Integrated monitoring and sampling method and system for red-bed groundwater

Abstract

The application discloses an integrated monitoring and sampling method and system for red-bed groundwater, and belongs to the technical field of groundwater monitoring. The method comprises the following steps: deploying the system and initializing, collecting water level and equipment state information, calculating initial water level monitoring benchmark parameters in combination with a water level monitoring logic model, and generating an intelligent water pumping control execution sequence through a fuzzy control algorithm and a fluid dynamics model in real time with water level data and anti-clogging water pump structure parameters. The method takes the well flushing flow benchmark parameter as a constraint, combines water quality sensor data, and obtains a large-flow well flushing operation path through a dynamic pressure regulation algorithm and a water quality standard judgment model. Based on the well flushing end point parameter, the method combines a small-flow sampling pressure benchmark and pipeline characteristics, and obtains a steady-state sampling execution scheme through a double-pump linkage control algorithm and a bubble suppression model. The method collects data and generates an integrated monitoring and sampling report. The application realizes the intelligentization and standardization of red-bed groundwater monitoring and sampling, and improves the monitoring efficiency and data accuracy.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of groundwater monitoring, and in particular relates to an integrated monitoring and sampling method and system for red-bed groundwater. BACKGROUND

[0002] Red-bed groundwater refers to groundwater occurring in red clastic rock strata. The hydrogeological conditions of red-bed groundwater are complex. Red-bed groundwater often has characteristics such as high suspended solids, high TDS, and high iron and manganese. In addition, the monitoring well of red-bed groundwater is often deep, with less water and a deep water level, which brings many challenges to monitoring and sampling. In the prior art, red-bed groundwater monitoring and sampling are often operated in a step-by-step mode. The links such as water level monitoring, pumping control, well washing and purification, and water sample collection lack coordinated linkage, and there are problems such as poor equipment adaptability, complicated operation process, and low data accuracy. Specifically, the traditional method does not consider the dynamic fluctuation characteristics of red-bed well water during water level monitoring, and the baseline parameters are set fixedly, which leads to insufficient pumping control accuracy. In the well washing process, the pressure and flow rate regulation lacks intelligent feedback, and it is difficult to adapt to the complex characteristics of red-bed water. In the sampling stage, bubbles are easily generated due to pressure fluctuation, which affects the representativeness of the water sample. Therefore, an integrated method that can realize the whole process of monitoring, control, well washing, and sampling is urgently needed to improve the efficiency and quality of red-bed groundwater monitoring and sampling. SUMMARY

[0003] In view of the deficiencies of the prior art, the application provides an integrated monitoring and sampling method and system for red-bed groundwater. The system is deployed and initialized, water level and equipment state information are collected, initial water level monitoring baseline parameters are calculated in combination with a water level monitoring logic model, and intelligent pumping control execution sequences are generated through a fuzzy control algorithm and a fluid dynamics model in combination with real-time water level data and anti-clogging pump structure parameters. A large-flow well washing operation path is obtained through a dynamic pressure regulation algorithm and a water quality standard determination model in combination with water quality sensor data and with the well washing flow rate baseline parameter as a constraint. A steady-state sampling execution scheme is obtained through a double-pump linkage control algorithm and a bubble suppression model in combination with a small-flow sampling pressure baseline and pipeline characteristics based on well washing end point parameters. Data is summarized and an integrated monitoring and sampling report is generated. The application realizes the intelligentization and standardization of red-bed groundwater monitoring and sampling, and improves the monitoring efficiency and data accuracy.

[0004] To achieve the above-mentioned purpose, the application provides the following technical scheme:

[0005] The integrated monitoring and sampling method for red-bed groundwater comprises the following steps:

[0006] S1: In response to the preset red layer groundwater monitoring requirements, deploy the monitoring sampling system and complete the initialization configuration, real-time acquisition of well water level data and equipment running state information, combined with the preset water level monitoring logic model, calculate the initial water level monitoring reference parameter; the initial water level monitoring reference parameter includes the initial water level reference value, the equipment starting threshold and the data acquisition interval;

[0007] S2: Based on the initial water level monitoring reference parameter, combined with the real-time collected water level data and the anti-clogging water pump structure parameter, through the fuzzy control algorithm and the fluid dynamics model, the intelligent pumping control execution sequence is generated;

[0008] S3: Based on the intelligent pumping control execution sequence, with the preset well flushing flow reference parameter as the constraint, combined with the real-time monitoring water quality data of the water quality sensor, through the dynamic pressure regulation algorithm and the water quality standard determination model, the large flow well flushing operation path is obtained;

[0009] S4: Based on the end point parameter of the large flow well flushing operation path, combined with the preset small flow sampling pressure reference and the pipeline fluid characteristic parameter, through the double pump linkage control algorithm and the bubble suppression model, the steady-state sampling execution scheme is obtained;

[0010] S5: Based on the completion signal of the steady-state sampling execution scheme, the water level final monitoring data and the equipment running full cycle record are summarized, through the closed-loop control logic and the data archiving rule, the integrated monitoring sampling report is generated.

[0011] Specifically, the deployment of the monitoring sampling system and the completion of the initialization configuration include:

[0012] The intelligent pumping control device and the well flushing and sampling integrated device are assembled to form a monitoring sampling system, which is lowered to a preset depth in the target red layer monitoring well;

[0013] The intelligent pumping control device includes a water regime sensing start-stop system and an anti-clogging water pump; the water regime sensing start-stop system includes an ultrasonic water level sensor, a capacitive water level sensor and a control module; the sound wave frequency of the ultrasonic water level sensor is used to avoid red layer well water bubbles and suspended particles; the electrode of the capacitive water level sensor is made of titanium alloy material, and the electrode spacing is 2cm; the control module adopts a fuzzy control algorithm to include the water level change rate and the duration into the decision model, and starts and stops the anti-clogging water pump according to the water level change, and the control module is built-in temperature sensor, combined with the current value for overload protection;

[0014] The integrated well washing and sampling device includes a variable speed water pump system, an intelligent switching and evaluation unit, and a sampling pipeline assembly. The variable speed water pump system includes a vector control frequency converter and a dual-pump linkage mechanism. The vector control frequency converter establishes a nonlinear mapping model between motor speed and head. The dual-pump linkage mechanism includes a main pump and an auxiliary pump, with the auxiliary pump compensating for pipeline pressure fluctuations through bypass adjustment. The intelligent switching and evaluation unit includes a turbidity sensor, a conductivity sensor, and a control circuit. The control circuit dynamically calculates the well washing time based on well depth, well diameter, and initial turbidity.

[0015] Specifically, the calculation process of the initial water level monitoring reference parameters in S1 includes:

[0016] S1.1: Start the deployed monitoring and sampling system, and begin real-time collection of well water level data through the ultrasonic water level sensor and capacitive water level sensor of the intelligent pumping control device, while recording equipment operating status information; the equipment operating status information includes sensor signal transmission delay, water pump initial current and pipeline sealing parameters;

[0017] S1.2: Input the real-time collected well water level data into the preset water level monitoring logic model. The water level monitoring logic model performs trend decomposition and noise filtering on the well water level data based on time series analysis, and preliminarily calculates the initial water level reference value and the equipment start-up threshold. The equipment start-up threshold is the water level critical value that triggers the start and stop of the water pump.

[0018] S1.3: The water level monitoring logic model automatically generates data acquisition intervals based on the fluctuation characteristics of the initial water level reference value;

[0019] S1.4: With a defined data acquisition interval as the period, the water level monitoring logic model performs a moving average correction on the initial water level reference value to generate a corrected water level reference value;

[0020] S1.5: Integrate the obtained corrected water level reference value with the equipment start-up threshold into a dynamic water level threshold, and output it to the fluid dynamics model.

[0021] Specifically, the steps of S2 include:

[0022] S2.1: Extract the corrected initial water level reference value and equipment start-up threshold from the initial water level monitoring reference parameters, compare the corrected initial water level reference value with the real-time collected well water level data, and obtain the water level deviation value and change rate parameter.

[0023] S2.2: Call the anti-clogging pump structural parameters and input them into the fluid dynamics model. The fluid dynamics model introduces the Navier-Stokes equations and calculates the correlation between the impeller diameter and the critical pumping rate in the anti-clogging pump structural parameters to obtain the critical pumping rate at different water levels. At the same time, it calculates the maximum head under the corresponding operating conditions by combining the impeller diameter parameter. The anti-clogging pump structural parameters include impeller diameter, radius of curvature, installation angle, spiral guide groove size, and surface coating characteristics.

[0024] S2.3: The equipment start-up threshold in the water level deviation value, change rate parameter and initial water level monitoring benchmark parameter, together with the critical pumping rate and maximum head under different water levels, are input into the fuzzy control algorithm. The fuzzy control algorithm makes decisions based on preset rules and generates an intelligent pumping control execution sequence that includes pump start-up and shutdown timing, operating power adjustment parameters and anti-clogging control instructions.

[0025] Specifically, the generation process of the intelligent pumping control execution sequence in S2.3 includes:

[0026] The start and stop times are determined based on the equipment start threshold and water level deviation, thus obtaining the pump start and stop sequence.

[0027] Based on the matching relationship between the maximum head and the critical pumping rate, the power distribution ratio is calculated to obtain the operating power adjustment parameters.

[0028] By combining the characteristics of the spiral guide channel and outputting the impeller speed correction value according to the critical pumping rate, the intelligent pumping control execution sequence is obtained.

[0029] Specifically, the steps of S3 include:

[0030] S3.1: Extract the final flow rate value in the intelligent pumping control execution sequence as the well washing start flow rate benchmark, call the preset well washing flow rate benchmark parameters, and determine the flow range constraints and corresponding pipeline Reynolds number thresholds for the high flow rate well washing stage.

