Underground water and seawater intrusion early warning method and system based on hydrology and water quality on-line monitoring data

Abstract

The invention provides an underground water and seawater intrusion early warning method and system based on hydrology and water quality on-line monitoring data, and the method comprises the steps: firstly obtaining a hydrology and water quality on-line monitoring data set containing underground water dynamic, seawater dynamic and environment related monitoring information; identifying and verifying a trigger signal of underground water and seawater invasion from the hydrology and water quality on-line monitoring data set to form an invasion trigger signal set, then constructing a diffusion blocking deduction model based on the invasion trigger signal set in combination with geological blocking and hydrology driving information, simulating invasion diffusion path changes under different blocking strategies, and finally obtaining a diffusion blocking result. And rehearsing various prevention and control technology application effects by using a diffusion blocking deduction model to generate a prevention and control rehearsing result, and finally generating an early warning report containing a diffusion trend, a key blocking node and an adaptive prevention and control technology based on the prevention and control rehearsing result, and sending the early warning report to a target monitoring management terminal. Therefore, the accuracy and timeliness of underground water and seawater intrusion early warning are improved.

Description

Technical Field

[0001] This invention relates to the field of computer technology, and more specifically, to a method and system for early warning of groundwater and seawater intrusion based on online hydrological and water quality monitoring data. Background Technology

[0002] In the field of groundwater management and protection, seawater intrusion is a serious threat to the water resource security and ecological environment of coastal areas. With the rapid economic development and continuous population growth in coastal areas, the extraction of groundwater is increasing, and changes in the marine environment are further exacerbating the risk of seawater intrusion.

[0003] Currently, the monitoring and early warning of seawater intrusion into groundwater mainly rely on traditional monitoring methods, such as periodic manual sampling and analysis of groundwater quality parameters, and obtaining dynamic information of groundwater through a limited number of monitoring points. These methods have obvious limitations. On the one hand, the frequency of manual sampling and analysis is low, making it impossible to obtain real-time information on changes in groundwater quality and to detect early signs of seawater intrusion in a timely manner. On the other hand, the limited number of monitoring points cannot fully reflect the complex dynamics of the groundwater system, and the judgment of the diffusion path and trend of seawater intrusion is not accurate enough.

[0004] Furthermore, most existing early warning methods are based on analysis of single monitoring data, lacking comprehensive consideration of groundwater dynamics, seawater dynamics, and related environmental factors. They cannot accurately identify triggering signals for groundwater and seawater intrusion, nor can they effectively evaluate the application effects of different prevention and control technologies at different stages of seawater intrusion. As a result, the accuracy and timeliness of early warnings are insufficient, making it difficult to meet the actual needs of groundwater protection and management. Summary of the Invention

[0005] In view of the aforementioned problems, and in conjunction with the first aspect of the present invention, embodiments of the present invention provide a groundwater-seawater intrusion early warning method based on online hydrological and water quality monitoring data, the method comprising: A set of online hydrological and water quality monitoring data is obtained, which includes groundwater dynamic monitoring data blocks, seawater dynamic monitoring data blocks, and environmental correlation monitoring data blocks. The groundwater dynamic monitoring data blocks record groundwater flow disturbance information and water quality variation information. The seawater dynamic monitoring data blocks record seawater infiltration front information and water quality diffusion information. The environmental correlation monitoring data blocks record geological barrier information and hydrological driving information of the monitoring area. The trigger signals for groundwater and seawater intrusion are identified from the set of online hydrological and water quality monitoring data. The correlation of the trigger signals is verified by associating the monitoring information recorded in the groundwater dynamic monitoring data block, the monitoring information recorded in the seawater dynamic monitoring data block, and the monitoring information recorded in the environmental correlation monitoring data block, thus forming a set of intrusion trigger signals. Based on the intrusion trigger signal set, and combined with the interaction between the geological barrier information and hydrological driving information recorded in the environmental associated monitoring data block, a diffusion blocking simulation model is constructed. The diffusion blocking simulation model is used to simulate the path changes of groundwater and seawater intrusion diffusion under different blocking strategies. The diffusion blocking simulation model is used to simulate the application effects of various prevention and control technologies at different diffusion stages of groundwater and seawater intrusion, and to generate prevention and control simulation results that include the adaptability of prevention and control technologies and the performance of groundwater and seawater intrusion diffusion inhibition. Based on the results of the prevention and control simulation, a groundwater and seawater intrusion early warning report is generated, which includes the groundwater and seawater intrusion diffusion trend, key blocking nodes of groundwater and seawater intrusion, and appropriate prevention and control technologies. The groundwater and seawater intrusion early warning report is then sent to the target monitoring and management terminal.

[0006] Furthermore, embodiments of the present invention also provide a groundwater and seawater intrusion early warning system based on online hydrological and water quality monitoring data, comprising: A processor; a machine-readable storage medium for storing machine-executable instructions of the processor; wherein the processor is configured to execute the aforementioned groundwater and seawater intrusion early warning method based on online hydrological and water quality monitoring data by executing the machine-executable instructions.

[0007] In another aspect, embodiments of the present invention also provide a computer program product, the computer program product including machine-executable instructions, the machine-executable instructions being stored in a computer-readable storage medium, the processor of a computer device reading the machine-executable instructions from the computer-readable storage medium, the processor executing the machine-executable instructions, causing the computer device to execute the above-mentioned groundwater and seawater intrusion early warning method based on online hydrological and water quality monitoring data.

[0008] Based on the above, by acquiring an online hydrological and water quality monitoring data set containing groundwater dynamic monitoring data blocks, seawater dynamic monitoring data blocks, and environmental correlation monitoring data blocks, it is possible to comprehensively and in real-time grasp the dynamic information of groundwater, seawater, and environmental correlation factors. Trigger signals can be identified and their correlations verified from the online hydrological and water quality monitoring data set, forming an intrusion trigger signal set. This effectively improves the accuracy and reliability of trigger signal identification, avoiding misjudgments and omissions. Based on the intrusion trigger signal set, a diffusion blocking simulation model is constructed. Combining the interaction between geological barrier information and hydrological driving information, it can simulate the path changes of groundwater and seawater intrusion diffusion under different blocking strategies. Using this diffusion blocking simulation model, the application effects of various prevention and control technologies can be simulated, generating prevention and control simulation results. This can intuitively demonstrate the adaptability and inhibition performance of different prevention and control technologies at different stages of seawater intrusion. The final generated groundwater and seawater intrusion early warning report includes information such as diffusion trends, key blocking nodes, and adapted prevention and control technologies, improving the accuracy and timeliness of groundwater and seawater intrusion early warning and effectively protecting groundwater resources and the ecological environment of coastal areas. Attached Figure Description

[0009] Figure 1 This is a schematic diagram of the execution flow of the groundwater and seawater intrusion early warning method based on online hydrological and water quality monitoring data provided in this embodiment of the invention.

[0010] Figure 2 This is a schematic diagram of exemplary hardware and software components of a groundwater and seawater intrusion early warning system based on online hydrological and water quality monitoring data provided in an embodiment of the present invention. Detailed Implementation

[0011] The present invention will now be described in detail with reference to the accompanying drawings. Figure 1 This is a flowchart illustrating a groundwater and seawater intrusion early warning method based on online hydrological and water quality monitoring data, according to an embodiment of the present invention. The following is a detailed description of this groundwater and seawater intrusion early warning method based on online hydrological and water quality monitoring data.

[0012] Step S110: Obtain a set of online hydrological and water quality monitoring data, which includes groundwater dynamic monitoring data blocks, seawater dynamic monitoring data blocks, and environmental correlation monitoring data blocks. The groundwater dynamic monitoring data blocks record groundwater flow disturbance information and water quality variation information. The seawater dynamic monitoring data blocks record seawater infiltration front information and water quality diffusion information. The environmental correlation monitoring data blocks record geological barrier information and hydrological driving information of the monitoring area.

[0013] This step first requires constructing a three-dimensional monitoring network covering the target monitoring area. The deployment of groundwater dynamic monitoring data blocks must consider the geological structure and groundwater flow characteristics of the monitoring area, selecting representative monitoring points. Each monitoring point is equipped with a multi-parameter sensor to collect information on groundwater flow disturbance and water quality variation. Flow disturbance information includes dynamic parameters such as changes in the flow direction and flow rate fluctuations at different depths. Water quality variation information covers changes in the concentration of various ionic components in the groundwater, dissolved oxygen content, pH value, and other water quality indicators. These sensors employ a waterproof and corrosion-resistant housing design to adapt to the complex underground environment, and the collected data is transmitted to the data processing center in real time via wired or wireless communication.

[0014] The seawater dynamic monitoring data blocks are primarily deployed in transitional zones near the coastline, including beaches, intertidal zones, and nearshore areas. Seawater infiltration monitoring wells and water quality diffusion monitoring profiles are installed in these areas. Pressure sensors and water content sensors are installed in the infiltration wells to monitor changes in the position and pressure of the seawater infiltration front, i.e., infiltration front information. The water quality diffusion monitoring profile consists of a series of water quality sensors deployed perpendicular to the coastline. These sensors can detect the diffusion concentration and diffusion distance of characteristic ions (such as chloride and sodium ions) in seawater within the groundwater, forming water quality diffusion information.

[0015] The environmental monitoring data block covers the entire target area, including inland regions and coastal zones. Geological barrier information is obtained through geological exploration and geophysical methods, combined with the deployment of geological sensors to monitor parameters such as lithology, porosity, and permeability of different geological layers. These parameters collectively reflect the geological layers' ability to block seawater intrusion. The collection of hydrological driving information includes rainfall monitoring stations, surface runoff monitoring stations, and water level monitoring stations. Rainfall monitoring stations record rainfall intensity and distribution at different times, surface runoff monitoring stations monitor changes in the flow rate and velocity of surface water, and water level monitoring stations monitor real-time changes in the water levels of rivers, lakes, and other surface water bodies. These data collectively constitute hydrological driving information, reflecting the driving effects on groundwater and seawater movement.

[0016] During data acquisition, all monitoring data blocks must adhere to a unified time synchronization protocol to ensure the consistency of timestamps. Simultaneously, to protect data privacy and prevent the leakage of sensitive information, transmitted data is encrypted using encryption algorithms. Data storage employs a distributed storage architecture and an access control mechanism, ensuring that only authorized personnel can access and process the monitoring data. Furthermore, a data backup and recovery mechanism is established, with regular backups of the monitoring data to prevent data loss.

[0017] Step S120: Identify the trigger signals for groundwater and seawater intrusion from the set of online hydrological and water quality monitoring data, verify the correlation of the trigger signals by associating the monitoring information recorded in the groundwater dynamic monitoring data block, the monitoring information recorded in the seawater dynamic monitoring data block, and the monitoring information recorded in the environmental correlation monitoring data block, and form an intrusion trigger signal set.

[0018] This step involves in-depth analysis and feature extraction of various monitoring information based on the acquired online hydrological and water quality monitoring data set. This aims to identify trigger signals that may indicate groundwater / seawater intrusion, and to ensure the reliability of these trigger signals through multi-source information correlation verification, ultimately forming a set of intrusion trigger signals. This process requires the comprehensive application of signal processing, feature recognition, and data correlation analysis techniques to screen and analyze massive amounts of monitoring data.

[0019] Step S121: Extract abnormal flow signal features from the flow disturbance information recorded in the groundwater dynamic monitoring data block. The abnormal flow signal features reflect sudden changes in the direction of groundwater flow and non-periodic fluctuations in the groundwater flow rate. Record the monitoring time and monitoring location corresponding to the abnormal flow signal features.

[0020] Step S1211: Extract flow direction data for all monitoring locations within a continuous monitoring period from the flow disturbance information recorded in the groundwater dynamic monitoring data block. The flow direction data records the flow direction of each monitoring location within a unit of time.

[0021] In this sub-step, the flow disturbance information collected from the groundwater dynamic monitoring data block is first preprocessed. Raw flow direction data for all monitoring locations over multiple consecutive monitoring periods are retrieved from the database. This data is stored in time series format, with each data point containing a monitoring location identifier, monitoring time, and corresponding flow direction value. Flow direction values ​​are typically expressed in angles, ranging from 0 to 360 degrees, each corresponding to a different water flow direction. Missing value imputation and noise filtering are performed on the raw data. Missing value imputation uses interpolation based on data from adjacent time points, while noise filtering uses a moving average filter to eliminate the influence of random interference. The processed data is then arranged according to monitoring location and monitoring time order, forming a structured flow direction dataset.

[0022] Step S1212: Observe the change angle of the flow direction in adjacent monitoring cycles at each monitoring location, and count the number of times the change angle of the flow direction at each monitoring location exceeds the range of natural fluctuations within a preset time period.

