A visual groundwater level dynamic monitoring system

Abstract

The application relates to the technical field of groundwater monitoring, and discloses a visual groundwater level dynamic monitoring system, which comprises a multidimensional monitoring module, a quality verification module, a dynamic monitoring module, a precision evaluation module and a visual management module. The system obtains monitoring point distribution management data, groundwater sensing data and hydraulic test data through the multidimensional monitoring module, the quality verification module evaluates the fidelity index of measured data, quantifies data quality in real time and self-checks, the multidimensional monitoring has high fidelity, the dynamic monitoring module evaluates the hydraulic coupling state of well type monitoring points and aquifers, generates a coupling index, realizes early warning of well body health state, the precision evaluation module evaluates the accuracy of no-well type monitoring points, generates an accuracy index, improves the regional monitoring network density and coverage capacity, the visual management module judges the deviation level of measured data, the abnormal level of the hydraulic coupling state, the stability level of the monitoring points, outputs results and response measures, and intelligent operation and maintenance response is more timely.

Description

Technical Field

[0001] This invention relates to the field of groundwater monitoring technology, specifically a visual groundwater level dynamic monitoring system. Background Technology

[0002] Visualized groundwater level dynamic monitoring integrates IoT sensing, GIS geographic information, big data analysis, and 3D simulation technologies. Through a comprehensive sensing network, data transmission hub, intelligent analysis platform, and visualized control terminal, it achieves digital perception, intelligent judgment, and intuitive presentation of groundwater levels. Monitoring methods are mainly divided into two categories: well monitoring and wellless monitoring. Well monitoring is currently the most mainstream and accurate method, including dedicated monitoring wells with stable and reliable data, and reused wells that are only used as supplementary monitoring and are easily affected by mining interference. Wellless monitoring is suitable for areas where wells are difficult to deploy, such as karst areas, permafrost areas, and densely populated urban areas. It mainly includes lower-precision geophysical exploration techniques used for large-scale surveys, in-situ sensor burial suitable for temporary or harsh environment monitoring, and lower-precision remote sensing indirect inversion techniques used for large-scale macroscopic monitoring of watersheds or continents.

[0003] Currently, traditional groundwater level dynamic monitoring systems struggle to detect the accumulation of zero-point drift in sensor probes in a timely manner. Filter pipes within monitoring wells are not only prone to clogging by sediment but also suffer from imbalances in permeability and structural strength. Long-term operation of the well pipes can lead to filter media blockage and well wall collapse, resulting in delayed or distorted water level monitoring responses. Furthermore, multi-layer aquifer monitoring wells may experience cross-flow between shallow and deep water layers due to water shut-off failure, resulting in mixed water levels that fail to accurately reflect the true dynamics of the target aquifer. Additionally, inconsistent data formats at different monitoring points easily create data silos, hindering regional joint analysis. The lack of real-time quality verification and automatic outlier removal mechanisms allows erroneous data to infiltrate the database, compromising monitoring accuracy and stability. Summary of the Invention

[0004] To address the shortcomings of existing technologies, this invention provides a visualized dynamic groundwater level monitoring system, which has the advantages of high fidelity in multi-dimensional monitoring and more timely intelligent operation and maintenance response. It solves the problems of traditional dynamic groundwater level monitoring systems that are difficult to detect drift accumulation and lack real-time quality verification and automatic outlier removal mechanisms.

[0005] To achieve the above objectives, the present invention provides the following technical solution: a visualized groundwater level dynamic monitoring system, comprising a multi-dimensional monitoring module, a quality verification module, a dynamic monitoring module, an accuracy assessment module, and a visualized management module; The multidimensional monitoring module acquires distribution management data, groundwater sensing data, and hydraulic test data of all monitoring points by connecting to a database, dual redundant probe water level gauges, ultrasonic imaging detection devices, and sensing devices, and classifies them into well network datasets, sensing datasets, and test datasets. The quality verification module evaluates the fidelity of the measured data in the target area based on the well network dataset and the sensor dataset, and generates a corresponding fidelity index. ; The dynamic monitoring module evaluates the hydraulic coupling state between each well monitoring point and the aquifer based on the well network dataset, sensor dataset, and experimental dataset, and generates a corresponding coupling index. ; The accuracy assessment module evaluates the accuracy of each well-free monitoring point based on the sensor dataset and the experimental dataset, and generates a corresponding accuracy index. ; The visualization management module is set with a fixed range of fidelity thresholds. Coupling threshold range and precise threshold range Combined with the fidelity index Coupling index and accuracy index It determines the deviation level of measured data in the target area, the anomaly level of the hydraulic coupling state between well monitoring points and aquifers, and the stability level of data without well monitoring points, and outputs the corresponding judgment results and response measures.

[0006] Preferably, the well network dataset includes the monitoring methods and the number of monitoring points within the target area, wherein the monitoring methods include well-based monitoring, wellless monitoring, and hybrid monitoring.

[0007] Preferably, the sensor dataset includes synchronized minute-level water level readings from dual redundant probes at each monitoring point, maximum permissible deviation of the dual probes, standard atmospheric pressure, on-site measured atmospheric pressure, standard density of pure water, local gravitational acceleration, water level temperature expansion coefficient, standard temperature value for water level measurement, on-site measured water temperature value, maximum tolerance for environmental error compensation, total theoretical sampling times, effective sampling times, calibration reference response lag time for wellless monitoring points, measured water level response lag time, calibration reference signal-to-noise ratio, measured signal-to-noise ratio, measured water level value, and inverted water level value.

[0008] Preferably, the test dataset includes the initial stable water output, initial drawdown, initial specific well capacity, initial Lurong value, initial aquifer water conductivity, and initial water level response lag time of each well during well completion and acceptance; the real-time stable water output, real-time drawdown, real-time specific well capacity, real-time Lurong value, real-time aquifer water conductivity, and real-time water level response lag time measured in the current micro-water test; the maximum allowable attenuation of specific well capacity; the maximum allowable attenuation of Lurong value; the maximum allowable deviation of water conductivity; the total length of the well casing; and the total length of well casing defects measured by ultrasonic imaging.

