Integrated monitoring and sampling method and system for red-bed underground water

Abstract

The invention discloses an integrated monitoring and sampling method and system for red-bed groundwater, and belongs to the technical field of groundwater monitoring, and the method comprises the steps: deploying and initializing a system, collecting water level and equipment state information, calculating initial water level monitoring reference parameters in combination with a water level monitoring logic model, and obtaining real-time water level data and anti-blocking water pump structure parameters; generating an intelligent water pumping control execution sequence through a fuzzy control algorithm and a fluid dynamic model; a large-flow well washing operation path is obtained through a dynamic pressure adjusting algorithm and a water quality standard judgment model by taking the well washing flow reference parameter as a constraint and combining data of a water quality sensor; obtaining a steady-state sampling execution scheme through a double-pump linkage control algorithm and a bubble suppression model on the basis of the well washing end point parameters in combination with a small-flow sampling pressure reference and pipeline characteristics; summarizing data and generating an integrated monitoring sampling report; according to the invention, intelligentization and standardization of red layer underground water monitoring sampling are realized, and monitoring efficiency and data accuracy are improved.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of groundwater monitoring, and particularly 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, has a small amount of water, and has 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 fixed, 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 capable of realizing the cooperation of 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 a well washing flow baseline parameter and water quality sensor data. 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 a well washing end point parameter. 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: The integrated monitoring and sampling method for red-bed groundwater comprises the following steps: S1: In response to a preset red-bed groundwater monitoring requirement, a monitoring and sampling system is deployed and initialized, real-time well water level data and equipment operation state information are collected, and initial water level monitoring baseline parameters are calculated in combination with a preset water level monitoring logic model. The initial water level monitoring baseline parameters include an initial water level baseline value, an equipment start threshold, and a data collection 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 well washing operation path, combined with the preset low-flow sampling pressure benchmark and pipeline fluid characteristic parameters, a steady-state sampling execution scheme is obtained through the dual-pump linkage control algorithm and bubble suppression model. 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.

[0005] Specifically, 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.

[0006] Specifically, the calculation process of the initial water level monitoring reference parameters in S1 includes: S1.1: Start the monitoring and sampling system deployed, and start real-time collection of well water level data through the ultrasonic water level sensor and the capacitive water level sensor of the intelligent pumping control device, while recording equipment operation state information; the equipment operation state information includes sensor signal transmission delay, water pump initial current, and pipeline sealing parameter; S1.2: Input the real-time collected well water level data into a preset water level monitoring logic model, which performs trend decomposition and noise filtering on the well water level data based on a time series analysis method, to preliminarily calculate an initial water level reference value and an equipment start threshold; the equipment start threshold is a water level critical value for triggering the start and stop of the water pump; S1.3: The water level monitoring logic model automatically generates a data collection interval based on the fluctuation characteristics of the initial water level reference value; S1.4: The water level monitoring logic model performs a moving average correction on the initial water level reference value at a determined data collection interval, to generate a corrected water level reference value; S1.5: Integrate the corrected water level reference value and the equipment start threshold into a dynamic water level threshold, and output to a fluid dynamics model.

[0007] Specifically, the specific steps of S2 include: S2.1: Extract the corrected initial water level reference value and the equipment start 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 a water level deviation value and a change rate parameter; S2.2: Call the anti-clogging water pump structure parameters, and input the anti-clogging water pump structure parameters into the fluid dynamics model; the fluid dynamics model introduces the Navier-Stokes equation, and performs associated calculation on the impeller diameter in the anti-clogging water pump structure parameters and the critical pumping rate at different water levels, to obtain the critical pumping rate at different water levels, and simultaneously calculate the maximum lift under the corresponding working condition in combination with the impeller diameter parameter; the anti-clogging water pump structure parameters include impeller diameter, curvature radius, installation angle, spiral guide groove size, and surface coating characteristics; S2.3: Input the water level deviation value, the change rate parameter, and the equipment start threshold in the initial water level monitoring reference parameters, and the critical pumping rate and the maximum lift at different water levels into a fuzzy control algorithm; the fuzzy control algorithm makes decisions based on preset rules, to generate an intelligent pumping control execution sequence including water pump start and stop timing, running power adjustment parameters, and anti-clogging control instructions.

[0008] Specifically, the generation process of the intelligent pumping control execution sequence in S2.3 includes: Determine the start time and the stop time based on the equipment start threshold and the water level deviation value, to obtain the water pump start and stop timing; The power distribution ratio is calculated based on the matching relationship between the maximum lift and the critical pumping rate, and the operation power adjustment parameter is obtained. In combination with the characteristics of the spiral flow guide groove, a correction value of the impeller rotating speed is output according to the critical pumping rate, and an intelligent pumping control execution sequence is obtained.

