A pumped storage power station slope groundwater level dynamic monitoring system

Abstract

The application relates to the technical field of liquid level measurement, in particular to a dynamic monitoring system for the underground water level of the slope of a pumped storage power station. The air jumping index of each monitoring point is obtained by analyzing the change of the extrusion pressure in the current statistical period, and the abnormal degree of the extrusion pressure of each monitoring point is comprehensively determined in combination with the state of adjacent monitoring points, so as to identify the air wall interference. Then, according to the turbidity of the slurry, the duration and the pressure difference change law of the upper and lower adjacent depth layers, the plugging delay degree of each monitoring point is calculated. Then, the correction coefficient is derived in combination with the abnormal degree of the extrusion pressure and the plugging delay degree, the original pressure data is dynamically compensated and attenuated, so as to strip the air wall false height and the silt delay, and the real extrusion pressure in each direction is obtained. Finally, the real extrusion pressure is used to convert the absolute underground water level elevation in real time through the principle of fluid static pressure. The application can improve the accuracy and timeliness of underground water level monitoring.

Description

Technical Field

[0001] This application belongs to the field of liquid level measurement technology, specifically relating to a dynamic monitoring system for groundwater level on the slope of a pumped storage power station. Background Technology

[0002] Pumped-storage hydroelectric power stations are typically built into mountainsides. The slopes between the upper and lower reservoirs consist of complex soil, weathered rock, and a network of underground fissures. During extreme rainstorms, large amounts of rainwater seep into the ground along the slopes. To prevent excessive water accumulation inside the mountain and the resulting outward pushing force that could lead to landslides, it is usually necessary to drill holes and install water level monitoring pipes inside the mountain to dynamically monitor the groundwater level.

[0003] However, existing technologies have significant drawbacks: During heavy downpours, air trapped in soil crevices is compressed downwards, creating air walls. These air walls not only block groundwater from entering the measuring pipe but also frequently compress the sensor, causing drastic fluctuations in the sensor data and leading to false alarms or distorted water level calculations. Furthermore, groundwater infiltration within slopes often follows specific rock fissures, forming extremely fast and destructive underground jets. Because existing filter pipes are continuous, the water flow mixes together, resulting in a static, stagnant water level. This makes it impossible to identify the direction of localized water flow. The internal continuity also means that localized high-pressure jets are diluted by the stagnant water, masking the true dangerous water pressure and leading to delayed groundwater level warnings. Existing technologies cannot distinguish between air walls (interference noise) and underground jets (real danger).

[0004] In addition, existing technologies cannot solve the problem of pressure transmission lag caused by siltation in the measuring tube. Summary of the Invention

[0005] To address the aforementioned problems, this application provides a dynamic monitoring system for groundwater levels on slopes of pumped-storage power stations. The system includes: The data acquisition module is used to acquire the original extrusion pressure and mud-water turbidity at any monitoring point; The extrusion pressure anomaly determination module is used to calculate the air vibration index based on the fluctuation characteristics of the original extrusion pressure at any monitoring point within the current statistical period, and to determine the extrusion pressure anomaly at any monitoring point by combining the abnormal state of the air vibration index at monitoring points in the same direction in adjacent monitoring locations; The compression hysteresis degree determination module is used to calculate the compression hysteresis degree of any monitoring point based on the mud and water turbidity of any monitoring point within the current statistical period; The blockage hysteresis degree determination module is used to calculate the blockage hysteresis degree of any monitoring point based on the pressure difference change pattern between any monitoring point and monitoring points in the same direction of adjacent depth layers above and below; The correction coefficient generation module is used to obtain the corrected degree of extrusion lag based on the degree of extrusion lag and the degree of blockage lag, and to generate the extrusion pressure correction coefficient for any monitoring point based on the degree of extrusion pressure anomaly and the corrected degree of extrusion lag. The absolute groundwater level elevation determination module is used to correct the original extrusion pressure using the extrusion pressure correction coefficient to obtain the true extrusion pressure at any monitoring point, and to calculate the absolute groundwater level elevation at any monitoring point based on the true extrusion pressure.

[0006] In one embodiment, the extrusion pressure anomaly determination module is further configured to: Obtain the extrusion pressure fluctuation curve of any monitoring point within the current statistical period; Based on the peak detection algorithm, all extreme points on the extrusion pressure fluctuation curve are extracted to divide the extrusion pressure fluctuation curve into multiple line segments, and the absolute value of the difference between the maximum and minimum extrusion pressure on any of the multiple line segments is obtained. The number of line segments of the extrusion pressure fluctuation curve, the number of abnormal line segments of the extrusion pressure fluctuation curve, and the mean of the absolute values ​​of the differences of all line segments of the extrusion pressure fluctuation curve are obtained. The abnormal line segments are line segments whose absolute values ​​of the differences exceed a first preset threshold. The air vibration index of any monitoring point is obtained based on the number of line segments, the number of abnormal line segments, and the mean of the absolute values ​​of the differences.

[0007] In one embodiment, the extrusion pressure anomaly determination module is further configured to: Obtain the number of adjacent monitoring locations that are horizontally adjacent to the monitoring location of any of the monitoring points, wherein each monitoring location includes at least one monitoring point; The number of monitoring points with abnormal air vibration index in the adjacent monitoring locations that are in the same direction as any of the monitoring points is obtained. The monitoring points with abnormal air vibration index are those whose air vibration index exceeds a second preset threshold. The degree of pressure abnormality at any monitoring point is obtained based on the air vibration index, the number of adjacent monitoring locations, and the number of monitoring points with abnormal air vibration index.

[0008] In one embodiment, the compression hysteresis degree determination module is further configured to: Obtain the average mud and water turbidity at any of the monitoring points within the current statistical period; In a single precipitation event, the cumulative time during which the average value of the mud and water turbidity continuously exceeds a preset safety threshold is obtained; The degree of compression lag at any monitoring point is obtained based on the average turbidity of the mud and water and the cumulative time.

[0009] In one embodiment, the congestion hysteresis degree determination module is further configured to: Obtain the absolute value of the first pressure difference, which is the absolute value of the difference between the average original extrusion pressure of any monitoring point in the current statistical period and the average original extrusion pressure in the previous statistical period. Obtain the second absolute pressure difference, which is the absolute value of the difference between the average original extrusion pressure of the current statistical period and the average original extrusion pressure of the previous statistical period of the monitoring points in the same direction of the upper adjacent layer of any monitoring point. Obtain the absolute value of the third pressure difference, which is the absolute value of the difference between the average original extrusion pressure of the monitoring points in the same direction in the current statistical period and the average original extrusion pressure of the previous statistical period in the next lower layer of any monitoring point. The degree of blockage delay at any monitoring point is determined based on the first absolute value of the pressure difference, the second absolute value of the pressure difference, and the third absolute value of the pressure difference.