[0031] S3.2: Input the final flow rate value and the well washing flow rate reference parameters into the dynamic pressure adjustment algorithm. The dynamic pressure adjustment algorithm takes the Reynolds number threshold as the core constraint. First, it calculates the current Reynolds number and matches the corresponding turbulence state through the final flow rate value. Then, it constructs a flow-pressure mapping relationship model based on historical data, calculates the maximum allowable flushing pressure in combination with the turbulence state, and finally generates the initial adjustment parameters of the pump speed according to the conversion relationship between the maximum allowable flushing pressure and the pump head and the speed-head characteristic curve.

[0032] S3.3: Based on the generated initial adjustment parameters of the water pump speed, start the high-flow well washing operation. The dynamic pressure adjustment algorithm adjusts the water pump speed according to the real-time collected pipeline flow feedback, and compares the feedback flow with the flow range constraint. If the feedback flow exceeds the flow range constraint, the water pump speed is dynamically adjusted according to the deviation ratio.

[0033] S3.4: During the high-flow well washing operation, water quality data is collected in real time by the turbidity sensor and conductivity sensor in the integrated well washing and sampling device, and the water quality data is transmitted to the water quality compliance judgment model; the water quality data includes turbidity value, sediment content and conductivity fluctuation value;

[0034] S3.5: The water quality compliance judgment model analyzes the received real-time water quality data based on the target turbidity value of the preset evaluation standard and well washing flow benchmark parameter. If the water quality does not meet the standard, the flushing path node parameters are dynamically corrected according to the deviation between the current turbidity and the target value. If the water quality meets the standard, the current node is marked as the well washing endpoint.

[0035] S3.6: Integrate the pressure regulation records, flow stability parameters, and flushing path node parameter correction results determined by the dynamic pressure regulation algorithm output, and form a complete high-flow well flushing operation path.

[0036] Specifically, the steps of S4 include:

[0037] S4.1: Extract the pressure and flow values ​​at the end of the high-flow-rate well washing operation path as the initial conditions for sampling. The pressure value is the final outlet pressure of the well washing stage, and the flow value is the actual flow rate at the end of the well washing.

[0038] S4.2: Call the preset low-flow sampling pressure reference and pipeline fluid characteristic parameters; the pipeline fluid characteristic parameters include the inner diameter, inner wall roughness and structural parameters of the food-grade silicone hose and the gradually expanding interface;

[0039] S4.3: Input the initial pressure value, flow rate value, small flow sampling pressure reference, and pipeline fluid characteristic parameters into the dual-pump linkage control algorithm. The dual-pump linkage control algorithm calculates the difference between the target pressure and the initial pressure, and combines it with the pipeline friction coefficient to obtain the main pump basic flow rate parameter and the auxiliary pump bypass compensation value; the pipeline friction coefficient is determined based on the inner wall roughness.

[0040] S4.4: Input the pipeline fluid characteristic parameters and the main pump basic flow parameters into the bubble suppression model. The bubble suppression model calculates the critical flow velocity of the pipeline using the Hagen-Poiseuille law, and at the same time introduces the dissolved gas content in the real-time collected water level data to generate the inert gas injection sequence.

[0041] S4.5: The bubble suppression model optimizes the gas-liquid balance in the sampling pipeline based on the pipeline critical flow rate and inert gas injection timing, and outputs the pipeline pressure stability threshold and the upper limit of bubble content control.

[0042] S4.6: The dual-pump linkage control algorithm combines the auxiliary pump bypass compensation value and the pipeline pressure stability threshold to generate motor speed adjustment parameters through PWM technology;

[0043] S4.7: Integrate motor speed regulation parameters, pressure maintenance curve, inert gas injection sequence and upper limit of bubble content control to form a steady-state sampling execution scheme.

[0044] Specifically, in S4, the dual-pump linkage control algorithm uses a small flow sampling pressure benchmark, with the main pump maintaining the base pressure and the auxiliary pump calculating the pulse frequency according to the bubble suppression model to compensate for pressure fluctuations.

[0045] Specifically, the integrated monitoring and sampling report in S5 includes water level change curves, well washing time and water quality index change trends, sampling pressure stability analysis, equipment operation status record table and abnormal situation handling log.

[0046] An integrated monitoring and sampling system for dealing with red-bed groundwater includes: an initialization module, a water level monitoring module, a control module, a high-flow-rate well flushing control module, a steady-state sampling control module, and a report generation module;

[0047] The initialization module is used to monitor the physical deployment of the sampling system, device self-test, and parameter initialization.

[0048] The water level monitoring module is used to collect water level data in the well in real time and calculate the initial water level reference value by combining it with the preset water level monitoring logic model.

[0049] The control module generates an intelligent pumping control execution sequence based on the initial water level reference value and equipment structural parameters, realizing intelligent management of pump start-up and shutdown, power adjustment and anti-clogging operation;

[0050] The high-flow well washing control module is used to start the high-flow well washing process based on the intelligent pumping control execution sequence, and to purify the water in the well through pressure regulation and water quality monitoring.

[0051] The steady-state sampling control module is used to switch to a low-flow sampling mode after well washing is completed, and achieves stable pressure sampling through dual-pump linkage and bubble suppression technology;

[0052] The report generation module is used to summarize the monitoring data throughout the entire process and generate an integrated monitoring sampling report after verification through closed-loop logic.

[0053] Compared with the prior art, the beneficial effects of the present invention are:

[0054] 1. This invention proposes an integrated monitoring and sampling system for red-bed groundwater, and optimizes and improves its architecture, operation steps and processes. The system has the advantages of simple process, low investment and operating costs and low production and working costs.

[0055] 2. This invention proposes an integrated monitoring and sampling method for red-bed groundwater. By constructing a closed-loop system encompassing monitoring, control, well flushing, sampling, and reporting, it achieves intelligent and standardized monitoring and sampling of red-bed groundwater. Through a water level monitoring logic model, benchmark parameters are dynamically generated. Combined with fuzzy control and fluid dynamics models, the pumping strategy is optimized. This approach accurately responds to water level changes, avoiding equipment idling or overloading, and utilizes an anti-clogging pump structure design to reduce fine particle deposition, significantly improving the system's stability and adaptability.

[0056] 3. This invention proposes an integrated monitoring and sampling method for red-bed groundwater. It achieves precise control of high-flow well washing through dynamic pressure regulation and water quality compliance determination. Combined with dual-pump linkage and bubble suppression technology, it ensures pressure stability and bubble content control for low-flow sampling. This effectively reduces the interference of high suspended solids and high mineralization characteristics of red-bed groundwater on monitoring data. Furthermore, the full-cycle data archiving and closed-loop verification mechanism ensures the traceability and accuracy of monitoring results. Attached Figure Description

[0057] Figure 1 This is a schematic diagram of the integrated monitoring and sampling method for red-bed groundwater according to the present invention;

[0058] Figure 2 This is a flowchart illustrating the principle of the integrated monitoring and sampling method for red-bed groundwater according to the present invention. Detailed Implementation Example

[0059] Please see Figure 1 and Figure 2 The present invention provides an embodiment of an integrated monitoring and sampling method for red-bed groundwater, comprising the following steps:

[0060] S1: In response to the preset red-bed groundwater monitoring requirements, deploy the monitoring and sampling system and complete the initial configuration, collect the water level data in the well and the equipment operation status information in real time, and calculate the initial water level monitoring benchmark parameters in combination with the preset water level monitoring logic model; the initial water level monitoring benchmark parameters include the initial water level benchmark value, the equipment start threshold and the data acquisition interval;

[0061] S2: Based on the initial water level monitoring benchmark parameters, combined with the real-time collected water level data and anti-clogging pump structural parameters, an intelligent pumping control execution sequence is generated through fuzzy control algorithm and fluid dynamics model;

[0062] S3: Based on the intelligent pumping control execution sequence, with the preset well washing flow rate benchmark parameters as constraints, combined with real-time monitoring of water quality data by water quality sensors, a high-flow well washing operation path is obtained through dynamic pressure regulation algorithm and water quality compliance judgment model.

[0063] S4: Based on the endpoint parameters of the high-flow-rate well washing operation path, combined with the preset low-flow-rate sampling pressure benchmark and pipeline fluid characteristic parameters, a steady-state sampling execution scheme is obtained through a dual-pump linkage control algorithm and a bubble suppression model.

[0064] The dual-pump linkage control algorithm in S4 uses a small flow sampling pressure benchmark. The main pump maintains the base pressure, while the auxiliary pump calculates the pulse frequency according to the bubble suppression model to compensate for pressure fluctuations.

[0065] Furthermore, in dual-pump linkage control, two independent pumps work together. The main pump is the core equipment responsible for the main flow output and pressure supply. It needs to be lowered to the target depth downhole, such as the working section of a drilling, water well, or sampling well, to directly contact the downhole fluid. It is responsible for transporting the fluid from downhole to the surface pipeline or sampling system. Its function is to provide basic flow and initial pressure to meet the main needs of high-flow well washing, fluid transportation, or sampling. The auxiliary pump is a bypass auxiliary regulator. The auxiliary pump is not lowered downhole but installed on the surface pipeline near the wellhead. It is usually connected to the main pipeline of the main pump through a bypass pipeline, i.e., bypass compensation design. Its function is to dynamically compensate for the flow and pressure output by the main pump through the bypass pipeline: when the output pressure or flow of the main pump deviates from the target value, the auxiliary pump adjusts the total pipeline pressure by increasing the bypass flow or diverting the flow, thus achieving precise control.

[0066] S5: Based on the completion signal of the steady-state sampling execution scheme, it summarizes the final water level monitoring data and the full cycle record of equipment operation, and generates an integrated monitoring and sampling report through closed-loop control logic and data archiving rules.

[0067] The integrated monitoring and sampling report in S5 includes water level change curves, well washing time and water quality index change trends, sampling pressure stability analysis, equipment operation status record table, and abnormal situation handling log.