[0009] Specifically, the specific steps of S3 include: S3.1: Extracting a final flow value in the intelligent pumping control execution sequence as a well flushing start flow reference, calling a preset well flushing flow reference parameter, determining a flow range constraint and a corresponding pipe Reynolds number threshold of a large flow well flushing stage; S3.2: Inputting the final flow value and the well flushing flow reference parameter into a dynamic pressure adjustment algorithm, the dynamic pressure adjustment algorithm taking the Reynolds number threshold as a core constraint, first calculating the current Reynolds number through the final flow value and matching the corresponding turbulent flow state, then constructing a flow-pressure mapping relationship model based on historical data, calculating the maximum allowable flushing pressure in combination with the turbulent flow state, and finally generating a water pump rotating speed initial adjustment parameter according to the conversion relationship between the maximum allowable flushing pressure and the water pump lift and the rotating speed-lift characteristic curve; S3.3: Starting the large flow well flushing operation based on the generated water pump rotating speed initial adjustment parameter, the dynamic pressure adjustment algorithm adjusting the water pump rotating speed according to the real-time collected pipe flow feedback, and comparing the feedback flow with the flow range constraint, if the feedback flow exceeds the flow range constraint, then dynamically adjusting the water pump rotating speed according to the deviation proportion; S3.4: In the process of starting the large flow well flushing operation, the turbidity sensor and the conductivity sensor in the well flushing and sampling integrated device are used to collect water quality data in real time, and the water quality data is transmitted to a water quality standard judgment model; the water quality data includes turbidity value, sand content and conductivity fluctuation value; S3.5: The water quality standard judgment model analyzes the received real-time water quality data based on the preset evaluation standard and the target turbidity value of the well flushing flow reference parameter, if it does not meet the standard, the flushing path node parameter is dynamically corrected according to the deviation of the current turbidity and the target value, if it meets the standard, the current node is marked as the well flushing end point; S3.6: Integrating the pressure adjustment record output by the dynamic pressure adjustment algorithm, the flow stability parameter and the flushing path node parameter correction result determined by the water quality standard judgment model, a complete large flow well flushing operation path is formed.

[0010] Specifically, the specific steps of S4 include: S4.1: Extracting a pressure value and a flow value at the end point of the large flow well flushing operation path as the sampling start initial conditions, wherein the pressure value is the final outlet pressure in the well flushing stage, and the flow value is the actual flow at the end of the well flushing; S4.2: Call the preset small flow sampling pressure reference and pipeline fluid characteristic parameters; the pipeline fluid characteristic parameters include the inner diameter, inner wall roughness of food-grade silicone hose and structural parameters of the gradually expanding interface; S4.3: Input the initial pressure value, flow value, small flow sampling pressure reference and pipeline fluid characteristic parameters into the double-pump linkage control algorithm; the double-pump linkage control algorithm obtains the main pump basic flow parameter and auxiliary pump bypass compensation value by calculating the difference between the target pressure and the initial pressure and combining the pipeline resistance coefficient; the pipeline resistance coefficient is determined based on the inner wall roughness; S4.4: Input the pipeline fluid characteristic parameters and the main pump basic flow parameter into the bubble suppression model; the bubble suppression model calculates the pipeline critical flow rate by the Hagen-Poiseuille law and generates the inert gas injection timing by introducing the dissolved gas content in the real-time collected water level data; S4.5: The bubble suppression model optimizes the gas-liquid equilibrium state in the sampling pipeline based on the pipeline critical flow rate and the inert gas injection timing, and outputs the pipeline pressure stability threshold and the upper limit of the bubble content control; S4.6: The double-pump linkage control algorithm combines the auxiliary pump bypass compensation value and the pipeline pressure stability threshold to generate the motor speed regulation parameter by PWM technology; S4.7: Integrate the motor speed regulation parameter, the pressure maintenance curve, the inert gas injection timing and the upper limit of the bubble content control to form a steady-state sampling execution scheme.

[0011] Specifically, the double-pump linkage control algorithm in S4 maintains the basic pressure of the main pump according to the small flow sampling pressure reference, and the auxiliary pump compensates the pressure fluctuation according to the pulse frequency calculated by the bubble suppression model.

[0012] Specifically, the integrated monitoring and sampling report in S5 includes the water level change curve, the well flushing time, the water quality index change trend, the sampling pressure stability analysis, the equipment operation state record table and the abnormal situation processing log.

[0013] The integrated monitoring and sampling system for red-bed groundwater includes an initialization module, a water level monitoring module, a control module, a large-flow well flushing control module, a steady-state sampling control module and a report generation module. The initialization module is used for monitoring and sampling system physical deployment, device self-checking and parameter initialization. The water level monitoring module is used for real-time acquisition of well water level data and calculation of the initial water level reference value in combination 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 the device structure parameters, and realizes intelligent management of water pump start-stop, power regulation 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.

[0014] Compared with the prior art, the beneficial effects of the present invention are: 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.

[0015] 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.

[0016] 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

[0017] Figure 1 This is a schematic diagram of the integrated monitoring and sampling method for red-bed groundwater according to the present invention; 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

[0018] 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: 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 operation 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; 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; S3: Based on the intelligent pumping control execution sequence, taking 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 judgment model, the large flow well flushing operation path is obtained; 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; The double pump linkage control algorithm in S4 according to the small flow sampling pressure reference, the main pump maintains the basic pressure, and the auxiliary pump compensates the pressure fluctuation according to the pulse frequency calculated by the bubble suppression model.

[0019] Further, in the double pump linkage control, two independent pumps work together. The main pump is the core equipment that undertakes the main flow output and pressure supply, needs to be lowered to the target depth of the well, such as the operation section of the well, the sampling well or the sampling well, directly contacts with the downhole fluid, is responsible for transporting the fluid from the downhole to the ground pipeline or the sampling system, and its function is to provide basic flow and initial pressure to meet the main demand of large flow well flushing, fluid transportation or sampling. The auxiliary pump is a bypass auxiliary adjustment installed on the ground pipeline near the wellhead. The auxiliary pump is not lowered into the downhole, but installed on the ground pipeline near the wellhead. It is usually connected with the main pipeline of the main pump through a bypass pipeline, that is, a bypass compensation design. Its role is to dynamically compensate 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 pressure of the pipeline by increasing the bypass flow or shunt to realize precise control.