[0010] In one embodiment, the correction coefficient generation module is further configured to: Obtain the hysteresis compensation weight coefficient and the extrusion pressure anomaly weight coefficient; Based on the hysteresis compensation weight coefficient, the extrusion pressure anomaly weight coefficient, the degree of extrusion pressure anomaly, and the corrected degree of extrusion hysteresis, the extrusion pressure correction coefficient for any monitoring point is generated.

[0011] In one embodiment, the absolute groundwater level elevation determination module is further used for: The actual extrusion pressure at any monitoring point is determined by multiplying the extrusion pressure correction factor and the original extrusion pressure.

[0012] In one embodiment, the absolute groundwater level elevation determination module is further used for: Obtain the vertical elevation of any of the monitoring points; Based on the vertical elevation and the actual compressive force, the absolute groundwater level elevation of any monitoring point is determined.

[0013] In one embodiment, the system further includes: The alarm module is used to obtain the dynamic water level elevation of the pumped storage power station slope based on the absolute groundwater level elevation, and to trigger a disaster prevention alarm when the dynamic water level elevation exceeds a first threshold or the rate of rise of the dynamic water level elevation exceeds a second threshold.

[0014] In one embodiment, the system further includes: a reset module; The reset module is used to: reset the cumulative time to zero when the average turbidity of the mud and water at any of the monitoring points falls back and remains below the preset safety threshold for more than a set period of time.

[0015] The embodiments of this application provide the following beneficial effects: A dynamic monitoring system for groundwater levels on pumped-storage power station slopes is provided. By analyzing the changes in extrusion pressure within the current statistical period, the air movement index of each monitoring point is obtained. Combined with the status of adjacent monitoring points, the degree of extrusion pressure anomaly at each monitoring point is determined, thereby identifying air wall interference. Subsequently, based on the turbidity of the mud and water, the duration, and the pressure difference variation between adjacent depth layers, the degree of blockage hysteresis at each monitoring point is calculated. Next, a correction coefficient is derived by combining the degree of extrusion pressure anomaly and the degree of blockage hysteresis, dynamically compensating and attenuating the original pressure data, thereby eliminating the artificial height of the air wall and the sludge hysteresis, and obtaining the true extrusion pressure in all directions. Finally, the absolute groundwater level elevation is calculated in real time using this true extrusion pressure through the principle of hydrostatic pressure. The embodiments of this application can improve the accuracy and timeliness of groundwater level monitoring. Attached Figure Description

[0016] Figure 1 This is a block diagram of a dynamic monitoring system for groundwater level on the slope of a pumped storage power station provided by the present invention; Figure 2 This is a block diagram of another dynamic monitoring system for groundwater level on the slope of a pumped storage power station provided by the present invention. Detailed Implementation

[0017] Figure 1 This is a block diagram illustrating a dynamic monitoring system for groundwater levels on the slope of a pumped-storage power station, according to an exemplary embodiment. Figure 1 As shown in the figure, this application provides a dynamic monitoring system for groundwater level on the slope of a pumped storage power station, which may include the following modules: The data acquisition module is used to acquire the original extrusion pressure and mud-water turbidity at any monitoring point.

[0018] The extrusion pressure anomaly determination module is used to calculate the air vibration index based on the fluctuation characteristics of the original extrusion pressure at any monitoring point within the current statistical period, and to determine the degree of extrusion pressure anomaly at any monitoring point by combining the abnormal state of the air vibration index at monitoring points in the same direction in adjacent monitoring locations.

[0019] The compression hysteresis degree determination module is used to calculate the compression hysteresis degree of any monitoring point based on the mud and water turbidity of any monitoring point within the current statistical period.

[0020] The blockage hysteresis degree determination module is used to calculate the blockage hysteresis degree of any monitoring point based on the pressure difference change pattern between any monitoring point and the monitoring points in the same direction of the adjacent depth layers above and below.

[0021] The correction coefficient generation module is used to obtain the corrected degree of extrusion lag based on the degree of extrusion lag and the degree of blockage lag, and to generate the extrusion pressure correction coefficient for any monitoring point based on the degree of extrusion pressure anomaly and the corrected degree of extrusion lag.

[0022] The absolute groundwater level elevation determination module is used to correct the original extrusion pressure using the extrusion pressure correction coefficient to obtain the true extrusion pressure at any monitoring point, and to calculate the absolute groundwater level elevation at any monitoring point based on the true extrusion pressure.

[0023] For example, this application provides a dynamic monitoring system for groundwater levels on pumped-storage power station slopes. The system initially operates through a data acquisition module. During system deployment, multiple specially designed water level monitoring pipes are arranged in a grid pattern along the slope area, with each pipe serving as an independent monitoring location. Within each monitoring pipe, a measurement chamber is installed every 0.5 meters vertically. Each chamber is further divided into four independent quadrants (east, south, west, and north) by cross-shaped baffles. Each quadrant contains a high-frequency micro-differential pressure sensor and an optical turbidity probe. The data acquisition module collects the raw pressure values ​​and mud-water turbidity at each monitoring point at a high frequency of 100Hz, providing the raw data stream for subsequent analysis.

[0024] Next, the module for determining the degree of extrusion pressure anomaly begins operation. This module first segments the extrusion pressure fluctuation curve of any monitoring point within a 1-second statistical period into multiple line segments using a peak detection algorithm. Since air is compressible while water is incompressible, the presence of an air wall causes drastic pressure fluctuations, manifested as a large difference between the maximum and minimum values ​​within a line segment. Based on this, the module calculates the fluctuation amplitude of each line segment and counts the number of abnormal line segments exceeding a threshold, ultimately quantifying an air fluctuation index. Subsequently, to distinguish between air wall interference and genuine underground jets, the module also examines spatial consistency: if the air fluctuation index of a monitoring point is abnormal, but no abnormalities are observed at adjacent monitoring locations in the same direction (e.g., all due east), it is determined that the anomaly is likely caused by localized jets rather than a large-area air wall, and the degree of extrusion pressure anomaly is adjusted accordingly.

