes, making it difficult to effectively adjust predicted pathways and control measures. Therefore, this application proposes a technical solution that can dynamically adjust pollutant migration pathways based on real-time data, improving the accuracy and response speed of the assessment through a risk assessment mechanism.

[0034] In the specific implementation process, the concentration gradient of pollutants is first calculated to obtain the migration trend of pollutants in groundwater, taking into account the influence of geological media characteristics on pollutant diffusion. By comparing these concentration gradients with preset thresholds, if the concentration gradient exceeds the threshold, the threshold monitoring mechanism is triggered to promptly identify sudden environmental variables, such as rainfall or changes in geological fissures. These factors can significantly affect the migration process of pollutants, especially in soluble rocks, where sudden rainfall events may cause drastic changes in groundwater flow velocity, thereby accelerating the diffusion of pollutants.

[0035] The influence of geological media characteristics on pollutant diffusion includes: In areas with soluble rock, groundwater flow is often affected by fissures and caverns. Assuming a dense cavern structure exists in an area, groundwater flows rapidly through these caverns. Due to the presence of these caverns, pollutants diffuse rapidly in a short time, causing a rapid increase in pollutant concentration near the caverns, thus accelerating pollutant migration. In this case, the permeability of the caverns is very high, therefore the diffusion rate of pollutants is also fast, and the diffusion path exhibits non-uniform characteristics. Conversely, in areas with lower permeability and denser soil or rock, groundwater flow is restricted, and the migration rate of pollutants is significantly slowed. Assuming the rock medium in this area is low-permeability materials such as clay or shale, the water flow is slow, and pollutants accumulate in this area, limiting the diffusion rate. Furthermore, the high porosity and strong adsorption of fine-particle media such as clay can lead to the interaction between pollutants and the medium. Strong adsorption occurs between different geological media, maintaining a high concentration of pollutants in the area and reducing their ability to migrate further. In another example, if groundwater flows through sandstone layers with high permeability and relatively fast flow velocity, pollutants migrate quickly and spread over a wide area. However, if heavy metals such as lead and cadmium are present in the groundwater, these pollutants may adsorb onto minerals in the sandstone, slowing their migration. Nevertheless, with continued water flow, pollutants may still spread to distant areas. These examples demonstrate that the characteristics of geological media—such as permeability, porosity, adsorption properties, and the distribution of fissures and caverns—significantly influence pollutant diffusion. In soluble rocks, the presence of caverns and fissures can accelerate pollutant diffusion, while in low-permeability areas, pollutants may accumulate, affecting their further diffusion range and speed. Therefore, accurately understanding and integrating geological media characteristic data is crucial for predicting pollutant migration pathways and formulating pollution control strategies.

[0036] The monitoring mechanism identifies variable types by analyzing abnormal peaks in the data stream and inputs them into the risk assessment module. In another scenario, geological changes such as fissure expansion can also trigger rapid pollutant infiltration. These geological changes can also be identified by the mechanism. Based on the identified sudden variables, the interaction energy between pollutants and environmental variables is quantified. Specifically, the Gibbs free energy change principle can be used to calculate the potential energy difference between pollutant molecules and environmental factors. This energy value reflects the driving force of pollutant migration. For example, under sudden rainfall conditions, the increased hydrodynamic energy will promote the accelerated diffusion of pollutants from high-concentration areas to low-concentration areas. The calculation result can be expressed in joules per mole and used as the basis for generating risk assessment results. For example, in the monitoring data of water sources, if the concentration gradient is 0.5 mg / L per meter... If the concentration exceeds the threshold of 0.3 mg / L, the mechanism triggers the identification of sudden rainfall variables and classifies it as a high-risk level. Simultaneously, the energy quantification results show that the diffusion process is spontaneous. Based on this, the risk assessment module outputs a high-risk result and generates correction parameters. These correction parameters are used to update the pollutant migration path to reflect the dynamic characteristics of accelerated diffusion in particle trajectory simulation. In different water source scenarios, such as areas with dense fissures or developed karst caves, the assessment process can further incorporate the influence of temperature variables to reflect the thermodynamic contribution of seasonal changes, thus making the risk assessment results more comprehensive. Through the above methods, not only can the prediction bias caused by the inability of traditional methods to dynamically capture sudden variables be solved, but also the dynamic correction of pollutant migration paths can be achieved, improving the accuracy of risk identification and prediction and reducing simulation errors.