[0009] Preferably, the fidelity index The calculation process is as follows: S11. Based on the well network dataset and sensor dataset, extract the distribution management data and groundwater level sensor data of all monitoring points within the target area, and record the number of monitoring points within the target area as follows: ; S12. Calculate monitoring points for the target area. Dual probe consistency coefficient ; S13, Based on monitoring points Dual probe consistency coefficient Determine the monitoring point The validity of a single sampling is used to determine the initial water level value before error compensation. The determination rules are as follows: If the consistency coefficient of the dual probes If the reading is ≥0, the dual probes are considered to be working properly, and this sampling is considered valid. The arithmetic mean of the water level readings from both probes is taken as the monitoring point. Initial water level before error compensation If the consistency coefficient of the dual probes If the value is less than 0, it indicates that at least one probe's data is abnormal, and this sampling is invalid. A new monitoring point should be selected. The previous normal minute-level water level final value is used as the current water level initial value. ; If monitoring point If more than three consecutive invalid samples are collected, the monitoring point will be marked. In offline mode, take monitoring points The final value of the water level in the first normal minute before going offline is used as the initial value of the current water level. ; S14. To address water level measurement errors caused by environmental factors such as air pressure and temperature, adjust the initial water level value. Perform error compensation and calculate monitoring points. Standard water level value after error compensation and error compensation coefficient ; S15. For the target area, calculate the monitoring points within the statistical period. Sampling integrity coefficient ; S16. Calculate monitoring points for the target area. stability index ; S17. Calculate the percentage of well-type monitoring points within the target area. The proportion of well-free monitoring ; S18. Based on S11-S17, calculate the fidelity index of the well network in the target area using a weighted method. .

[0010] Preferably, the coupling index The calculation process is as follows: S21. Based on the well network dataset and the test dataset, extract well monitoring points. Distribution management data and hydraulic test data; S22, Calculation well monitoring point Specific well capacity decay coefficient ; S23, Calculation well monitoring point Permeability attenuation coefficient ; S24, Calculation well monitoring point Hierarchical isolation ; S25, Calculation well monitoring point Structural integrity ; S26, Calculation well monitoring point Response sensitivity ; S27. Based on S21-S26, calculate the well-type monitoring points using a weighted method. Coupling index .

[0011] Preferably, the accuracy index The calculation process is as follows: S31. Based on the sensor dataset and the experimental dataset, extract well-free monitoring points. Groundwater sensing data and hydraulic test data; S32, Calculation of well-free monitoring points Temporal consistency coefficient ; S33, Calculation of well-free monitoring points effective coefficient of signal ; S34, Calculate the number of well-free monitoring points Inversion error rate ; S35. Based on S31-S34, calculate the number of well-less monitoring points using a weighted method. Accuracy Index .

[0012] Preferably, the deviation level assessment process is as follows: Let the upper limit of the fidelity threshold range be denoted as The lower limit of the fidelity threshold range is denoted as ; If the fidelity index of the target area > This indicates that the deviation of the measured data in the target area is low, with a deviation level of 1. Response measures include marking the target area in green, continuing the application process of the well network data for the target area, and maintaining the current monitoring frequency. ≤ Fidelity index of the target area ≤ This indicates a moderate deviation in the measured data for the target area, with a deviation level of 2. Response measures include marking the target area in yellow, continuing the application process of the well network data for the target area, automatically calibrating the sensor parameters, and increasing the monitoring frequency. If the fidelity index of the target area... < This indicates a high degree of deviation in the measured data for the target area, with a deviation level of 3. Response measures include marking the target area in red, suspending the application of well network data for the target area, reminding management personnel to conduct on-site repairs of offline monitoring equipment, and carrying out a comprehensive verification of all monitoring equipment until the fidelity index of the target area is reached. ≥ This allows for the restoration of the application process for well network data in the target area.

[0013] Preferably, the anomaly level assessment process is as follows: Let the upper limit of the coupling threshold interval be denoted as Let the lower limit of the coupling threshold interval be denoted as ; If the coupling index > This indicates that the hydraulic coupling between the corresponding well monitoring point and the aquifer is normal, with an anomaly level of 1. Response measures include marking the well monitoring point in blue, continuing the application process of the groundwater sensor data from the corresponding monitoring point, and maintaining the current monitoring frequency. ≤ Coupling Index ≤ This indicates a slight anomaly in the hydraulic coupling between the corresponding well monitoring point and the aquifer, with an anomaly level of 2. Response measures include marking the well monitoring point as gray, continuing the application process of the groundwater sensor data for the corresponding monitoring point, automatically initiating well sludge flushing operations, and increasing the monitoring frequency. If the coupling index... < This indicates a severe anomaly in the hydraulic coupling between the corresponding well monitoring point and the aquifer, with an anomaly level of 3. Response measures include marking the well monitoring point as orange, suspending the application of groundwater sensor data from the corresponding monitoring point, reminding management personnel to conduct on-site inspections and ultrasonic imaging to locate well defects and develop repair plans, and simultaneously increasing the monitoring frequency of adjacent monitoring points until the coupling index of the corresponding monitoring point is reached. ≥ This allows for the restoration of the application process for groundwater sensor data at the corresponding monitoring point.

[0014] Preferably, the stability level assessment process is as follows: Let the upper limit of the precise threshold range be denoted as The lower limit of the precise threshold interval is denoted as ; If the accuracy index > This indicates that the monitoring status of the corresponding wellless monitoring point is stable, with a stability level of 1. Response measures include marking the wellless monitoring point in purple, continuing the application process of the groundwater sensor data from the corresponding wellless monitoring point, and maintaining the current monitoring frequency. ≤Accuracy Index ≤ This indicates a slight anomaly in the monitoring status of the corresponding wellless monitoring point, with a stability level of 2. Response measures include marking the wellless monitoring point in black, continuing the application process of the groundwater sensor data from the corresponding wellless monitoring point, automatically optimizing signal identification and inversion parameters, and increasing the monitoring frequency. If the accuracy index... < This indicates a severely abnormal monitoring status at the corresponding wellless monitoring point, with a stability level of 3. Response measures include marking the wellless monitoring point in pink, suspending the application of groundwater sensor data from the corresponding wellless monitoring point, reminding management personnel to conduct on-site calibration and equipment verification, and simultaneously increasing the monitoring frequency of adjacent monitoring points until the accuracy index of the corresponding wellless monitoring point is restored. ≥ This allows for the restoration of the application process for groundwater sensor data from the corresponding wellless monitoring point.

[0015] Compared with the prior art, the present invention provides a visualized groundwater level dynamic monitoring system, which has the following beneficial effects: 1. The multi-dimensional monitoring module of the present invention uses dual-redundancy probes, ultrasonic imaging, and multi-device linkage acquisition to uniformly construct three datasets of well networks, sensors, and tests, achieving full coverage of well-type and non-well-type monitoring, solving the problems of single data source, invisibility of well body status, and lack of standardized acquisition at non-well-type points in traditional systems, greatly improving the integrity of raw data and scene adaptability. The quality verification module calculates the fidelity index through the consistency of dual probes, environmental error compensation, and weighted calculation of sampling integrity , automatically identifies abnormal sampling and offline status, realizes real-time quantification and self-verification of data quality, avoids data distortion caused by air pressure drift, temperature interference, and signal packet loss from the source, and has high multi-dimensional monitoring fidelity.