[0025] Meanwhile, the modules for determining the degree of squeezing hysteresis and the degree of blockage hysteresis address the issue of sediment interference. The squeezing hysteresis module obtains the average turbidity of the mud and water within the current statistical period and tracks the duration of continuous turbidity exceeding the threshold during a single precipitation event. Multiplying and normalizing these two values ​​yields a squeezing hysteresis degree, reflecting the "viscous" effect of pressure transmission caused by sediment adhesion. The blockage hysteresis degree determination module compares the pressure change rate at the current monitoring point with that of monitoring points in the same direction at adjacent depth layers. If the pressure response of the current layer is significantly slower than that of the adjacent layers, it indicates that the inlet may be severely blocked, unable to detect changes in actual external water pressure, and thus calculates a blockage hysteresis degree.

[0026] Then, the correction coefficient generation module integrates the above results, fusing the squeezing hysteresis degree with the blockage hysteresis degree to obtain a corrected squeezing hysteresis degree. Next, a dynamic squeezing pressure correction coefficient is calculated using the corrected squeezing hysteresis degree and the squeezing pressure anomaly degree. This coefficient can intelligently adjust the correction direction according to the current disturbance type: for pressure inflation caused by air walls, the coefficient will be less than 1 to weaken the pressure; for pressure hysteresis caused by silt, the coefficient will be greater than 1 to compensate for the pressure.

[0027] Finally, the absolute groundwater level determination module multiplies the original compressive pressure by a compressive pressure correction factor to obtain the true compressive pressure after removing interference. Combined with the pre-marked physical elevation of the monitoring point, the absolute groundwater level elevation at that point is calculated based on the principle of hydrostatic pressure. By integrating the elevation data from all points, the system provides high-fidelity water level data for subsequent disaster prevention and alarm systems.

[0028] The above process in the embodiments of this application, from data acquisition, multi-dimensional interference identification, dynamic correction to water level inversion, forms a complete logical closed loop, which effectively solves the problem that the existing technology cannot distinguish between the false height of the air wall, sediment stagnation and the real jet pressure.

[0029] In one possible implementation, the extrusion pressure anomaly determination module is further configured to: Obtain the extrusion pressure fluctuation curve of any monitoring point within the current statistical period.

[0030] Based on the peak detection algorithm, all extreme points on the extrusion pressure fluctuation curve are extracted to divide the extrusion pressure fluctuation curve into multiple line segments, and the absolute value of the difference between the maximum and minimum extrusion pressure on any of the multiple line segments is obtained.

[0031] The number of line segments of the extrusion pressure fluctuation curve, the number of abnormal line segments of the extrusion pressure fluctuation curve, and the mean of the absolute values ​​of the differences of all line segments of the extrusion pressure fluctuation curve are obtained. The abnormal line segments are line segments whose absolute values ​​of the differences exceed a first preset threshold.

[0032] The air vibration index of any monitoring point is obtained based on the number of line segments, the number of abnormal line segments, and the mean of the absolute values ​​of the differences.

[0033] For example, the module for determining the degree of extrusion pressure anomaly employs a sophisticated algorithm to calculate the air fluctuation index, thereby accurately quantifying air wall interference. When heavy rain causes high-pressure air to surround the sensor, due to the compressibility of air, it exhibits violent fluctuations of contraction and rebound when impacted by water flow. In contrast, the actual static water pressure change is gradual and incompressible over a very short period (e.g., 1 second). Based on this fundamental physical difference, the module uses a 1-second statistical period to extract the fluctuation curve of the original extrusion pressure over time at any monitoring point within the current period. Within the current 1-second statistical period, with the time series as the horizontal axis and the 100 discrete extrusion pressure data points collected by the sensor in real time as the vertical axis, the discrete data points are connected sequentially according to time to construct a discrete "time-pressure" fluctuation curve for that period.

[0034] To quantify this fluctuation, the module first invokes a peak detection algorithm. This algorithm scans the extrusion pressure fluctuation curve, identifying all local maxima and minima, which form the "skeleton" of the curve's fluctuation. Subsequently, the algorithm uses these extreme points as dividing boundaries to segment the entire continuous fluctuation curve into N interconnected line segments. Each line segment represents the continuous trajectory of extrusion pressure during a single rise or fall.

[0035] Based on this, the module performs feature analysis on each line segment. For each line segment, it calculates the maximum and minimum values ​​of the compressive pressure at its endpoints (or within the interval), and obtains the absolute value of the difference between the two, denoted as A. This value A directly reflects the intensity of pressure fluctuations within that time period. The module compares this value A with a preset first threshold T1 (i.e., the first preset threshold, for example, an engineering experience value of 1, with units consistent with pressure units). If A is less than T1, it indicates that the pressure change is gradual, consistent with the fluctuation characteristics of pure water pressure, and is judged as normal fluctuation; conversely, if A is greater than or equal to T1, it indicates that the pressure within the line segment has experienced violent oscillations, which is a typical manifestation of high-pressure bubble rupture and expansion, and therefore the line segment is judged as an abnormal fluctuation line segment.

[0036] T1 represents the baseline background noise and upper limit of normal small fluctuations of the micro-differential pressure sensor under pure still water or uniform and stable seepage conditions. It relies on a combination of factory calibration and field background testing. The peak-to-peak noise of the monitoring tube is measured in a static water column in the laboratory, and the baseline of water pressure fluctuation at its actual installation depth is measured in a dry season with no rainfall on the slope. The maximum fluctuation amplitude of the two measurements is taken and multiplied by a certain safety margin (such as 1.5 times) as the final value of T1.

[0037] After calculating the number N1 of abnormal fluctuation segments throughout the entire statistical period, if N1 equals 0, then the air vibration index is directly set. The value is assigned to 0; if N1 is greater than 0, the module further calculates the arithmetic mean of the A values ​​on these abnormal line segments, denoted as . This mean reflects the average intensity of air wall interference.

[0038] For example, the air turbulence index at the j-th monitoring point of the i-th monitoring location in the m-th layer. It can be obtained from the following formula: Formula 1 in, This represents the number of line segments on the extrusion pressure fluctuation curve at the j-th monitoring point of the i-th monitoring location in the m-th layer. This value is not zero. To normalize the results, the calculation results are mapped to a range that is easy to compare (such as 0 to 1).

[0039] It should be noted that the norm function processing involved in this application all adopts the min-max normalization method, the purpose of which is to linearly map the actual calculated values ​​of each feature to the dimensionless interval [0,1]. In this normalization process, the minimum value of each scenario is fixed at 0 (representing the initial lower limit of the physical state or a state without anomalies). Moreover, if the normalized object is greater than the maximum value in the corresponding min-max normalization method, the normalization result is limited to 1; if the normalized object is less than the minimum value in the corresponding min-max normalization method, the normalization result is limited to 0.

[0040] When calculating the air turbulence index, the maximum value of the normalized treatment is set as: the theoretical maximum hydrostatic pressure limit that the monitoring point may experience at this depth layer.