[0037] The monitoring mechanism identifies variable types by analyzing abnormal peaks in the data stream. The specific process is as follows: First, the real-time monitoring system continuously collects information such as water quality parameters, meteorological data, and environmental variables from the water source. This data is collected and uploaded in real time through sensors and monitoring stations. The monitoring mechanism continuously monitors this data, comparing the current data with historical data in real time. When a sudden and significant change in a data point, such as rainfall, flow velocity, or pollutant concentration, exceeds the preset normal fluctuation range, these sudden changes are considered "abnormal peaks." Based on this, the monitoring mechanism identifies and confirms variable types using a set anomaly detection algorithm. For example, based on the magnitude and frequency of changes in key environmental parameters such as rainfall, flow velocity, and temperature... To determine whether these fluctuations fall within the scope of natural changes, if a sudden change deviates from historical data trends and is related to factors such as water flow and precipitation, the monitoring mechanism will identify it as a variable of "sudden rainfall" or "water flow change." If changes occur in geological fissures, such as fissures opening due to earthquakes or construction, the monitoring mechanism will identify geological variables related to abrupt changes in groundwater flow. By identifying abnormal peak values, potential risk factors for pollutant migration can be determined, and migration path predictions can be adjusted in a timely manner to avoid ignoring the pollution spread risks that may arise from sudden events. This method ensures that the prediction of pollutant migration paths can be updated in a timely manner with real-time environmental changes, improving the accuracy of water source safety assessments and the ability to respond to sudden risks.

[0038] Imagine a water source in a soluble rock area where groundwater flows through a region rich in karst caves and fissures. In this region, the groundwater flow is rapid, and pollutants such as heavy metals or organic pollutants can quickly diffuse through these caves and fissures. If heavy rainfall occurs, the increased water flow further accelerates pollutant migration. The risk assessment module first collects real-time water quality and environmental data from the source area, such as pollutant concentration and rainfall. It calculates the pollutant concentration gradient and compares it to a preset threshold. If the concentration gradient exceeds the preset threshold (e.g., 0.5 mg / L), a threshold monitoring mechanism is triggered to identify sudden variables such as heavy rainfall and determine if the risk of pollutant diffusion has accelerated. Next, the assessment module quantifies the interaction energy between pollutants and environmental variables such as rainfall. For example, rainfall increases the dynamic energy of water flow, thus accelerating pollutant diffusion. The energy quantification results are used to generate a risk assessment report and adjust the simulation parameters of pollutant migration paths to update pollutant migration predictions in real time. Through dynamic adjustments, more precise pollution control measures can be provided, and areas requiring priority intervention can be identified in a timely manner to ensure the safety of the water source.

[0039] Step S5: Based on the risk assessment results, obtain the degradation parameters of the water source and adjust the migration path of pollutants. Combine the correction coefficient and multi-source data to perform particle trajectory simulation, optimize the migration trajectory of pollutants, and obtain the predicted concentration distribution of pollutants at different time periods and spatial locations. Specifically, based on the risk assessment results, obtain the biodegradation parameters of the water source under geological conditions from the historical database, determine the correction coefficient of the biodegradation process on the migration path, and optimize the correction coefficient by integrating the risk acceleration judgment results to generate the adjusted migration path parameters. The correction coefficient reflects the degree of influence of biodegradation on pollutant concentration.

[0040] Specifically, in order to address the problem that traditional methods for predicting pollutant migration paths in water sources fail to adequately consider the impact of biodegradation processes on pollutant migration paths, especially in complex soluble rocks, this application obtains biodegradation parameters from historical databases and optimizes pollutant migration path predictions based on pollutant diffusion risk levels and correction coefficients, so as to more accurately reflect the impact of biodegradation on pollutant concentration changes.

[0041] In practice, the process begins by obtaining biodegradation parameters that match the current geological conditions of soluble rocks from historical databases, based on the assessed level of pollutant diffusion risk. These parameters mainly include data such as biodegradation rate constants and half-lives, which are derived from past groundwater monitoring cases. Next, by querying historical databases, rapid indexing technology is used to extract degradation parameters suitable for the current risk scenario, providing a reliable basis for subsequent path correction.