[0016] 2. The dynamic monitoring module of the present invention calculates the coupling index from multiple dimensions such as specific well capacity attenuation, permeability change, layered isolation degree, structural integrity, and response sensitivity for well-type monitoring points , accurately reflects hidden diseases such as filter pipe blockage, aquifer cross-layer, and well pipe damage in the well body, realizes early warning of the health status of the well body, and avoids long-term distortion of water level monitoring caused by well body failure. The accuracy evaluation module constructs a precision index for non-well-type monitoring points through time series consistency, signal validity, and inversion accuracy , uniformly quantifies the problems of lag, noise, and error in non-well-type monitoring, makes up for the shortcomings of traditional non-well-type monitoring without quantitative evaluation and uncontrollable accuracy, improves the density and coverage ability of the regional monitoring network. The visualization management module automatically classifies based on the three indexes and threshold intervals, intuitively presents data deviation, well body abnormality, and point stability status with color identification, automatically triggers response measures such as calibration, flushing, and maintenance, realizes rapid positioning of abnormalities and closed-loop disposal of operation and maintenance, transforms traditional passive repair into active prediction and control, improves the intelligent level and decision-making support ability of groundwater dynamic monitoring, and has more timely intelligent operation and maintenance response. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 is the system flow chart of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0018] The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0019] Embodiment Please refer to Figure 1Table 1 shows the experimental data of the fidelity index, Table 2 shows the experimental data of the coupling index, and Table 3 shows the experimental data of the precision index. This invention provides a visualized groundwater level dynamic monitoring system, which includes a multi-dimensional monitoring module, a quality verification module, a dynamic monitoring module, a precision assessment module, and a visualization management module. The multidimensional monitoring module acquires distribution management data, groundwater sensing data, and hydraulic test data of all monitoring points by connecting to a database, dual redundant probe water level gauges, ultrasonic imaging detection devices, and sensing equipment, and classifies them into well network datasets, sensing datasets, and test datasets. The well network dataset includes the monitoring methods and the number of monitoring points within the target area. The monitoring methods include well-based monitoring, well-less monitoring, and hybrid monitoring. The sensor dataset includes synchronized minute-level water level readings from dual redundant probes at each monitoring point, maximum permissible deviation of the dual probes, standard atmospheric pressure, measured atmospheric pressure in the field, standard density of pure water, local gravitational acceleration, water level temperature expansion coefficient, standard temperature value for water level measurement, measured water temperature value in the field, maximum tolerance for environmental error compensation, total theoretical sampling times, effective sampling times, calibration reference response lag time for wellless monitoring points, measured water level response lag time, calibration reference signal-to-noise ratio, measured signal-to-noise ratio, measured water level value, and inverted water level value. The test dataset includes the initial stable water output, initial drawdown, initial specific well capacity, initial Lurong value, initial aquifer electrical conductivity, and initial water level response lag time of each well during well completion and acceptance; the real-time stable water output, real-time drawdown, real-time specific well capacity, real-time Lurong value, real-time aquifer electrical conductivity, real-time water level response lag time measured by the current micro-water test; the maximum allowable attenuation of specific well capacity; the maximum allowable attenuation of Lurong value; the maximum allowable deviation of electrical conductivity; the total length of the well casing; and the total length of well casing defects measured by ultrasonic imaging. The quality verification module evaluates the fidelity of measured data in the target area based on the well network dataset and sensor dataset, and generates a corresponding fidelity index. ; Fidelity Index The calculation process is as follows: S11. Based on the well network dataset and sensor dataset, extract the distribution management data and groundwater level sensor data of all monitoring points within the target area, and record the number of monitoring points within the target area as follows: ; S12. Calculate monitoring points for the target area. Dual probe consistency coefficient , This is used to quantify the consistency of data from the same source at the same monitoring point, and its expression is as follows: In the formula, Indicates monitoring point Independent probe in , Indicates monitoring point Independent probe in , , Indicates monitoring point Inside, independent probe Independent probe Synchronous minute-level water level readings This indicates the maximum permissible deviation of the dual-redundant probe; Specifically, monitoring points When there are no well-type monitoring points or no redundant probe points, The default value is 1, which is included in subsequent calculations. S13, Based on monitoring points Dual probe consistency coefficient Determine the monitoring point The validity of a single sampling is used to determine the initial water level value before error compensation. The determination rules are as follows: If the consistency coefficient of the dual probes If the reading is ≥0, the dual probes are considered to be working properly, and this sampling is considered valid. The arithmetic mean of the water level readings from both probes is taken as the monitoring point. Initial water level before error compensation If the consistency coefficient of the dual probes If the value is less than 0, it indicates that at least one probe's data is abnormal, and this sampling is invalid. A new monitoring point should be selected. The previous normal minute-level water level final value is used as the current water level initial value. ; If monitoring point If more than three consecutive invalid samples are collected, the monitoring point will be marked. In offline mode, take monitoring points The final value of the water level in the first normal minute before going offline is used as the initial value of the current water level. ; S14. To address water level measurement errors caused by environmental factors such as air pressure and temperature, adjust the initial water level value. Perform error compensation and calculate monitoring points. Standard water level value after error compensation and error compensation coefficient Its expression is as follows: In the formula, Indicates standard atmospheric pressure. Indicates monitoring point Atmospheric pressure was measured on site. This indicates the standard density of pure water. Indicates monitoring point Local gravitational acceleration, This represents the coefficient of thermal expansion of water level. This indicates the standard temperature value for water level measurement. Indicates monitoring point Actual measured water temperature value on site; In the formula, This indicates the maximum tolerance for environmental error compensation; S15. For the target area, calculate the monitoring points within the statistical period. Sampling integrity coefficient Used to quantify monitoring points The continuity and integrity of data is expressed as follows: In the formula, Indicates the number of monitoring points within the statistical period. The theoretical total number of samplings, Indicates the number of monitoring points within the statistical period. The effective number of samples; S16. Calculate monitoring points for the target area. stability index This is used to quantify the overall data quality of a single monitoring point, and its expression is as follows: If monitoring point The monitoring method is well-type monitoring. In the formula, , and All are weights, and satisfy the following conditions: , Indicates well-type monitoring points The stability index; If monitoring point The monitoring method is wellless monitoring. In the formula, , and All are weights, and , , Indicates a well-free monitoring point The stability index; S17. Calculate the percentage of well-type monitoring points within the target area. The proportion of well-free monitoring Its expression is as follows: In the formula, This indicates the number of well-type monitoring points within the target area. Indicates the number of well-free monitoring points within the target area; S18. Based on S11-S17, calculate the fidelity index of the well network in the target area using a weighted method. This is used to quantify the overall fidelity of groundwater level monitoring data in the target area, and its expression is as follows: If the monitoring method for the entire target area is well-type monitoring In the formula, This represents the weighting coefficient of a single-well monitoring point, satisfying... , Indicates the fidelity index of the well network for monitoring wells in the target area; If the monitoring method for the entire target area is wellless monitoring... In the formula, This represents the weighting coefficient of a single well-less monitoring point, satisfying... , This indicates the fidelity index of the well network without wells in the target area; If the monitoring method for the target area is hybrid monitoring In the formula, This represents the average stability index of well-type monitoring points within the target area. This represents the average stability index of the target area without well-type monitoring points. Indicates the fidelity index of the mixed monitoring well network in the target area; The following is the fidelity index experimental data, as shown in Table 1: Table 1: Fidelity Index Experimental Data In Table 1, the fidelity index experimental data shows that target area A was selected as the experimental target. Monitoring points 1, 2 and 3 are well-type monitoring points, while monitoring points 4 and 5 are non-well-type monitoring points. The statistical period is 1 hour, and the minute-level sampling is once per minute. Consistency coefficient of dual probes at all monitoring points If all values ​​are ≥0, the sampling is considered valid. There are no consecutive invalid sampling cases. The initial water level value is taken as the arithmetic mean of the two probes. For wellless samples, the measured value of a single probe is taken. The weights are set as follows: , , , , , ; The visual management module has a fixed range of fidelity thresholds. Used to quickly determine the deviation level and fidelity threshold range of well network monitoring data. The calibration method is as follows: By utilizing a well network data monitoring database, regional samples with varying degrees of data deviation were screened, covering situations such as low data deviation (excellent fidelity), moderate data deviation (acceptable fidelity), and high data deviation (fidelity failure). Core monitoring indicators of the well network data in the sample areas (such as sensor parameters, point signals, data acquisition frequency, and numerical stability), device calibration records, and subsequent verification results of data application (such as data accuracy and application suitability) were extracted. Different candidate threshold ranges were set. In each calibration experiment, the data deviation level of the sample area was classified according to the candidate threshold range, and the matching degree between the classification results and the actual data exploration conclusions was recorded. Then, combined with regional well network dynamic monitoring data, the fidelity index of the sample