[0041] This index cleverly combines the proportional factor (N1 / N) of abnormal segments with the average amplitude of abnormal fluctuations. The scaling factor reflects the temporal coverage of the airwall interference, while the average amplitude reflects the intensity of the interference. Therefore, when A higher value indicates that the monitoring point has not only been frequently impacted by air barriers during the current statistical period, but also that each impact has been very strong, making the data highly reliable in terms of air barrier contamination. Conversely, A smaller value indicates that the pressure fluctuations are stable and the data is accurate and reliable.

[0042] This application embodiment transforms the complex physical phenomena of the air wall into quantifiable anomaly indicators through mathematical modeling, providing a clear and objective basis for subsequent data correction.

[0043] In one possible implementation, the extrusion pressure anomaly determination module is further configured to: Obtain the number of adjacent monitoring locations that are horizontally adjacent to the monitoring location of any of the monitoring points, wherein each monitoring location includes at least one monitoring point.

[0044] The number of monitoring points with abnormal air vibration index in the adjacent monitoring locations that are in the same direction as any of the monitoring points is obtained. The monitoring points with abnormal air vibration index are those whose air vibration index exceeds a second preset threshold.

[0045] The degree of pressure abnormality at any monitoring point is obtained based on the air vibration index, the number of adjacent monitoring locations, and the number of monitoring points with abnormal air vibration index.

[0046] For example, after calculating the air vibration index of a single monitoring point, the module for determining the degree of extrusion pressure anomaly does not directly classify it as an anomaly. Instead, it further introduces spatial neighborhood analysis logic. This is because air walls caused by torrential rain are usually distributed over a wide area along fissures or pores in the strata, simultaneously affecting multiple monitoring points within a region, especially those in the same direction. In contrast, underground jets, which are of real concern, while possessing strong impact force, are often confined to specific rock fissure channels, exhibiting locality. To accurately distinguish between these two different sources of anomalies, the module implements the following spatial correlation analysis steps.

[0047] First, the system deploys multiple water level monitoring pipes in a grid pattern across the slope area, with each pipe representing an independent monitoring location. For the j-th monitoring point at the i-th monitoring location currently being analyzed (e.g., the sensor facing due east of a monitoring point in the second layer), the module queries its geographical adjacency on the horizontal plane to obtain the number of directly adjacent monitoring locations, denoted as F. This F value represents the connectivity of the local grid, providing a benchmark for determining whether anomalies possess spatial consistency.

[0048] Next, the module checks these F adjacent monitoring locations. Specifically, it examines the air agitation index of the monitoring point within each adjacent location that is at the same depth level as the current monitoring point and has the same orientation (e.g., both are due east, using true north as the absolute reference; the orientation is the same as the current monitoring point). The module counts the number of monitoring points at the same depth and in the same orientation whose air agitation index exceeds a second preset threshold T2 (e.g., 0.5), i.e., the number of points exhibiting anomalies, denoted as F1.

[0049] Among them, T2 is used to physically define the critical point between "random interference from sporadic bubbles" and "periodic excitation of large-scale air walls". It can be calibrated using a water-air two-phase flow simulation test bench. Water flow impacts with different air contents are applied to the outer wall of the monitoring tube. When the energy of the high-frequency component output by the micro differential pressure sensor exceeds 50% of the total energy, the corresponding air vibration index is recorded, which is used as the critical threshold for identifying severe air wall interference.

[0050] For example, the degree of extrusion pressure anomaly at the j-th monitoring point of the i-th monitoring location in the m-th layer. It can be obtained from the following formula: Formula 2 Where F is the number of adjacent monitoring locations, which is not zero.

[0051] The brilliance of this formula lies in its logical reasoning ability. If the air vibration index at the current monitoring point itself... The value is very high, and at the same time, the monitoring points in the same direction in the surrounding adjacent locations also generally show anomalies (i.e., F1 / F is close to 1), then... It will remain at a high level, which the system determines to be widespread airwall interference; subsequent corrections need to reduce this pressure. Conversely, if the current monitoring point... The value is very high, but most of the monitoring points in the same direction in adjacent locations are normal (i.e., F1 / F is very small), then... This will be significantly reduced. This situation indicates that the pressure fluctuations currently felt at the monitoring point are not originating from a large-scale air wall, but rather from a localized underground jet impacting from a specific direction. At this point, the degree of abnormal compressive pressure... A smaller value means that the system should not downplay the pressure data, but rather treat it as a true change in water pressure.

[0052] This application embodiment successfully eliminates misjudgments caused by real physical phenomena such as jet impact through this spatial comparison mechanism, ensuring that only genuine air wall interference is marked as high anomaly, thus providing an important guarantee for the correct generation of subsequent correction coefficients.

[0053] In one possible implementation, the compression hysteresis determination module is further configured to: Obtain the average mud and water turbidity at any of the monitoring points within the current statistical period.

[0054] In a single precipitation event, the cumulative time during which the average turbidity of the muddy water continuously exceeds a preset safety threshold is recorded.

[0055] The degree of compression lag at any monitoring point is obtained based on the average turbidity of the mud and water and the cumulative time.

[0056] For example, the module for determining the degree of pressure hysteresis is primarily responsible for quantifying the pressure transmission hysteresis effect caused by turbidity in the muddy water. During heavy rain, groundwater seeping into the monitoring pipe often carries a large amount of sediment. As this highly turbid muddy water passes through the inlet holes (such as louvers) on the outer wall of the monitoring pipe, sediment particles gradually deposit around the openings, forming a dense layer similar to a "filter cake." As the filter cake thickens, the actual external groundwater pressure takes longer to be transmitted to the internal sensors, resulting in "sluggish" sensor readings that lag behind actual water level changes. To accurately assess this hysteresis, the module employs the following composite algorithm.

[0057] First, within each statistical period, the module acquires real-time turbidity data of the mud and water using optical turbidity probes deployed at monitoring points, and calculates the average value for the current period, denoted as . Instantaneous turbidity alone cannot describe the cumulative effect of clogging, because the degree of clogging caused by short-term high turbidity surges may differ from that caused by sustained moderate turbidity buildup. Therefore, the module introduces a time dimension: recording the cumulative time during which the average turbidity continuously exceeds a preset safety threshold in a single precipitation or seepage event, denoted as [missing value]. .

[0058] Optionally, the system further includes a reset module (not shown in the figure), which is used to reset the cumulative time to zero when the average turbidity of the mud and water at any of the monitoring points falls back and remains below a preset safety threshold for more than a set period of time.