[0023] For the preprocessed flow direction data, the angle of change of flow direction at each monitoring location within adjacent monitoring periods is calculated. Specifically, for any two consecutive monitoring time points t and t+1, the flow direction angle θt at time t and the flow direction angle θt+1 at time t+1 are obtained, and the absolute difference between the two is calculated as Δθ = |θt+1 - θt|. If Δθ is greater than 180 degrees, the angle of change of flow direction is taken as 360 degrees minus Δθ to obtain the minimum angle of change. Then, based on historical long-term monitoring data, the natural fluctuation range of flow direction at each monitoring location is determined through statistical analysis. This natural fluctuation range reflects the normal variation range of groundwater flow direction under conditions without external interference (such as seawater intrusion). For each monitoring location, the number of times the angle of change of flow direction exceeds its natural fluctuation range within a preset time period (such as one month or one quarter) is counted and denoted as N1.

[0024] Step S1213: Extract flow rate data for all monitoring locations within a continuous monitoring period from the flow disturbance information recorded in the groundwater dynamic monitoring data block. The flow rate data records the flow speed at each monitoring location per unit time.

[0025] Similar to extracting flow direction data, flow velocity data for all monitoring locations within a continuous monitoring period is extracted from flow disturbance information. Flow velocity data represents the distance groundwater travels per unit time, typically measured in meters per day or centimeters per second. The raw flow velocity data undergoes preprocessing, including handling missing values ​​and removing noise, using the same methods as for flow direction data preprocessing. The preprocessed flow velocity data is then organized according to monitoring location and time sequence to form a flow velocity time series dataset.

[0026] Step S1214: Observe the change in flow rate at each monitoring location within adjacent monitoring periods, and count the number of times the change in flow rate at each monitoring location exceeds the natural fluctuation range within a preset time period.

[0027] Calculate the variation in flow rate within adjacent monitoring periods at each monitoring location. For two consecutive monitoring time points t and t+1, the flow rates are vt and vt+1, respectively, and the variation amplitude Δv = |vt+1 - vt| / vt (when vt is not zero). Similarly, determine the natural fluctuation range of flow rate at each monitoring location based on historical data. This natural fluctuation range reflects the fluctuation of flow rate under normal hydrological conditions. Count the number of times the flow rate variation amplitude exceeds its natural fluctuation range within a preset time period, denoted as N2.

[0028] Step S1215: For each monitoring location, determine whether the number of times the flow direction changes and the number of times the flow rate changes independently reach the preset frequency.

[0029] The preset frequency, denoted as N0, is an empirical value determined based on the flow disturbance characteristics during historical seawater intrusion events. For each monitoring location, it is determined whether N1 is greater than or equal to N0, and simultaneously whether N2 is greater than or equal to N0. When at least one of N1 and N2 reaches or exceeds N0, it is preliminarily considered that there may be abnormal flow at that monitoring location.

[0030] Step S1216: Mark the monitoring location where the number of flow direction changes reaches a preset frequency as the flow direction abnormal location, and extract the flow direction data of the flow direction abnormal location as the direction abnormal data segment.

[0031] Monitoring locations where the number of flow direction changes (N1) reaches or exceeds the preset frequency (N0) are marked as flow direction anomaly locations. All flow direction data for these anomaly locations within a preset time period are extracted from the flow direction dataset to form a flow direction anomaly data segment. This segment includes not only the flow direction angle value but also the corresponding monitoring time and location information for subsequent analysis.

[0032] Step S1217: Mark the monitoring location where the number of flow rate changes reaches a preset frequency as the flow rate abnormal location, and extract the flow rate data of the flow rate abnormal location as the flow rate abnormal data segment.

[0033] Similarly, monitoring locations where the number of flow rate changes N2 reaches or exceeds the preset frequency N0 are marked as flow rate anomaly locations. Flow rate data for these anomaly locations within a preset time period are extracted from the flow rate dataset to form a flow rate anomaly data segment, which also includes monitoring time and location information.

[0034] Step S1218: Record the directional anomaly data segment and the velocity anomaly data segment respectively, and mark the monitoring location and monitoring time corresponding to each of the directional anomaly data segment and the velocity anomaly data segment, which together constitute the abnormal flow signal characteristics.

[0035] The extracted directional and velocity anomaly data segments are aggregated, and each segment is marked with a clear monitoring location identifier and a detailed monitoring timestamp. These data segments collectively constitute the anomalous flow signal characteristics, which can reflect sudden changes in groundwater flow direction and non-periodic fluctuations in flow velocity. For example, if at a monitoring location the flow direction suddenly changes from west to east to east to west within a short period of time, and this change occurs at a preset frequency within a preset time period, while the flow velocity also exhibits several large fluctuations exceeding the natural range, these data will be integrated into the anomalous flow signal characteristics for that location.

[0036] Step S122: Extract abnormal water quality signal features from the water quality variation information recorded in the groundwater dynamic monitoring data block. The abnormal water quality signal features reflect the sudden increase of key groundwater components and the unnatural adjustment of groundwater component ratios. Record the monitoring time and monitoring location corresponding to the abnormal water quality signal features.

[0037] Step S1221: Preprocess the water quality index data in the water quality variation information, including data standardization and preliminary screening of outliers.

[0038] Water quality variability information includes various water quality indicators, such as chloride ion concentration, sodium ion concentration, calcium ion concentration, magnesium ion concentration, sulfate ion concentration, pH value, and dissolved oxygen. First, the raw data for each indicator are standardized, converting indicators with different dimensions into dimensionless relative values. The standardization method used is Z-score standardization, where for a measured value x of an indicator, the standardized value is (x-μ) / σ, where μ is the historical average of the indicator and σ is the historical standard deviation. This standardization process ensures the comparability of various water quality indicators.

[0039] Then, a box plot method is used for preliminary screening of outliers. For each standardized water quality indicator data, its quartiles Q1 (lower quartile) and Q3 (upper quartile) are calculated, and the boundaries of outliers are determined as Q1-k×(Q3-Q1) and Q3+k×(Q3-Q1), where k is a constant (usually 1.5 or 3). Data points exceeding these boundaries are considered preliminary outliers, and the monitoring time and location corresponding to these outliers are recorded.

[0040] Step S1222: Identify key components of groundwater and determine the normal concentration range and component ratio range of key components.

[0041] Key components of groundwater refer to characteristic components that are indicative of seawater intrusion, determined based on the geological background and seawater composition characteristics of the study area. Typically, seawater has high concentrations of chloride and sodium ions, thus these two ions are often considered key components. Other characteristic ions, such as sulfate and magnesium ions, may also be included. Based on long-term historical monitoring data, statistical methods are used to determine the normal concentration ranges of each key component under different seasons and hydrological conditions, as well as the normal ratio ranges between each key component (e.g., the chloride-to-sulfate ratio, the sodium-to-calcium ratio, etc.).

[0042] Step S1223: Monitor the concentration changes of each key component to determine whether there is a sudden increase.

[0043] For the concentration data of key components at each monitoring location, their time-series trends are analyzed. When the concentration of a key component suddenly increases from below the detection limit or far below the normal concentration range to near or above the upper limit of the normal concentration range within a short period of time (such as within several monitoring cycles), it is determined to be a sudden increase. For example, if the chloride ion concentration in the groundwater of an inland monitoring location has been at an extremely low level for a long time, and a significant increase in chloride ion concentration is suddenly detected in a monitoring session, exceeding the normal range, then a sudden increase in chloride ion concentration is considered to have occurred at that location.

[0044] Step S1224: Analyze the proportional relationship between key components to determine whether there are any unnatural adjustments.

[0045] The actual proportions of key components at each monitoring location are calculated and compared with the normal proportion range. When the actual proportions exceed the normal range, and this exceedance is not caused by normal hydrogeochemical processes (such as ion exchange, adsorption, and desorption), it is determined that the component proportions have undergone unnatural adjustments. For example, under normal circumstances, the ratio of sodium ions to chloride ions in groundwater is relatively stable. If this ratio suddenly changes significantly and cannot be explained by the local geochemical background, it is considered that an unnatural adjustment has occurred.

[0046] Step S1225: Record the monitoring time and location when a key component suddenly appears or the component ratio is not naturally adjusted, and extract the corresponding water quality data as abnormal water quality data segments.

[0047] For cases identified as sudden increases in key components or unnatural adjustments in component proportions, the monitoring time and location of the occurrence are recorded in detail. Water quality index data corresponding to these times and locations are extracted from the water quality variation information dataset to form abnormal water quality data segments. These abnormal water quality data segments include the concentration values, proportion values, and other relevant water quality index data of the key components.

[0048] Step S1226: Integrate all abnormal water quality data fragments to form abnormal water quality signal features.

[0049] Multiple extracted abnormal water quality data fragments were integrated and arranged according to monitoring location and time sequence to form abnormal water quality signal characteristics. This virtual blockage sign can clearly reflect the sudden increase of key groundwater components and the unnatural adjustment of component proportions.

[0050] Step S123: Extract abnormal permeation signal features from the permeation front information recorded in the seawater dynamic monitoring data block. The abnormal permeation signal features reflect the sudden forward movement of the seawater permeation location and the unexpected expansion of the seawater permeation range. Record the monitoring time and monitoring location corresponding to the abnormal permeation signal features.

[0051] Step S1231: Perform spatiotemporal analysis on the permeation front information to determine the benchmark location and benchmark range of the seawater permeation front.

[0052] The infiltration front information includes the coordinates of the infiltration front location and the boundary data of the infiltration range at different monitoring times. First, based on infiltration front information from historical monitoring data during periods without seawater intrusion, a baseline location for the seawater infiltration front is determined. This baseline location is typically a relatively stable boundary. Simultaneously, the baseline infiltration range is determined, which is the maximum range of seawater infiltration under normal tidal and wave action. The determination of the baseline location and baseline range needs to consider the influence of natural factors such as tidal cycles and seasonal climate changes, and is obtained through statistical analysis of multi-year historical data.

[0053] Step S1232: Monitor the position of the seawater infiltration front at different monitoring time points and calculate the offset from the reference position.

[0054] For each monitoring time point, the actual location coordinates of the seawater permeation front are extracted from the permeation front information, and the horizontal distance offset and vertical depth offset between the actual location and the reference location are calculated. The horizontal distance offset reflects the movement of the permeation front in the horizontal direction (usually inland), while the vertical depth offset reflects the change of the permeation front in the vertical direction.

[0055] Step S1233: Determine whether the position of the penetration front has suddenly shifted forward, i.e., whether the offset exceeds the preset forward shift threshold.

[0056] The preset forward displacement threshold is determined based on the characteristics of the forward displacement of the infiltration front in historical seawater intrusion events. This threshold takes into account the changes in the position of the infiltration front caused by normal tidal fluctuations. When the calculated horizontal distance offset exceeds the forward displacement threshold, it is determined that the seawater infiltration position has experienced a sudden forward displacement. For example, if during a high tide, the horizontal forward displacement of the infiltration front is much greater than the forward displacement during a normal high tide, and this displacement fails to return to the baseline position after the tide recedes, then a sudden forward displacement is considered to have occurred.

[0057] Step S1234: Analyze the changes in seawater infiltration range and calculate the difference between the actual infiltration range and the reference range.

[0058] The actual permeability range is obtained by analyzing the boundary data in the permeability front information, and is usually expressed as area or volume. The difference between the actual permeability range and the baseline range is calculated; when the actual range is greater than the baseline range, the difference is positive, and vice versa.

[0059] Step S1235: Determine whether the penetration range has expanded unexpectedly, i.e., whether the difference exceeds the preset expansion threshold.

[0060] The expansion threshold is determined based on the normal fluctuation range of the infiltration range. When the difference between the actual infiltration range and the baseline range exceeds the expansion threshold, the infiltration range is considered to have expanded unexpectedly. For example, if the seawater infiltration range continues to expand beyond the normal seasonal variation range under conditions of prolonged drought and low surface runoff, it is considered to have expanded unexpectedly.

[0061] Step S1236: Record the monitoring time and monitoring location corresponding to the sudden forward movement of the permeation front or the unexpected expansion of the permeation range, and extract the corresponding permeation data as abnormal permeation data segments.

[0062] In cases of sudden forward movement of the permeation front or unexpected expansion of the permeation range, the monitoring time and location (i.e., the location of the permeation monitoring well) are recorded. Data such as permeation pressure, water cut, and permeation location coordinates corresponding to these times and locations are extracted from the permeation front information dataset to form abnormal permeation data segments.

[0063] Step S1237: Integrate all anomalous penetration data fragments to form anomalous penetration signal characteristics.