area under different deviation intervention intensities was simulated. The changing trend was analyzed, and the upper and lower limits of the candidate threshold intervals were adjusted. Multiple verification experiments were conducted to record the impact of threshold interval settings on data deviation level assessment and subsequent well network data application. For each candidate threshold interval, the collected sample monitoring data and dynamic simulation results were used as inputs. The number of times low-deviation areas were misjudged as high-deviation areas due to improper interval range settings (counted as over-warning), the number of times high-deviation areas were misjudged as low-deviation areas (counted as insufficient warning), and the degree of fit between the data deviation level classification results and the subsequent well network application effectiveness (such as data fit, system stability, monitoring verification efficiency, etc.) were counted. Finally, the interval range that minimizes both over-warning rate and insufficient warning rate and has the highest degree of fit with the subsequent well network application effectiveness was selected as the fidelity threshold interval. The preferred range; Table 1 shows the fidelity threshold range in the fidelity index experimental data. The preferred range is 0.7-0.9. Based on the judgment, Fidelity index of target area A < This indicates that the deviation of the measured data in the target area is moderate, with a deviation level of 2. Response measures include marking the target area in yellow, continuing the application process of the well network data in the target area, automatically calibrating the sensor parameters of the device, and increasing the monitoring frequency. The dynamic monitoring module evaluates the hydraulic coupling state between each well monitoring point and the aquifer based on the well network dataset, sensor dataset, and experimental dataset, and generates the corresponding coupling index. ; Coupling index The calculation process is as follows: S21. Based on the well network dataset and the test dataset, extract well monitoring points. Distribution management data and hydraulic test data; S22, Calculation well monitoring point Specific well capacity decay coefficient Used for quantifying well-type monitoring points The degree of decay over time reflects the blockage status of the filter pipe and the surrounding aquifer, and its expression is as follows: In the formula, Indicates well-type monitoring points Stable water output during well completion acceptance. Indicates well-type monitoring points Drawdown during well completion acceptance Indicates well-type monitoring points Initial well capacity at the time of well completion acceptance; In the formula, Indicates well-type monitoring points The stable water output measured in the current micro-water test, Indicates well-type monitoring points The water level drawdown measured in the current micro-water test, Indicates well-type monitoring points Real-time specific well capacity measured by current micro-water test; In the formula, This indicates the maximum allowable attenuation of the well capacity, if the well monitoring point... Real-time specific well capacity measured by current micro-water test Initial specific well capacity at well completion acceptance The attenuation coefficient of the specific well capacity is then calculated using the attenuation formula. If well-type monitoring points Real-time specific well capacity measured by current micro-water test ≥ Initial specific well capacity at well completion acceptance Then the well capacity attenuation coefficient will be... A value of 1 is directly assigned (indicating that there is no well body attenuation and no deterioration in water permeability). Specifically, well-type monitoring points Specific well capacity decay coefficient When the value is ≤0, it indicates that the filter pipe and the surrounding aquifer are severely blocked, and the water permeability is basically lost. S23, Calculation well monitoring point Permeability attenuation coefficient Its expression is as follows: In the formula, Indicates well-type monitoring points The initial Lv Rong value at the time of well completion acceptance. This represents the real-time Lürren value measured in the current micro-water test. This indicates the maximum allowable attenuation of the Lü Rong value, if it is a well-type monitoring point. Real-time Lü Rong value measured by current micro-water test > Initial Lvrong value during well completion acceptance The attenuation coefficient of permeability is then calculated using the attenuation formula. If well-type monitoring points Real-time Lü Rong value measured by current micro-water test ≤ Initial Lvrong value at well completion acceptance Then the permeability attenuation coefficient will be... A value of 1 is directly assigned (indicating that there is no well body attenuation and no deterioration in water permeability). Specifically, well-type monitoring points Permeability attenuation coefficient When the value is ≤0, it also indicates that the filter pipe and the surrounding aquifer are severely blocked, and the permeability is basically lost. The change in the permeability of the aquifer and the filter pipe is quantified by the Lü Rong value to help verify the blockage status and form a cross-verification with the specific well capacity. S24, Calculation well monitoring point Hierarchical isolation Its expression is as follows: In the formula, This represents the electrical conductivity of the aquifer measured by the current pressure test. Indicates well-type monitoring points The electrical conductivity of the aquifer at the time of well completion acceptance. This indicates the maximum permissible deviation of the water's electrical conductivity; Specifically, by observing the changes in the electrical conductivity of the target aquifer, the water-stopping effect of the well can be quantified, and it can be determined whether there are problems such as aquifer cross-layering or water-stopping failure. S25, Calculation well monitoring point Structural integrity Its expression is as follows: In the formula, Indicates well-type monitoring points Total length of well casing Indicates well-type monitoring points detected by ultrasonic imaging. Total length of well casing defects; Specifically, well casing defects include types such as deformation, rupture, misalignment, collapse, and corrosion perforation. Ultrasonic imaging can be used to quantify the degree of damage to the physical structure of the well casing, and can intuitively reflect the development status and severity of structural defects such as well wall collapse, well casing deformation, rupture, and misalignment. S26, Calculation well monitoring point Response sensitivity This is used to quantify the well's response to aquifer water level dynamics, reflecting the lag and distortion risk of water level data. Its expression is as follows: In the formula, Indicates well-type monitoring points Water level response lag time during well completion testing This indicates the response lag time measured in the current pressure test, if it is a well-type monitoring point. The response lag time measured in the current pressure test ≥ Water level response lag time during well completion testing The response sensitivity is then calculated using the ratio formula. If well-type monitoring points The response lag time measured in the current pressure test <Water level response lag time during well completion testing Then the response sensitivity A direct value of 1 indicates that the well's response to aquifer water level dynamics has not diminished. S27. Based on S21-S26, calculate the well-type monitoring points using a weighted method. Coupling index Its expression is as follows: In the formula, , , , and All are weights, and satisfy the following conditions: ; Specifically, by quantifying the hydraulic coupling state between the monitoring well and the aquifer through multi-dimensional parameters, the health level of the well body can be accurately evaluated, ensuring that the monitoring data truly reflects the groundwater dynamics of the target aquifer, and providing core quantitative support for the stable operation of the groundwater monitoring network and the reliability of the data. The following are the experimental data for the coupling index, as shown in Table 2: Table 2: Experimental Data for the Coupling Index In Table 2, the coupling index experimental data were used, with well monitoring point a selected as the experimental target. The weights are set as follows: , , , , ; The visualization management module has a fixed range of coupling thresholds. It is used to quickly determine the anomaly level and coupling threshold range of the hydraulic coupling state between well monitoring points and aquifers. The calibration method is as follows: Using a groundwater well network coupling monitoring database, monitoring point samples with different hydraulic coupling states were screened, covering various situations such as normal hydraulic coupling (excellent coupling), slight anomaly (moderate coupling), and severe anomaly (coupling failure). Core hydraulic coupling monitoring data (such as well wall permeability, aquifer connectivity, water level response rate, and well structure integrity), well maintenance records, and subsequent application results of groundwater sensing data (such as data reliability and water level monitoring accuracy) were extracted from the sample monitoring points. Different candidate threshold ranges were set. In each calibration experiment, the hydraulic coupling anomaly level of the sample monitoring points was classified according to the candidate threshold range. The matching degree between the classification results and the actual coupling state exploration conclusions was recorded. Combined with regional groundwater dynamic monitoring data, the coupling index of the sample monitoring points under different coupling remediation intervention intensities was simulated. The changing trend was analyzed, and the upper and lower limits of the candidate threshold intervals were adjusted. Multiple sets of verification experiments were conducted to record the impact of threshold interval settings on the assessment of coupling anomaly levels and subsequent applications of groundwater sensor data. For each candidate threshold interval, the collected sample monitoring data and dynamic simulation results were used as inputs. The number of times normal coupling monitoring points were misclassified as abnormal monitoring points due to improper interval settings (counted as over-warning), the number of times severely abnormal monitoring points were misclassified as normal monitoring points (counted as under-warning), and the degree of fit between the hydraulic coupling anomaly level classification results and the subsequent groundwater monitoring application effectiveness (such as data fit, monitoring stability, and remediation efficiency) were calculated. Finally, the interval range that minimizes both the over-warning rate and the under-warning rate and has the highest degree of fit with the subsequent groundwater monitoring application effectiveness was selected as the coupling threshold interval. The preferred range; Table 2 shows the coupling threshold range in the experimental data of the coupling index. The preferred range is 0.7-0.9. Based on the judgment, Coupling index of well monitoring point a < This indicates a slight anomaly in the hydraulic coupling state between the corresponding well monitoring point and the aquifer, with an anomaly level of 2. Response measures include marking the well monitoring point as gray, continuing the application process of the groundwater sensing data of the corresponding monitoring point, automatically initiating the sludge flushing operation in the well, and increasing the monitoring frequency. The accuracy assessment module evaluates the accuracy of each well-free monitoring point based on the sensor dataset and the experimental dataset, and generates a corresponding accuracy index. ; Accuracy Index The calculation process is as follows: S31. Based on the sensor dataset and the experimental dataset, extract well-free monitoring points. Groundwater