[0068] In summary, the integrated monitoring and sampling method for red bed groundwater proposed in this application addresses the unique hydrogeological conditions of red bed groundwater, such as high suspended solids, high mineralization, and large water level fluctuations. It constructs a closed-loop intelligent system covering the entire process of monitoring, control, well flushing, sampling, and reporting. Through multi-module collaborative linkage, this method achieves intelligent management throughout the entire lifecycle, from system deployment to data archiving. It solves the technical pain points of traditional red bed groundwater monitoring, such as poor equipment adaptability, fragmented operation processes, and low data accuracy. Its core advantage lies in the deep integration of dynamic water level monitoring, intelligent pumping control, precise well flushing and purification, stable sampling control, and full-cycle data management, forming a dedicated technical solution adapted to the complex environment of red beds, possessing irreplaceable environmental adaptability and data reliability.

[0069] The deployment of the monitoring and sampling system and the completion of initial configuration include:

[0070] The intelligent pumping control device and the integrated well washing and sampling device are assembled to form a monitoring and sampling system, which is then lowered into the target red layer monitoring well at a preset depth.

[0071] The intelligent pumping control device includes a water level sensor-activated start / stop system and an anti-clogging pump. The water level sensor-activated start / stop system comprises an ultrasonic water level sensor, a capacitive water level sensor, and a control module. The ultrasonic water level sensor uses a sound wave frequency to avoid air bubbles and suspended particles in the red-bed well water. The electrodes of the capacitive water level sensor are made of titanium alloy with an electrode spacing of 2 cm. The control module uses a fuzzy control algorithm to incorporate the water level change rate and duration into the decision model, and starts and stops the anti-clogging pump according to the water level change. The control module also has a built-in temperature sensor and uses current values ​​for overload protection.

[0072] The integrated well washing and sampling device includes a variable speed water pump system, an intelligent switching and evaluation unit, and a sampling pipeline assembly. The variable speed water pump system includes a vector control frequency converter and a dual-pump linkage mechanism. The vector control frequency converter establishes a nonlinear mapping model between motor speed and head. The dual-pump linkage mechanism includes a main pump and an auxiliary pump, with the auxiliary pump compensating for pipeline pressure fluctuations through bypass adjustment. The intelligent switching and evaluation unit includes a turbidity sensor, a conductivity sensor, and a control circuit. The control circuit dynamically calculates the well washing time based on well depth, well diameter, and initial turbidity.

[0073] Furthermore, the nonlinear mapping model of the vector control frequency converter obtains head data at different speeds through offline experiments, and uses the least squares method to fit the speed-head curve equation.

[0074] It should be noted that the collaborative assembly design of the intelligent pumping control device and the integrated well washing and sampling device solves the problems of poor pipeline connection and signal transmission delay when traditional equipment is deployed separately. Through mechanical structure optimization, the device can adapt to the characteristics of red layer well bodies that are prone to collapse and have irregular well diameters during the lowering process, thus avoiding equipment jamming or damage.

[0075] The calculation process of the initial water level monitoring reference parameters in S1 includes:

[0076] S1.1: Start the deployed monitoring and sampling system, and begin real-time collection of well water level data through the ultrasonic water level sensor and capacitive water level sensor of the intelligent pumping control device, while recording equipment operating status information; the equipment operating status information includes sensor signal transmission delay, water pump initial current and pipeline sealing parameters;

[0077] S1.2: Input the real-time collected well water level data into the preset water level monitoring logic model. The water level monitoring logic model performs trend decomposition and noise filtering on the well water level data based on time series analysis, and preliminarily calculates the initial water level reference value and the equipment start-up threshold. The equipment start-up threshold is the water level critical value that triggers the start and stop of the water pump.

[0078] Furthermore, the time series analysis method of the water level monitoring logic model adopts a seasonal trend decomposition algorithm to decompose the water level data into trend terms, seasonal terms and residual terms, and removes noise interference in the residual terms through sliding window filtering.

[0079] Furthermore, the process of constructing the water level monitoring logical model includes:

[0080] (1) Collect historical water level data of the target red layer monitoring well, and statistically analyze the daily fluctuation range, seasonal variation trend and sudden interference characteristics of the water level. Clarify the trend, periodic, random fluctuation and outlier distribution patterns of the data. The historical water level data shall include data for at least one complete hydrological year. For example, seasonal variation trends include a rise of 0.8-1.5 meters during the rainy season and a drop of 0.5-1 meters during the dry season. Sudden interference characteristics include short-term sudden rises caused by rainfall and instantaneous jumps caused by well wall collapse.

[0081] (2) Based on the characteristics of long-term slow change + short-term fluctuation + occasional anomaly of red bed groundwater level, time series analysis method is selected as the core algorithm. The time series analysis method is the existing technology in this field and is not the inventive solution of this application. It will not be described in detail here.

[0082] (3) Preset initial parameters based on historical data characteristics, including the time window for trend decomposition, the standard deviation threshold for noise filtering, and the weight coefficients for benchmark calculation, and reserve a dynamic adjustment interface so that parameters can be corrected based on real-time data feedback to construct a water level monitoring logic model. The initial time window for trend decomposition is set to 24 hours to adapt to the daily change cycle of red layer water level. The initial standard deviation threshold for noise filtering is set to 0.3 meters, corresponding to the fluctuation range of the 95% confidence interval in historical data. In the weight coefficients for benchmark calculation, the weight of recent data is 0.7 and the weight of long-term data is 0.3, highlighting timeliness.

[0083] Furthermore, the specific steps in S1.2 include:

[0084] (1) Receive the real-time collected well water level data. First, perform integrity verification on the well water level data and check whether there are missing values, abnormal jump values ​​or format errors in the data sequence one by one. For missing values, if the continuous missing time does not exceed 1.5 times the preset minimum data collection interval, the linear interpolation method of adjacent valid data is used to fill it. If the missing time exceeds the threshold, it is marked as a data fault and the missing time period is recorded. For abnormal jump values, the average difference between the current data and the previous 3 consecutive valid data is calculated. When the difference exceeds 3 times the standard deviation of the historical data, it is judged as an abnormal value and removed. At the same time, the original abnormal record is retained for subsequent equipment status analysis. After the verification is completed, all valid water level data are uniformly converted into the standardized format of timestamp-water level value. The timestamp is accurate to the second and the water level value is retained to two decimal places to ensure that the data format meets the input requirements of time series analysis.

[0085] (2) Input the standardized water level data into the time series analysis algorithm, and use the seasonal trend decomposition algorithm to perform multi-level decomposition on the standardized water level data, including: First, extract the trend term from the data, process the original data by the sliding window smoothing method, and dynamically set the window size according to the water level data collection frequency. When the collection interval is 1 minute, the window is set to 30 data points, and when the collection interval is 5 minutes, the window is set to 12 data points. By weighted averaging of the data within the window, short-term fluctuations are weakened, highlighting the long-term trend characteristics of water level changes over time, and forming a trend sequence; Second, separate the seasonal components and analyze the periodicity of historical water level data. If there are obvious daily or monthly water level change characteristics in the area where the monitoring well is located, the seasonal fluctuations in the current data are identified by the period matching algorithm, and an independent seasonal term sequence is generated; Finally, calculate the residual term, subtract the trend term and seasonal term from the original water level data to obtain the residual sequence containing random noise and sudden interference, and realize the multi-level trend decomposition of the water level data;

[0086] (3) Perform noise filtering operation on the residual sequence obtained by decomposition. The residual data is processed by wavelet threshold denoising algorithm, including: firstly, select a wavelet basis function suitable for the characteristics of groundwater level data. Usually, the db4 wavelet is selected as the decomposition basis function. The residual sequence is decomposed into three layers of wavelets to obtain wavelet coefficients of different frequency bands. Then, set a noise threshold. By calculating the standard deviation of the decomposition coefficients of each layer, the noise filtering threshold of each layer is determined by the adaptive threshold method. The threshold of high frequency coefficients is relatively low to retain effective details, and the threshold of low frequency coefficients is relatively high to filter the main noise. Wavelet coefficients exceeding the threshold are shrunk and their amplitude is adjusted to the threshold range. Then, the processed coefficients are reconstructed into the filtered residual sequence by wavelet inverse transform. At the same time, the reconstructed residual sequence is smoothed twice by combining the moving average filtering method to further eliminate high frequency random noise and obtain purified residual data. The wavelet threshold denoising algorithm and wavelet decomposition are existing technologies in this field and are not the inventive solutions of this application. They will not be described in detail here.

[0087] (4) The trend term, seasonal term and purification residual term after integration are superimposed to generate a denoised water level sequence. This sequence retains the main characteristics of water level changes and removes most of the noise interference. The initial water level benchmark value is calculated based on the denoised water level sequence. The sliding window mean method is adopted. The window duration is set to 24 hours. The window is slid once every data collection cycle. The arithmetic mean of all water level data in each window is calculated. The stability of the mean of 5 consecutive windows is tested. When the fluctuation range of the mean of adjacent windows is less than 0.05 meters, the mean of the last window is taken as the initial water level benchmark value. If the fluctuation range exceeds the threshold, the window duration is extended to 48 hours and recalculated until a stable benchmark value is obtained.

[0088] (5) Using the initial water level reference value as a reference, and combining the hydrological characteristics of the red bed groundwater, the equipment start-up threshold is set. First, the historical water level fluctuation data is analyzed, and the maximum deviation of the water level from the reference value under normal operating conditions is statistically analyzed. 1.2 times this deviation is used as the basic threshold reference value. Then, the actual use of the monitoring well is considered. If it is used for water resource monitoring, the basic reference value is multiplied by a correction factor of 0.8. If it is used for anti-sudden water monitoring, the correction factor is multiplied by 1.2. At the same time, the sensor stability parameters in the real-time equipment operation status information are introduced. The final determined equipment start-up threshold includes the high water level start-up threshold and the low water level stop threshold. The high water level start-up threshold is the initial water level reference value plus the calculated positive threshold. The low water level stop threshold is the initial water level reference value minus the negative threshold, forming a complete pump start-up and stop critical value system.