[0042] Then, using a first-order degradation model, the biodegradation correction coefficient for pollutants is calculated. This correction coefficient is derived from an exponential function of the degradation rate multiplied by time, quantifying the degree of pollutant concentration decay over time and thus optimizing the simulation accuracy of migration paths. Next, the risk assessment results are combined with an acceleration factor to optimize the application of the correction coefficient. Based on the risk acceleration judgment results, if the risk level is high, the acceleration factor is multiplied by the original correction coefficient to adjust the migration path parameters. For example, in a high-risk scenario, the correction coefficient may be reduced by 20%, simulating a faster decay rate of pollutant concentration. The acceleration factor is a quantification of the risk acceleration judgment results; for example, if the risk is high, the factor is 1.5. During fusion, it is multiplied by the original correction coefficient to adjust the value of the correction coefficient. The process of quantifying the risk acceleration judgment results into an acceleration factor is to dynamically adjust the correction coefficient of pollutant migration paths based on the risk assessment results, thereby more accurately reflecting the impact of environmental changes on pollutant diffusion. The goal of risk acceleration judgment is to quantify the pollutant migration acceleration effect caused by sudden environmental changes such as sudden rainfall or accelerated groundwater flow, and to convert this effect into an acceleration factor to optimize migration path prediction. Assuming that in soluble rock... In the water source area of ​​Shijiazhuang, the original pollutant migration path prediction showed that the expected decay rate of pollutant concentration in a certain area was 0.1 mg / L per hour. If, during the simulation, a sudden rainfall was identified, which led to an increase in flow rate and accelerated the diffusion of pollutants, after risk acceleration assessment, it was believed that the pollutant migration rate in this area would increase by 50%. Therefore, an acceleration factor of 1.5 was set and applied to the original correction coefficient, resulting in a diffusion rate of 0.1 mg / L * 1.5 = 0.15 mg / L. Through this adjustment, the diffusion trend of pollutants can be predicted more accurately, providing real-time and precise support for subsequent prevention and control measures. The first-order degradation model is a commonly used mathematical model, belonging to the prior art, used to describe the process of pollutants degrading (decaying) in the environment over time. This model assumes that the degradation rate of pollutants is proportional to their current concentration, that is, the degradation rate of pollutants is a linear process related to concentration. In this application, the first-order degradation model is used to calculate the correction coefficient of the biodegradation process on the pollutant migration path. Specifically, biodegradation is one of the main reasons for the reduction of pollutant concentration in groundwater, especially in water source management, where the degradation rate has a significant impact on the long-term diffusion of pollutants.

[0043] After generating the adjusted migration path parameters, particle trajectory simulation is performed in conjunction with real-time water quality parameters to update the spatial distribution prediction of pollutants. By applying the optimized correction coefficient, the mitigation effect of biodegradation on pollutant concentration can be simulated more accurately, ensuring that the simulation results reflect the specific impact of biodegradation on pollutant diffusion, thereby improving the accuracy of path prediction and providing a scientific basis for the subsequent formulation of water source pollution prevention and control strategies.

[0044] Among them, particle trajectory simulation combining correction coefficients and multi-source data includes: The adjusted migration path parameters and multi-source data are obtained, and the correction coefficients are fused using the particle trajectory simulation method to generate the concentration distribution prediction results of pollutants in different time periods and spatial locations of the water source landform. The uncertainty of spatial heterogeneity is quantified to obtain a high-precision pollutant distribution map, which includes the spatial distribution and temporal evolution characteristics of pollutant concentration.

[0045] Specifically, traditional methods fail to effectively consider the spatiotemporal variability of pollutants in water sources under complex geological conditions, especially the influence of soluble rocks on the migration path and concentration distribution of pollutants, resulting in low accuracy of water source safety assessment results. Based on multi-source data fusion and real-time data monitoring, this application can accurately predict the migration trajectory of pollutants under different time and space conditions, thereby making the optimization of water source safety assessment and control measures more precise.