[0020] S5: Based on the completion signal of the steady-state sampling execution scheme, the water level final monitoring data and the equipment operation full cycle record are summarized, and through the closed-loop control logic and the data archiving rule, the integrated monitoring sampling report is generated.

[0021] The integrated monitoring sampling report in S5 includes the water level change curve, the well flushing time and the water quality index change trend, the sampling pressure stability analysis, the equipment operation state record table and the abnormal situation processing log.

[0022] In summary, the integrated monitoring and sampling method of red-bed groundwater in the present application is aimed at the special hydrogeological conditions of red-bed groundwater, such as high suspended solids, high mineralization, and large water level fluctuation. It constructs an intelligent system of monitoring-control-well flushing-sampling-reporting closed loop. Through the cooperation of multiple modules, it realizes the intelligent management of the whole cycle from system deployment to data archiving, solves the technical problems of poor equipment adaptability, fragmented operation process, and low data accuracy in traditional red-bed groundwater monitoring. The core advantage is the deep integration of water level dynamic monitoring, intelligent pumping control, precise well flushing and purification, stable sampling control, and whole cycle data management, forming a special technical solution suitable for red-bed complex environment, which has irreplaceable environmental adaptability and data reliability.

[0023] The deployment of the monitoring and sampling system and the completion of the initialization configuration include: The intelligent pumping control device and the well flushing and sampling integrated device are assembled to form a monitoring and sampling system, which is lowered to a preset depth in the target red-bed monitoring well. 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-bed well water bubbles and suspended particles. The electrodes of the capacitive water level sensor are made of titanium alloy material, and the electrode spacing is 2 cm. The control module uses 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. The control module is built-in temperature sensor, which combines with current value for overload protection. The well flushing and sampling integrated 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 double pump linkage mechanism. The vector control frequency converter establishes a non-linear mapping model of motor speed and lift. The double pump linkage mechanism includes a main pump and an auxiliary pump, and the auxiliary pump compensates for the pressure fluctuation of the pipeline through a bypass adjustment.

[0024] Further, the non-linear mapping model of the vector control frequency converter obtains lift data at different speeds through offline experiments, and uses the least squares method to fit the speed-lift curve equation.

[0025] It should be noted that the collaborative assembly design of the intelligent pumping control device and the well flushing and sampling integrated device solves the problem 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-bed well body collapse and irregular well diameter during lowering, avoiding equipment jamming or damage.

[0026] The calculation process of the initial water level monitoring reference parameter in S1 includes: S1.1: Start the deployed monitoring sampling system, and start real-time collection of well water level data through the ultrasonic water level sensor and the capacitive water level sensor of the intelligent pumping control device, while recording equipment operation state information; the equipment operation state information includes sensor signal transmission delay, initial current of the water pump, and pipeline sealing parameter; S1.2: Input the real-time collected well water level data into a 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 a time series analysis method, and initially calculates an initial water level reference value and an equipment start threshold; the equipment start threshold is a water level critical value for triggering the start and stop of the water pump; Further, the time series analysis method of the water level monitoring logic model uses a seasonal trend decomposition algorithm to decompose the water level data into a trend item, a seasonal item, and a residual item, and removes noise interference in the residual item through sliding window filtering.

[0027] Further, the construction process of the water level monitoring logic model includes: (1) Collect historical water level data of the target red layer monitoring well, and statistically analyze water level daily fluctuation amplitude, seasonal variation trend, and sudden disturbance characteristics, and determine trend items, periodic items, random fluctuation items, and abnormal value distribution rules of the data, wherein the historical water level data at least includes data of one complete hydrological year, for example, seasonal variation trend such as 0.8-1.5 meters of rise in the rainy season and 0.5-1 meter of drop in the dry season, and sudden disturbance characteristics such as short-time sudden rise caused by rainfall and instantaneous jump caused by well wall collapse; (2) Based on the characteristics of long-term slow change, short-term fluctuation, and occasional abnormality of red layer groundwater level, a time series analysis method is selected as the core algorithm, wherein the time series analysis method is prior art content in the field and is not the inventive scheme of the present application, and will not be described here; (3) According to the characteristics of the historical data, preset initial parameters are included, such as time window for trend decomposition, standard deviation threshold for noise filtering, and weight coefficient for reference value calculation, and a dynamic adjustment interface is reserved, which can correct the parameters according to real-time data feedback to construct the water level monitoring logic model, wherein the time window for trend decomposition is initially set to 24 hours to adapt to the daily change period of the red layer water level, the standard deviation threshold for noise filtering is initially set to 0.3 meters, which corresponds to the fluctuation range of the 95% confidence interval in the historical data, and the weight coefficient for reference value calculation is 0.7 for recent data and 0.3 for long-term data, which highlights the timeliness.