[0089] Further, based on the above, it can be summarized as follows: After real-time water level data is collected, it first undergoes data integrity and format verification, outliers are removed and missing values ​​are filled, and it is converted into a standardized timestamp-water level value format; then it enters the time series decomposition stage, sequentially extracting the trend term, separating the seasonal components, and calculating the residual term to complete the multi-level decomposition of the water level data; the residual term is filtered for noise using a combination of wavelet threshold denoising and moving average filtering to obtain a purified residual sequence; the trend term, seasonal term, and purified residual term are superimposed to generate a denoised water level sequence, and a stable initial water level benchmark value is calculated and verified using the sliding window mean method; finally, based on the initial benchmark value, combined with historical fluctuation data, monitoring purpose correction coefficients, and sensor status parameters, the high water level start threshold and low water level stop threshold are determined, completing the calculation of the initial water level benchmark value and the equipment start threshold.

[0090] S1.3: The water level monitoring logic model automatically generates data acquisition intervals based on the fluctuation characteristics of the initial water level reference value, realizing intelligent adaptation of monitoring density;

[0091] Furthermore, the specific steps in S1.3 include:

[0092] (1) Based on the calculated initial water level benchmark value, extract the corresponding original water level data sequence and timestamp information to construct a fluctuation analysis dataset containing time-water level values. The dataset is then preprocessed. First, outliers are removed. The criteria for outlier determination are: if the deviation of a single water level value from the average of three adjacent valid water level values ​​exceeds 5% of the initial water level benchmark value, it is marked as an abnormal fluctuation point and retained but recorded separately to avoid outliers interfering with the overall fluctuation feature analysis. Second, the continuity of the original water level data sequence is checked. If there is missing data and the missing duration exceeds the preset minimum analysis period, the missing segment is filled by linear interpolation. The minimum analysis period is set to 1 hour. The linear interpolation method is the existing technology in this field and is not an inventive solution of this application. It will not be elaborated here.

[0093] (2) For the preprocessed water level data sequence, calculate multiple fluctuation characteristic quantitative indicators. First, calculate the water level fluctuation amplitude, that is, the difference between the maximum and minimum water level values ​​within a set time window to obtain the absolute fluctuation amplitude within the window; at the same time, calculate the relative fluctuation amplitude, that is, the ratio of the absolute fluctuation amplitude to the initial water level reference value, expressed as a percentage; second, calculate the fluctuation frequency, count the number of times the water level value exceeds the initial water level reference value within a unit time by 2% above or below, the more times, the higher the fluctuation frequency; third, calculate the fluctuation rate, obtain the instantaneous fluctuation rate by the ratio of the water level difference between two adjacent water level data points to the time interval, and then take the absolute value of the instantaneous rate within a unit time and calculate the average fluctuation rate; fourth, calculate the fluctuation variance, calculate the sum of squares of the deviations between the water level data within the time window and the initial water level reference value, and then divide by the number of data to obtain the variance value reflecting the degree of fluctuation dispersion.

[0094] (3) Based on the calculated volatility quantification indicators, establish volatility level classification standards, including: classifying the four indicators of relative volatility amplitude, volatility frequency, average volatility rate, and volatility variance into three levels: low volatility, medium volatility, and high volatility. Among them, when the relative volatility amplitude is less than 1%, the volatility frequency is less than 2 times per hour, the average volatility rate is less than 0.01 m / min, and the volatility variance is less than 0.001 square meters, it is judged as low volatility level; when the relative volatility amplitude is between 1% and 3%, the volatility frequency is 2 to 5 times per hour, the average volatility rate is between 0.01 and 0.03 m / min, and the volatility variance is between 0.001 and 0.005 square meters, it is judged as medium volatility level; when the relative volatility amplitude is greater than 3%, the volatility frequency is greater than 5 times per hour, the average volatility rate is greater than 0.03 m / min, and the volatility variance is greater than 0.005 square meters, it is judged as high volatility level; the results of the four indicators are weighted and voted, and the level with the most votes is the final volatility level;

[0095] (4) Based on the final fluctuation level, the corresponding initial data collection interval range is preset, wherein the initial collection interval range for low fluctuation level is 10-15 minutes, for medium fluctuation level it is 5-10 minutes, and for high fluctuation level it is 1-5 minutes.

[0096] (5) Dynamically verify the initial matching acquisition interval. Set the verification period to 2 hours. Calculate the matching degree between the actual fluctuation characteristics and the acquisition interval within each verification period. The matching degree is calculated as follows: statistically analyze whether the water level fluctuation within the acquisition interval is effectively captured. That is, if there is a fluctuation exceeding 2% of the initial water level reference value within the interval, and the start and end points of the fluctuation are covered by data points, it is determined to be effectively captured; otherwise, it is determined to be missed. When the proportion of missed occurrences to the total number of fluctuations exceeds the first preset threshold, it indicates that the current acquisition interval is too large, and the interval is shortened by 20%. When the proportion of effectively captured occurrences to the total number of data points is lower than the second preset threshold, it indicates that the current acquisition interval is too small, and the interval is extended by 20%. After 3 verification periods, if the acquisition interval is not adjusted again, it is determined as the final data acquisition interval; if it still needs adjustment, the verification process is repeated until it stabilizes. In this invention, the first preset threshold is set to 10%, and the second preset threshold is set to 50%.

[0097] S1.4: With a defined data acquisition interval as the period, the water level monitoring logic model performs a moving average correction on the initial water level reference value to generate a corrected water level reference value;

[0098] Furthermore, the specific steps in S1.4 include:

[0099] (1) Based on the determined data collection interval, set the period of the moving average correction. The correction period is consistent with the data collection interval. That is, the moving average correction is triggered after each data collection is completed. At the same time, the size of the sliding window is determined according to the data collection interval. The setting of the window size must ensure that it covers at least 1 hour of water level data in order to balance the timeliness and stability of the correction. At the same time, the sliding window is initialized to an empty dataset and waits for new water level data to be continuously input according to the collection interval.

[0100] (2) Whenever new water level data is generated according to the collection interval, the new water level data along with the corresponding timestamp is added to the sliding window. Before adding the data, the validity of the new data needs to be verified. The verification standard is consistent with the outlier judgment in the fluctuation characteristic analysis. That is, if the deviation between the new water level value and the average value of the last 3 valid data in the window exceeds the current initial water level benchmark threshold, it is marked as suspicious data, temporarily added to the window but marked separately, and does not participate in the current correction calculation; if the deviation does not exceed the current initial water level benchmark threshold, it is determined as valid data and is added to the window normally. At the same time, when the number of data in the window reaches the set window size, the earliest data point is removed according to the first-in-first-out principle to ensure that the window always maintains a fixed number of the latest data and realizes sliding update. In this invention, the current initial water level benchmark threshold is 5% of the current initial water level benchmark value.

[0101] (3) Weighting is performed on the effective data within the sliding window using the time decay weighting method, where data closer to the current time is assigned a higher weight to enhance the influence of recent data on the correction result. The weighting rules are as follows: the weight of the latest data within the window is set to 1.5 times the base weight, and the remaining data decrease sequentially in chronological order. The weight difference between adjacent data is 0.1 times the base weight, and the sum of the weights of all data is 1. The base weight is calculated by dividing 1 by the number of effective data within the window. After weighting is completed, each data value is multiplied by its corresponding weight and summed to obtain the weighted average result, which serves as the initial correction value for this sliding average.

[0102] (4) Compare the preliminary correction value with the current initial water level reference value, calculate the percentage deviation between the two, the percentage deviation is equal to the difference between the preliminary correction value and the initial water level reference value, then divide by the initial water level reference value and multiply by 100%;

[0103] (5) Set a stability verification threshold. When the absolute value of the deviation percentage is less than the minimum deviation threshold, the preliminary correction value is determined to be stable and can be directly used as the correction result. When the absolute value of the deviation percentage is between the minimum and maximum deviation thresholds, the preliminary correction value is smoothed and the weighted average of the preliminary correction value and the initial water level reference value is taken as the correction result. When the absolute value of the deviation percentage is greater than the maximum deviation threshold, it is determined that there may be abnormal fluctuations. The initial water level reference value is not updated for the time being, and only the deviation situation is recorded. When this situation occurs 3 times in a row, the data re-examination process is triggered. The minimum deviation threshold is selected as 1%, and the maximum deviation threshold is selected as 3%.

[0104] (6) After stability verification, the final corrected water level benchmark value is determined and replaced with the original initial water level benchmark value as the benchmark for the next moving average correction. At the same time, the complete record of this correction is stored, including the correction timestamp, all data values ​​and weights in the window, the initial correction value, the deviation percentage, the final correction value, etc., forming a correction history ledger.

[0105] S1.5: Integrate the obtained corrected water level reference value with the equipment start-up threshold into a dynamic water level threshold, and output it to the fluid dynamics model.

[0106] The specific steps of S2 include:

[0107] S2.1: Extract the corrected initial water level reference value and equipment start-up threshold from the initial water level monitoring reference parameters, compare the corrected initial water level reference value with the real-time collected well water level data, and obtain the water level deviation value and change rate parameter.

[0108] Furthermore, the function of calculating water level deviation and rate of change parameters quantifies the degree of deviation and trend of the real-time water level from the dynamic benchmark value. Given the characteristics of red-bed groundwater prone to sudden surges and seepage, this function can identify abnormal water level changes 5-10 minutes in advance, providing a sufficient time window for decision-making regarding the start and stop of anti-clogging pumps.

[0109] S2.2: Call the anti-clogging pump structural parameters and input them into the fluid dynamics model. The fluid dynamics model introduces the Navier-Stokes equations and calculates the correlation between the impeller diameter and the critical pumping rate in the anti-clogging pump structural parameters to obtain the critical pumping rate at different water levels. At the same time, it calculates the maximum head under the corresponding operating conditions by combining the impeller diameter parameter. The anti-clogging pump structural parameters include impeller diameter, radius of curvature, installation angle, spiral guide groove size, and surface coating characteristics.