[0064] By integrating the various abnormal seepage data segments in chronological order, an abnormal seepage signal feature is formed. This abnormal seepage signal feature can reflect the sudden forward movement of the seawater seepage location and the unexpected expansion of the seepage range, directly reflecting the abnormal dynamics of seawater seepage into the ground.

[0065] Step S124: Extract abnormal diffusion signal features from the water quality diffusion information recorded in the seawater dynamic monitoring data block. The abnormal diffusion signal features reflect the sudden increase in the diffusion concentration of seawater components and the unnatural increase in the diffusion distance of seawater components. Record the monitoring time and monitoring location corresponding to the abnormal diffusion signal features.

[0066] Step S1241: Determine the baseline concentration and baseline distance for the diffusion of seawater components.

[0067] The baseline concentration for seawater component diffusion refers to the background concentration of marine characteristic components (such as chloride ions) in groundwater under conditions unaffected by seawater intrusion. This background concentration is obtained through statistical analysis of groundwater monitoring data far from the coastline. The baseline distance refers to the maximum distance that marine characteristic components diffuse from the coastline inland under normal conditions, and is also determined based on historical data.

[0068] Step S1242: Extract concentration data and diffusion distance data of marine characteristic components at different locations along the water quality diffusion monitoring profile.

[0069] The water quality diffusion monitoring profile consists of multiple monitoring points extending inland from the coastline in a direction perpendicular to the coastline. For each monitoring time point, the concentration data of marine characteristic components at each monitoring point on the profile are extracted, and the actual distance of each monitoring point from the coastline, i.e., the diffusion distance data, is also recorded.

[0070] Step S1243: Determine whether there is a sudden increase in the diffusion concentration of marine characteristic components, that is, whether the concentration at a certain monitoring point suddenly exceeds the preset multiple of the baseline concentration.

[0071] The preset multiple is determined based on the severity of seawater intrusion and the sensitivity of the study area, for example, set at 1.5 or 2 times the baseline concentration. When the concentration of a marine characteristic component at a monitoring point suddenly increases within a short period and exceeds the preset multiple of the baseline concentration, it is considered a sudden increase. For example, if the chloride ion concentration at a monitoring point has been stable near the baseline concentration for a long time, and suddenly increases to more than twice the baseline concentration in a monitoring session, it is considered a sudden increase.

[0072] Step S1244: Determine whether the diffusion distance of marine characteristic components has increased unnaturally, that is, whether the location of the monitoring point that reaches the baseline concentration has moved inland beyond the preset distance.

[0073] The preset distance is determined based on the fluctuation range of diffusion distance under normal circumstances. When the location of a marine characteristic component reaching the baseline concentration moves inland a distance exceeding the preset distance compared to the same period in history, the diffusion distance is considered to have increased unnaturally. For example, in the absence of special hydrological events (such as heavy rain or floods), if the location of a monitoring point reaching the baseline chloride ion concentration moves significantly inland compared to the same period of the previous year, the diffusion distance is considered to have increased unnaturally.

[0074] Step S1245: Record the monitoring time and location corresponding to the sudden increase in the diffusion concentration of marine characteristic components or the unnatural increase in diffusion distance, and extract the corresponding diffusion data as abnormal diffusion data segments.

[0075] For monitoring points exhibiting sudden increases in diffusion concentration or unnatural increases in diffusion distance, their monitoring time and location (i.e., the coordinates of the monitoring point on the diffusion monitoring profile) are recorded. Data such as the concentration of marine characteristic components and diffusion distance corresponding to these times and locations are extracted from the water quality diffusion information dataset to form abnormal diffusion data segments.

[0076] Step S1246: Integrate all anomalous diffusion data fragments to form anomalous diffusion signal characteristics.

[0077] By integrating the various abnormal diffusion data segments according to the monitoring profile and time sequence, an abnormal diffusion signal feature is formed. This abnormal diffusion signal feature reflects the sudden increase in the diffusion concentration of seawater components and the unnatural increase in diffusion distance, which is a direct manifestation of seawater intrusion in terms of water quality.

[0078] Step S125: Extract abnormal barrier signal features from the geological barrier information recorded in the environmental correlation monitoring data block. The abnormal barrier signal features reflect the sudden decrease in the permeability resistance of the geological layer and the unexpected enhancement of the pore connectivity of the geological layer. Record the monitoring time and monitoring location corresponding to the abnormal barrier signal features.

[0079] Step S1251: Extract the permeability resistance data of different geological layers within a continuous monitoring period from the geological barrier information recorded in the environmental correlation monitoring data block. The permeability resistance data records the barrier strength of each geological layer per unit time.

[0080] Geological barrier information includes the physical and mechanical properties of different geological layers (such as clay, sand, and bedrock). Permeability resistance data is a crucial component, reflecting the ability of geological layers to impede the permeation of fluids (such as groundwater and seawater). Permeability resistance data can be obtained through laboratory tests and in-situ tests. Laboratory tests involve permeability experiments on collected rock core samples to obtain the permeability coefficient, which is then used to calculate the permeability resistance. In-situ tests directly determine the permeability resistance of geological layers through methods such as pumping and injection tests. Permeability resistance data for each geological layer within a continuous monitoring period (e.g., monthly or quarterly) is extracted from the geological barrier information dataset. This data is organized by geological layer number and monitoring time.

[0081] Step S1252: Observe the change in permeability resistance of each geological layer within adjacent monitoring periods, and count the number of times the change in permeability resistance of each geological layer is lower than the natural decrease range within a preset time period.

[0082] For each geological layer, the change in seepage resistance within adjacent monitoring periods is calculated, i.e., the seepage resistance value of the later period minus the seepage resistance value of the previous period, yielding the change in seepage resistance ΔR. The natural reduction range is the normal range of seepage resistance reduction determined based on the long-term stability of the geological layer and slow processes such as natural weathering and erosion. When ΔR is negative (i.e., seepage resistance decreases) and the absolute value of ΔR is greater than the upper limit of the natural reduction range, the change is considered to be below the natural reduction range. The number of times the seepage resistance change of each geological layer is below the natural reduction range within a preset time period (e.g., one year) is recorded as N3.

[0083] Step S1253: Extract pore connectivity data of different geological layers within a continuous monitoring period from the geological barrier information recorded in the environmental correlation monitoring data block. The pore connectivity data records the degree of pore connectivity of each geological layer within a unit time.

[0084] Pore ​​connectivity data reflects the connectivity of pores, fractures, and other spaces within geological strata, and is a crucial factor influencing fluid permeability. Pore connectivity data can be obtained through geophysical exploration methods such as ground-penetrating radar and acoustic wave testing, or indirectly by measuring parameters such as porosity and specific surface area of ​​geological strata. Pore connectivity data for each geological stratum within a continuous monitoring period was extracted from the geological barrier information dataset and organized according to geological stratum number and monitoring time.

[0085] Step S1254: Observe the change in pore connectivity of each geological layer within adjacent monitoring periods, and count the number of times the change in pore connectivity of each geological layer exceeds the natural enhancement range within a preset time period.

[0086] Calculate the change in pore connectivity ΔC (pore connectivity value of the later period minus the pore connectivity value of the previous period) within adjacent monitoring periods for each geological layer. The natural enhancement range is the normal enhancement range of pore connectivity determined based on the natural evolution process of the geological layer. When ΔC is positive (i.e., pore connectivity is enhanced) and ΔC is greater than the upper limit of the natural enhancement range, the change is considered to be higher than the natural enhancement range. Count the number of times the change in pore connectivity of each geological layer is higher than the natural enhancement range within a preset time period, and denote it as N4.

[0087] Step S1255: For each geological layer, determine whether the number of changes in permeability resistance and the number of changes in pore connectivity independently reach the preset frequency.

[0088] The preset frequency N0 also applies here. For each geological layer, it is determined whether N3 is greater than or equal to N0, and simultaneously whether N4 is greater than or equal to N0. When N3 or N4 reaches or exceeds N0, it is considered that the barrier performance of that geological layer may be abnormal.

[0089] Step S1256: Mark the geological layers whose permeability resistance changes at a preset frequency as geological layers with abnormal resistance, and extract the permeability resistance data of the geological layers with abnormal resistance as a data segment with abnormal resistance.

[0090] Geological layers where N3 reaches or exceeds N0 are marked as geological layers with abnormal resistance. All permeability resistance data for this geological layer within a preset time period are extracted from the permeability resistance data to form a resistance anomaly data segment, which includes the geological layer number, monitoring time, and permeability resistance value.

[0091] Step S1257: Geological layers whose pore connectivity changes reach a preset frequency are marked as connectivity anomalous geological layers, and the pore connectivity data of the connectivity anomalous geological layers are extracted as connectivity anomalous data segments.

[0092] Geological layers with N4 reaching or exceeding N0 are marked as connectivity anomaly layers. Pore connectivity data for these layers within a preset time period are extracted from the pore connectivity data to form connectivity anomaly data segments, including the geological layer number, monitoring time, and pore connectivity value.

[0093] Step S1258: Record the resistance anomaly data segment and the connectivity anomaly data segment respectively, and label the geological layer and monitoring time corresponding to each of the resistance anomaly data segment and the connectivity anomaly data segment, which together constitute the anomaly blocking signal characteristics.

[0094] By aggregating the resistance and connectivity anomaly data segments, and labeling their respective geological layers and monitoring times, these segments collectively constitute the characteristics of the anomalous blocking signal. This characteristic reflects a sudden decrease in the permeability resistance of the geological layer and an unexpected increase in pore connectivity, indicating a decline in the geological layer's ability to block seawater intrusion.

[0095] Step S126: Extract anomalous driving signal features from the hydrological driving information recorded in the environmental associated monitoring data block. The anomalous driving signal features reflect sudden surges in rainfall and non-periodic enhancement of surface runoff. Record the monitoring time and monitoring location corresponding to the anomalous driving signal features.

[0096] Step S1261: Extract rainfall data and surface runoff data from hydrological driving information to determine their normal variation range and periodic characteristics.

[0097] The rainfall data in the hydrological driving information is collected by multiple rainfall monitoring stations distributed within the monitoring area, including daily rainfall, monthly rainfall, and annual rainfall; the surface runoff data is collected by surface runoff monitoring stations, including parameters such as runoff volume and runoff depth. Based on historical rainfall and surface runoff data over many years, the seasonal variation patterns and interannual trends are analyzed to determine the normal rainfall range and normal surface runoff variation range for different periods (such as rainy season and dry season), as well as the periodic characteristics of rainfall and surface runoff (such as seasonal cycles).

[0098] Step S1262: Monitor real-time rainfall to determine if there is a sudden surge, i.e., the rainfall exceeds the upper limit of the normal range in a short period of time.

[0099] Real-time rainfall data is transmitted to the data processing center in real time through rainfall monitoring stations. For a single rainfall event, the rainfall amount per unit time (rainfall intensity) is calculated and compared with the upper limit of the normal rainfall range for that period. When the rainfall intensity exceeds the upper limit of the normal range and the duration reaches a certain threshold (e.g., 1 hour), it is determined to be a sudden surge in rainfall. For example, during the dry season, the normal range of daily rainfall in a certain area is low. If the rainfall on a certain day suddenly reaches or exceeds the maximum daily rainfall during the rainy season, it is considered a sudden surge.

[0100] Step S1263: Monitor real-time surface runoff and determine whether non-periodic enhancement occurs, i.e., surface runoff flow or velocity exceeds the range of normal periodic changes.

[0101] Normal variations in surface runoff typically exhibit seasonal cycles, with higher flow rates and velocities during the rainy season and vice versa during the dry season. Real-time monitoring of surface runoff flow and velocity is crucial, comparing these values ​​to historical ranges for the same period. When surface runoff flow or velocity exceeds this range, and this exceedance is not due to a normal seasonal cycle, it is considered an intensification of non-periodic patterns. For example, an abnormal increase in surface runoff flow due to sudden upstream reservoir discharge or other anthropogenic factors during the non-rainy season is considered an intensification of non-periodic patterns.

[0102] Step S1264: Record the monitoring time and location corresponding to sudden surges in rainfall or non-periodic enhancement of surface runoff, and extract the corresponding hydrological data as anomaly-driven data segments.

[0103] For instances of sudden surges in rainfall, the monitoring time and location of the rainfall monitoring station are recorded; for non-periodic intensifications of surface runoff, the monitoring time and location of the surface runoff monitoring station are recorded. Rainfall and surface runoff data corresponding to these times and locations are extracted from the hydrological driving information dataset to form anomaly driving data segments.

[0104] Step S1265: Integrate all abnormal driving data fragments to form abnormal driving signal characteristics.