[0059] For example, this cumulative time It has an intelligent reset mechanism: when the average turbidity of the muddy water drops and remains below the preset safety threshold for a set period of time (e.g., 1 hour), the system determines that the precipitation event has ended and then... Reset to zero to avoid the indefinite impact of historical data on the current state.

[0060] For example, the degree of compression hysteresis at the j-th monitoring point of the i-th monitoring location in the m-th layer. It can be obtained from the following formula: Formula 3 in, To normalize the process, the maximum value for normalization is set as the historical limit of filter cake accumulation at the inlet of the monitoring pipe when calculating the degree of compression lag.

[0061] The physical significance of this formula lies in simulating the growth process of the filter cake. This reflects the concentration of "building materials" (silt particles) provided by the muddy water. This reflects the "construction time." The product of the two represents the total accumulated amount of filter cake. The larger the product, the more severe the blockage of the louver inlet holes, and the stronger the hysteresis effect of pressure transmission. Finally, the product result is mapped to a standardized degree value through the normalization function norm(), outputting the degree of squeezing hysteresis. The larger this value, the more severe the siltation, and the greater the lag between the internally measured extrusion pressure and the actual external water pressure. In other words, the actual extrusion pressure needs to be corrected upwards.

[0062] Furthermore, when the average turbidity of the muddy water drops but the time it remains below the preset safety threshold does not exceed the set duration, the current cumulative time remains unchanged, and the accumulation continues until the average turbidity of the muddy water exceeds the preset safety threshold again.

[0063] The embodiments of this application, by simultaneously considering turbidity intensity and duration and introducing an event reset mechanism, can dynamically and accurately assess the development stage of sediment blockage and its impact on pressure measurement.

[0064] In one possible implementation, the congestion hysteresis degree determination module is further configured to: Obtain the absolute value of the first pressure difference, which is the absolute value of the difference between the average original extrusion pressure of any monitoring point in the current statistical period and the average original extrusion pressure in the previous statistical period.

[0065] Obtain the second absolute pressure difference, which is the absolute value of the difference between the average original extrusion pressure of the current statistical period and the average original extrusion pressure of the previous statistical period of the monitoring points in the same direction of the upper adjacent layer of any monitoring point.

[0066] Obtain the absolute value of the third pressure difference, which is the absolute value of the difference between the average original extrusion pressure of the current statistical period and the average original extrusion pressure of the previous statistical period of the monitoring points in the same direction of the lower adjacent layer of any monitoring point.

[0067] The degree of blockage delay at any monitoring point is determined based on the first absolute value of the pressure difference, the second absolute value of the pressure difference, and the third absolute value of the pressure difference.

[0068] For example, the blockage hysteresis determination module is specifically designed to identify a more extreme operating condition: the monitoring pipe inlet is blocked by silt, causing the internal pressure data to "freeze" and become unresponsive to changes in the external water level. Unlike the "slow-to-respond" handling of the squeezing hysteresis module, complete blockage will cause the pressure response to completely fail, requiring identification by comparing the formation pressure gradient.

[0069] The core logic of this module is based on the principle of continuity in formation pressure transmission. Under stable geological conditions, water pressure changes in adjacent depth layers (e.g., 0.5 meters apart) in the same horizontal direction should be synchronous and correlated. If the rate of change in an intermediate layer lags significantly behind that of the layers above and below it, it is highly likely that the sensor inlet at that layer is blocked. The specific calculation process is as follows: First, the module calculates the average original compressive pressure at the j-th monitoring point of the i-th monitoring location at the m-th layer within the current statistical period, and then calculates the difference between this average and the average from the previous statistical period to obtain the absolute value of the difference. Similarly, obtain the absolute value of the difference between monitoring points at the same location and in the same direction on the upper layer (m-1th layer). and the lower layer (m+1th layer) .

[0070] Next, the module sets a trigger condition: if Less than and The average value indicates that the pressure change response at this level is lagging, which triggers the calculation of the degree of blockage hysteresis. Based on this, the initial hysteresis value S is calculated. For example, the initial hysteresis value S can be obtained by the following formula: Formula 4 S essentially measures the total extent to which the current layer is "lagging behind" the layers above and below it.

[0071] Meanwhile, when the calculation location is at the top layer of the overall monitoring pipe network (i.e., this layer lacks a corresponding upper neighboring layer), only the pressure difference data of the lower neighboring layer is used for calculation, that is, the hysteresis value at this time. Similarly, when the calculation location is at the bottom layer (i.e., this layer lacks the corresponding lower neighboring layer), only the pressure difference data of the upper neighboring layer is used for calculation, that is, the hysteresis value at this time. .

[0072] Next, a polarity determination is performed. If the value of S is negative, it means that the pressure change in this layer is actually faster than that in the adjacent layers, which does not conform to the physical logic of blockage causing lag. Therefore, S is forcibly set to 0, and the degree of blockage lag is set accordingly. The value is 0. Only when S is greater than 0 is it processed by the normalization function norm(), and the final output is a congestion lag level between 0 and 1. .

[0073] For example, the degree of congestion delay at the j-th monitoring point of the i-th monitoring location in the m-th layer. It can be obtained from the following formula: Formula 5 When calculating the degree of blockage hysteresis, the maximum value of the normalized treatment is set as: the absolute value of the maximum true water pressure gradient difference that has occurred between two adjacent depth layers under historical extreme rainstorm conditions on the slope of the power station.

[0074] The higher the value, the greater the suspicion that the inlet of this layer is completely blocked, and the sensor reading can no longer keep up with changes in external water pressure.

[0075] Furthermore, the corrected squeezing hysteresis degree at the j-th monitoring point of the i-th monitoring location in the m-th layer. It can be obtained from the following formula: Formula 6 In this formula, when Approaching the degree of compression hysteresis after correction at time 1 Approaching 1; when not fully blocked, the corrected degree of squeezing hysteresis. Depend on It plays a dominant role.

[0076] This application embodiment introduces adjacent layers as reference benchmarks, eliminating the need to rely on absolute pressure values. By comparing the relative rate of pressure change, it can sensitively capture the moment when the sensor "fails," providing a key diagnostic indicator for the system and ensuring that it does not provide false safety information under extreme blockage conditions.

[0077] In one possible implementation, the correction coefficient generation module is further configured to: Obtain the hysteresis compensation weight coefficient and the extrusion pressure anomaly weight coefficient.

[0078] Based on the hysteresis compensation weight coefficient, the extrusion pressure anomaly weight coefficient, the degree of extrusion pressure anomaly, and the corrected degree of extrusion hysteresis, the extrusion pressure correction coefficient for any monitoring point is generated.