[0046] In a specific embodiment, water quality parameters, environmental variables, and rainfall data are first acquired in real time through a multi-source data acquisition system to construct an initial dataset. This dataset is then spatiotemporally gridded with geological medium characteristics to support subsequent particle trajectory simulation. The acquired, adjusted migration path parameters and real-time water quality data are used to refine the spatiotemporal resolution of pollutant migration paths, while also considering the interaction between pollutants and the geological medium. Next, correction coefficients, such as biodegradation rate constants and degradation process correction coefficients, are fused in the particle trajectory simulation. These correction coefficients are used as weighting factors to adjust the velocity and diffusion term calculations during particle diffusion. Specifically, by simulating particle movement in the groundwater flow field, particle positions are iteratively calculated and their diffusion variance is adjusted to accurately reflect the non-uniform migration of pollutants in soluble rocks, especially considering the influence of features such as fissures and caverns. Here, the particles are not actual physical particles, but rather mathematical models of pollutant concentrations set up through numerical simulation to simulate pollutant migration paths, representing the trajectory of pollutants during groundwater flow. By simulating the movement of these virtual particles, combined with correction coefficients and real-time multi-source data, the diffusion paths and concentration changes of pollutants can be accurately predicted.

[0047] Furthermore, based on the simulation results, intermediate trajectory data is generated, and the velocity vector of each particle is calculated and scaled using Darcy's law and correction coefficients to reflect the effect of biodegradation on the migration path. The accuracy of the simulation results is verified by comparing them with similar paths in a historical database, ensuring that the corrected path predictions are consistent with actual pollutant migration trends. In this process, the model is optimized to adapt to the complexity of the soluble rock fracture network by adjusting the flow field and diffusion characteristics.

[0048] Scaling using Darcy's Law and correction factors refers to calculating the velocity of each particle in particle trajectory simulations using Darcy's Law and adjusting the simulation results using correction factors to reflect the impact of biodegradation and other environmental factors on pollutant migration paths. The following details how Darcy's Law and correction factors are used together to solve technical problems in this process: Darcy's Law describes the fundamental laws of groundwater flow. In pollutant migration path simulations, flow velocity directly affects the migration speed of pollutants. Darcy's Law derives the displacement of each particle by calculating the velocity of groundwater flow. Specifically, Darcy's Law yields the velocity vector of each particle, i.e., the direction and magnitude of the water flow velocity, which determines the migration direction and speed of the particle in the water flow. Correction factors quantify the impact of biodegradation on pollutant concentration decay and adjust the particle migration paths during the simulation. First, Darcy's Law is used to calculate the velocity vector of groundwater flow (the velocity of each particle). The flow velocity is determined by the permeability coefficient and hydraulic gradient of the geological medium, directly affecting the migration speed of pollutants in groundwater. Then, a correction coefficient is used to adjust the particle migration velocity. This correction coefficient reflects the influence of factors such as biodegradation and adsorption on the pollutant migration path. For example, in areas with high pollutant degradation rates, the correction coefficient may decrease, leading to a slower particle diffusion rate; in areas with low permeability, the correction coefficient may increase, indicating limited pollutant diffusion and a more concentrated path. Based on the flow velocity calculated using Darcy's law and the adjustment of the correction coefficient, particles are iteratively updated in the spatiotemporal grid according to the speed and direction of the water flow. Each time a particle is updated, its displacement in the next time period needs to be calculated based on the new flow velocity and correction coefficient. In this way, the pollutant migration path not only considers the influence of groundwater flow but also accurately reflects the impact of environmental changes such as biodegradation and temperature changes on pollutant diffusion, thus providing more precise technical support for pollution control in water sources.

[0049] Subsequently, based on the density distribution of particles in the spatiotemporal grid, the concentration distribution prediction results of pollutants at different time periods and spatial locations are generated. At this time, the simulated concentration distribution not only considers the migration of pollutants in groundwater, but also incorporates geological medium characteristic data and dynamically adjusts the grid resolution to ensure that the concentration prediction reflects the changes in the fast flow path in soluble rocks. For example, through short-term simulations such as 24 hours and long-term simulations such as one week, the concentration field in different time periods is generated, revealing the spatial evolution trend of pollutant concentration and helping to identify high-risk areas.

[0050] Finally, considering the spatial heterogeneity in soluble rocks, the Monte Carlo method is used to quantify uncertainty. By randomly perturbing geological parameters such as permeability, the diffusion of pollutants under different geological conditions is simulated, and the statistical variance of the simulation results is calculated. The uncertainty of the prediction is represented by the confidence interval. This process can not only provide accurate predictions of pollutant concentrations, but also provide decision-makers with risk boundaries and response strategies.

[0051] By combining the above steps, the resulting pollutant distribution map clearly shows the spatial distribution and temporal evolution characteristics of pollutant concentrations, thus providing precise decision support and intervention basis for pollution prevention and control in water sources.