[0028] Further, the specific steps of S1.2 include: (1) Receive real-time collected well water level data, first check the integrity of the well water level data, one by one to check whether there are missing values, abnormal jump values or format errors in the data sequence, among them, for missing values, if the continuous missing time length does not exceed 1.5 times of the preset minimum data collection interval, the linear interpolation method of the adjacent effective data is used to fill in; If the missing time length exceeds the threshold, it is marked as a data fault and the missing period is recorded, for abnormal jump values, by calculating the average difference of the current data and the previous 3 continuous effective data, when the difference exceeds 3 times of the historical data standard deviation, it is determined as an abnormal value and is rejected, at the same time, the original abnormal record is kept for subsequent equipment state analysis, after the check, all effective water level data is uniformly converted into the standardized format of timestamp-water level value, among them, the timestamp is accurate to seconds, the water level value is retained to two decimal places, and the data format is ensured to meet the input requirements of time series analysis; (2) The standardized water level data is input into the time series analysis algorithm, and the seasonal trend decomposition algorithm is used to decompose the standardized water level data in multiple layers, including: first, extract the trend item in the data, process the original data by sliding window smoothing method, the window size is dynamically set according to the collection frequency of water level data, when the collection interval is 1 minute, the window is set to 30 data points, when the collection interval is 5 minutes, the window is set to 12 data points, the weighted average of the data in the window weakens the short-term fluctuations, highlights the long-term trend characteristics of water level change with time, and forms a trend sequence; Secondly, separate the seasonal component, analyze the periodicity of the 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 fluctuation in the current data is identified by the period matching algorithm, and an independent seasonal item sequence is generated; Finally, calculate the residual term, subtract the trend term and the seasonal term from the original water level data, get the residual sequence containing random noise and sudden interference, realize the multi-layer trend decomposition of water level data; (3) Perform noise filtering operation on the residual sequence obtained by decomposition, and process the residual data by using wavelet threshold denoising algorithm, including: first, select a wavelet base function suitable for the characteristics of groundwater level data, usually select db4 wavelet as the decomposition base function, and perform 3-layer wavelet decomposition on the residual sequence to obtain wavelet coefficients of different frequency bands; then set the noise threshold, calculate the standard deviation of each layer of decomposition coefficients, and determine the noise filtering threshold of each layer by using the adaptive threshold method, wherein the threshold of high frequency coefficient is relatively low to retain effective details, and the threshold of low frequency coefficient is relatively high to filter main noise, and the wavelet coefficients exceeding the threshold are contracted to adjust the amplitude to the threshold range, and then the processed coefficients are reconstructed into the filtered residual sequence by wavelet inverse transform, and at the same time, the reconstructed residual sequence is smoothed again by using the moving average filtering method to further eliminate high frequency random noise, and the purified residual data is obtained, wherein the wavelet threshold denoising algorithm and the wavelet decomposition are prior art contents in the field, and are not the inventive scheme of the present application, and will not be described here; (4) Integrate the processed trend item, seasonal item and purified residual item, superimpose the three to generate the denoised water level sequence, which retains the main change characteristics of the water level and removes most of the noise interference, calculates the initial water level reference value based on the denoised water level sequence, uses the data mean value method in the sliding window, sets the window length to 24 hours, slides the window every data collection period, calculates the arithmetic mean value of all water level data in each window, and performs stability test on the mean values of the continuous 5 windows, when the fluctuation amplitude of the mean values of adjacent windows is less than 0.05 meters, the mean value of the last window is taken as the initial water level reference value; if the fluctuation amplitude exceeds the threshold, the window length is extended to 48 hours to recalculate, until a stable reference value result is obtained; (5) Take the initial water level reference value as the reference reference, set the device starting threshold according to the hydrological characteristics of the red bed groundwater, first analyze the historical water level fluctuation data, and calculate the maximum amplitude of the water level deviating from the reference value under normal working conditions, and take 1.2 times of the amplitude as the basic threshold reference value; then consider the actual use of the monitoring well, if used for water resource monitoring, multiply the basic reference value by a correction coefficient of 0.8; if used for water inrush prevention monitoring, multiply the basic reference value by a correction coefficient of 1.2; at the same time, introduce the sensor stability parameter in the real-time device running state information, and finally determine the device starting threshold, including the high water level starting threshold and the low water level stopping threshold, the high water level starting threshold is the initial water level reference value plus the calculated positive threshold, and the low water level stopping threshold is the initial water level reference value minus the negative threshold, forming a complete water pump start-stop critical value system. Further, based on the above, it can be concluded that after real-time water level data collection, data integrity and format verification is performed first, abnormal values are removed and missing values are filled, and the data is converted to a standardized timestamp-water level value format; then it enters the time series decomposition link, and the trend item, seasonal component and residual item are extracted in turn to complete the multi-layer decomposition of the water level data; the residual item is filtered by combining wavelet threshold denoising and moving average filtering to obtain the purified residual sequence; the trend item, seasonal item and purified residual item are superimposed to generate a denoised water level sequence, and a stable initial water level reference value is calculated and verified by the sliding window mean method; finally, based on the initial reference value, combined with historical fluctuation data, monitoring purpose correction coefficient and sensor state parameters, the high water level start threshold and low water level stop threshold are determined to complete the calculation of the initial water level reference value and the equipment start threshold.