[0110] Furthermore, the function of calling the structural parameters of the anti-clogging water pump enables precise matching between equipment characteristics and hydrological conditions. The parameter combination of impeller diameter, radius of curvature, and installation angle is specifically optimized for the characteristics of high sediment content in red groundwater, so that the water flow forms a strong vortex at the impeller, reducing sediment deposition and extending the water pump dredging cycle to more than 3 times that of traditional equipment.

[0111] Furthermore, the introduction of the Navier-Stokes equations enables accurate simulation of the flow state of groundwater in red beds. This model can calculate the velocity and pressure fields of water flow under different water levels and flow rates, accurately predict the trajectory of sediment in the pump, and provide a theoretical basis for determining the critical pumping rate.

[0112] Furthermore, the specific steps of S2.2 include:

[0113] (1) Retrieve the complete set of structural parameters of the anti-clogging water pump from the equipment parameter database of the monitoring and sampling system, including core parameters such as impeller diameter, radius of curvature, installation angle, spiral guide groove size and surface coating characteristics, and verify the integrity of the retrieved parameters, checking one by one whether there are missing parameters, incorrect format or abnormal values ​​that exceed the physical reasonable range;

[0114] (2) Standardize and convert the verified structural parameters, unify the parameter units, and organize them into a structured data format according to the input requirements of the fluid dynamics model, including geometric parameter group, surface characteristic parameter group and motion parameter group. After the conversion is completed, input the standardized parameters into the fluid dynamics model in batches through the fluid dynamics model interface to establish the mapping relationship between parameters and model calculation module.

[0115] (3) Load the Navier-Stokes equation into the fluid dynamics model. This equation is used to describe the momentum conservation law of fluid. The equation form includes inertial terms, viscosity terms, pressure gradient terms and gravity terms. Set the boundary conditions of the Navier-Stokes equation according to the structural characteristics of the anti-clogging pump. Among them, set the no-slip boundary condition on the impeller surface, that is, the relative velocity of the fluid on the impeller surface is zero; set the symmetrical boundary condition on the inner wall of the pump casing, and ignore the interference of the pump casing on the fluid flow; set the pressure boundary condition and the flow boundary condition at the inlet and outlet respectively. The inlet pressure is equal to the hydrostatic pressure corresponding to the current water level, and the outlet pressure is initially set to atmospheric pressure. The Navier-Stokes equation is the prior art in this field and is not the inventive solution of this application. It will not be described in detail here.

[0116] (4) Based on the Navier-Stokes equations, establish a correlation model between impeller diameter and critical pumping rate, including: firstly, calculate the flow field distribution under different impeller diameters through the model, extract the fluid velocity field data at the impeller outlet, and analyze the nonlinear relationship between velocity distribution and impeller diameter. The critical pumping rate is defined as the minimum pumping velocity to avoid the deposition of suspended particles on the impeller surface. The critical condition is determined by calculating the force balance of particles in the flow field: when the viscous resistance generated by the fluid velocity is greater than the settling resistance of the particles, the particles are in a suspended state, and the corresponding velocity is the critical pumping rate. For different water levels, adjust the boundary conditions and repeat the calculation to obtain a table of the correspondence between water level and critical pumping rate. A set of critical pumping rate data is recorded for every N meters change in water level, forming a complete dataset of critical pumping rates under different water levels.

[0117] (5) Based on the calculation of the critical pumping rate, the maximum head under different operating conditions is calculated using a fluid dynamics model. The head calculation formula is based on the law of conservation of energy and is equal to the energy increment obtained by a unit weight of fluid through the pump, including the static pressure energy increment, kinetic energy increment, and positional potential energy increment. The pressure difference, fluid velocity difference, and height difference at the pump inlet and outlet are extracted from the model, and the three energy increments are calculated and summed. Combined with the impeller diameter parameter, the influence of the impeller diameter on the head is analyzed. For the operating conditions corresponding to the critical pumping rate at different water levels, the maximum head is calculated one by one to ensure that the head calculation results match the critical pumping rate, forming a correlation database of water level-critical pumping rate-maximum head.

[0118] The influence of impeller diameter on head is as follows: at the same rotational speed, the larger the impeller diameter, the greater the centrifugal force obtained by the fluid and the higher the head. However, due to the limitations of pump structure and power, there is a limit head corresponding to the maximum diameter.

[0119] S2.3: The equipment start-up threshold in the water level deviation value, change rate parameter, and initial water level monitoring benchmark parameters, together with the critical pumping rate and maximum head at different water levels, are input into the fuzzy control algorithm. The fuzzy control algorithm makes decisions based on preset rules and generates an intelligent pumping control execution sequence that includes pump start-up and shutdown timing, operating power adjustment parameters, and anti-clogging control instructions. The fuzzy control algorithm is existing technology in this field and is not an inventive solution of this application, so it will not be described in detail here.

[0120] Furthermore, in this invention, the preset rules are as follows: a drop in water level exceeding 5cm within 3 seconds triggers pump shutdown; fluctuations within 10 seconds of less than 1cm result in steady-state operation. Rapidly stopping the pump when a sudden drop in water level is detected avoids pump idling and further well collapse. Moreover, the steady-state operation rules reduce unnecessary start-ups and shutdowns, saving energy.

[0121] Furthermore, the specific steps of S2.3 include:

[0122] (1) Collect all parameters to be input, including water level deviation value, water level change rate parameter, equipment start-up threshold, critical pumping rate and maximum head at different water levels, and preprocess them to obtain clear parameters after preprocessing.

[0123] (2) Convert the preprocessed clear parameters into fuzzy linguistic variables, determine the fuzzy subset and membership function of each parameter. For the water level deviation value, it is divided into five fuzzy subsets: negative large, negative small, zero, positive small, and positive large. The membership function adopts the triangular function. The water level change rate is divided into five subsets: negative fast, negative slow, zero, positive slow, and positive fast. Taking the equipment start-up threshold as a reference, the ratio of the water level deviation value to the start-up threshold is divided into five subsets: much lower, slightly lower, close to, slightly higher, and much higher. The critical pumping rate and maximum head are divided into five subsets: low, medium-low, medium, medium-high, and high according to their percentages to the rated value. The membership value of each parameter belonging to each fuzzy subset is calculated through the membership function to complete the fuzzification conversion. The membership function calculation formula is the prior art in this field and is not an inventive solution of this application. It will not be elaborated here.

[0124] (3) Based on the actual needs of monitoring red-bed groundwater, a fuzzy rule base is constructed. The rules adopt the if-then form and cover the correspondence between the combination of input parameters and the output control quantity. For example, rule 1: if the water level deviation value is positive and the rate of change is positive and fast, and the current rate is much lower than the critical pumping rate, then the pump start-stop sequence is to start immediately, the operating power adjustment parameter is high power, and the anti-clogging control command is high-frequency flushing; rule 2: if the water level deviation value is zero and the rate of change is zero, and the current rate is close to the critical pumping rate, then the pump start-stop sequence is to maintain the current state, the operating power adjustment parameter is medium power, and the anti-clogging control command is regular flushing. The rule base contains at least 20 basic rules, covering different water level states, rate characteristics and equipment parameter combination scenarios.

[0125] (4) Match the fuzzy membership degree of the input parameters with the preconditions in the rule base, calculate the trigger strength of each rule, and the trigger strength is equal to the minimum value of the membership degree of all preconditions of the rule;

[0126] (5) Fuzzy reasoning is performed using the Mamdani reasoning method. Based on the trigger strength and the fuzzy subset of the conclusion part of each rule, a fuzzy set of each output control quantity is generated. The reasoning results of all rules are synthesized, and the fuzzy sets of the same output quantity are merged using the maximum membership method to obtain the total fuzzy sets of the pump start-stop sequence, the operating power adjustment parameters and the anti-clogging control command. The Mamdani reasoning method is the prior art in this field and is not an inventive solution of this application. It will not be described in detail here.

[0127] (6) The synthesized fuzzy output is converted into clear control parameters, and the clear value of each output is calculated by the centroid method. The centroid method is the prior art in this field and is not an inventive solution of this application. It will not be described in detail here.

[0128] (7) The clarified control parameters are integrated into an intelligent pumping control execution sequence containing time nodes, power values ​​and instruction codes. The sequence format meets the input requirements of the equipment control interface.

[0129] The generation process of the intelligent pumping control execution sequence in S2.3 includes:

[0130] The start and stop times are determined based on the equipment start threshold and water level deviation, thus obtaining the pump start and stop sequence.

[0131] Based on the matching relationship between the maximum head and the critical pumping rate, the power distribution ratio is calculated to obtain the operating power adjustment parameters.

[0132] By combining the characteristics of the spiral guide channel and outputting the impeller speed correction value according to the critical pumping rate, the intelligent pumping control execution sequence is obtained.

[0133] The specific steps of S3 include:

[0134] S3.1: Extract the final flow rate value in the intelligent pumping control execution sequence as the well washing start flow rate benchmark, call the preset well washing flow rate benchmark parameters, and determine the flow range constraints and corresponding pipeline Reynolds number thresholds for the high flow rate well washing stage.

[0135] Furthermore, the well-washing start-up flow rate benchmark adopts the final flow rate value of the intelligent pumping control execution sequence, realizing a seamless connection between the pumping and well-washing processes and avoiding the problem of sudden flow changes in traditional step-by-step operations.

[0136] Furthermore, the setting of the well-washing flow rate benchmark parameters is tailored to the low permeability coefficient of the red aquifer, ensuring sufficient flushing intensity while avoiding well-surround formation disturbance caused by excessive flushing. Compared with traditional high-flow-rate blind flushing, it can reduce the amount of water used for well washing and protect the aquifer structure.