[0052] Step S6: Compare the concentration distribution prediction results with the preset risk assessment standards to determine the distribution of priority intervention areas; Step S6 further includes: comparing the concentration distribution prediction results with the preset risk assessment standards, marking grid cells with concentrations higher than the risk standards, calculating the risk index of each cell; prioritizing based on the risk index, and generating a priority intervention area distribution map by combining spatial location information.

[0053] The distribution map of priority intervention areas is fed back to the threshold monitoring mechanism, and subsequent identification of sudden variables is processed in a loop to obtain an updated distribution map of intervention areas.

[0054] Specifically, existing technologies fail to effectively integrate real-time water quality data and environmental change factors into pollutant migration path prediction, thus failing to accurately identify high-risk areas. This application can update pollutant concentration distribution in real time and dynamically generate priority intervention area distribution maps based on risk assessment standards, further optimizing pollution prevention and control measures for water sources.

[0055] In the specific implementation process, such as Figure 4As shown, the predicted concentration distribution results at different times and spatial locations in the water source area are compared with preset risk assessment standards, such as a pollutant concentration upper limit of 0.5 mg / L, to identify areas where the concentration exceeds the preset threshold. Grid cells with concentrations higher than the risk standard are marked, and a risk index for each cell is calculated. A priority sequence is generated by sorting the indices, with the priority ranking based on the concentration gradient trend and geological permeability. The risk index is calculated using a weighted method of multiplying the concentration value by the permeability coefficient; for example, when the concentration is 1.2 mg / L and the permeability is 0.8, the index is 0.96. When sorting, high-index areas are prioritized; combined with spatial location information such as latitude and longitude coordinates, distribution maps are drawn to highlight the boundaries of high-priority areas. This method of generating distribution maps is beneficial for quickly locating key areas for prevention and control and improving intervention efficiency. For example, in the scenario of soluble rocks, assuming that the prediction results show that the concentration distribution in a certain area is 0.3-1.5 mg / L, after comparing it with the standard of 0.5 mg / L, the generated distribution map marks the densely populated areas of karst caves with concentrations exceeding 1.0 mg / L as the highest priority. Spatial location information includes grid coordinates such as 105 degrees east longitude and 25 degrees north latitude, which is beneficial for the on-site team to accurately deploy barrier measures.

[0056] In some cases, this technical solution can also be extended to scenarios with multiple types of pollutants. For example, for heavy metals and organic pollutants, their concentration distribution prediction results are calculated separately and jointly evaluated to generate a comprehensive risk distribution map, thereby taking into account the interaction energy between pollutants and further refining the distribution information of spatial location and groundwater flow layer depth. This optimization makes the pollution prevention and control of water sources more adaptable and able to cope with more complex pollution situations.

[0057] Through the above steps, this application can accurately predict the diffusion path of pollutants, identify high-risk areas in real time, and generate a distribution map of priority intervention areas, thereby effectively improving the accuracy and efficiency of pollution prevention and control in water sources.

[0058] This application also introduces a dynamic feedback mechanism, which can monitor the spread of pollutants in real time and adjust the migration path according to emergencies, thereby improving the accuracy and response capability of pollution prevention and control in water sources.

[0059] In this embodiment, the above steps use the generated priority intervention area distribution map to show the concentration distribution and spatial distribution of pollutants in the water source area. This distribution map is then fed back to the threshold monitoring mechanism for further processing. If the distribution map shows that the pollutant concentration or diffusion trend exceeds a preset threshold, such as a concentration exceeding 0.5 mg / L, the threshold monitoring mechanism will trigger a sudden variable identification mechanism to analyze in real time the factors that may accelerate the diffusion of pollutants, such as sudden rainfall or changes in geological fissures.

[0060] After identifying the sudden variable, the correction coefficients of the migration path are updated based on degradation parameters such as biodegradation rate and half-life under similar soluble rock geological conditions in historical databases. For example, if a sudden rainfall causes a sharp increase in the concentration gradient, relevant degradation parameters are extracted from the database and the biodegradation coefficient is adjusted, such as changing the original coefficient from 0.7 to 0.6, thereby optimizing the simulation results. These adjustments can quantify the impact of the sudden event on pollutant diffusion. For example, the corrected migration path shows that the risk area has shrunk by 10%.