[0029] S1.3: The water level monitoring logic model automatically generates data collection intervals based on the fluctuation characteristics of the initial water level reference value, achieving intelligent adaptation of monitoring density; Further, the specific steps of S1.3 include: (1) Based on the calculated initial water level reference value, the corresponding original water level data sequence and timestamp information are extracted, a fluctuation analysis data set containing time-water level value is constructed, and the data set is preprocessed. First, abnormal values are removed, and the abnormal value determination standard is that the deviation of a single water level value from the average value of the adjacent three effective water level values exceeds 5% of the initial water level reference value, which is marked as an abnormal fluctuation point and is retained but recorded separately to avoid interference with the overall fluctuation characteristic analysis. Secondly, the continuity of the original water level data sequence is checked. If there is data missing and the missing duration exceeds the preset minimum analysis period, the missing section is filled by linear interpolation, wherein the minimum analysis period is set to 1 hour, and the linear interpolation method is a prior art in the art and is not the inventive scheme of the present application, which will not be described here. (2) For the preprocessed water level data sequence, calculate multiple fluctuation characteristic quantitative indicators. First, calculate the water level fluctuation amplitude, which is the difference between the maximum water level value and the minimum water level value in the time window to obtain the absolute fluctuation amplitude in the window. At the same time, calculate the relative fluctuation amplitude, which is the ratio of the absolute fluctuation amplitude to the initial water level reference value, expressed in percentage. Second, calculate the fluctuation frequency, which is the number of times the water level value exceeds the initial water level reference value by 2% in a unit of time. The more the number, the higher the fluctuation frequency. Third, calculate the fluctuation rate, which is the ratio of the water level difference between two adjacent water level data points to the time interval to obtain the instantaneous fluctuation rate. Then take the absolute value of the instantaneous rate in a unit of time and average it to obtain the average fluctuation rate. Fourth, calculate the fluctuation variance, which is the sum of the squares of the deviations of the water level data in the time window from the initial water level reference value, divided by the number of data to obtain the variance value reflecting the dispersion degree of fluctuation. (3) According to the calculated fluctuation quantification index, the fluctuation grade division standard is established, including: the relative fluctuation amplitude, the fluctuation frequency, the average fluctuation rate and the fluctuation variance are divided into three grades of low fluctuation, medium fluctuation and high fluctuation respectively. Among them, when the relative fluctuation amplitude is less than 1%, the fluctuation frequency is less than 2 times per hour, the average fluctuation rate is less than 0.01 meters / minute, and the fluctuation variance is less than 0.001 square meters, it is determined as low fluctuation grade; when the relative fluctuation amplitude is between 1%-3%, the fluctuation frequency is 2-5 times per hour, the average fluctuation rate is between 0.01-0.03 meters / minute, and the fluctuation variance is between 0.001-0.005 square meters, it is determined as medium fluctuation grade; when the relative fluctuation amplitude is greater than 3%, the fluctuation frequency is greater than 5 times per hour, the average fluctuation rate is greater than 0.03 meters / minute, and the fluctuation variance is greater than 0.005 square meters, it is determined as high fluctuation grade; the grade results of the four indexes are weighted voting, and the grade with the most votes is the final fluctuation grade; (4) Based on the final fluctuation grade divided, the corresponding initial data collection interval range is preset, wherein the initial collection interval range corresponding to the low fluctuation grade is 10-15 minutes, the medium fluctuation grade is 5-10 minutes, and the high fluctuation grade is 1-5 minutes; (5) The initial matching collection interval is dynamically checked, and the checking period is set to 2 hours. In each checking period, the matching degree of the actual fluctuation characteristics and the collection interval is calculated, wherein the matching degree calculation method is: whether the water level fluctuation in the collection interval is effectively captured is counted, that is, if there is a fluctuation of more than 2% of the initial water level reference value in the interval, and the start and end points of the fluctuation are covered by the data points, it is determined as effective capture; otherwise, it is determined as missing. When the proportion of missing times to the total fluctuation times exceeds the first preset threshold, it indicates that the current collection interval is too large, and the shortening range is 20% of the current interval; when the proportion of effective capture times to the total data points is less than the second preset threshold, it indicates that the current collection interval is too small, and the extension range is 20% of the current interval. After 3 checking periods, if the collection interval is not adjusted again, it is determined as the final data collection interval; if it still needs to be adjusted, the checking process is repeated until it is stable, wherein in the present application, the first preset threshold is set to 10%, and the second preset threshold is set to 50%.