[0105] By integrating the various anomaly-driven data segments in chronological order, an anomaly-driven signal feature is formed. This feature reflects the sudden surge in rainfall and the non-periodic enhancement of surface runoff, which may accelerate or alter the process of seawater intrusion.

[0106] Step S127: The extracted abnormal flow signal features, abnormal water quality signal features, abnormal infiltration signal features, abnormal diffusion signal features, abnormal blocking signal features, and abnormal driving signal features are correlated according to the monitoring time and monitoring location. The combination of at least three of the above abnormal signal features appearing at the same monitoring time and the same monitoring location is retained to form an intrusion trigger signal set.

[0107] First, a multi-dimensional index table is established, with index entries including monitoring time and monitoring location. The six anomalous signal characteristics (abnormal flow, abnormal water quality, abnormal infiltration, abnormal diffusion, abnormal blockage, and abnormal driving) are then matched with the monitoring time and location in the index table. For each monitoring time point and each monitoring location, the anomalous signal characteristics that appeared at that time and location are examined.

[0108] Then, the association condition is set as the simultaneous occurrence of at least three types of abnormal signal features at the same monitoring time and location. For example, if at a certain monitoring time T and at a certain monitoring location P, abnormal flow signal features, abnormal water quality signal features, and abnormal seepage signal features are simultaneously observed, then the association condition is met. For combinations of abnormal signal features that meet the association condition, they are extracted.

[0109] Finally, all the abnormal signal features that meet the criteria are combined and summarized to form an intrusion trigger signal set. Each element in the intrusion trigger signal set contains a set of associated abnormal signal features, as well as corresponding monitoring time and monitoring location information. Through the above multi-signal association verification, the false positives caused by a single signal anomaly can be effectively reduced, and the reliability of the intrusion trigger signal can be improved.

[0110] Step S130: Based on the intrusion trigger signal set, and combined with the interaction between the geological barrier information and hydrological driving information recorded in the environmental correlation monitoring data block, a diffusion blocking simulation model is constructed. The diffusion blocking simulation model is used to simulate the path changes of groundwater and seawater intrusion diffusion under different blocking strategies.

[0111] After obtaining the set of intrusion triggering signals, it is necessary to further analyze the diffusion patterns of seawater intrusion and construct a diffusion blocking simulation model capable of simulating the effects of different blocking strategies. This diffusion blocking simulation model is based on the set of intrusion triggering signals, fully considering the interaction between geological barrier information and hydrological driving information. Through mathematical modeling and computer simulation techniques, it achieves dynamic simulation of the seawater intrusion diffusion path.

[0112] Step S131: Extract the monitoring location and signal strength corresponding to each trigger signal from the intrusion trigger signal set to determine the initial starting point and initial diffusion intensity of groundwater and seawater intrusion.

[0113] Each trigger signal in the intrusion trigger signal set corresponds to a specific monitoring location and signal strength. Signal strength is determined by comprehensively considering the severity of various anomalous signal characteristics contained within the trigger signal. For example, the exceedance multiple of key component concentrations in anomalous water quality signals and the distance of the forward movement of the seepage front in anomalous seepage signals can both serve as indicators of signal strength. By quantitatively evaluating the signal strength of each trigger signal, the monitoring location corresponding to the trigger signal with the highest signal strength is selected as the initial starting point for groundwater-seawater intrusion and diffusion. The initial diffusion intensity is then determined based on the overall intensity of the trigger signals at this initial starting point; the greater the intensity, the stronger the initial diffusion capacity.

[0114] Step S132: Extract geological barrier intensity data for different regions from the geological barrier information recorded in the environmental correlation monitoring data block, and determine the natural barrier capacity of each region against groundwater and seawater intrusion and diffusion.

[0115] The target monitoring area is divided into multiple small grid regions, each serving as a basic calculation unit. Parameters such as lithology, thickness, porosity, permeability, and seepage resistance of the strata within each grid region are extracted from geological barrier information. These parameters collectively determine the geological barrier strength of the region. The analytic hierarchy process (AHP) or fuzzy comprehensive evaluation method is used to comprehensively weight these parameters, yielding the geological barrier strength data for each grid region. A higher geological barrier strength value indicates a stronger natural barrier against seawater intrusion and diffusion. For example, clay layer regions typically have higher geological barrier strength, while sandy soil layer regions have relatively lower geological barrier strength.

[0116] Step S133: Extract hydrological driving intensity data of different regions from the hydrological driving information recorded in the environmental correlation monitoring data block, and determine the natural driving capacity of each region for groundwater and seawater intrusion and diffusion.

[0117] Similarly, using grid areas as units, hydrological parameters such as rainfall, surface runoff depth, groundwater recharge, and water level difference are extracted from the hydrological driving information for each grid area. These parameters reflect the driving effect of the area on groundwater and seawater movement. Using a method similar to that used for geological barrier strength, these hydrological parameters are comprehensively weighted to obtain the hydrological driving intensity data for each grid area. A higher hydrological driving intensity value indicates a stronger natural driving force for seawater intrusion and diffusion in that area. For example, areas with abundant rainfall and large surface runoff typically have higher hydrological driving intensity.

[0118] Step S134: Analyze the interaction between the natural barrier capacity corresponding to the geological barrier strength data and the natural driving capacity corresponding to the hydrological driving strength data, and determine the combined effect of promoting the diffusion of groundwater and seawater intrusion and hindering the diffusion of groundwater and seawater intrusion in each region.

[0119] Step S1341: Determine the characteristic performance of geological barrier strength based on historical monitoring data. The characteristic performance is set based on the comprehensive performance of geological layer permeability resistance and geological layer pore connectivity. The more significant the characteristic performance, the stronger the natural barrier ability.

[0120] Historical monitoring data includes information on geological barriers and corresponding seawater intrusion situations in various regions during and before seawater intrusion events. The characteristics of geological barrier strength are comprehensively considered based on two key parameters: geological layer permeability resistance and pore connectivity. Higher permeability resistance and weaker pore connectivity result in more pronounced characteristics of geological barrier strength and stronger natural barrier capacity. Through correlation analysis of geological layer permeability resistance and pore connectivity with the degree of seawater intrusion in historical data, evaluation criteria for the characteristics of geological barrier strength are established, classifying geological barrier strength into different characteristic levels, such as strong, medium, and weak.

[0121] Step S1342: Determine the characteristic performance of hydrological driving intensity based on historical monitoring data. The characteristic performance is set based on the comprehensive performance of rainfall and surface runoff. The more significant the characteristic performance, the stronger the natural driving force.

[0122] Similarly, based on the correlation analysis of historical monitoring data on rainfall, surface runoff, and the velocity and extent of seawater intrusion, the characteristic manifestations of hydrological driving intensity were determined. The greater the rainfall and the stronger the surface runoff, the more significant the characteristic manifestations of hydrological driving intensity, and the stronger the natural driving force. Hydrological driving intensity was also classified into different characteristic manifestation levels.

[0123] Step S1343: Based on the degree of influence of geological barriers and hydrological driving on diffusion in historical groundwater seawater intrusion events, set the influence weight of geological barriers and the influence weight of hydrological driving.

[0124] This study collects data on multiple historical groundwater-seawater intrusion events and analyzes the contributions of geological barriers and hydrological driving factors to the diffusion of seawater intrusions. The degree of contribution can be measured by statistically analyzing indicators such as changes in the rate and extent of seawater intrusion diffusion under the influence of different factors. Based on the magnitude of the contribution, a weight ω1 is assigned to the influence of geological barriers, and a weight ω2 is assigned to the influence of hydrological driving factors, with ω1 + ω2 = 1. For example, in areas with complex geological structures and significant geological barriers, ω1 can be assigned a larger value; while in areas with drastic changes in hydrological conditions and significant hydrological driving factors, ω2 can be assigned a larger value.

[0125] Step S1344: For each region, normalize its geological barrier intensity characteristics and hydrological driving intensity characteristics to the same numerical range.

[0126] To ensure comparability between the characteristics of geological barrier intensity and hydrological driving intensity, normalization is necessary. The normalized values ​​are typically set to a range of [0,1]. For geological barrier intensity characteristics, different levels of characteristics are converted to corresponding normalized values, such as 1 for strong, 0.5 for medium, and 0 for weak. The specific conversion method is determined based on the distribution characteristics of historical data. The normalization method for hydrological driving intensity characteristics is similar to that for geological barrier intensity, converting its characteristic levels to normalized values ​​within the range of [0,1].

[0127] Step S1345: Combining the geological barrier influence weight and the hydrological driving influence weight, analyze the interaction between the normalized geological barrier intensity characteristics and the normalized hydrological driving intensity characteristics in each region, and determine the dominant relationship between the driving force and the hindering force of groundwater and seawater intrusion and diffusion in each region.

[0128] For each region, calculate the driving force index and the hindering force index. The hindering force index I_resistance = normalized geological barrier intensity characteristic × ω1, and the driving force index I_push = normalized hydrological driving intensity characteristic × ω2. Then compare the magnitudes of I_push and I_resistance.

[0129] Step S1346: If the result of the normalized hydrological driving intensity characteristics combined with the hydrological driving influence weight in any region is stronger than the result of the normalized geological barrier intensity characteristics combined with the geological barrier influence weight, then the driving effect of groundwater and seawater intrusion diffusion is dominant in that region.

[0130] That is, when I_push > I_resist, the effect of promoting the intrusion and diffusion of seawater in this area is greater than the effect of hindering it, and seawater is more likely to diffuse in this area.

[0131] Step S1347: If the result of the normalized geological barrier intensity characteristics combined with the geological barrier influence weight in any region is stronger than the result of the normalized hydrological driving intensity characteristics combined with the hydrological driving influence weight, then the effect of hindering the intrusion and diffusion of groundwater and seawater is dominant in that region.

[0132] That is, when I_resistance > I_push, the effect of hindering the diffusion of seawater intrusion in this area is greater than the effect of pushing, and the diffusion of seawater in this area is suppressed.

[0133] Step S1348: Based on the judgment results of the dominant relationship between the driving force and the hindering force of groundwater and seawater intrusion and diffusion in each region, determine the comprehensive effect of the driving force and the hindering force of groundwater and seawater intrusion and diffusion in each region.

[0134] The combined effect can be represented by a combined effect index. For example, when the driving force is dominant, the combined effect index is positive, and the larger the value, the stronger the driving force. When the hindering force is dominant, the combined effect index is negative, and the larger the absolute value, the stronger the hindering force. When I_push ≈ I_hindering, the combined effect index is zero, indicating that the driving and hindering forces are basically balanced. The combined effect index quantitatively reflects the combined impact of each region on the diffusion of seawater intrusion.

[0135] Step S135: Taking the initial starting point of groundwater and seawater intrusion and diffusion as the center, divide the diffusion priority areas according to the order of the strength of the driving force of groundwater and seawater intrusion and diffusion in each area, and each diffusion priority area corresponds to a diffusion priority.

[0136] Based on the comprehensive effect index of each region, a positive comprehensive effect index indicates the presence of a driving force in that region. Using the initial starting point as the center, the distance between each region and the initial starting point is calculated. Combining this distance with the strength of the driving force (i.e., the magnitude of the comprehensive effect index), regions exhibiting a driving force are ranked by diffusion priority. Regions closer to the initial starting point and with a stronger driving force have higher diffusion priority. Regions with the same or similar priorities are grouped into diffusion priority regions, such as high-priority regions, medium-priority regions, and low-priority regions, each corresponding to a specific diffusion priority.

[0137] Step S136: Set up virtual blocking nodes in each diffusion priority area, with each virtual blocking node corresponding to a potential blocking measure application location.

[0138] Within each diffusion priority zone, virtual blocking nodes are selected and set up at appropriate locations based on the area's geological conditions, hydrological characteristics, and human activities. The placement of virtual blocking nodes should consider their ability to effectively intercept or mitigate seawater intrusion diffusion paths, such as selecting key locations in narrow channels or geologically weak zones within the area. Each virtual blocking node is pre-configured with multiple possible blocking measures, such as physical interception (e.g., impermeable walls, cutoff walls), chemical treatment (e.g., injecting chemicals to alter soil and rock properties), and bioremediation, with each measure corresponding to different blocking effect parameters.

[0139] Step S137: Based on the initial diffusion intensity of groundwater and seawater intrusion, the combined effect of pushing and hindering in each region, and the location of virtual blocking nodes, construct a diffusion blocking simulation model to simulate the changes in the diffusion path of groundwater and seawater intrusion under different virtual blocking node activation states.