[0061] The updated correction coefficients and path adjustment results will be re-input into the particle trajectory simulation. By adjusting the diffusion rate and concentration changes in the simulation, the prediction of pollutant migration will be further refined. Through this cyclical feedback mechanism, the prediction of pollutant concentration distribution can be continuously optimized in real time in response to environmental changes, thereby improving the accuracy of pollutant migration paths and the timeliness of prevention and control strategies.

[0062] Ultimately, the updated intervention area distribution map will include new priority rankings and precise spatial location information. For example, priority intervention areas such as areas with dense karst caves and fissures will be prioritized based on factors such as concentration gradients and permeability, and their specific location coordinates, such as 105 degrees east longitude and 25 degrees north latitude, will be marked to support the precise deployment of on-site prevention and control measures. Through this dynamic feedback loop, pollution prevention and control strategies for water sources can be adjusted in a timely manner after a sudden environmental incident, ensuring the efficiency and accuracy of prevention and control work.

[0063] Through the coordination of the above steps, this application achieves high-precision prediction of the migration path of pollutants in water sources, significantly improving the accuracy and efficiency of water source safety management and control.

[0064] Example 2: The above describes the water source safety assessment method integrating multi-source data in the embodiments of this application. The following describes the water source safety assessment system integrating multi-source data in the embodiments of this application. Please refer to... Figure 5 One embodiment of the water source safety assessment system integrating multi-source data in this application includes: Data acquisition unit 01 is used to acquire multi-source data from the water source, including real-time water quality parameters and environmental variable datasets. The path simulation unit 02 is used to simulate the migration process of pollutants using numerical simulation methods, and to generate the preliminary migration path of pollutants in the water source area by analyzing multi-source data. Concentration distribution unit 03 is used to integrate the geological medium characteristics data of the water source with the preliminary migration path, calculate the interaction between pollutants and the medium through the adsorption isotherm method, and obtain the concentration gradient calculation results; Risk assessment unit 04 is used to analyze the concentration gradient calculation results, determine the risk of pollutant diffusion, quantify the interaction energy between pollutants and environmental variables, and generate risk assessment results. The path adjustment unit 05 is used to obtain the degradation parameters of the water source based on the risk assessment results, adjust the migration path of pollutants, combine the correction coefficient and multi-source data to perform particle trajectory simulation, optimize the migration trajectory of pollutants, and obtain the predicted concentration distribution of pollutants at different time periods and spatial locations. The distribution prediction unit 06 is used to compare the concentration distribution prediction results with the preset risk assessment standards to determine the distribution of priority intervention areas.

[0065] Through the synergistic cooperation of the above-mentioned components, this application further achieves high-precision prediction of the migration path of pollutants in water sources, significantly improving the accuracy and efficiency of water source safety management and prevention and control.

[0066] Those skilled in the art will understand that, for the sake of convenience and brevity, the specific working process of the described system and unit can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.

[0067] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0068] The above-described embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this application.

Claims

1. A method for assessing the safety of water sources by integrating multi-source data, characterized in that, The method includes: Step S1: Obtain multi-source data from the water source, including real-time water quality parameters and environmental variable datasets; Step S2: Simulate the migration process of pollutants using numerical simulation methods, and generate preliminary migration paths of pollutants within the water source area by analyzing the multi-source data; Step S3: Integrate the geological medium characteristic data of the water source with the preliminary migration path, and calculate the interaction between pollutants and the medium using the adsorption isotherm method to obtain the concentration gradient calculation results; Step S4: Analyze the concentration gradient calculation results, determine the pollutant diffusion risk, quantify the interaction energy between pollutants and environmental variables, and generate risk assessment results; Step S5: Based on the risk assessment results, obtain the degradation parameters of the water source, adjust the migration path of pollutants, combine the correction coefficient and the multi-source data to perform particle trajectory simulation, optimize the migration trajectory of pollutants, and obtain the predicted concentration distribution of pollutants at different time periods and spatial locations. Step S6: Compare the concentration distribution prediction results with the preset risk assessment criteria to determine the distribution of priority intervention areas.

2. The method according to claim 1, characterized in that, The step S1 of obtaining multi-source data on the water source includes: Real-time water quality parameters and rainfall data are obtained from monitoring stations and meteorological sensors through a multi-source data acquisition system to obtain the initial dataset of pollutant concentration distribution and environmental variables. The initial dataset is divided into spatiotemporal grids to generate a gridded data structure that supports particle trajectory simulation. The gridded data structure includes discretized representations of time and space dimensions for subsequent numerical simulation of pollutant migration paths.