[0030] S1.4: The water level monitoring logic model performs moving average correction on the initial water level reference value with the determined data collection interval as the period to generate the corrected water level reference value; Further, the specific steps of S1.4 include: (1) Based on the determined data collection interval, set the period of moving average correction, the correction period is consistent with the data collection interval, that is, trigger the moving average correction once after completing the data collection once, at the same time, determine the size of the sliding window according to the data collection interval, and the setting of the window size needs to ensure that at least 1 hour of water level data is covered, so as to balance the timeliness and stability of the correction, at the same time, initialize the sliding window as an empty data set, and wait for new water level data to be input continuously according to the collection interval; (2) Whenever new water level data is generated according to the collection interval, the new water level data is added to the sliding window together with the corresponding time stamp, wherein the new data needs to be verified for validity before being added, and the verification standard is consistent with the abnormal value determination in the fluctuation characteristic analysis, that is, if the deviation of the new water level value from the average value of the last three valid data in the window exceeds the current initial water level reference threshold value, it is marked as suspicious data, temporarily added to the window but separately marked, and does not participate in the correction calculation this time; if the deviation does not exceed the current initial water level reference threshold value, it is determined as valid data, and is normally added to the window, 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, so that the window always maintains a fixed number of the latest data, realizes sliding update, and in the present application, the current initial water level reference threshold value is 5% of the current initial water level reference value; (3) The valid data in the sliding window is weighted and distributed, and the time decay weight method is adopted, that is, the data closer to the current time is given higher weight, so as to enhance the influence of recent data on the correction result. The weight distribution rule is that the weight of the latest data in the window is set to 1.5 times of the basic weight, and the rest of the data is sequentially decreased according to the time sequence, the weight difference between adjacent data is 0.1 times of the basic weight, and the sum of the weights of all data is 1. The calculation method of the basic weight is 1 divided by the number of valid data in the window. After completing the weight distribution, multiply each data value by the corresponding weight and sum, to obtain the weighted average result as the preliminary correction value of this moving average; (4) Compare the preliminary correction value with the current initial water level reference value, calculate the deviation percentage, and the deviation percentage is equal to the difference between the preliminary correction value and the initial water level reference value, divided by the initial water level reference value, multiplied by 100%; (5) Set the stability check threshold value, when the absolute value of the deviation percentage is less than the minimum value of the deviation threshold value, it is determined that the preliminary correction value is stable, and the preliminary correction value can be directly used as the correction result; when the absolute value of the deviation percentage is between the minimum value of the deviation threshold value and the maximum value of the deviation threshold value, the preliminary correction value is smoothed, and the weighted average value 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 value of the deviation threshold value, it is determined that there may be abnormal fluctuations, and the initial water level reference value is not updated, only the deviation of this time is recorded, and when such a situation occurs for three times in succession, the data re-inspection process is triggered, wherein the minimum value of the deviation threshold value is 1%, and the maximum value of the deviation threshold value is 3%; (6) After the stability check, the final corrected water level reference value is determined, the original initial water level reference value is replaced with the final corrected water level reference value, and the final corrected water level reference value is used as the reference for the next moving average correction, and the complete record of the correction is stored, including the correction timestamp, all data values and weights in the window, the preliminary correction value, the deviation percentage, the final correction value and other information, forming a correction history account. S1.5: The obtained corrected water level reference value and the device start threshold value are integrated into a dynamic water level threshold value, which is output to the fluid dynamics model.

[0031] The specific steps of S2 include: S2.1: Extract the corrected initial water level reference value and the device start threshold value from the initial water level monitoring reference parameter, 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 the change rate parameter; Further, the calculation function of the water level deviation value and the change rate parameter quantifies the deviation degree and the change trend of the real-time water level and the dynamic reference value. In view of the characteristics that the red layer groundwater is prone to sudden gushing and seepage, this function can identify abnormal water level changes 5-10 minutes in advance, and provide sufficient time window for the start-stop decision of the anti-clogging water pump.

[0032] S2.2: Call the anti-clogging water pump structure parameter, and input the anti-clogging water pump structure parameter into the fluid dynamics model. The fluid dynamics model introduces the Navier-Stokes equation, and performs correlation calculation on the impeller diameter in the anti-clogging water pump structure parameter and the critical pumping rate to obtain the critical pumping rate under different water levels, and calculates the maximum lift under the corresponding working condition in combination with the impeller diameter parameter; the anti-clogging water pump structure parameter includes the impeller diameter, the curvature radius, the installation angle, the size of the spiral guide groove and the surface coating characteristics; Further, the calling function of the anti-clogging water pump structure parameter realizes the accurate matching of the device characteristics and the hydrological conditions. The parameter combination of the impeller diameter, the curvature radius and the installation angle is specially optimized for the characteristics of high sand content of the red layer groundwater, so that the water flow forms a strong vortex at the impeller, reduces the sediment deposition, and prolongs the water pump dredging period to more than three times of the traditional device.

[0033] Further, the introduction of the Navier-Stokes equation realizes the accurate simulation of the flow state of red layer groundwater. The model can calculate the flow velocity field and pressure field under different water levels and flow rates, accurately predict the movement trajectory of sediment in the pump, and provide a theoretical basis for determining the critical pumping rate.