[0079] For example, the correction coefficient generation module plays the core role of data fusion and decision-making. It is responsible for combining the previously calculated degree of extrusion pressure anomaly (representing the artificial height of the air wall) and the corrected degree of extrusion lag (representing the pressure lag caused by sediment) to generate an intelligent extrusion pressure correction coefficient. The core function of this coefficient is to determine whether the original pressure value should be "increased" or "decreased" to restore the true groundwater level.

[0080] Specifically, the module pre-sets two key empirical parameters: the hysteresis compensation weight coefficient. and the weighting coefficient of extrusion pressure anomaly In typical engineering applications, An empirical value of 0.6 can be taken. An empirical value of 0.4 is acceptable. The setting of these two coefficients reflects the system's level of trust in and handling strategies for different interference sources.

[0081] For example, the compressive pressure correction coefficient at the j-th monitoring point of the i-th monitoring location in the m-th layer. It can be obtained from the following formula: Formula 7 The following derives the logic behind the application of this formula. When the monitoring point is primarily affected by silt blockage, the corrected squeezing hysteresis is high, while the squeezing pressure anomaly (from the air wall) is low. In this case, the positive term in the formula... Play a leading role, making Multiplying such a coefficient greater than 1 by the original compressive pressure yields an increased true compressive pressure value, thus compensating for the lower sensor readings caused by sediment lag and restoring a higher actual external water level. Conversely, when the monitoring point is primarily affected by airflow interference, the compressive pressure anomaly is higher, while the lag is lower. In this case, the negative term... Play a leading role, making Multiplying a coefficient less than 1 by the original extrusion pressure will effectively reduce the artificially high pressure caused by the impact of high-pressure bubbles and eliminate false water level spikes.

[0082] This module also imposes strict physical constraints on the range of parameter values. Specific limitations and Abnormal weight coefficient A constraint less than 1 is crucial, as it ensures that even under extreme, single airwall disturbances, It will at most approach 1 but will not equal or exceed 1, thus ensuring the correction factor. Negative values ​​will not occur. This avoids the physical paradox of the actual compressive force becoming negative due to over-correction. Through this weighted dynamic addition and subtraction, the correction coefficient... It is not a fixed calibration value, but an intelligent variable that can be automatically adjusted according to the real-time working conditions on site (whether there is more mud or more air bubbles), thus achieving adaptive compensation for the two main sources of interference.

[0083] In one possible implementation, the absolute groundwater level elevation determination module is further used for: The actual extrusion pressure at any monitoring point is determined by multiplying the extrusion pressure correction factor and the original extrusion pressure.

[0084] For example, the absolute groundwater level determination module performs the most crucial data transformation step: applying the pressure coefficient, corrected by a complex algorithm, to the original data to obtain the "real compressive force" that characterizes the actual physical state. This process is simple and direct, and it is the culmination of the entire complex signal processing and interference identification work in the early stages.

[0085] The core operation of this module is a multiplication operation. Specifically, for any monitoring point in the system (e.g., the m-th layer, the i-th monitoring position, the j-th monitoring point), two values ​​are first read from the data cache: one is the raw extrusion pressure of the current statistical period, directly collected by the high-frequency sensor and synchronized with time. Secondly, the extrusion pressure correction coefficient is calculated and issued in real time by the correction coefficient generation module. Subsequently, the module performs a multiplication operation to obtain the actual extrusion force. .

[0086] For example, the actual compressive force at the j-th monitoring point at the i-th monitoring location in the m-th layer. It can be obtained from the following formula: Formula 8 This simple multiplication formula contains profound implications for physical compensation. Let's review the correction coefficients. The generation process has integrated interference information from multiple dimensions: including the degree of extrusion anomaly reflecting the frequency and intensity of the air wall impact, and the degree of corrected extrusion hysteresis reflecting the siltation status. When When the value is greater than 1, the multiplication operation acts as a "gain amplification," increasing the original pressure value, which may appear "too small" due to inlet blockage or delayed pressure transmission, to match the pressure applied by the actual external water level. When When the value is less than 1, the multiplication operation acts as an "attenuation filter," correcting the original pressure value, which fluctuates violently due to the impact of high-pressure bubbles and contains a large number of "burrs" and false peaks, downward, thus eliminating the artificially high components caused by the air wall.

[0087] This embodiment of the application, through dynamic adjustment of this multiplication, ensures that the final true extrusion pressure, regardless of whether the initial pressure is too low or too high, is maximally free from the dual interference of air walls and silt. This value no longer includes the jumping noise from bubble bursts and overcomes the response lag caused by mud and water blockage. It purely reflects the true hydrostatic pressure of the groundwater body at the monitoring point's location due to water level changes. Therefore, this step is the core link in achieving the "high-fidelity" monitoring objective, providing reliable input parameters for subsequent calculations of absolute water level elevation based on hydrostatics.

[0088] In one possible implementation, the absolute groundwater level elevation determination module is further used for: Obtain the vertical elevation of any of the monitoring points.

[0089] Based on the vertical elevation and the actual compressive force, the absolute groundwater level elevation of any monitoring point is determined.

[0090] For example, after obtaining the pure, real pressure, the absolute groundwater level determination module uses the classic principle of hydrostatic pressure to convert it into an absolute groundwater level elevation with clear physical meaning and engineering application value. This conversion process establishes a direct link between the electrical signal (pressure value) and the spatial location (elevation), enabling the monitoring data to be directly understood and applied by the disaster prevention command center.

[0091] The module first needs to obtain a key prior constant: the vertical elevation of the j-th monitoring point at the i-th monitoring location in the m-th layer. This elevation is precisely measured and recorded in the system using high-precision geodetic surveying equipment (such as GPS RTK) or a level when the system is deployed and the water level monitoring pipes are drilled and installed. This represents the physical height of the pressure sensor relative to a fixed reference surface (such as absolute altitude), which is a fixed and known constant.

[0092] The module then invokes the fundamental formula of hydrostatics: pressure P = density ρ × gravitational acceleration g × liquid column height h. In this scenario, the actual compressive force... The sensor actually acts as the pressure P, and what needs to be solved is the height h of the underground water column above the sensor.

[0093] Transform the formula as follows Formula 9 Where ρ is the density of groundwater, which can be preset based on local water quality sampling and analysis; g is the local gravitational acceleration, which is a known physical constant.