sensing data and hydraulic test data; S32, Calculation of well-free monitoring points Temporal consistency coefficient This is used to quantify the temporal synchronization between monitoring data and aquifer water level dynamics, reflecting the degree of lag in the monitoring response to aquifer water level changes. Its expression is as follows: In the formula, Indicates a well-free monitoring point The baseline response lag time during calibration testing This indicates the water level response lag time within the current monitoring period. If there are no well-type monitoring points... Water level response lag time during the current monitoring period ≥Reference response lag time during calibration test The timing consistency coefficient is then calculated using the ratio formula. If there are no well-type monitoring points Water level response lag time during the current monitoring period <Reference response lag time during calibration test Then the timing consistency coefficient A value of 1 is directly assigned (indicating that the monitoring response is sensitive to changes in aquifer water level). S33, Calculation of well-free monitoring points effective coefficient of signal This is used to quantify the ability of a monitoring point to acquire effective signals from the aquifer, reflecting the effectiveness of signal penetration, transmission, and identification. Its expression is as follows: In the formula, Indicates a well-free monitoring point The baseline signal-to-noise ratio during calibration testing This represents the measured signal-to-noise ratio within the current monitoring period. If there are no well-type monitoring points... Measured signal-to-noise ratio during the current monitoring period <Reference signal-to-noise ratio during calibration test The effective coefficient of the signal is then calculated using the ratio formula. If there are no well-type monitoring points Measured signal-to-noise ratio during the current monitoring period ≥ Reference signal-to-noise ratio during calibration test Then the effective coefficient of the signal will be... A value of 1 is directly assigned (indicating that the monitoring point's ability to acquire effective signals from the aquifer has not been attenuated). Specifically, for in-situ sensor buried monitoring points, the signal-to-noise ratio (SNR) is the ratio of the effective water level signal of the sensor to the environmental interference noise; for geophysical monitoring points, the SNR is the ratio of the effective response signal of the target aquifer to the background interference signal of the stratum; and for remote sensing inversion monitoring points, the SNR is the ratio of the effective inversion signal to atmospheric and surface interference noise. S34, Calculate the number of well-free monitoring points Inversion error rate This is used to quantify the accuracy of water level inversion, and its expression is as follows: In the formula, Indicates a well-free monitoring point The measured water level value, Indicates a well-free monitoring point The inverted water level value; S35. Based on S31-S34, calculate the number of well-less monitoring points using a weighted method. Accuracy Index Its expression is as follows: In the formula, , and All are weights, and satisfy the following conditions: ; The following is the experimental data for the Precision Index, as shown in Table 3: Table 3: Experimental Data for the Precision Index In Table 3, the wellless monitoring point b was selected as the experimental target in the precision index experimental data. The weights are set as follows: , , ; The visualization management module has a fixed range of precise thresholds. Used to quickly determine the stability level of wellless monitoring points, with precise threshold ranges. The calibration method is as follows: Using a groundwater wellless monitoring database, samples of monitoring points with varying degrees of stability were screened, covering situations such as stable (excellent accuracy), slight anomalies (moderate accuracy), and severe anomalies (accuracy failure). Core monitoring data (e.g., signal strength, inversion accuracy, environmental interference, equipment consistency), parameter optimization records, and subsequent application results of groundwater sensor data (e.g., data reliability, monitoring accuracy) were extracted from these samples. Different candidate threshold ranges were set, and in each calibration experiment, the stability levels of the sample monitoring points were classified based on these candidate threshold ranges. The matching degree between the classification results and the actual monitoring status survey conclusions was recorded. Combined with regional groundwater dynamic monitoring data, the accuracy index of the sample monitoring points under different parameter optimization intervention intensities was simulated. The changing trend was analyzed, and the upper and lower limits of the candidate threshold intervals were adjusted. Multiple sets of verification experiments were conducted to record the impact of threshold interval settings on stability level assessment and subsequent groundwater sensor data application. For each candidate threshold interval, the collected sample monitoring data and dynamic simulation results were used as inputs. The number of times high-stability monitoring points were misclassified as low-stability monitoring points due to improper interval settings (counted as over-assessment), the number of times low-stability monitoring points were misclassified as high-stability monitoring points (counted as under-assessment), and the degree of fit between the monitoring stability level classification results and the subsequent groundwater monitoring application effectiveness (such as data fit, system stability, calibration and verification efficiency, etc.) were calculated. Finally, the interval range that minimizes both over-assessment and under-assessment rates and has the highest degree of fit with the subsequent groundwater monitoring application effectiveness was selected as the accurate threshold interval. The preferred range; Table 3 shows the accuracy threshold range in the accuracy index experimental data. The preferred range is 0.5-0.8. Based on the judgment, <Accuracy Index of Well-less Monitoring Point b> < This indicates a slight anomaly in the monitoring status of the corresponding wellless monitoring point, with a stability level of 2. Response measures include marking the wellless monitoring point in black, continuing the application process of the groundwater sensor data of the corresponding wellless monitoring point, automatically optimizing signal identification and inversion parameters, and increasing the monitoring frequency. The visual management module has a fixed range of fidelity thresholds. Coupling threshold range and precise threshold range Combined with the fidelity index Coupling index and accuracy index It determines the deviation level of the measured data in the target area, the anomaly level of the hydraulic coupling state between the well monitoring point and the aquifer, and the stability level without well monitoring points, and outputs the corresponding judgment results and response measures. The deviation level assessment process is as follows: Let the upper limit of the fidelity threshold range be denoted as The lower limit of the fidelity threshold range is denoted as ; If the fidelity index of the target area > This indicates that the deviation of the measured data in the target area is low, with a deviation level of 1. Response measures include marking the target area in green, continuing the application process of the well network data for the target area, and maintaining the current monitoring frequency. ≤ Fidelity index of the target area ≤ This indicates a moderate deviation in the measured data for the target area, with a deviation level of 2. Response measures include marking the target area in yellow, continuing the application process of the well network data for the target area, automatically calibrating the sensor parameters, and increasing the monitoring frequency. If the fidelity index of the target area... < This indicates a high degree of deviation in the measured data for the target area, with a deviation level of 3. Response measures include marking the target area in red, suspending the application of well network data for the target area, reminding management personnel to conduct on-site repairs of offline monitoring equipment, and carrying out a comprehensive verification of all monitoring equipment until the fidelity index of the target area is reached. ≥ This allows for the restoration of the application process for well network data in the target area; The anomaly level assessment process is as follows: Let the upper limit of the coupling threshold interval be denoted as Let the lower limit of the coupling threshold interval be denoted as ; If the coupling index > This indicates that the hydraulic coupling between the corresponding well monitoring point and the aquifer is normal, with an anomaly level of 1. Response measures include marking the well monitoring point in blue, continuing the application process of the groundwater sensor data from the corresponding monitoring point, and maintaining the current monitoring frequency. ≤ Coupling Index ≤ This indicates a slight anomaly in the hydraulic coupling between the corresponding well monitoring point and the aquifer, with an anomaly level of 2. Response measures include marking the well monitoring point as gray, continuing the application process of the groundwater sensor data for the corresponding monitoring point, automatically initiating well sludge flushing operations, and increasing the monitoring frequency. If the coupling index... < , indicating that the hydraulic coupling state between the corresponding well-type monitoring point and the aquifer is severely abnormal, with an abnormal level of 3. The response measures include marking the well-type monitoring point orange, suspending the application process of groundwater sensing data for the corresponding monitoring point, reminding the management to conduct on-site inspection and ultrasonic imaging detection, locating the well body defects and formulating a repair plan, simultaneously increasing the monitoring frequency of the adjacent monitoring points around, and resuming the application process of groundwater sensing data for the corresponding monitoring point when the coupling index of the corresponding monitoring point ≥ . The stable level assessment process is as follows: Record the upper limit of the accurate threshold interval as , and record the lower limit of the accurate threshold interval as ; If the accurate index > , it indicates that the monitoring state of the corresponding non-well-type monitoring point is stable, with a stable level of 1. The response measures include marking the non-well-type monitoring point purple, continuing the application process of groundwater sensing data for the corresponding non-well-type monitoring point, maintaining the current monitoring frequency. If ≤ accurate index ≤ , it indicates that the monitoring state of the corresponding non-well-type monitoring point is slightly abnormal, with a stable level of 2. The response measures include marking the non-well-type monitoring point black, continuing the application process of groundwater sensing data for the corresponding non-well-type monitoring point, automatically optimizing the signal recognition and inversion parameters, and increasing the monitoring frequency. If the accurate index < , it indicates that the monitoring state of the corresponding non-well-type monitoring point is severely abnormal, with a stable level of 3. The response measures include marking the non-well-type monitoring point pink, suspending the application process of groundwater sensing data for the corresponding non-well-type monitoring point, reminding the management to conduct on-site calibration and equipment verification, simultaneously increasing the monitoring frequency of the adjacent monitoring points around, and resuming the application process of groundwater sensing data for the corresponding non-well-type monitoring point when the accurate index of the corresponding non-well-type monitoring point ≥ .