[0137] S3.2: Input the final flow rate value and the well washing flow rate reference parameters into the dynamic pressure adjustment algorithm. The dynamic pressure adjustment algorithm takes the Reynolds number threshold as the core constraint. First, it calculates the current Reynolds number and matches the corresponding turbulence state through the final flow rate value. Then, it constructs a flow-pressure mapping relationship model based on historical data, calculates the maximum allowable flushing pressure in combination with the turbulence state, and finally generates the initial adjustment parameters of the pump speed according to the conversion relationship between the maximum allowable flushing pressure and the pump head and the speed-head characteristic curve.

[0138] Furthermore, the pipeline Reynolds number threshold is typically less than or equal to 2000. The constraint function of the pipeline Reynolds number threshold ensures that the water flow is in a laminar state during well washing, avoiding pipeline wear and secondary suspension of sediment caused by turbulence. In red bed groundwater environments with high sediment content, this function extends the service life of the pipeline.

[0139] Furthermore, the specific steps in S3.2 include:

[0140] (1) Receive the final flow rate value and well washing flow rate reference parameters determined in S3.1, and perform integrity verification on the parameters;

[0141] (2) Convert the final flow rate value into the fluid velocity using the velocity calculation formula, where the velocity calculation formula is the first variable divided by the product of pi and the second variable. The first variable is the ratio of the final flow rate value to 3600, and the second variable is the square of half the pipe inner diameter.

[0142] (3) Obtain the fluid kinematic viscosity parameters of the red bed groundwater, and combine them with the inner diameter of the pipe to obtain the Reynolds number. The Reynolds number is equal to the product of the flow velocity and the inner diameter of the pipe, divided by the fluid kinematic viscosity.

[0143] (4) Compare the calculated current Reynolds number with the Reynolds number threshold determined in S3.1 to match the corresponding turbulence state;

[0144] (5) Call the flow-pressure correlation data in the historical well washing database and filter it. Use the least squares method to perform curve fitting on the filtered flow-pressure correlation data to construct a flow-pressure mapping relationship model. Substitute the flow percentage corresponding to the current final flow value into the flow-pressure mapping relationship model to obtain the basic pressure value. The flow-pressure mapping relationship model is divided into linear segment and nonlinear segment.

[0145] (6) Correct the base pressure value according to the matched turbulence state;

[0146] For example, under laminar flow conditions, a 15% compensation pressure is added to the base pressure value, and the compensated pressure must not exceed 80% of the upper limit of the pipeline design pressure; under safe turbulent flow conditions, the base pressure value is used directly without correction.

[0147] (7) Convert the maximum allowable flushing pressure into the corresponding pump head. The conversion formula is that the head is equal to the maximum allowable flushing pressure divided by the product of fluid density and gravitational acceleration. Add the total pipeline resistance loss to the quotient.

[0148] (8) Call the speed-head characteristic curve of the vector control frequency converter, find the speed value corresponding to the target head, and the percentage of the speed value to the rated speed is the initial speed adjustment parameter;

[0149] (9) Perform boundary verification on the initial speed adjustment parameters. If the parameters exceed the boundary, automatically adjust to the nearest boundary value and record the reason for adjustment. Finally, generate a set of initial adjustment parameters for water pump speed that includes speed percentage, adjustment accuracy, and response delay.

[0150] S3.3: Based on the generated initial adjustment parameters of the water pump speed, start the high-flow well washing operation. The dynamic pressure adjustment algorithm adjusts the water pump speed according to the real-time collected pipeline flow feedback, and compares the feedback flow with the flow range constraint. If the feedback flow exceeds the flow range constraint, the water pump speed is dynamically adjusted according to the deviation ratio.

[0151] Furthermore, the real-time rotation speed adjustment function based on pipeline flow feedback solves the problem of flow attenuation caused by silt blockage during the well washing process of red-bed groundwater.

[0152] Furthermore, the specific steps of S3.3 include:

[0153] (1) Before starting the high-flow well washing operation, call the flow range constraint determined by S3.1 and the initial adjustment parameters of the water pump speed generated by S3.2 to check the parameters, check whether the speed parameters are within the normal operating range of the equipment, and whether the flow range constraint matches the upper limit of the pipeline design flow. At the same time, perform a readiness check on the well washing equipment, including the opening and closing status of pipeline valves, the connection stability of turbidity sensor and flow sensor, and the preheating status of water pump motor. After confirming that all parameters are correct and the equipment status is normal, generate a start command and prepare to enter the well washing operation stage.

[0154] (2) Start the well cleaning pump according to the initial adjustment parameters of the pump speed, set the initial running time to a stabilization period of 30 seconds, collect the initial flow value through the electromagnetic flow sensor built into the pipeline, record the average flow during the stabilization period as the initial feedback flow, and monitor auxiliary parameters such as pump outlet pressure and motor current. If the initial feedback flow does not reach 50% of the flow range constraint within 30 seconds, immediately stop the operation and trigger fault diagnosis to check for pipeline blockage or pump no-load problems.

[0155] It should be noted that during the initial stabilization period of 30 seconds, no speed adjustment is performed, and only basic data is collected in real time.

[0156] (3) After the stabilization period ends, the dynamic pressure regulation algorithm enters the real-time regulation stage. The flow sensor continuously collects pipeline flow data and generates a flow feedback value every 2 seconds. This includes: preprocessing the collected pipeline flow data, first removing outliers, then using the moving average method of three adjacent valid data to calculate the smooth flow value, and storing the smooth flow value in association with the corresponding timestamp to form a flow feedback time series.

[0157] (4) Compare the preprocessed smooth flow value with the flow range constraint determined in S3.1 in real time, calculate the flow deviation parameter, and determine the range in which the smooth flow value is located. The deviation parameter includes absolute deviation and relative deviation.

[0158] (5) Determine the deviation ratio level based on the relative deviation value. When the absolute value of the relative deviation is less than or equal to the preset lower limit of the deviation threshold, it is a slight deviation and no speed adjustment is required. When the absolute value of the relative deviation is greater than the preset lower limit of the deviation threshold and less than the preset upper limit of the deviation threshold, it is a moderate deviation and regular speed adjustment is required. When the absolute value of the relative deviation is greater than the preset upper limit of the deviation threshold, it is a serious deviation and emergency speed adjustment is required and an early warning is issued.

[0159] (6) Calculate the speed adjustment amount based on the deviation ratio level, wherein the speed adjustment amount is equal to the product of the current speed adjustment parameter, the relative deviation, and the adjustment coefficient, and the adjustment coefficient is set according to the deviation level;

[0160] (7) The calculated speed adjustment amount is converted into a vector control inverter control signal and output to the water pump motor through pulse width modulation (PWM) technology. The adjustment response time is controlled within 0.5 seconds, and after adjustment, a 10-second effect observation period is entered. During this period, the flow feedback value is continuously collected and a new smooth flow value is calculated. It is compared with the flow range constraint again. If the absolute value of the new relative deviation drops to within the preset deviation threshold, the adjustment is deemed effective and the current speed is maintained. If the deviation does not decrease or increases instead, the cause of the deviation is analyzed, the adjustment amount is recalculated, and the adjustment coefficient is increased to 1.0 for a second adjustment. If the adjustment is ineffective for 3 consecutive times, a manual intervention warning is triggered. The PWM technology is prior art in this field and is not an inventive solution of this application. It will not be described in detail here.

[0161] (8) For each speed adjustment, record the adjustment time, speed before adjustment, adjustment amount, speed after adjustment, flow deviation change and corresponding pressure change value to form a dynamic adjustment log.

[0162] S3.4: During the high-flow well washing operation, water quality data is collected in real time by the turbidity sensor and conductivity sensor in the integrated well washing and sampling device, and the water quality data is transmitted to the water quality compliance judgment model; the water quality data includes turbidity value, sediment content and conductivity fluctuation value;

[0163] S3.5: The water quality compliance judgment model analyzes the received real-time water quality data based on the target turbidity value of the preset evaluation standard and well washing flow benchmark parameter. If the water quality does not meet the standard, the flushing path node parameters are dynamically corrected according to the deviation between the current turbidity and the target value. If the water quality meets the standard, the current node is marked as the well washing endpoint.

[0164] Furthermore, the preset evaluation standard of the water quality compliance judgment model is that the turbidity is less than or equal to 10 NTU and the conductivity fluctuates less than or equal to 5% for 5 consecutive minutes. Here, NTU represents turbidity unit, which is a standard unit used to measure the degree of turbidity of water.

[0165] Furthermore, the preset evaluation criteria of the water quality compliance judgment model are designed to address the phenomenon of false clearing that is prone to occur in red-bed groundwater. By continuously monitoring conductivity fluctuations, it avoids water sample contamination caused by prematurely ending well washing and improves the accuracy of subsequent sampling data.

[0166] S3.6: Integrate the pressure regulation records, flow stability parameters, and flushing path node parameter correction results determined by the dynamic pressure regulation algorithm output, and form a complete high-flow well flushing operation path.

[0167] The specific steps of S4 include:

[0168] S4.1: Extract the pressure and flow values ​​at the end of the high-flow-rate well washing operation path as the initial conditions for sampling. The pressure value is the final outlet pressure of the well washing stage, and the flow value is the actual flow rate at the end of the well washing.

[0169] S4.2: Call the preset low-flow sampling pressure reference and pipeline fluid characteristic parameters; the pipeline fluid characteristic parameters include the inner diameter, inner wall roughness and structural parameters of the food-grade silicone hose and the gradually expanding interface;

[0170] S4.3: Input the initial pressure value, flow rate value, small flow sampling pressure reference, and pipeline fluid characteristic parameters into the dual-pump linkage control algorithm. The dual-pump linkage control algorithm calculates the difference between the target pressure and the initial pressure, and combines it with the pipeline friction coefficient to obtain the main pump basic flow rate parameter and the auxiliary pump bypass compensation value; the pipeline friction coefficient is determined based on the inner wall roughness.