3. The method according to claim 1, characterized in that, Step S2 further includes: Based on the real-time water quality parameters and environmental variable dataset, the finite difference method is used to simulate convection and dispersion processes, generating preliminary migration paths of pollutants in the groundwater of the water source. By integrating real-time data to update the spatiotemporal resolution of the initial migration path, a high-resolution pollutant migration trajectory is obtained. The high-resolution pollutant migration trajectory includes information on the spatial location and temporal evolution of pollutants in groundwater.

4. The method according to claim 1, characterized in that, Step S3 further includes: Geological media characteristic data of the water source are obtained, the preliminary migration path is integrated with the geological media characteristic data, and the surface affinity between pollutants and media is calculated using the adsorption isotherm fitting method to obtain the coupled concentration gradient calculation results. The concentration gradient calculation results reflect the spatial distribution trend of pollutants in groundwater.

5. The method according to claim 4, characterized in that, Step S4 further includes: The calculated concentration gradient after coupling is compared with a preset threshold. If the concentration gradient calculation result exceeds the preset threshold, the threshold monitoring mechanism is triggered to identify sudden variables, including sudden rainfall or changes in geological fissures. Based on the aforementioned sudden variables, the interaction energy between pollutants and environmental variables is quantified to generate risk assessment results, which are used to update the adjustment parameters of pollutant migration pathways.

6. The method according to claim 1, characterized in that, Step S5 further includes: Based on the risk assessment results, biodegradation parameters under the geological conditions of the water source are obtained from the historical database, the correction coefficient of the biodegradation process on the migration path is determined, and the risk acceleration judgment results are integrated to optimize the correction coefficient and generate the adjusted migration path parameters. The correction coefficient reflects the degree of influence of biodegradation on pollutant concentration.

7. The method according to claim 6, characterized in that, In step S5, particle trajectory simulation is performed by combining the correction coefficient and the multi-source data, including: The adjusted migration path parameters and the multi-source data are obtained, and the correction coefficients are fused using the particle trajectory simulation method to generate the concentration distribution prediction results of pollutants in different time periods and spatial locations of the water source landform. The uncertainty of spatial heterogeneity is quantified to obtain a high-precision pollutant distribution map, wherein the pollutant distribution map includes the spatial distribution and temporal evolution characteristics of pollutant concentration.

8. The method according to claim 1, characterized in that, Step S6 further includes: The concentration distribution prediction results are compared with the preset risk assessment standards, and grid cells with concentrations higher than the risk standards are marked. The risk index of each cell is then calculated. Prioritize the areas based on the risk index and generate a distribution map of priority intervention areas by combining spatial location information.

9. The method according to claim 8, characterized in that, After generating the priority intervention area distribution map, the following steps are also included: The priority intervention area distribution map is fed back to the threshold monitoring mechanism, and subsequent sudden variable identification is processed in a loop to obtain an updated intervention area distribution map.

10. A water source safety assessment system integrating multi-source data, used to implement the water source safety assessment method integrating multi-source data as described in any one of claims 1-9, characterized in that, The system includes: The data acquisition unit is used to acquire multi-source data from the water source, including real-time water quality parameters and environmental variable datasets. The path simulation unit is used to simulate the migration process of pollutants using numerical simulation methods, and to generate preliminary migration paths of pollutants within the water source area by analyzing the multi-source data. The concentration distribution unit is used to integrate the geological medium characteristic data of the water source with the preliminary migration path, and calculate the interaction between pollutants and the medium through the adsorption isotherm method to obtain the concentration gradient calculation results. The risk assessment unit is used to analyze the concentration gradient calculation results, determine the risk of pollutant diffusion, quantify the interaction energy between pollutants and environmental variables, and generate risk assessment results. The path adjustment unit is used to obtain the degradation parameters of the water source based on the risk assessment results, adjust the migration path of pollutants, combine the correction coefficient and the multi-source data to perform particle trajectory simulation, optimize the migration trajectory of pollutants, and obtain the predicted concentration distribution of pollutants at different time periods and spatial locations. The distribution prediction unit is used to compare the concentration distribution prediction results with preset risk assessment standards to determine the distribution of priority intervention areas.

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