[0020] In this embodiment, a full-chain closed-loop optimization is formed from data acquisition, quality control, well body health, non-well-type monitoring accuracy to visualization decision-making, significantly overcoming the hidden defects of traditional groundwater monitoring systems. The multi-dimensional monitoring module uses dual-redundant probes, ultrasonic imaging and multi-device linkage acquisition to uniformly construct three major data sets of well network, sensing and test, achieving full coverage of well-type and non-well-type monitoring, solving the problems of single data source, invisible well body state and lack of standardized acquisition for non-well-type points in traditional systems, greatly improving the integrity of raw data and scene adaptability. The quality verification module calculates the fidelity index through the consistency of dual probes, environmental error compensation and sampling integrity weighting It automatically identifies abnormal sampling and offline status, enabling real-time quantification and self-verification of data quality. This avoids data distortion caused by pressure drift, temperature interference, and signal packet loss from the source. The multi-dimensional monitoring offers high fidelity. The dynamic monitoring module, targeting well-type monitoring points, calculates the coupling index from multiple dimensions, including specific well capacity decay, permeability changes, stratification isolation, structural integrity, and response sensitivity. It accurately reflects hidden problems such as filter pipe blockage, aquifer cross-contamination, and well pipe damage, enabling early warning of well health status and avoiding long-term distortion of water level monitoring due to well failure. The accuracy assessment module is designed for wellless monitoring points, constructing an accuracy index through temporal consistency, signal validity, and inversion accuracy. This system unifies and quantifies the lag, noise, and error issues of well-free monitoring, overcoming the shortcomings of traditional well-free monitoring such as lack of quantitative assessment and uncontrollable accuracy. It improves the density and coverage of the regional monitoring network. The visualization management module automatically classifies data based on three indices and threshold ranges, and intuitively presents data deviations, well anomalies, and point stability status with color indicators. It automatically triggers response measures such as calibration, flushing, and maintenance, enabling rapid anomaly location and closed-loop operation and maintenance. It transforms traditional passive emergency repair into proactive prediction and control, improves the intelligence level and decision support capabilities of groundwater dynamic monitoring, and makes intelligent operation and maintenance response more timely.