[0034] Further, the specific steps of S2.2 include: (1) Call the complete set of structural parameters of the anti-clogging water pump from the device parameter database of the monitoring sampling system, including the core parameters such as impeller diameter, curvature radius, installation angle, spiral guide groove size, and surface coating characteristics, and perform integrity check on the called parameters, one by one to check whether there are parameter missing, format error or abnormal value exceeding the physical reasonable range; (2) Standardize the structural parameters that pass the check, unify the parameter units, and arrange 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, the standardized parameters are batch input into the fluid dynamics model through the fluid dynamics model interface, and the mapping relationship between the parameters and the model calculation module is established; (3) Load the Navier-Stokes equation in the fluid dynamics model. The equation is used to describe the law of conservation of momentum of fluid. The equation form includes inertia term, viscosity term, pressure gradient term and gravity term. According to the structural characteristics of the anti-clogging water pump, the boundary conditions of the Navier-Stokes equation are set. The no-slip boundary condition is set on the surface of the impeller, i.e. the relative velocity of the fluid on the surface of the impeller is zero. The symmetric boundary condition is set on the inner wall of the pump shell, ignoring the disturbance of the pump shell to the fluid flow. The pressure boundary condition and the flow boundary condition are set on the water inlet and the water outlet respectively. The pressure of the water inlet is equal to the static water pressure corresponding to the current water level, and the pressure of the water outlet is initially set to atmospheric pressure. The Navier-Stokes equation is a prior art in the field and is not part of the inventive concept of the present application, so it is not described here; (4) Based on the Navier-Stokes equation, an associated model of impeller diameter and critical pumping rate is established, including: first, calculate the flow field distribution under different impeller diameters through the model, extract the fluid velocity field data at the outlet of the impeller, and analyze the nonlinear relationship between the velocity distribution and the impeller diameter. The critical pumping rate is defined as the minimum pumping speed to prevent suspended particles from depositing on the surface of the impeller. The critical condition is determined by calculating the force balance of the particles in the flow field: when the viscous resistance of 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 level heights, adjust the boundary conditions and repeat the calculation to obtain the corresponding relationship table of water level and critical pumping rate, where a set of critical pumping rate data is recorded for every N meters of water level change, forming a complete set of critical pumping rate data under different water levels; (5) On the basis of calculating the critical pumping rate, the maximum lift under different working conditions is calculated through the fluid dynamics model, wherein the lift calculation formula is based on the law of conservation of energy, and is equal to the energy increment obtained by the unit weight fluid passing through the water pump, including the static pressure energy increment, kinetic energy increment and position potential energy increment. In the model, the pressure difference, fluid flow rate difference and height difference of the water pump inlet and outlet are extracted, and the three energy increments are calculated and summed. Combined with the impeller diameter parameter, the influence law of the impeller diameter on the lift is analyzed, and the maximum lift is calculated for the working condition corresponding to the critical pumping rate under different water levels, so as to ensure that the lift calculation result matches the critical pumping rate, and form a water level-critical pumping rate-maximum lift correlation database.

[0035] The influence law of the impeller diameter on the lift includes: at the same speed, the larger the impeller diameter, the greater the centrifugal force obtained by the fluid, and the higher the lift, but limited by the pump body structure and power, there is a maximum diameter corresponding to the limit lift.

[0036] S2.3: The water level deviation value, the change rate parameter and the equipment starting threshold in the initial water level monitoring reference parameter are input into the fuzzy control algorithm together with the critical pumping rate and the maximum lift under different water levels, the fuzzy control algorithm makes a decision based on the preset rule, and generates an intelligent pumping control execution sequence including the water pump start-stop time sequence, the running power adjustment parameter and the anti-blocking control instruction, wherein the fuzzy control algorithm is prior art content in the field, and is not the inventive scheme of the present application, and will not be described here.

[0037] Further, in the present application, the preset rule is that the water level is reduced by more than 5 cm within 3 seconds to trigger the pump to stop, and the fluctuation is less than 1 cm within 10 seconds to execute stable state operation. When the water level drops suddenly, the pump is stopped quickly, which can avoid the water pump running empty and the further collapse of the well body, and the stable state operation rule reduces unnecessary start and stop and saves energy consumption.

[0038] Further, the specific steps of S2.3 include: (1) Collecting various parameters to be input, including water level deviation value, water level change rate parameter, equipment starting threshold, critical pumping rate and maximum lift under different water levels, and pre-processing to obtain clear parameters after pre-processing; (2) The pre-processed clear parameters are converted into fuzzy language variables, and the fuzzy subsets and membership functions of each parameter are determined. For the water level deviation value, five fuzzy subsets, i.e. negative large, negative small, zero, positive small and positive large, are divided, and the membership function adopts a triangular function. For the water level change rate, five subsets, i.e. negative fast, negative slow, zero, positive slow and positive fast, are divided. The ratio of the water level deviation value to the starting threshold value is divided into five subsets, i.e. far below, slightly below, close to, slightly above and far above. The critical pumping rate and the maximum lift are divided into five subsets, i.e. low, medium low, medium, medium high and high, according to their percentage of the rated value. The membership degree values of each parameter belonging to each fuzzy subset are calculated through the membership function, and the fuzzy conversion is completed. The membership function calculation formula is the prior art in the field, and is not the creative scheme of the present application, and will not be described here. (3) A fuzzy rule base is constructed based on the actual needs of red-bed groundwater monitoring. The rules adopt the form of if-then, covering the corresponding relationship between the input parameter combination and the output control quantity. For example, rule 1: if the water level deviation value is positive large, the change rate is positive fast, and the current rate is far below the critical pumping rate, then the water pump start-stop timing is immediate start, the running power adjustment parameter is high power, and the anti-blocking control instruction is high-frequency flushing. Rule 2: if the water level deviation value is zero, the change rate is zero, and the current rate is close to the critical pumping rate, then the water pump start-stop timing is to maintain the current state, the running power adjustment parameter is medium power, and the anti-blocking control instruction is regular flushing. The rule base contains at least 20 basic rules, covering different water level states, rate characteristics and device parameter combination scenarios. (4) The fuzzy membership of the input parameters is matched with the premise conditions in the rule base, and the triggering strength of each rule is calculated, which is equal to the minimum value of the membership degrees of all premise conditions of the rule. (5) The Mamdani reasoning method is used for fuzzy reasoning. According to the triggering strength of each rule and the fuzzy subsets of the conclusion part, the fuzzy sets of each output control quantity are generated. The reasoning results of all rules are synthesized, and the fuzzy sets of the same output quantity are combined by using the maximum membership degree method, to obtain the total fuzzy sets of the water pump start-stop timing, the running power adjustment parameter and the anti-blocking control instruction respectively. The Mamdani reasoning method is the prior art in the field, and is not the creative scheme of the present application, and will not be described here. (6) The synthesized fuzzy output quantity is converted into clear control parameters, and the barycentric method is used to calculate the clear values of each output quantity. The barycentric method is the prior art in the field, and is not the creative scheme of the present application, and will not be described here. (7) The clear control parameters are integrated into an intelligent pumping control execution sequence containing time nodes, power values and instruction codes, and the sequence format meets the input requirements of the device control interface.