[0171] Furthermore, the implementation process of S4.3 includes: firstly, collecting and verifying the initial pressure and flow rate, the small flow rate sampling pressure benchmark and pipeline characteristic parameters, and calculating the pipeline friction coefficient based on the inner wall roughness and Reynolds number after standardization and conversion; calculating the difference between the target and initial pressure and correcting the pressure loss effect, determining the basic flow parameters of the main pump in combination with the flow-pressure relationship model, and calculating the bypass compensation value of the auxiliary pump based on the pressure difference; verifying the matching of the dual pump parameters and adjusting them to meet the requirements, outputting parameters and setting a dynamic correction mechanism to achieve dual pump coordinated control to achieve the target sampling pressure and flow rate.

[0172] S4.4: Input the pipeline fluid characteristic parameters and the main pump basic flow parameters into the bubble suppression model to generate the inert gas injection sequence; the bubble suppression model calculates the pipeline critical flow velocity through the Hagen-Poiseuille law, and at the same time introduces the dissolved gas content in the real-time collected water level data to generate the inert gas injection sequence. The Hagen-Poiseuille law is the prior art in this field and is not an inventive solution of this application, so it will not be described in detail here.

[0173] Furthermore, the implementation process of S4.4 includes: firstly, collecting and verifying pipeline fluid characteristic parameters, main pump basic flow parameters, and dissolved gas content data; after standardization and conversion, calculating the pipeline critical flow velocity based on Hagen-Poiseuille's law; analyzing dissolved gas supersaturation and assessing bubble risk level; setting basic parameters for inert gas injection based on risk level, generating initial injection sequence, and correcting sequence parameters based on real-time data; monitoring the suppression effect through bubble sensors, dynamically optimizing injection parameters and model coefficients, and finally outputting a stable inert gas injection sequence to achieve effective suppression of bubbles in the pipeline.

[0174] S4.5: The bubble suppression model optimizes the gas-liquid balance in the sampling pipeline based on the pipeline critical flow rate and inert gas injection timing, and outputs the pipeline pressure stability threshold and the upper limit of bubble content control.

[0175] Furthermore, the implementation process of S4.5 includes: firstly, assessing the initial gas-liquid equilibrium state and marking abnormal indicators of pressure and bubble content; adjusting the main pump flow rate with the critical flow rate as a constraint, while refining the inert gas injection sequence, adding pressure fluctuation triggering mechanisms and targeted measures for bubble types; constructing a gas-liquid two-phase flow model to simulate the equilibrium state, calculating the pipeline pressure stability threshold and the upper limit of bubble content control; monitoring the optimization effect in real time, providing feedback to correct parameters, and finally solidifying the thresholds and establishing a dynamic update mechanism to achieve continuous optimization of the gas-liquid equilibrium state.

[0176] S4.6: The dual-pump linkage control algorithm combines the auxiliary pump bypass compensation value and the pipeline pressure stability threshold to generate motor speed adjustment parameters through PWM technology;

[0177] S4.7: Integrate motor speed regulation parameters, pressure maintenance curve, inert gas injection sequence and upper limit of bubble content control to form a steady-state sampling execution scheme.

[0178] Example 2:

[0179] Another embodiment of the present invention provides an integrated monitoring and sampling system for dealing with red-bed groundwater, comprising:

[0180] Initialization module, water level monitoring module, control module, high-flow well washing control module, steady-state sampling control module, report generation module;

[0181] The initialization module is used to monitor the physical deployment of the sampling system, device self-test, and parameter initialization, providing a hardware foundation and environmental verification for subsequent monitoring and sampling processes.

[0182] The water level monitoring module is used to collect water level data in the well in real time, and calculate the initial water level reference value by combining it with the preset water level monitoring logic model, so as to provide dynamic threshold basis for pumping control.

[0183] The control module generates an intelligent pumping control execution sequence based on the initial water level reference value and equipment structural parameters, realizing intelligent management of pump start-up and shutdown, power regulation and anti-clogging operation;

[0184] The high-flow well washing control module is used to start the high-flow well washing process based on the intelligent pumping control execution sequence. It achieves water purification in the well through pressure regulation and water quality monitoring, and ensures the accuracy of subsequent sampling data.

[0185] The steady-state sampling control module is used to switch to a low-flow sampling mode after well washing is completed. It achieves stable pressure sampling through dual-pump linkage and bubble suppression technology to ensure water sample quality.

[0186] The report generation module is used to summarize the monitoring data throughout the entire process and generate an integrated monitoring and sampling report after verification through closed-loop logic, so as to achieve data traceability and result visualization.

[0187] The water level monitoring module includes: a water level data acquisition unit, a status recording unit, and a water level monitoring logic unit;

[0188] The water level data acquisition unit collects water level data in the well in real time through ultrasonic water level sensors and capacitive water level sensors, and records information such as instantaneous water level and change amplitude according to timestamps;

[0189] The status recording unit is used to record equipment status information such as sensor signal stability, water pump operating current, and pipeline pressure in real time, providing support for data validity verification.

[0190] The water level monitoring logic unit is used to perform trend decomposition and noise filtering on water level data through a built-in time series analysis model, preliminarily calculate the initial water level reference value and equipment start-up threshold, generate data acquisition intervals based on water level fluctuation characteristics, correct the initial water level reference value through moving average, and finally output the dynamic water level threshold to the control module.

[0191] The control module includes: a data fusion unit, an analysis unit, and a control decision unit;

[0192] The data fusion unit is used to integrate dynamic water level reference values, real-time water level deviation values, change rate parameters, and anti-clogging pump structural parameters.

[0193] The analysis unit is used to introduce the Navier-Stokes equations, simulate the water flow state under different water levels, calculate the impeller structural parameters and critical pumping rate, and output the maximum head and critical velocity for the corresponding working conditions.

[0194] The control decision unit is used to generate an intelligent pumping control execution sequence based on preset rules, integrating water level deviation, critical pumping rate and maximum head data, including pump start-up and shutdown sequence, power adjustment parameters and anti-clogging instructions.

[0195] The preset rules are as follows: if the water level drops by more than 5cm in 3 seconds, the pump will be stopped; if the fluctuation is less than 1cm in 10 seconds, the pump will run in a stable state.

[0196] The high-flow well washing control module includes: a well washing parameter configuration unit, a dynamic pressure regulation unit, and a compliance judgment unit;

[0197] The well washing parameter configuration unit is used to call the preset well washing flow reference parameters, set the Reynolds number constraint and the initial outlet pressure range, and provide target thresholds for dynamic adjustment.

[0198] The dynamic pressure regulation unit is used to adjust the speed of the variable speed water pump in real time through the PID control algorithm. It maintains the outlet pressure stability with the well washing flow rate as a constraint. When the flow rate drops by more than 10% within 5 minutes, it automatically extends the well washing time by 10 minutes and increases it to 60L / min in stages to ensure the flushing intensity.

[0199] The water quality monitoring and compliance determination unit is used to collect water quality data in real time through turbidity and conductivity sensors; it has a built-in water quality compliance determination model to determine the completion of well washing and output the endpoint parameters.

[0200] The steady-state sampling control module includes: a mode switching unit, a dual-pump linkage adjustment unit, and a pipeline optimization unit;

[0201] The mode switching unit is used to receive the well washing completion signal, switch the variable speed water pump system from high flow well washing mode to low flow sampling mode, and simultaneously adjust the status of pipeline valves.

[0202] The dual-pump linkage regulation unit is used to adjust the base flow of the main pump based on the small flow sampling pressure reference, and the auxiliary pump compensates for pipeline pressure fluctuations through bypass. It also uses PWM technology to control motor speed fluctuations to ensure flow stability.

[0203] The pipeline optimization unit is used to calculate the critical flow velocity of the pipeline based on the Hagen-Poiseuille law, and to control the Reynolds number of the water flow through the design of food-grade silicone hoses and gradually expanding interfaces; combined with the dissolved gas content in the water level data, it generates an inert gas injection sequence to ensure the bubble content at the interface.

[0204] The embodiments of the present invention have been described above with reference to the accompanying drawings. However, the present invention is not limited to the specific embodiments described above. The specific embodiments described above are merely illustrative and not restrictive. Those skilled in the art can make changes, modifications, substitutions and variations to the above embodiments under the guidance of the present invention without departing from the spirit and scope of the present invention. All of these variations are within the protection scope of the present invention.

Claims

1. An integrated monitoring and sampling method for addressing red-bed groundwater, characterized in that, include: S1: In response to the preset red-bed groundwater monitoring requirements, deploy the monitoring and sampling system and complete the initial configuration, collect the water level data in the well and the equipment operation status information in real time, and calculate the initial water level monitoring benchmark parameters in combination with the preset water level monitoring logic model; the initial water level monitoring benchmark parameters include the initial water level benchmark value, the equipment start threshold and the data acquisition interval; S2: Based on the initial water level monitoring benchmark parameters, combined with the real-time collected water level data and anti-clogging pump structural parameters, an intelligent pumping control execution sequence is generated through fuzzy control algorithm and fluid dynamics model; S3: Based on the intelligent pumping control execution sequence, with the preset well washing flow rate benchmark parameters as constraints, combined with real-time monitoring of water quality data by water quality sensors, a high-flow well washing operation path is obtained through dynamic pressure regulation algorithm and water quality compliance judgment model. S4: Based on the endpoint parameters of the high-flow-rate well washing operation path, combined with the preset low-flow-rate sampling pressure benchmark and pipeline fluid characteristic parameters, a steady-state sampling execution scheme is obtained through a dual-pump linkage control algorithm and a bubble suppression model. S5: Based on the completion signal of the steady-state sampling execution scheme, summarize the final water level monitoring data and the full cycle record of equipment operation, and generate an integrated monitoring and sampling report through closed-loop control logic and data archiving rules; The specific steps of S2 include: S2.1: Extract the corrected initial water level reference value and equipment start-up threshold from the initial water level monitoring reference parameters, compare the corrected initial water level reference value with the real-time collected well water level data, and obtain the water level deviation value and change rate parameter. S2.2: Call the anti-clogging pump structural parameters and input them into the fluid dynamics model. The fluid dynamics model introduces the Navier-Stokes equations and calculates the correlation between the impeller diameter and the critical pumping rate in the anti-clogging pump structural parameters to obtain the critical pumping rate at different water levels. At the same time, it calculates the maximum head under the corresponding operating conditions by combining the impeller diameter parameter. The anti-clogging pump structural parameters include impeller diameter, radius of curvature, installation angle, spiral guide groove size, and surface coating characteristics. S2.3: The equipment start-up threshold in the water level deviation value, change rate parameter and initial water level monitoring benchmark parameter, together with the critical pumping rate and maximum head under different water levels, are input into the fuzzy control algorithm. The fuzzy control algorithm makes decisions based on preset rules and generates an intelligent pumping control execution sequence that includes pump start-up and shutdown timing, operating power adjustment parameters and anti-clogging control instructions.