[0021] The threshold is set to facilitate comparison. The size of the threshold depends on the amount of sample data and the number of bases set by those skilled in the art for each set of sample data; as long as it does not affect the ratio between the parameter and the quantized value, it is acceptable.

[0022] The above formulas are all derived from software simulation using a large amount of data and are selected to be close to the actual values. The coefficients in the formulas are set by those skilled in the art according to the actual situation. The above description is only a preferred embodiment of the present invention, but the protection scope of the present invention is not limited thereto. Any equivalent substitutions or changes made by those skilled in the art within the technical scope disclosed in the present invention, based on the technical solution and inventive concept of the present invention, should be covered within the protection scope of the present invention.

Claims

1. A visual groundwater level dynamic monitoring system, characterized in that: It includes a multi-dimensional monitoring module, a quality verification module, a dynamic monitoring module, an accuracy assessment module, and a visualization management module; The multidimensional monitoring module acquires distribution management data, groundwater sensing data, and hydraulic test data of all monitoring points by connecting to a database, dual redundant probe water level gauges, ultrasonic imaging detection devices, and sensing devices, and classifies them into well network datasets, sensing datasets, and test datasets. The quality verification module evaluates the fidelity of the measured data in the target area based on the well network dataset and the sensor dataset, and generates a corresponding fidelity index. ; The dynamic monitoring module evaluates the hydraulic coupling state between each well monitoring point and the aquifer based on the well network dataset, sensor dataset, and experimental dataset, and generates a corresponding coupling index. ; The accuracy assessment module evaluates the accuracy of each well-free monitoring point based on the sensor dataset and the experimental dataset, and generates a corresponding accuracy index. ; The visualization management module is set with a fixed range of fidelity thresholds. Coupling threshold range and precise threshold range Combined with the fidelity index Coupling index and accuracy index It determines the deviation level of measured data in the target area, the anomaly level of the hydraulic coupling state between well monitoring points and aquifers, and the stability level of data without well monitoring points, and outputs the corresponding judgment results and response measures.

2. The visualized groundwater level dynamic monitoring system according to claim 1, characterized in that: The well network dataset includes the monitoring methods and the number of monitoring points within the target area. The monitoring methods include well-based monitoring, wellless monitoring, and hybrid monitoring.

3. The visualized groundwater level dynamic monitoring system according to claim 2, characterized in that: The sensor dataset includes synchronized minute-level water level readings from dual redundant probes at each monitoring point, maximum permissible deviation of the dual probes, standard atmospheric pressure, measured atmospheric pressure in the field, standard density of pure water, local gravitational acceleration, water level temperature expansion coefficient, standard temperature value for water level measurement, measured water temperature value in the field, maximum tolerance for environmental error compensation, total theoretical sampling times, effective sampling times, calibration reference response lag time for wellless monitoring points, measured water level response lag time, calibration reference signal-to-noise ratio, measured signal-to-noise ratio, measured water level value, and inverted water level value.

4. The visualized groundwater level dynamic monitoring system according to claim 3, characterized in that: The test dataset includes the initial stable water output, initial drawdown, initial specific well capacity, initial Lurong value, initial aquifer water conductivity, and initial water level response lag time of each well during well completion and acceptance; the real-time stable water output, real-time drawdown, real-time specific well capacity, real-time Lurong value, real-time aquifer water conductivity, and real-time water level response lag time measured in the current micro-water test; the maximum allowable attenuation of specific well capacity; the maximum allowable attenuation of Lurong value; the maximum allowable deviation of water conductivity; the total length of the well casing; and the total length of well casing defects measured by ultrasonic imaging.