[0094] Therefore, for the j-th monitoring point at the i-th monitoring location in the m-th layer, its absolute groundwater level elevation... The precise formula for calculation is as follows: Formula 10 Where ρ represents the density of groundwater and g is the gravitational acceleration constant. The derivation process is as follows: The pressure sensed by the sensor comes from the weight of the water column above it. The total pressure at the bottom of the water column (sensor location) is equal to the height of the water column multiplied by the density and gravitational acceleration. Therefore, the absolute elevation of the top of the water column (i.e., the free surface of the groundwater) is equal to the fixed elevation Z where the sensor is located, plus the height h of this water column. The module substitutes the previously calculated actual compressive force into the formula to accurately calculate the absolute elevation of the groundwater level at that moment. This value is the direct basis for subsequent safety assessments and alarm threshold determinations.

[0095] The embodiments of this application transform complex mechanical measurement results into intuitive and easy-to-understand water level information for engineering purposes.

[0096] Figure 2 This is a block diagram illustrating another dynamic monitoring system for groundwater levels on the slope of a pumped-storage power station, according to an exemplary embodiment. For example... Figure 2 As shown, the system may further include the following modules: The alarm module is used to obtain the dynamic water level elevation of the pumped storage power station slope based on the absolute groundwater level elevation, and to trigger a disaster prevention alarm when the dynamic water level elevation exceeds a first threshold or the rate of rise of the dynamic water level elevation exceeds a second threshold.

[0097] For example, the alarm module is the final execution link in the entire monitoring chain, responsible for transforming the analyzed water level data into concrete safety protection actions. Its core task is to implement dynamic and intelligent disaster prevention and early warning based on the high-fidelity water level information output by the preceding modules, completely solving the problem of untimely early warning or frequent false alarms caused by high data noise in existing technologies.

[0098] The module operates as follows: First, the system deploys multiple water level monitoring pipes in a grid pattern across the slope area. Each pipe provides absolute groundwater level elevation data for multiple depth layers and directions. The alarm module receives real-time water level elevation values ​​from all monitoring points calculated by the absolute groundwater level elevation determination module. To obtain a robust estimate of the overall slope safety situation, the module can calculate the average water level elevation of all valid monitoring points, or take the highest water level value at a specific key section as the current dynamic water level elevation of the slope.

[0099] After acquiring the dynamic water level elevation, the alarm module simultaneously executes two independent threshold comparison logics. The first is an absolute water level height comparison: the module has a pre-stored safety warning line (first threshold) set based on geological survey reports and slope stability analysis. Once the real-time dynamic water level elevation exceeds this warning line, it means that the hydrostatic pressure inside the slope has reached or exceeded the design limit, posing a landslide risk, and the module will immediately trigger the first-level dynamic water level over-limit alarm. The second is a water level rise rate comparison: the module continuously records the rate of change of the dynamic water level elevation over time, i.e., the speed at which the water level rises. Under heavy rainfall conditions, even if the current absolute water level has not yet reached the warning line, if the rise rate shows an abnormal surge (e.g., a rapid rise in water level within a short period), this often indicates that a more dangerous seepage or jetting event is occurring. Once the rate exceeds the second threshold (this value is a priori empirical value, obtainable through experiments), the module can immediately trigger the second-level dynamic disaster prevention alarm.

[0100] These two levels of alarm signals will be pushed in real time to the power plant's disaster prevention command center, the mobile terminals of on-site personnel, or the superior monitoring platform via wired or wireless networks in the form of sound and light, and data messages. Because the input dynamic water level elevation has been processed by the complex algorithm of this application, the influence of the false height of the air wall and the stagnation of sediment has been eliminated, so the judgment basis of the alarm module is extremely reliable.

[0101] The embodiments of this application fundamentally avoid false alarms of high water levels caused by bubble impact during rainstorms, and also eliminate serious accidents caused by low water level readings due to silt blockage, thus delaying alarms. This provides pumped storage power stations with high-fidelity, low-delay dynamic disaster prevention and early warning capabilities under extreme rainstorm conditions.

[0102] In one possible implementation, the system may further include: multiple water level monitoring tubes, the outer wall of which is provided with an asymmetrical array of Venturi-type flow guide louvers, the bottom of which is provided with a centrifugal sand collection trough, and the top of which is provided with an array of hydrophobic capillary exhaust valves; the inside of the water level monitoring tube is provided with an inner measuring cavity, the inner measuring cavity is provided with transverse water-stop rings at predetermined intervals in the vertical direction, and is radially divided into four independent and non-communicating quadrant chambers within the same height layer by a cross baffle, each quadrant chamber independently encapsulating a high-frequency micro differential pressure sensor.

[0103] For example, the hardware structure design is the physical basis for achieving high-precision data acquisition and interference identification, especially the special construction of the water level monitoring pipe. This design fundamentally solves the pain points of traditional water filter pipes, which are unable to identify the direction of water flow due to structural defects and are easily affected by air walls and sediment, providing high-quality raw data for all the aforementioned algorithms.

[0104] The outer wall of the water level monitoring pipe features an asymmetric Venturi-style flow-guiding louver array. When external pore water or underground jets impact the pipe wall, this specially structured louver does not simply allow water to rush in directly. Instead, it utilizes the Venturi effect to forcibly change the direction of water flow, creating a tangential vortex upon entering the outer cavity. This vortex design offers two major advantages: first, centrifugal force throws heavier sediment particles in the water towards the bottom of the outer cavity, causing them to settle in a specially designed centrifugal sand collection tank, significantly reducing the amount of sediment entering the inner measuring cavity; second, the orderly vortex helps guide high-pressure air bubbles entrained in the water to the top of the pipe, where they are automatically discharged through an array of hydrophobic capillary vent valves, effectively mitigating the air wall effect.

[0105] More crucially, the design of the inner measurement chamber is key. Traditional monitoring tubes have a continuous internal space where water flow from all directions mixes, causing the sensor to only measure the average pressure after mixing, completely losing directional information. This application installs transverse water-stop rings at predetermined intervals (e.g., 0.5m) along the vertical direction within the measurement chamber, dividing the long tube into multiple independent measurement layers and isolating the hydraulic connection between layers, thus achieving vertical layered monitoring. Within the same height layer, a cross-shaped baffle radially divides the circular space into four independent and non-communicating quadrant chambers (corresponding to the east, south, west, and north directions, respectively). Each quadrant chamber independently encapsulates a high-frequency micro-differential pressure sensor.

[0106] In this way, when a rapid underground jet occurs along a specific rock fissure within the slope, this water flow will only enter the corresponding quadrant chamber from the corresponding direction (e.g., due east), while the water in the chambers in the other three directions remains relatively still. The system can therefore independently acquire pressure data from all four directions. If only the pressure sensor in the east direction experiences a sudden increase, while the pressure in other directions remains unchanged, the system can immediately determine the presence of a localized jet from the east. Combined with the aforementioned algorithm, this high-fidelity directional data allows the system to accurately identify the actual jet and avoid misinterpreting it as a large-scale air wall.