[0140] The diffusion blocking simulation model is constructed using numerical simulation methods such as the finite element method or the finite difference method. The target region is discretized into a computational grid, with each grid cell corresponding to a region. The model's input parameters include the location of the initial diffusion initiation point, the initial diffusion intensity, the combined effect of each region (combined effect index), the location of the virtual blocking nodes, and the parameters of each blocking measure.

[0141] The model's governing equations are based on groundwater dynamics and solute transport theories, considering physicochemical processes such as convection, dispersion, and adsorption during seawater intrusion. In the model, the initial diffusion intensity serves as the initial condition, and the combined effect index of each region influences the flow velocity and direction of groundwater, thus affecting the seawater diffusion path. The activation state of virtual blocking nodes is achieved by changing the hydraulic parameters (such as permeability coefficient and porosity) of the grid cells containing the node. When a virtual blocking node is activated (i.e., a blocking measure is applied), the hydraulic parameters of the region containing that virtual blocking node change accordingly, thereby altering the seawater diffusion path.

[0142] By numerically solving the governing equations, the model can simulate the spatiotemporal distribution characteristics of seawater intrusion diffusion under different virtual blocking node activation states (i.e., different combinations of blocking strategies), including diffusion range, diffusion rate, and concentration distribution, thereby obtaining the changes in the diffusion path. The model's output results are displayed in a visual manner, such as dynamic diffusion range plots and concentration contour maps, facilitating intuitive analysis of the effects of different blocking strategies.

[0143] Step S140: Use the diffusion blocking simulation model to simulate the application effects of various prevention and control technologies at different diffusion stages of groundwater and seawater intrusion, and generate prevention and control simulation results that include the adaptability of prevention and control technologies and the performance of groundwater and seawater intrusion diffusion inhibition.

[0144] After constructing the diffusion blocking simulation model, it is necessary to use this model to conduct a preliminary evaluation of the application effects of various prevention and control technologies at different diffusion stages of seawater intrusion. By setting different diffusion stages and corresponding combinations of prevention and control technologies, the inhibitory effect on the diffusion of seawater intrusion can be simulated.

[0145] Step S141: Review various prevention and control technologies applicable to groundwater and seawater intrusion, and determine the mechanism of action of each technology. Among them, physical interception technology blocks the spread of groundwater and seawater intrusion by constructing barriers, chemical adsorption technology inhibits the spread of groundwater and seawater intrusion by adsorbing seawater components, and biological purification technology reduces the activity of seawater components through biological action to inhibit the spread of groundwater and seawater intrusion.

[0146] Common groundwater seawater intrusion prevention and control technologies include physical interception technology, chemical adsorption technology, biological purification technology, hydrological regulation technology (such as artificial recharge), and engineering seepage prevention technology. This embodiment focuses on physical interception technology, chemical adsorption technology, and biological purification technology.

[0147] The mechanism of physical interception technology is to construct physical barriers along the path of seawater intrusion, such as impermeable walls, diaphragm walls, and cement-soil mixing pile curtains. These barriers have extremely low permeability and can directly block the continued spread of seawater, thus changing the path of seawater intrusion.

[0148] Chemisorption technology involves injecting or placing materials with adsorption properties (such as activated carbon, zeolite, ion exchange resins, etc.) into groundwater affected by seawater intrusion. These materials can selectively adsorb characteristic components in seawater (such as chloride ions, sodium ions, etc.), reducing the concentration of these components in the groundwater and thus inhibiting the diffusion of seawater components.

[0149] Biological purification technology utilizes specific microbial communities or plant roots to reduce the activity or concentration of characteristic components in seawater through biological metabolism, absorption, and transformation processes. For example, some microorganisms can convert nitrate ions in seawater into nitrogen gas, and some salt-tolerant plants can absorb sodium ions from groundwater, thereby purifying water quality and inhibiting the spread of seawater intrusion.

[0150] Step S142: Divide the groundwater-seawater intrusion and diffusion process into an initial diffusion stage, a rapid diffusion stage, and a stable diffusion stage. Each diffusion stage corresponds to different groundwater-seawater intrusion and diffusion intensity and groundwater-seawater intrusion and diffusion range characteristics.

[0151] Step S1421: Extract the initial diffusion intensity data and initial diffusion range data of groundwater and seawater intrusion diffusion from the diffusion blocking simulation model.

[0152] In diffusion blocking simulation models, after initializing the model parameters, the diffusion intensity at the start of the simulation is the initial diffusion intensity data, and the diffusion range at this moment is the initial diffusion range data. The initial diffusion intensity data can be represented by the seawater composition concentration gradient or diffusion flux at the initial starting point, while the initial diffusion range data is represented by the area or volume of the diffusion region including the initial starting point.

[0153] Step S1422: Set the criteria for determining the initial diffusion stage. When the groundwater seawater intrusion diffusion range does not exceed a preset multiple of the range corresponding to the initial diffusion range data and the groundwater seawater intrusion diffusion intensity does not exceed a preset multiple of the intensity corresponding to the initial diffusion intensity data, it is determined to be the initial diffusion stage of groundwater seawater intrusion.

[0154] The preset multiples are determined based on historical data of seawater intrusion and model simulation results. For example, the preset multiple for diffusion range is set to 2 times, and the preset multiple for diffusion intensity is set to 1.5 times. Once initial diffusion begins, the diffusion range and intensity are monitored in real time during the model simulation. If the diffusion range does not exceed twice the initial diffusion range, and the diffusion intensity does not exceed 1.5 times the initial diffusion intensity, it is determined that the current stage is the initial diffusion phase. The characteristics of the initial diffusion phase are a small diffusion range, a slow diffusion speed, and the impact of the intrusion limited to the area near the initial starting point.

[0155] Step S1423: Set the criteria for determining the rapid diffusion stage. When the groundwater seawater intrusion diffusion range exceeds a preset multiple of the range corresponding to the initial diffusion range data and the groundwater seawater intrusion diffusion intensity exceeds a preset multiple of the intensity corresponding to the initial diffusion intensity data, it is determined to be the rapid diffusion stage of groundwater seawater intrusion.

[0156] Similarly, based on historical data and model results, criteria for determining the rapid diffusion stage are established. For example, when the diffusion range exceeds twice the initial diffusion range and the diffusion intensity exceeds 1.5 times the initial diffusion intensity, it is determined to have entered the rapid diffusion stage. During the rapid diffusion stage, due to strong hydrological driving forces and weak geological barriers, the speed of seawater intrusion and diffusion accelerates significantly, and the diffusion range expands rapidly, making it a critical stage for prevention and control.

[0157] Step S1424: Set the criteria for determining the stable diffusion stage. When the variation range of groundwater seawater intrusion diffusion is lower than the preset range within a continuous preset time period and the variation range of groundwater seawater intrusion diffusion intensity is lower than the preset range within a continuous preset time period, it is determined to be the stable diffusion stage of groundwater seawater intrusion.

[0158] The preset time period can be set to one month or one quarter, and the preset range is a small percentage (e.g., 5%). When the change in the diffusion range within a consecutive preset time period (e.g., the ratio of the difference in diffusion range between two adjacent time periods to the diffusion range of the previous time period) is less than 5%, and the change in diffusion intensity is also less than 5%, it is determined to have entered the stable diffusion stage. The characteristics of the stable diffusion stage are that the rate of seawater intrusion and diffusion slows down, and the diffusion range and diffusion intensity tend to stabilize. At this time, the intrusion situation is relatively stable, but continuous monitoring and control are still required.

[0159] Step S1425: Extract real-time groundwater-seawater intrusion diffusion range data and real-time groundwater-seawater intrusion diffusion intensity data from the simulation process of the diffusion blocking model, and compare the real-time groundwater-seawater intrusion diffusion range data and the real-time groundwater-seawater intrusion diffusion intensity data with the initial diffusion stage judgment criteria, the rapid diffusion stage judgment criteria, and the stable diffusion stage judgment criteria, respectively.

[0160] During the operation of the diffusion blocking simulation model, the model outputs real-time data on the diffusion range and diffusion intensity according to the set time step. This real-time data is then compared with the judgment criteria for each of the three stages to determine the current diffusion stage of the seawater intrusion in the simulation.

[0161] Step S1426: When the real-time groundwater seawater intrusion diffusion range data and the real-time groundwater seawater intrusion diffusion intensity data meet the initial diffusion stage determination criteria, mark the current groundwater seawater intrusion diffusion process as being in the initial diffusion stage.

[0162] Step S1427: When the real-time groundwater seawater intrusion diffusion range data and the real-time groundwater seawater intrusion diffusion intensity data meet the rapid diffusion stage judgment criteria, mark the current groundwater seawater intrusion diffusion process as being in the rapid diffusion stage.

[0163] Step S1428: When the real-time groundwater seawater intrusion diffusion range data and the real-time groundwater seawater intrusion diffusion intensity data meet the criteria for determining the stable diffusion stage, mark the current groundwater seawater intrusion diffusion process as being in the stable diffusion stage.

[0164] By following the steps above, the diffusion stage of seawater intrusion can be automatically identified and marked during the model simulation process.

[0165] Step S143: Set virtual blocking parameters for each prevention and control technology in the diffusion blocking simulation model. The virtual blocking parameters reflect the change in blocking strength after the application of the prevention and control technology. Among them, physical interception technology corresponds to the improvement of the blocking strength of the virtual blocking node, and chemical adsorption technology corresponds to the decrease in the concentration of seawater components in the groundwater-seawater intrusion diffusion path.

[0166] To simulate the effects of different prevention and control technologies in the diffusion blocking simulation model, it is necessary to set corresponding virtual blocking parameters for each technology. Virtual blocking parameters are quantitative indicators that reflect the degree of impact of blocking measures on seawater intrusion and diffusion.

[0167] Physical interception technology aims to enhance the barrier strength of virtual blocking nodes; therefore, the virtual blocking parameter is set as a barrier strength enhancement coefficient. When physical interception technology is applied to a virtual blocking node, the barrier strength (such as seepage resistance) of that node is multiplied by this enhancement coefficient, effectively blocking or slowing down seawater diffusion. The magnitude of the enhancement coefficient is determined based on the type of physical interception technology and design parameters, such as the thickness of the cutoff wall and the permeability of the material.

[0168] Chemisorption technology aims to reduce the concentration of seawater components; therefore, the virtual blocking parameter is set as a concentration reduction coefficient or adsorption capacity. When chemisorption technology is applied to a certain area, the seawater component concentration in that area in the model is multiplied by the concentration reduction coefficient, or the amount of adsorbed seawater is calculated based on the adsorption capacity, thereby reducing the concentration of seawater components along the diffusion path and inhibiting diffusion. The concentration reduction coefficient or adsorption capacity is determined based on the properties, amount, and deployment method of the adsorbent material.

[0169] Biological purification technology aims to reduce the activity of seawater components. The virtual blocking parameter can be set as an activity reduction coefficient or a conversion rate. After applying biological purification technology, the activity of seawater components in the model area will decrease, leading to a reduction in their diffusion capacity and environmental impact. The activity reduction coefficient or conversion rate is determined based on the species, quantity, and environmental conditions of the organisms.

[0170] Step S144: For the initial diffusion stage of groundwater-seawater intrusion, activate virtual blocking nodes in the low diffusion priority area in the diffusion blocking simulation model, apply a combination of physical interception technology and chemical adsorption technology to simulate the changes in the diffusion path of groundwater-seawater intrusion, and record the reduction in the diffusion range of groundwater-seawater intrusion as the diffusion inhibition performance.

[0171] In the initial diffusion stage, the seawater intrusion area is relatively small, mainly concentrated in the low-priority diffusion zone near the initial starting point. At this time, virtual blocking nodes within the low diffusion priority zone are activated, and a control strategy combining physical interception technology and chemical adsorption technology is adopted.

[0172] The specific operation involves setting corresponding virtual blocking parameters in the model, activating virtual blocking nodes in low-priority areas, applying physical interception technology to enhance the blocking strength of these nodes, and simultaneously applying chemical adsorption technology to reduce the concentration of seawater components in the area. Then, a diffusion blocking simulation model is run to simulate the changes in the seawater intrusion diffusion path under this control strategy.

[0173] During the simulation, changes in the spread range are monitored in real time and compared with the simulation results before the application of control technologies (control group). The reduction in the spread range and the percentage reduction are calculated, and the above data are recorded as the spread suppression performance at this stage. For example, if the spread range is reduced by 30% after the application of control technologies compared with the control group, then the spread suppression performance is a 30% reduction in the spread range.

[0174] Step S145: For the rapid diffusion stage of groundwater-seawater intrusion, activate virtual blocking nodes in the medium-to-high diffusion priority areas in the diffusion blocking simulation model, and apply a combination of physical interception technology, chemical adsorption technology and biological purification technology to simulate the changes in the diffusion rate of groundwater-seawater intrusion, and record the decrease in the diffusion rate of groundwater-seawater intrusion as the diffusion inhibition performance.