5. The visualized groundwater level dynamic monitoring system according to claim 4, characterized in that: The fidelity index The calculation process is as follows: S11. Based on the well network dataset and sensor dataset, extract the distribution management data and groundwater level sensor data of all monitoring points within the target area, and record the number of monitoring points within the target area as follows: ; S12. Calculate monitoring points for the target area. Dual probe consistency coefficient ; S13, Based on monitoring points Dual probe consistency coefficient Determine the monitoring point The validity of a single sampling is used to determine the initial water level value before error compensation. The determination rules are as follows: If the consistency coefficient of the dual probes If the reading is ≥0, the dual probes are considered to be working properly, and this sampling is considered valid. The arithmetic mean of the water level readings from both probes is taken as the monitoring point. Initial water level before error compensation If the consistency coefficient of the dual probes If the value is less than 0, it indicates that at least one probe's data is abnormal, and this sampling is invalid. A new monitoring point should be selected. The previous normal minute-level water level final value is used as the current water level initial value. ; If monitoring point If more than three consecutive invalid samples are collected, the monitoring point will be marked. In offline mode, take monitoring points The final value of the water level in the first normal minute before going offline is used as the initial value of the current water level. ; S14. To address water level measurement errors caused by environmental factors such as air pressure and temperature, adjust the initial water level value. Perform error compensation and calculate monitoring points. Standard water level value after error compensation and error compensation coefficient ; S15. For the target area, calculate the monitoring points within the statistical period. Sampling integrity coefficient ; S16. Calculate monitoring points for the target area. stability index ; S17. Calculate the percentage of well-type monitoring points within the target area. The proportion of well-free monitoring ; S18. Based on S11-S17, calculate the fidelity index of the well network in the target area using a weighted method. .

6. The visualized groundwater level dynamic monitoring system according to claim 5, characterized in that: The coupling index The calculation process is as follows: S21. Based on the well network dataset and the test dataset, extract well monitoring points. Distribution management data and hydraulic test data; S22, Calculation well monitoring point Specific well capacity decay coefficient ; S23, Calculation well monitoring point Permeability attenuation coefficient ; S24, Calculation well monitoring point Hierarchical isolation ; S25, Calculation well monitoring point Structural integrity ; S26, Calculation well monitoring point Response sensitivity ; S27. Based on S21-S26, calculate the well-type monitoring points using a weighted method. Coupling index .

7. The visualized groundwater level dynamic monitoring system according to claim 6, characterized in that: The accuracy index The calculation process is as follows: S31. Based on the sensor dataset and the experimental dataset, extract well-free monitoring points. Groundwater sensing data and hydraulic test data; S32, Calculation of well-free monitoring points Temporal consistency coefficient ; S33, Calculation of well-free monitoring points effective coefficient of signal ; S34, Calculate the number of well-free monitoring points Inversion error rate ; S35. Based on S31-S34, calculate the number of well-less monitoring points using a weighted method. Accuracy Index .

8. The visualized groundwater level dynamic monitoring system according to claim 7, characterized in that: The deviation level assessment process is as follows: Let the upper limit of the fidelity threshold range be denoted as The lower limit of the fidelity threshold range is denoted as ; If the fidelity index of the target area > This indicates that the deviation of the measured data in the target area is low, with a deviation level of 1. Response measures include marking the target area in green, continuing the application process of the well network data for the target area, and maintaining the current monitoring frequency. ≤ Fidelity index of the target area ≤ This indicates a moderate deviation in the measured data for the target area, with a deviation level of 2. Response measures include marking the target area in yellow, continuing the application process of the well network data for the target area, automatically calibrating the sensor parameters, and increasing the monitoring frequency. If the fidelity index of the target area... < This indicates a high degree of deviation in the measured data for the target area, with a deviation level of 3. Response measures include marking the target area in red, suspending the application of well network data for the target area, reminding management personnel to conduct on-site repairs of offline monitoring equipment, and carrying out a comprehensive verification of all monitoring equipment until the fidelity index of the target area is reached. ≥ This allows for the restoration of the application process for well network data in the target area.

9. The visualized groundwater level dynamic monitoring system according to claim 8, characterized in that: The anomaly level assessment process is as follows: Let the upper limit of the coupling threshold interval be denoted as Let the lower limit of the coupling threshold interval be denoted as ; If the coupling index > This indicates that the hydraulic coupling between the corresponding well monitoring point and the aquifer is normal, with an anomaly level of 1. Response measures include marking the well monitoring point in blue, continuing the application process of the groundwater sensor data from the corresponding monitoring point, and maintaining the current monitoring frequency. ≤ Coupling Index ≤ This indicates a slight anomaly in the hydraulic coupling between the corresponding well monitoring point and the aquifer, with an anomaly level of 2. Response measures include marking the well monitoring point as gray, continuing the application process of the groundwater sensor data for the corresponding monitoring point, automatically initiating well sludge flushing operations, and increasing the monitoring frequency. If the coupling index... < This indicates a severe anomaly in the hydraulic coupling between the corresponding well monitoring point and the aquifer, with an anomaly level of 3. Response measures include marking the well monitoring point as orange, suspending the application of groundwater sensor data from the corresponding monitoring point, reminding management personnel to conduct on-site inspections and ultrasonic imaging to locate well defects and develop repair plans, and simultaneously increasing the monitoring frequency of adjacent monitoring points until the coupling index of the corresponding monitoring point is reached. ≥ This allows for the restoration of the application process for groundwater sensor data at the corresponding monitoring point.

10. A visual groundwater level dynamic monitoring system according to claim 9, characterized in that: The stability level assessment process is as follows: Let the upper limit of the precise threshold range be denoted as The lower limit of the precise threshold interval is denoted as ; If the accuracy index > This indicates that the monitoring status of the corresponding wellless monitoring point is stable, with a stability level of 1. Response measures include marking the wellless monitoring point in purple, continuing the application process of the groundwater sensor data from the corresponding wellless monitoring point, and maintaining the current monitoring frequency. ≤Accuracy Index ≤ This indicates a slight anomaly in the monitoring status of the corresponding wellless monitoring point, with a stability level of 2. Response measures include marking the wellless monitoring point in black, continuing the application process of the groundwater sensor data from the corresponding wellless monitoring point, automatically optimizing signal identification and inversion parameters, and increasing the monitoring frequency. If the accuracy index... < This indicates a severely abnormal monitoring status at the corresponding wellless monitoring point, with a stability level of 3. Response measures include marking the wellless monitoring point in pink, suspending the application of groundwater sensor data from the corresponding wellless monitoring point, reminding management personnel to conduct on-site calibration and equipment verification, and simultaneously increasing the monitoring frequency of adjacent monitoring points until the accuracy index of the corresponding wellless monitoring point is restored. ≥ This allows for the restoration of the application process for groundwater sensor data from the corresponding wellless monitoring point.

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