2. The integrated monitoring and sampling method for addressing red-bed groundwater as described in claim 1, characterized in that, The deployment of the monitoring and sampling system and the completion of initial configuration include: The intelligent pumping control device and the integrated well washing and sampling device are assembled to form a monitoring and sampling system, which is then lowered into the target red layer monitoring well at a preset depth. The intelligent pumping control device includes a water level sensor-activated start / stop system and an anti-clogging pump. The water level sensor-activated start / stop system comprises an ultrasonic water level sensor, a capacitive water level sensor, and a control module. The ultrasonic water level sensor uses a sound wave frequency to avoid air bubbles and suspended particles in the red-bed well water. The electrodes of the capacitive water level sensor are made of titanium alloy with an electrode spacing of 2 cm. The control module uses a fuzzy control algorithm to incorporate the water level change rate and duration into the decision model, and starts and stops the anti-clogging pump according to the water level change. The control module also has a built-in temperature sensor and uses current values ​​for overload protection. The integrated well washing and sampling device includes a variable speed water pump system, an intelligent switching and evaluation unit, and a sampling pipeline assembly. The variable speed water pump system includes a vector control frequency converter and a dual-pump linkage mechanism. The vector control frequency converter establishes a nonlinear mapping model between motor speed and head. The dual-pump linkage mechanism includes a main pump and an auxiliary pump, with the auxiliary pump compensating for pipeline pressure fluctuations through bypass adjustment. The intelligent switching and evaluation unit includes a turbidity sensor, a conductivity sensor, and a control circuit. The control circuit dynamically calculates the well washing time based on well depth, well diameter, and initial turbidity.

3. The integrated monitoring and sampling method for addressing red-bed groundwater as described in claim 2, characterized in that, The calculation process of the initial water level monitoring reference parameters in S1 includes: S1.1: Start the deployed monitoring and sampling system, and begin real-time collection of well water level data through the ultrasonic water level sensor and capacitive water level sensor of the intelligent pumping control device, while recording equipment operating status information; the equipment operating status information includes sensor signal transmission delay, water pump initial current and pipeline sealing parameters; S1.2: Input the real-time collected well water level data into the preset water level monitoring logic model. The water level monitoring logic model performs trend decomposition and noise filtering on the well water level data based on time series analysis, and preliminarily calculates the initial water level reference value and the equipment start-up threshold. The equipment start-up threshold is the water level critical value that triggers the start and stop of the water pump. S1.3: The water level monitoring logic model automatically generates data acquisition intervals based on the fluctuation characteristics of the initial water level reference value; S1.4: With a defined data acquisition interval as the period, the water level monitoring logic model performs a moving average correction on the initial water level reference value to generate a corrected water level reference value; S1.5: Integrate the obtained corrected water level reference value with the equipment start-up threshold into a dynamic water level threshold, and output it to the fluid dynamics model.

4. The integrated monitoring and sampling method for addressing red-bed groundwater as described in claim 3, characterized in that, The generation process of the intelligent pumping control execution sequence in S2.3 includes: The start and stop times are determined based on the equipment start threshold and water level deviation, thus obtaining the pump start and stop sequence. Based on the matching relationship between the maximum head and the critical pumping rate, the power distribution ratio is calculated to obtain the operating power adjustment parameters. By combining the characteristics of the spiral guide channel and outputting the impeller speed correction value according to the critical pumping rate, the intelligent pumping control execution sequence is obtained.

5. The integrated monitoring and sampling method for addressing red-bed groundwater as described in claim 4, characterized in that, The specific steps of S3 include: S3.1: Extract the final flow rate value in the intelligent pumping control execution sequence as the well washing start flow rate benchmark, call the preset well washing flow rate benchmark parameters, and determine the flow range constraints and corresponding pipeline Reynolds number thresholds for the high flow rate well washing stage. S3.2: Input the final flow rate value and the well washing flow rate reference parameters into the dynamic pressure adjustment algorithm. The dynamic pressure adjustment algorithm takes the Reynolds number threshold as the core constraint. First, it calculates the current Reynolds number and matches the corresponding turbulence state through the final flow rate value. Then, it constructs a flow-pressure mapping relationship model based on historical data, calculates the maximum allowable flushing pressure in combination with the turbulence state, and finally generates the initial adjustment parameters of the pump speed according to the conversion relationship between the maximum allowable flushing pressure and the pump head and the speed-head characteristic curve. S3.3: Based on the generated initial adjustment parameters of the water pump speed, start the high-flow well washing operation. The dynamic pressure adjustment algorithm adjusts the water pump speed according to the real-time collected pipeline flow feedback, and compares the feedback flow with the flow range constraint. If the feedback flow exceeds the flow range constraint, the water pump speed is dynamically adjusted according to the deviation ratio. S3.4: During the high-flow well washing operation, water quality data is collected in real time by the turbidity sensor and conductivity sensor in the integrated well washing and sampling device, and the water quality data is transmitted to the water quality compliance judgment model; the water quality data includes turbidity value, sediment content and conductivity fluctuation value; S3.5: The water quality compliance judgment model analyzes the received real-time water quality data based on the target turbidity value of the preset evaluation standard and well washing flow benchmark parameter. If the water quality does not meet the standard, the flushing path node parameters are dynamically corrected according to the deviation between the current turbidity and the target value. If the water quality meets the standard, the current node is marked as the well washing endpoint. S3.6: Integrate the pressure regulation records, flow stability parameters, and flushing path node parameter correction results determined by the dynamic pressure regulation algorithm output, and form a complete high-flow well flushing operation path.

6. The integrated monitoring and sampling method for addressing red-bed groundwater as described in claim 5, characterized in that, The specific steps of S4 include: S4.1: Extract the pressure and flow values ​​at the end of the high-flow-rate well washing operation path as the initial conditions for sampling. The pressure value is the final outlet pressure of the well washing stage, and the flow value is the actual flow rate at the end of the well washing. S4.2: Call the preset low-flow sampling pressure reference and pipeline fluid characteristic parameters; the pipeline fluid characteristic parameters include the inner diameter, inner wall roughness and structural parameters of the food-grade silicone hose and the gradually expanding interface; S4.3: Input the initial pressure value, flow rate value, small flow sampling pressure reference, and pipeline fluid characteristic parameters into the dual-pump linkage control algorithm. The dual-pump linkage control algorithm calculates the difference between the target pressure and the initial pressure, and combines it with the pipeline friction coefficient to obtain the main pump basic flow rate parameter and the auxiliary pump bypass compensation value; the pipeline friction coefficient is determined based on the inner wall roughness. S4.4: Input the pipeline fluid characteristic parameters and the main pump basic flow parameters into the bubble suppression model. The bubble suppression model calculates the critical flow velocity of the pipeline using the Hagen-Poiseuille law, and at the same time introduces the dissolved gas content in the real-time collected water level data to generate the inert gas injection sequence. S4.5: The bubble suppression model optimizes the gas-liquid balance in the sampling pipeline based on the pipeline critical flow rate and inert gas injection timing, and outputs the pipeline pressure stability threshold and the upper limit of bubble content control. S4.6: The dual-pump linkage control algorithm combines the auxiliary pump bypass compensation value and the pipeline pressure stability threshold to generate motor speed adjustment parameters through PWM technology; S4.7: Integrate motor speed regulation parameters, pressure maintenance curve, inert gas injection sequence and bubble content control upper limit to form a steady-state sampling execution scheme.

7. The integrated monitoring and sampling method for addressing red-bed groundwater as described in claim 6, characterized in that, The dual-pump linkage control algorithm in S4 uses a small flow sampling pressure benchmark. The main pump maintains the base pressure, while the auxiliary pump calculates the pulse frequency according to the bubble suppression model to compensate for pressure fluctuations.

8. The integrated monitoring and sampling method for addressing red-bed groundwater as described in claim 7, characterized in that, The integrated monitoring and sampling report in S5 includes water level change curves, well washing time and water quality index change trends, sampling pressure stability analysis, equipment operation status record table, and abnormal situation handling log.

9. An integrated monitoring and sampling system for dealing with red-bed groundwater, used to implement the integrated monitoring and sampling method for dealing with red-bed groundwater as described in any one of claims 1-8, characterized in that, include: Initialization module, water level monitoring module, control module, high-flow well washing control module, steady-state sampling control module, report generation module; The initialization module is used to monitor the physical deployment of the sampling system, device self-test, and parameter initialization. The water level monitoring module is used to collect water level data in the well in real time and calculate the initial water level reference value by combining it with the preset water level monitoring logic model. The control module generates an intelligent pumping control execution sequence based on the initial water level reference value and equipment structural parameters, realizing intelligent management of pump start-up and shutdown, power adjustment and anti-clogging operation; The high-flow well washing control module is used to start the high-flow well washing process based on the intelligent pumping control execution sequence, and to purify the water in the well through pressure regulation and water quality monitoring. The steady-state sampling control module is used to switch to a low-flow sampling mode after well washing is completed, and achieves stable pressure sampling through dual-pump linkage and bubble suppression technology; The report generation module is used to summarize the monitoring data throughout the entire process and generate an integrated monitoring sampling report after verification through closed-loop logic.

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