[0175] The rapid diffusion phase is the period when seawater intrusion spreads most rapidly, requiring more robust control measures. At this stage, virtual blocking nodes in medium- to high diffusion priority areas should be activated, employing a combined strategy of physical interception, chemical adsorption, and biological purification technologies.

[0176] In the model, parameters for increasing the physical interception strength, decreasing the chemical adsorption concentration, and decreasing the biological purification activity are simultaneously set to activate virtual blocking nodes in high-priority areas. The model is then run to simulate the diffusion process under this combined control strategy, with a focus on monitoring changes in the diffusion rate.

[0177] The simulated diffusion rate was compared with that of the control group (without control measures), and the amount and percentage reduction in diffusion rate were calculated and recorded as the diffusion inhibition performance at that stage. For example, if the diffusion rate decreased by 40% compared to the control group, the diffusion inhibition performance would be described as a 40% reduction in diffusion rate.

[0178] Step S146: For the stable diffusion stage of groundwater and seawater intrusion, activate virtual blocking nodes in the entire region in the diffusion blocking simulation model, apply a combination of biological purification technology and long-term monitoring technology to simulate the maintenance of the groundwater and seawater intrusion diffusion range, and record the stable duration of the groundwater and seawater intrusion diffusion range as an indicator of the adaptability of prevention and control technology.

[0179] The key to control during the stable diffusion phase is to maintain the current diffusion range, prevent its further expansion, and gradually improve the water quality in the affected areas. At this time, virtual blocking nodes are activated throughout the region, and biological purification technology is mainly used for long-term water quality improvement, combined with long-term monitoring technology to monitor the diffusion dynamics in real time.

[0180] The model is configured with parameters for reducing the activity of bio-purification technology or for the conversion rate, activating virtual blocking nodes across the entire region to simulate the transformation and degradation processes of seawater components under bio-purification. Simultaneously, the application of long-term monitoring technology is simulated, with regular data acquisition and model update frequencies set.

[0181] During the simulation, changes in the spread range were monitored, and the duration for which the spread range remained stable (i.e., the change range was within a preset range) was recorded. This duration was used as an indicator of the adaptability of the control technologies. The longer the stable duration, the better the adaptability of the control technology combination during the stable spread phase.

[0182] Step S147: Record the diffusion inhibition performance of each prevention and control technology at different diffusion stages of groundwater and seawater intrusion and the adaptability index of the prevention and control technology, and integrate them to form the prevention and control simulation results.

[0183] The simulation results of applying different combinations of prevention and control technologies at different stages of the spread were compiled, and the spread suppression performance of each prevention and control technology (or combination) at each stage (such as the percentage reduction in spread range and percentage reduction in spread rate) and the adaptability indicators of the prevention and control technologies (such as the duration of stability) were recorded in detail. The above data were categorized and summarized according to the spread stage and the type of prevention and control technology to form a structured prevention and control simulation result. The prevention and control simulation result clearly demonstrates the application effects of various prevention and control technologies at different stages.

[0184] Step S150: Based on the results of the prevention and control simulation, generate a groundwater and seawater intrusion early warning report that includes the groundwater and seawater intrusion diffusion trend, key blocking nodes of groundwater and seawater intrusion and appropriate prevention and control technologies, and send the groundwater and seawater intrusion early warning report to the target monitoring and management terminal.

[0185] After obtaining the results of the prevention and control drills, it is necessary to conduct in-depth analysis and comprehensive evaluation to generate a comprehensive and instructive early warning report on groundwater and seawater intrusion, and send it to the relevant monitoring and management departments in a timely manner so that effective prevention and control measures can be taken.

[0186] Step S151: Extract the diffusion inhibition performance variation law of each groundwater and seawater intrusion diffusion stage from the prevention and control simulation results, combine it with the groundwater and seawater intrusion diffusion path simulation results in the diffusion blocking simulation model, predict the range and rate changes of groundwater and seawater intrusion diffusion within a preset time period in the future, and form a description of the groundwater and seawater intrusion diffusion trend.

[0187] For example, step S1511: extract diffusion inhibition performance data of the initial diffusion stage of groundwater and seawater intrusion from the prevention and control simulation results, count the diffusion inhibition performance data values ​​at different time points, analyze the change law of the diffusion inhibition performance data values ​​over time, and determine whether the diffusion inhibition performance data values ​​show an upward, downward or stable trend.

[0188] The results of the epidemic prevention and control drills include data on diffusion suppression performance at different time points in the initial diffusion phase (such as 1 day, 3 days, and 7 days after the simulation begins), including the percentage reduction in the diffusion range. These data are arranged chronologically, and curves depicting the changes in diffusion suppression performance over time are plotted. By analyzing the slope and curvature of the curves, the patterns of change are determined. For example, if the percentage reduction in the diffusion range gradually increases over time, it indicates an upward trend in diffusion suppression performance; if it gradually decreases, it indicates a downward trend; and if it remains essentially unchanged, it indicates a stable trend.

[0189] Step S1512: Extract the diffusion path simulation results of the initial diffusion stage of groundwater and seawater intrusion from the diffusion blocking simulation model, determine the main extension direction and extension speed of the groundwater and seawater intrusion diffusion path in this diffusion stage, and record the arrival time of groundwater and seawater intrusion diffusion at each key location on the diffusion path.

[0190] The initial diffusion stage simulation results output by the diffusion blocking model include coordinate changes of the diffusion front and the movement of isoconcentration lines. Analysis of these results determines the main extension direction of the diffusion path (e.g., northwest, southeast) and its extension rate (distance moved by the diffusion front per unit time). Simultaneously, key locations along the diffusion path are identified, such as important hydrogeological boundaries and urban water source protection zone boundaries, and the time it takes for seawater intrusion to reach these key locations is recorded.

[0191] Step S1513: Based on the variation law of diffusion inhibition performance and the simulation results of diffusion path in the initial diffusion stage of groundwater and seawater intrusion, assuming that the current intensity of prevention and control technology application is maintained, predict the changes in the extension direction and extension speed of the groundwater and seawater intrusion diffusion path in the future preset time period.

[0192] The preset time period is set according to the early warning requirements, such as the next 3 months or 6 months. Assuming that the intensity of the control technologies currently applied to the initial spread stage remains unchanged (i.e., the virtual blocking parameters remain unchanged) within the preset time period, the direction and speed of the spread path are predicted by combining the changing patterns of spread suppression performance and the extension characteristics of the spread path. For example, if the spread suppression performance shows an upward trend and the current extension speed is gradually slowing down, it is predicted that the future extension speed will continue to slow down, and the extension direction may be deflected due to the obstruction of control measures.

[0193] Step S1514: Analyze the expansion area of ​​groundwater and seawater intrusion within the predicted time period, compare the current groundwater and seawater intrusion range with the predicted groundwater and seawater intrusion range, and determine the magnitude of change in the groundwater and seawater intrusion range.

[0194] Based on the predicted diffusion path direction and speed, calculate the expansion area of ​​the diffusion range within a preset future time period. Compare the predicted diffusion range with the current (simulated time) diffusion range, calculate the area difference between the two, and then divide by the current diffusion range area to obtain the change in diffusion range (the percentage increase or decrease).

[0195] Step S1515: Extract diffusion suppression performance data of the rapid diffusion stage of groundwater and seawater intrusion from the prevention and control simulation results, repeat steps S1511 to S1514 above, and determine the change range and change rate of diffusion within a preset time period of the rapid diffusion stage of groundwater and seawater intrusion.

[0196] For the rapid diffusion stage, the same analysis method as the initial diffusion stage is used to extract the diffusion inhibition performance data of this stage, analyze its change law over time, extract the diffusion path simulation results, predict the extension direction and extension speed of the diffusion path in the future preset time period, and calculate the change range of diffusion range and the change range of diffusion rate (the percentage increase or decrease in diffusion rate).

[0197] Step S1516: Extract diffusion suppression performance data of groundwater and seawater intrusion during the stable diffusion stage from the prevention and control simulation results, analyze the stability of the diffusion range and diffusion rate of groundwater and seawater intrusion during this diffusion stage, and predict the duration of the diffusion range and diffusion rate of groundwater and seawater intrusion within a preset time period in the future.

[0198] The diffusion suppression performance data during the stable diffusion phase mainly reflects the stability of the diffusion range and diffusion rate. Analyzing this data determines the length of time that the diffusion range and diffusion rate can remain stable under the current control technologies. For example, if the diffusion range changes by less than 5% in the past month and the diffusion suppression performance data is stable, then it is predicted that the diffusion range and diffusion rate can remain stable within a predetermined time period (e.g., the next 6 months), with a duration of 6 months.

[0199] Step S1517: Integrate the prediction results of the initial diffusion stage, rapid diffusion stage, and stable diffusion stage of groundwater and seawater intrusion, and describe the range and rate of change of groundwater and seawater intrusion in chronological order to form a description of the groundwater and seawater intrusion diffusion trend.

[0200] The predictions from the three stages are integrated and organized chronologically (initial diffusion stage → rapid diffusion stage → stable diffusion stage). A detailed description is provided of the expansion or contraction of the seawater intrusion diffusion range, the acceleration or deceleration of the diffusion rate, and possible changes in the diffusion path within each stage. For example, in the initial diffusion stage, the diffusion range will slowly expand over the next three months, with the rate gradually increasing; after entering the rapid diffusion stage, the diffusion range will expand rapidly, reaching its peak rate; in the stable diffusion stage, the diffusion range and rate tend to stabilize. This description forms a complete picture of the groundwater seawater intrusion diffusion trend.

[0201] Step S152: Extract the virtual blocking node locations with the best diffusion inhibition performance under different groundwater and seawater intrusion diffusion stages from the diffusion blocking simulation model, mark the virtual blocking node locations as key blocking nodes for groundwater and seawater intrusion, and label the groundwater and seawater intrusion diffusion stage and applicable prevention and control technology type corresponding to each key blocking node for groundwater and seawater intrusion.

[0202] During the simulation of the diffusion blocking model, different combinations of virtual blocking nodes were activated for different diffusion stages. For each diffusion stage, the combinations of virtual blocking nodes with the best diffusion suppression performance (e.g., the largest reduction in diffusion range and the largest reduction in diffusion rate) were selected from the prevention and control simulation results, and the locations of the virtual blocking nodes contained in these combinations were extracted.

[0203] The locations of the aforementioned virtual blocking nodes are marked as key blocking nodes for groundwater and seawater intrusion. Each key blocking node is clearly labeled with its corresponding diffusion stage (initial, rapid, or stable stage) and the type of control technology applicable to that stage (such as physical interception technology, chemical adsorption technology, etc.). For example, if a key blocking node is located in a medium-priority area during the rapid diffusion stage, a combination of physical interception technology and biological purification technology is applicable.

[0204] Step S153: Extract the adaptability index of each prevention and control technology for different groundwater and seawater intrusion and diffusion stages from the prevention and control simulation results, screen out the prevention and control technology with the best adaptability index for each groundwater and seawater intrusion and diffusion stage, and form a list of adaptable prevention and control technologies for each groundwater and seawater intrusion and diffusion stage.

[0205] The results of the prevention and control drills include the adaptability indicators (such as stabilization duration, cost-effectiveness ratio, environmental impact, etc.) of each prevention and control technology at different stages of the spread. For each stage of the spread, the adaptability indicators of various prevention and control technologies are compared and ranked, and one or more prevention and control technologies with the best adaptability indicators are selected.

[0206] For example, in the initial diffusion stage, the combination of physical interception and chemical adsorption technologies showed the highest compatibility index (e.g., percentage reduction in diffusion range); in the rapid diffusion stage, the combination of all three technologies showed the best compatibility index; and in the stable diffusion stage, biological purification technology showed the longest compatibility index (e.g., duration of stability). The selected control technologies were then categorized according to diffusion stage to create a list of suitable control technologies for each stage, including the technology name, compatibility index value, and a brief description.

[0207] Step S154: Determine the structural order of the description of groundwater and seawater intrusion diffusion trend, information on key blocking nodes of groundwater and seawater intrusion, and the list of suitable prevention and control technologies in the groundwater and seawater intrusion early warning report, and organize the content in the order from early to late stages of groundwater and seawater intrusion diffusion.