[0107] The hardware structure of this application embodiment physically realizes sediment separation, bubble removal, and water flow direction identification, providing fundamental structural support for subsequent data processing algorithms.

Claims

1. A dynamic monitoring system for groundwater level on the slope of a pumped storage power station, characterized in that, The system includes: The data acquisition module is used to acquire the original extrusion pressure and mud-water turbidity at any monitoring point; The extrusion pressure anomaly determination module is used to calculate the air vibration index based on the fluctuation characteristics of the original extrusion pressure at any monitoring point within the current statistical period, and to determine the extrusion pressure anomaly at any monitoring point by combining the abnormal state of the air vibration index at monitoring points in the same direction in adjacent monitoring locations; The compression hysteresis degree determination module is used to calculate the compression hysteresis degree of any monitoring point based on the mud and water turbidity of any monitoring point within the current statistical period; The blockage hysteresis degree determination module is used to calculate the blockage hysteresis degree of any monitoring point based on the pressure difference change pattern between any monitoring point and monitoring points in the same direction of adjacent depth layers above and below; The correction coefficient generation module is used to obtain the corrected degree of extrusion lag based on the degree of extrusion lag and the degree of blockage lag, and to generate the extrusion pressure correction coefficient for any monitoring point based on the degree of extrusion pressure anomaly and the corrected degree of extrusion lag. The absolute groundwater level elevation determination module is used to correct the original extrusion pressure using the extrusion pressure correction coefficient to obtain the true extrusion pressure at any monitoring point, and to calculate the absolute groundwater level elevation at any monitoring point based on the true extrusion pressure.

2. The dynamic monitoring system for groundwater level on the slope of a pumped storage power station according to claim 1, characterized in that, The module for determining the degree of extrusion pressure anomaly is also used for: Obtain the extrusion pressure fluctuation curve of any monitoring point within the current statistical period; Based on the peak detection algorithm, all extreme points on the extrusion pressure fluctuation curve are extracted to divide the extrusion pressure fluctuation curve into multiple line segments, and the absolute value of the difference between the maximum and minimum extrusion pressure on any of the multiple line segments is obtained. The number of line segments of the extrusion pressure fluctuation curve, the number of abnormal line segments of the extrusion pressure fluctuation curve, and the mean of the absolute values ​​of the differences of all line segments of the extrusion pressure fluctuation curve are obtained. The abnormal line segments are line segments whose absolute values ​​of the differences exceed a first preset threshold. The air vibration index of any monitoring point is obtained based on the number of line segments, the number of abnormal line segments, and the mean of the absolute values ​​of the differences.

3. The dynamic monitoring system for groundwater level on the slope of a pumped storage power station according to claim 1, characterized in that, The module for determining the degree of extrusion pressure anomaly is also used for: Obtain the number of adjacent monitoring locations that are horizontally adjacent to the monitoring location of any of the monitoring points, wherein each monitoring location includes at least one monitoring point; The number of monitoring points with abnormal air vibration index in the adjacent monitoring locations that are in the same direction as any of the monitoring points is obtained. The monitoring points with abnormal air vibration index are those whose air vibration index exceeds a second preset threshold. The degree of pressure abnormality at any monitoring point is obtained based on the air vibration index, the number of adjacent monitoring locations, and the number of monitoring points with abnormal air vibration index.

4. The dynamic monitoring system for groundwater level on the slope of a pumped storage power station according to claim 1, characterized in that, The compression hysteresis degree determination module is also used for: Obtain the average mud and water turbidity at any of the monitoring points within the current statistical period; In a single precipitation event, the cumulative time during which the average value of the mud and water turbidity continuously exceeds a preset safety threshold is obtained; The degree of compression lag at any monitoring point is obtained based on the average turbidity of the mud and water and the cumulative time.

5. The dynamic monitoring system for groundwater level on the slope of a pumped storage power station according to claim 1, characterized in that, The module for determining the degree of congestion hysteresis is also used for: Obtain the absolute value of the first pressure difference, which is the absolute value of the difference between the average original extrusion pressure of any monitoring point in the current statistical period and the average original extrusion pressure in the previous statistical period. Obtain the second absolute pressure difference, which is the absolute value of the difference between the average original extrusion pressure of the current statistical period and the average original extrusion pressure of the previous statistical period of the monitoring points in the same direction of the upper adjacent layer of any monitoring point. Obtain the absolute value of the third pressure difference, which is the absolute value of the difference between the average original extrusion pressure of the current statistical period and the average original extrusion pressure of the previous statistical period of the monitoring points in the same direction of the lower adjacent layer of any monitoring point. The degree of blockage delay at any monitoring point is determined based on the first absolute value of the pressure difference, the second absolute value of the pressure difference, and the third absolute value of the pressure difference.

6. The dynamic monitoring system for groundwater level on the slope of a pumped storage power station according to claim 1, characterized in that, The correction coefficient generation module is also used for: Obtain the hysteresis compensation weight coefficient and the extrusion pressure anomaly weight coefficient; Based on the hysteresis compensation weight coefficient, the extrusion pressure anomaly weight coefficient, the degree of extrusion pressure anomaly, and the corrected degree of extrusion hysteresis, the extrusion pressure correction coefficient for any monitoring point is generated.

7. The dynamic monitoring system for groundwater level on the slope of a pumped storage power station according to claim 1, characterized in that, The absolute groundwater level elevation determination module is also used for: The actual extrusion pressure at any monitoring point is determined by multiplying the extrusion pressure correction factor and the original extrusion pressure.

8. The dynamic monitoring system for groundwater level on the slope of a pumped storage power station according to claim 1, characterized in that, The absolute groundwater level elevation determination module is also used for: Obtain the vertical elevation of any of the monitoring points; Based on the vertical elevation and the actual compressive force, the absolute groundwater level elevation of any monitoring point is determined.

9. The dynamic monitoring system for groundwater level on the slope of a pumped storage power station according to claim 1, characterized in that, The system also includes: The alarm module is used to obtain the dynamic water level elevation of the pumped storage power station slope based on the absolute groundwater level elevation, and to trigger a disaster prevention alarm when the dynamic water level elevation exceeds a first threshold or the rate of rise of the dynamic water level elevation exceeds a second threshold.

10. The dynamic monitoring system for groundwater level on the slope of a pumped storage power station according to claim 4, characterized in that, The system further includes: a reset module; The reset module is used to: reset the cumulative time to zero when the average turbidity of the mud and water at any of the monitoring points falls back and remains below the preset safety threshold for more than a set period of time.

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