[0208] The structure of the early warning report should be clear and concise, facilitating reading and comprehension. The content should be structured in the following order: first, an overview of the groundwater-seawater intrusion situation should be provided; then, following the diffusion stages from earliest to latest (initial diffusion stage → rapid diffusion stage → stable diffusion stage), the diffusion trend, key blocking points, and appropriate control technologies for each stage should be described sequentially. This organizational approach aligns with the development process of seawater intrusion, enabling readers to understand the intrusion situation and key control points step-by-step.

[0209] Step S155: Supplement the groundwater and seawater intrusion early warning report with implementation recommendations for each key blocking node of groundwater and seawater intrusion, including the best time window and application method for the corresponding prevention and control technologies. The deployment density and height of physical interception technologies at key blocking nodes of groundwater and seawater intrusion are predetermined.

[0210] For each critical blocking node, detailed implementation recommendations are provided based on its diffusion stage, geological conditions, and hydrological characteristics. The optimal time window refers to the best period within that stage for applying control technologies; for example, for critical blocking nodes in the initial diffusion stage, the optimal time window is within one week of the onset of invasion. Application methods include specific construction methods, material selection, and equipment configuration for the control technologies.

[0211] For physical interception technology, the layout density (such as how many anti-seepage piles are placed per meter) and layout height (depth of the anti-seepage wall and height above ground) of the key blocking nodes are determined in advance based on the geological structure (such as soil thickness and lithology) and design requirements. These parameters are clearly listed in the implementation recommendations to guide the actual engineering construction.

[0212] Step S156: Supplement the groundwater and seawater intrusion early warning report with application precautions for each suitable prevention and control technology, including water quality indicators and environmental parameters that need to be monitored during the application of the prevention and control technology, and the amount of adsorbent residue that needs to be monitored after the application of chemical adsorption technology is predetermined.

[0213] Each adaptive prevention and control technology has specific precautions for application, which need to be supplemented in the early warning report. Application precautions include construction safety requirements, material storage conditions, impact on the surrounding ecological environment, and countermeasures.

[0214] Meanwhile, the document clearly lists the water quality indicators and environmental parameters that need to be monitored during the application process. For example, after applying chemical adsorption technology, it is necessary to regularly monitor water quality indicators such as adsorbent residue, pH value, and dissolved oxygen in groundwater, as well as environmental parameters such as soil moisture content and temperature. Among these, the monitoring standards for adsorbent residue are determined in advance based on relevant environmental regulations and health risk assessments to ensure that they are below the safe threshold.

[0215] Step S157: Integrate all the above information to form a groundwater and seawater intrusion early warning report.

[0216] The report should integrate all information, including a description of the groundwater-seawater intrusion trend, information on key blocking nodes (including location, corresponding stage, applicable technology, and implementation recommendations), and a list of suitable prevention and control technologies (including technology name, compatibility indicators, and application precautions), into a complete groundwater-seawater intrusion early warning report, following a predetermined structural order. The report should use accurate, concise, and professional language, avoiding vague or ambiguous expressions.

[0217] Step S158: Send the groundwater and seawater intrusion early warning report to the target monitoring and management terminal.

[0218] Once the early warning report is completed, it is sent to the target monitoring and management terminal via secure communication channels (such as encrypted email or a dedicated data transmission network). The target monitoring and management terminal is typically a computer system or mobile terminal of the monitoring and management department. After receiving the report, relevant personnel can promptly understand the groundwater and seawater intrusion situation and take corresponding prevention and control measures based on the recommendations in the report. Simultaneously, a feedback mechanism is established to confirm that the target terminal has successfully received the report.

[0219] Based on the same inventive concept, please refer to Figure 2 This paper shows a schematic block diagram of a groundwater and seawater intrusion early warning system 100 based on online hydrological and water quality monitoring data, which is used to perform the above-mentioned inspection video stream processing method according to an embodiment of this application. The groundwater and seawater intrusion early warning system 100 based on online hydrological and water quality monitoring data may include a communication unit 110, a machine-readable storage medium 120, and a processor 130.

[0220] In this embodiment, both the machine-readable storage medium 120 and the processor 130 are located within the groundwater and seawater intrusion early warning system 100 based on online hydrological and water quality monitoring data, and are separately configured. However, it should be understood that the machine-readable storage medium 120 may also be independent of the groundwater and seawater intrusion early warning system 100 based on online hydrological and water quality monitoring data, and may be accessed by the processor 130 via a bus interface. Alternatively, the machine-readable storage medium 120 may also be integrated into the processor 130 and may communicate and interact with external systems through the communication unit 110.

[0221] The processor 130 is the control center of the groundwater and seawater intrusion early warning system 100 based on online hydrological and water quality monitoring data. It connects to various parts of the system via various interfaces and lines. By running or executing software programs and / or modules stored in the machine-readable storage medium 120, and by calling data stored in the machine-readable storage medium 120, it performs various functions and processes data of the groundwater and seawater intrusion early warning system 100, thereby providing overall monitoring of the system. Optionally, the processor 130 may include one or more processing cores; for example, the processor 130 may integrate an application processor and a modem processor, wherein the application processor mainly handles the operating system, user interface, and applications, and the modem processor mainly handles wireless communication. It is understood that the modem processor may also not be integrated into the processor. The machine-readable storage medium 120 is used to store machine-executable instructions for executing the scheme of this application, and the processor 130 is used to execute the machine-executable instructions stored in the machine-readable storage medium 120 to implement the inspection video stream processing method provided in the aforementioned method embodiments.

[0222] It should be noted that, in order to simplify the description of the present invention and thus help to understand one or more embodiments of the invention, multiple features may sometimes be grouped into one embodiment, drawing or description thereof in the foregoing description of the embodiments of the present invention.

Claims

1. A groundwater-seawater intrusion early warning method based on online hydrological and water quality monitoring data, characterized in that, The method includes: A set of online hydrological and water quality monitoring data is obtained, which includes groundwater dynamic monitoring data blocks, seawater dynamic monitoring data blocks, and environmental correlation monitoring data blocks. The groundwater dynamic monitoring data blocks record groundwater flow disturbance information and water quality variation information. The seawater dynamic monitoring data blocks record seawater infiltration front information and water quality diffusion information. The environmental correlation monitoring data blocks record geological barrier information and hydrological driving information of the monitoring area. The trigger signals for groundwater and seawater intrusion are identified from the set of online hydrological and water quality monitoring data. The correlation of the trigger signals is verified by associating the monitoring information recorded in the groundwater dynamic monitoring data block, the monitoring information recorded in the seawater dynamic monitoring data block, and the monitoring information recorded in the environmental correlation monitoring data block, thus forming a set of intrusion trigger signals. Based on the intrusion trigger signal set, and combined with the interaction between the geological barrier information and hydrological driving information recorded in the environmental associated monitoring data block, a diffusion blocking simulation model is constructed. The diffusion blocking simulation model is used to simulate the path changes of groundwater and seawater intrusion diffusion under different blocking strategies. The diffusion blocking simulation model is used to simulate the application effects of various prevention and control technologies at different diffusion stages of groundwater and seawater intrusion, and to generate prevention and control simulation results that include the adaptability of prevention and control technologies and the performance of groundwater and seawater intrusion diffusion inhibition. Based on the results of the prevention and control simulation, a groundwater and seawater intrusion early warning report is generated, which includes the groundwater and seawater intrusion diffusion trend, key blocking nodes of groundwater and seawater intrusion, and appropriate prevention and control technologies. The groundwater and seawater intrusion early warning report is then sent to the target monitoring and management terminal.

2. The groundwater and seawater intrusion early warning method based on online hydrological and water quality monitoring data according to claim 1, characterized in that, The step involves identifying groundwater-seawater intrusion trigger signals from the online hydrological and water quality monitoring data set, verifying the correlation of trigger signals by associating monitoring information recorded in groundwater dynamic monitoring data blocks, seawater dynamic monitoring data blocks, and environmental correlation monitoring data blocks, thereby forming an intrusion trigger signal set, including: Abnormal flow signal features are extracted from the flow disturbance information recorded in the groundwater dynamic monitoring data block. The abnormal flow signal features reflect sudden changes in the groundwater flow direction and non-periodic fluctuations in the groundwater flow rate. The monitoring time and monitoring location corresponding to the abnormal flow signal features are recorded. Abnormal water quality signal features are extracted from the water quality variation information recorded in the groundwater dynamic monitoring data block. The abnormal water quality signal features reflect the sudden increase of key groundwater components and the unnatural adjustment of the proportion of groundwater components. The monitoring time and monitoring location corresponding to the abnormal water quality signal features are recorded. Abnormal permeation signal features are extracted from the permeation front information recorded in the seawater dynamic monitoring data block. These abnormal permeation signal features reflect the sudden forward shift of the seawater permeation location and the unexpected expansion of the seawater permeation range. The monitoring time and monitoring location corresponding to the abnormal permeation signal features are recorded. Abnormal diffusion signal features are extracted from the water quality diffusion information recorded in the seawater dynamic monitoring data block. The abnormal diffusion signal features reflect the sudden increase in the diffusion concentration of seawater components and the unnatural increase in the diffusion distance of seawater components. The monitoring time and monitoring location corresponding to the abnormal diffusion signal features are recorded. Extract abnormal barrier signal features from the geological barrier information recorded in the environmental correlation monitoring data block. The abnormal barrier signal features reflect the sudden decrease in the permeability resistance of the geological layer and the unexpected enhancement of the pore connectivity of the geological layer. Record the monitoring time and monitoring location corresponding to the abnormal barrier signal features. Extract abnormal driving signal features from the hydrological driving information recorded in the environmental correlation monitoring data block. The abnormal driving signal features reflect the sudden surge in rainfall and the non-periodic enhancement of surface runoff. Record the monitoring time and monitoring location corresponding to the abnormal driving signal features. The extracted abnormal flow signal features, abnormal water quality signal features, abnormal infiltration signal features, abnormal diffusion signal features, abnormal barrier signal features, and abnormal driving signal features are correlated according to monitoring time and monitoring location. Combinations of at least three of the above abnormal signal features appearing simultaneously at the same monitoring time and the same monitoring location are retained to form an intrusion trigger signal set.

3. The groundwater and seawater intrusion early warning method based on online hydrological and water quality monitoring data according to claim 2, characterized in that, The extraction of abnormal flow signal features from the flow disturbance information recorded in the groundwater dynamic monitoring data block includes: The flow direction data of all monitoring locations within a continuous monitoring period is extracted from the flow disturbance information recorded in the groundwater dynamic monitoring data block. The flow direction data records the flow direction of each monitoring location within a unit of time. Observe the angle of change of flow direction at each monitoring location within adjacent monitoring periods, and count the number of times the angle of change of flow direction at each monitoring location exceeds the range of natural fluctuations within a preset time period; The flow rate data of all monitoring locations within a continuous monitoring period is extracted from the flow disturbance information recorded in the groundwater dynamic monitoring data block. The flow rate data records the flow speed of each monitoring location per unit time. Observe the change in flow rate at each monitoring location within adjacent monitoring periods, and count the number of times the change in flow rate at each monitoring location exceeds the natural fluctuation range within a preset time period; For each monitoring location, determine whether the number of changes in flow direction and the number of changes in flow rate independently reach the preset frequency; The monitoring location where the number of changes in flow direction reaches a preset frequency is marked as an abnormal flow direction location, and the flow direction data of the abnormal flow direction location is extracted as a flow direction abnormal data segment. The monitoring location where the flow rate changes a preset frequency is marked as an abnormal flow rate location, and the flow rate data at the abnormal flow rate location is extracted as a flow rate abnormal data segment. The directional anomaly data segments and the velocity anomaly data segments are recorded separately, and the monitoring locations and monitoring times corresponding to the directional anomaly data segments and the velocity anomaly data segments are marked respectively, which together constitute the abnormal flow signal characteristics.

4. The groundwater and seawater intrusion early warning method based on online hydrological and water quality monitoring data according to claim 2, characterized in that, The extraction of anomalous barrier signal features from the geological barrier information recorded in the environmental correlation monitoring data block includes: The permeability resistance data of different geological layers in a continuous monitoring period are extracted from the geological barrier information recorded in the environmental correlation monitoring data block. The permeability resistance data records the barrier strength of each geological layer per unit time. Observe the change in seepage resistance of each geological layer within adjacent monitoring periods, and count the number of times the change in seepage resistance of each geological layer is lower than the natural decrease range within a preset time period; Pore ​​connectivity data of different geological layers within a continuous monitoring period are extracted from the geological barrier information recorded in the environmental correlation monitoring data block. The pore connectivity data records the degree of pore connectivity of each geological layer within a unit time. Observe the changes in pore connectivity of each geological layer within adjacent monitoring periods, and count the number of times the changes in pore connectivity of each geological layer exceed the natural enhancement range within a preset time period; For each geological layer, determine whether the number of changes in permeability resistance and the number of changes in pore connectivity independently reach the preset frequency. Geological layers whose permeability resistance changes at a preset frequency are marked as geological layers with abnormal resistance, and the permeability resistance data of the geological layers with abnormal resistance are extracted as abnormal resistance data segments. Geological layers whose pore connectivity changes at a preset frequency are marked as connectivity anomalous geological layers, and the pore connectivity data of the connectivity anomalous geological layers are extracted as connectivity anomalous data fragments. The resistance anomaly data segment and the connectivity anomaly data segment are recorded separately, and the geological layer and monitoring time corresponding to each of the resistance anomaly data segment and the connectivity anomaly data segment are marked, which together constitute the characteristics of the anomaly blocking signal.

5. The groundwater and seawater intrusion early warning method based on online hydrological and water quality monitoring data according to claim 1, characterized in that, Based on the intrusion trigger signal set, and combined with the interaction between the geological barrier information and hydrological driving information recorded in the environmental correlation monitoring data block, a diffusion blocking inference model is constructed, including: Extract the monitoring location and signal strength corresponding to each trigger signal from the set of intrusion trigger signals to determine the initial starting point and initial diffusion intensity of groundwater and seawater intrusion. Geological barrier intensity data for different regions are extracted from the geological barrier information recorded in the environmental correlation monitoring data block to determine the natural barrier capacity of each region against groundwater and seawater intrusion and diffusion. Hydrological driving intensity data for different regions are extracted from the hydrological driving information recorded in the environmental correlation monitoring data block to determine the natural driving capacity of each region for groundwater and seawater intrusion and diffusion. The interaction between the natural barrier capacity corresponding to the geological barrier strength data and the natural driving capacity corresponding to the hydrological driving strength data is analyzed to determine the combined effect of promoting and hindering the intrusion and diffusion of groundwater and seawater in each region. Centered on the initial starting point of groundwater and seawater intrusion and diffusion, diffusion priority areas are divided according to the order of the strength of the driving force of groundwater and seawater intrusion and diffusion in each area, and each diffusion priority area corresponds to a diffusion priority. Virtual blocking nodes are set up in each diffusion priority area, and each virtual blocking node corresponds to a potential application location of a blocking measure. Based on the initial diffusion intensity of groundwater-seawater intrusion, the combined effect of pushing and hindering in each region, and the location of virtual blocking nodes, a diffusion blocking simulation model is constructed to simulate the changes in the diffusion path of groundwater-seawater intrusion under different virtual blocking node activation states.

6. The groundwater and seawater intrusion early warning method based on online hydrological and water quality monitoring data according to claim 5, characterized in that, The analysis examines the interaction between the natural barrier capacity corresponding to the geological barrier intensity data and the natural driving force corresponding to the hydrological driving intensity data, determining the combined effect of promoting and hindering groundwater and seawater intrusion in each region, including: The geological barrier strength is determined based on historical monitoring data. The characteristics are set based on the comprehensive performance of the geological layer's permeability resistance and pore connectivity. The more significant the characteristics, the stronger the natural barrier capacity. The characteristics of hydrological driving intensity are determined based on historical monitoring data. These characteristics are set based on the combined performance of rainfall and surface runoff. The more significant the characteristics, the stronger the natural driving force. Based on the degree of influence of geological barriers and hydrological driving on diffusion in historical groundwater-seawater intrusion events, the influence weights of geological barriers and hydrological driving are set. For each region, its geological barrier intensity characteristics and hydrological driving intensity characteristics are normalized to the same numerical range. By combining the weights of geological barrier influence and hydrological driving influence, the interaction between the normalized geological barrier intensity characteristics and the normalized hydrological driving intensity characteristics in each region is analyzed to determine the dominant relationship between the driving force and the hindering force of groundwater and seawater intrusion and diffusion in each region. If the result of the normalized hydrological driving intensity characteristics combined with the hydrological driving influence weight in any region is stronger than the result of the normalized geological barrier intensity characteristics combined with the geological barrier influence weight, then the region that promotes the diffusion of groundwater and seawater intrusion is dominant. If the result of the normalized geological barrier intensity characteristics combined with the geological barrier influence weight is stronger than the result of the normalized hydrological driving intensity characteristics combined with the hydrological driving influence weight in any region, then the effect of hindering the intrusion and diffusion of groundwater and seawater is dominant in that region. Based on the judgment results of the dominant relationship between the driving force and the hindering force of groundwater and seawater intrusion and diffusion in each region, the comprehensive effect of the driving force and the hindering force of groundwater and seawater intrusion and diffusion in each region is determined.

7. The groundwater and seawater intrusion early warning method based on online hydrological and water quality monitoring data according to claim 1, characterized in that, The diffusion blocking simulation model is used to simulate the application effects of various prevention and control technologies at different stages of groundwater and seawater intrusion, generating prevention and control simulation results that include the adaptability of prevention and control technologies and the performance of groundwater and seawater intrusion diffusion inhibition, including: To acquire a variety of control technologies applicable to groundwater and seawater intrusion prevention and control, and to determine the mechanism of action of each control technology, among which physical interception technology blocks the spread of groundwater and seawater intrusion by constructing barriers, chemical adsorption technology inhibits the spread of groundwater and seawater intrusion by adsorbing seawater components, and biological purification technology reduces the activity of seawater components through biological action to inhibit the spread of groundwater and seawater intrusion. The groundwater-seawater intrusion and diffusion process is divided into an initial diffusion stage, a rapid diffusion stage, and a stable diffusion stage. Each diffusion stage corresponds to different groundwater-seawater intrusion and diffusion intensities and ranges. In the diffusion blocking simulation model, virtual blocking parameters are set for each prevention and control technology. The virtual blocking parameters reflect the change in the blocking strength after the application of the prevention and control technology. Among them, physical interception technology corresponds to the improvement of the blocking strength of the virtual blocking node, and chemical adsorption technology corresponds to the decrease in the concentration of seawater components in the groundwater-seawater intrusion diffusion path. For the initial diffusion stage of groundwater-seawater intrusion, virtual blocking nodes in low diffusion priority areas are activated in the diffusion blocking simulation model. A combination of physical interception technology and chemical adsorption technology is applied to simulate the changes in the diffusion path of groundwater-seawater intrusion, and the reduction in the diffusion range of groundwater-seawater intrusion is recorded as the diffusion inhibition performance. In response to the rapid diffusion stage of groundwater-seawater intrusion, virtual blocking nodes in medium- and high diffusion priority areas are activated in the diffusion blocking simulation model. A combination of physical interception technology, chemical adsorption technology and biological purification technology is applied to simulate the changes in the diffusion rate of groundwater-seawater intrusion. The decrease in the diffusion rate of groundwater-seawater intrusion is recorded as the diffusion inhibition performance. For the stable diffusion stage of groundwater-seawater intrusion, virtual blocking nodes are activated in the diffusion blocking simulation model of the entire area. A combination of biological purification technology and long-term monitoring technology is applied to simulate the maintenance of the groundwater-seawater intrusion diffusion range. The stable duration of the groundwater-seawater intrusion diffusion range is recorded as an indicator of the adaptability of the prevention and control technology. Record the diffusion inhibition performance of each prevention and control technology at different diffusion stages of groundwater and seawater intrusion, and the adaptability indicators of the prevention and control technologies, and integrate them to form the prevention and control simulation results.

8. The groundwater and seawater intrusion early warning method based on online hydrological and water quality monitoring data according to claim 7, characterized in that, The process of groundwater and seawater intrusion and diffusion is divided into an initial diffusion stage, a rapid diffusion stage, and a stable diffusion stage, including: The initial diffusion intensity and initial diffusion range data of groundwater and seawater intrusion were extracted from the diffusion blocking simulation model. The criteria for determining the initial diffusion stage are set as follows: when the groundwater seawater intrusion diffusion range does not exceed a preset multiple of the range corresponding to the initial diffusion range data and the groundwater seawater intrusion diffusion intensity does not exceed a preset multiple of the intensity corresponding to the initial diffusion intensity data, it is determined to be the initial diffusion stage of groundwater seawater intrusion. The criteria for determining the rapid diffusion stage are set as follows: when the groundwater seawater intrusion diffusion range exceeds a preset multiple of the range corresponding to the initial diffusion range data and the groundwater seawater intrusion diffusion intensity exceeds a preset multiple of the intensity corresponding to the initial diffusion intensity data, it is determined to be the rapid diffusion stage of groundwater seawater intrusion. The criteria for determining the stable diffusion stage are set as follows: when the variation range of groundwater seawater intrusion diffusion range is lower than the preset range within a continuous preset time period and the variation range of groundwater seawater intrusion diffusion intensity is lower than the preset range within a continuous preset time period, it is determined to be the stable diffusion stage of groundwater seawater intrusion. Real-time groundwater-seawater intrusion diffusion range data and real-time groundwater-seawater intrusion diffusion intensity data are extracted from the simulation process of the diffusion blocking model. The real-time groundwater-seawater intrusion diffusion range data and the real-time groundwater-seawater intrusion diffusion intensity data are compared with the initial diffusion stage judgment criteria, the rapid diffusion stage judgment criteria, and the stable diffusion stage judgment criteria, respectively. When the real-time groundwater-seawater intrusion diffusion range data and the real-time groundwater-seawater intrusion diffusion intensity data meet the initial diffusion stage judgment criteria, the current groundwater-seawater intrusion diffusion process is marked as being in the initial diffusion stage. When the real-time groundwater-seawater intrusion diffusion range data and the real-time groundwater-seawater intrusion diffusion intensity data meet the criteria for determining the rapid diffusion stage, the current groundwater-seawater intrusion diffusion process is marked as being in the rapid diffusion stage. When the real-time groundwater seawater intrusion diffusion range data and the real-time groundwater seawater intrusion diffusion intensity data meet the criteria for determining a stable diffusion stage, the current groundwater seawater intrusion diffusion process is marked as being in a stable diffusion stage.

9. The groundwater and seawater intrusion early warning method based on online hydrological and water quality monitoring data according to claim 1, characterized in that, Based on the results of the prevention and control simulation, a groundwater and seawater intrusion early warning report is generated, which includes the groundwater and seawater intrusion diffusion trend, key blocking nodes of groundwater and seawater intrusion, and appropriate prevention and control technologies. Extract the diffusion inhibition performance of each groundwater and seawater intrusion diffusion stage from the prevention and control simulation results, and combine the groundwater and seawater intrusion diffusion path simulation results in the diffusion blocking simulation model to predict the range and rate changes of groundwater and seawater intrusion diffusion within a preset time period, thus forming a description of the groundwater and seawater intrusion diffusion trend. From the diffusion blocking simulation model, extract the virtual blocking node locations with the best diffusion inhibition performance under different groundwater and seawater intrusion diffusion stages, mark the virtual blocking node locations as key groundwater and seawater intrusion blocking nodes, and label the groundwater and seawater intrusion diffusion stage and applicable prevention and control technology type corresponding to each key groundwater and seawater intrusion blocking node. The adaptability index of each prevention and control technology at different groundwater and seawater intrusion and diffusion stages is extracted from the prevention and control simulation results. The prevention and control technology with the best adaptability index at each groundwater and seawater intrusion and diffusion stage is selected to form a list of adaptable prevention and control technologies for each groundwater and seawater intrusion and diffusion stage. The structural order of the description of the groundwater and seawater intrusion diffusion trend, the information of key blocking nodes of groundwater and seawater intrusion, and the list of suitable prevention and control technologies in the groundwater and seawater intrusion early warning report should be determined, and the content should be organized in the order of the groundwater and seawater intrusion diffusion stages from early to late. The groundwater and seawater intrusion early warning report is supplemented with implementation recommendations for each key blocking node of groundwater and seawater intrusion, including the best time window and application method for the corresponding prevention and control technologies. The density and height of physical interception technology at key blocking nodes of groundwater and seawater intrusion are determined in advance. The groundwater and seawater intrusion early warning report should be supplemented with the application precautions for each appropriate prevention and control technology, including the water quality indicators and environmental parameters that need to be monitored during the application of the prevention and control technology. The residual amount of adsorbent that needs to be monitored after the application of chemical adsorption technology should be determined in advance. Integrate all the above information to form a groundwater and seawater intrusion early warning report.

10. A groundwater and seawater intrusion early warning system based on online hydrological and water quality monitoring data, characterized in that, include: processor; A machine-readable storage medium for storing machine-executable instructions of the processor; The processor is configured to execute the groundwater and seawater intrusion early warning method based on online hydrological and water quality monitoring data as described in any one of claims 1 to 9 by executing the machine-executable instructions.

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