[0039] The generation process of the intelligent pumping control execution sequence in S2.3 includes: Determine the start time and stop time based on the device start threshold and water level deviation value, and obtain the water pump start-stop timing; Based on the matching relationship between the maximum lift and the critical pumping rate, calculate the power distribution ratio to obtain the running power adjustment parameter; Combined with the characteristics of the spiral flow guide groove, the critical pumping rate output impeller speed correction value is obtained to obtain the intelligent pumping control execution sequence.

[0040] The specific steps of S3 include: S3.1: Extract the final flow value in the intelligent pumping control execution sequence as the well flushing start flow reference, call the preset well flushing flow reference parameter, determine the flow range constraint and the corresponding pipe Reynolds number threshold of the large flow well flushing stage; Further, the well flushing start flow reference adopts the final flow value of the intelligent pumping control execution sequence, realizing seamless connection of the pumping and well flushing processes, and avoiding the flow mutation problem in traditional step-by-step operation.

[0041] Further, the setting of the well flushing flow reference parameter is aimed at the low permeability coefficient of the red layer aquifer, which can not only ensure sufficient flushing intensity, but also avoid wellbore formation disturbance caused by excessive flushing, compared with traditional large flow blind flushing, which can reduce the water consumption of well flushing and protect the structure of the aquifer.

[0042] S3.2: Input the final flow value and the well flushing flow reference parameter into the dynamic pressure regulation algorithm, which takes the Reynolds number threshold as the core constraint. First, calculate the current Reynolds number through the final flow value and match the corresponding turbulent state, then build a flow-pressure mapping relationship model based on historical data, calculate the maximum allowed flushing pressure combined with the turbulent state, and finally generate the initial adjustment parameter of the pump speed according to the conversion relationship between the maximum allowed flushing pressure and the pump lift and the speed-lift characteristic curve. Further, the pipe Reynolds number threshold is usually less than or equal to 2000, and the constraint function of the pipe Reynolds number threshold ensures that the water flow is in a laminar state during well flushing, avoiding pipe wear and secondary suspension of silt caused by turbulent flow. In the red layer environment with high silt content of underground water, this function prolongs the service life of the pipe.

[0043] Further, the specific steps of S3.2 include: (1) Receive the final flow value and the well flushing flow reference parameter determined in S3.1, and perform integrity check on the parameters; (2) converting the final flow value into the flow rate of the fluid through a flow rate calculation formula, wherein the flow rate calculation formula is a first variable divided by the product of pi and a second variable, the first variable is the ratio of the final flow value and 3600, and the second variable is one-half of the square of the inner diameter of the pipeline; (3) obtaining the fluid kinematic viscosity parameter of the red-layer groundwater, and combining the inner diameter of the pipeline to obtain the Reynolds number, wherein the Reynolds number is equal to the product of the flow rate and the inner diameter of the pipeline divided by the fluid kinematic viscosity; (4) comparing the calculated current Reynolds number with the Reynolds number threshold determined in S3.1 to match the corresponding turbulent flow state; (5) calling the flow-pressure correlation data in the historical well flushing database, and performing screening, adopting the least square method to perform curve fitting on the screened flow-pressure correlation data, constructing a flow-pressure mapping relationship model, and substituting the flow percentage corresponding to the current final flow value into the flow-pressure mapping relationship model to obtain a basic pressure value; the flow-pressure mapping relationship model is divided into a linear segment and a nonlinear segment; (6) correcting the basic pressure value according to the matched turbulent flow state; For example, in the low laminar flow state, 15% compensation pressure is added to the basic pressure value, and the compensated pressure should not exceed 80% of the upper limit of the pipeline design pressure; in the safe turbulent flow state, the basic pressure value is directly used without correction.

[0044] (7) converting the maximum allowable flushing pressure into the corresponding pump head, and the conversion formula is that the head is equal to the maximum allowable flushing pressure divided by the product of the fluid density and the gravitational acceleration, and the quotient obtained is added to the total resistance loss of the pipeline; (8) calling the speed-head characteristic curve of the vector control frequency converter to 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; (9) performing boundary check on the initial speed adjustment parameter, if the parameter exceeds the boundary, automatically adjusting to the nearest boundary value and recording the adjustment reason, and finally generating a set of pump speed initial adjustment parameters including speed percentage, adjustment accuracy and response delay.

[0045] S3.3: starting the high-flow well flushing operation based on the generated pump speed initial adjustment parameter, and the dynamic pressure adjustment algorithm adjusts the 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 pump speed is dynamically adjusted according to the deviation ratio; Further, based on the real-time speed adjustment function of the pipeline flow feedback, the problem of flow attenuation caused by silt blockage in the red-layer groundwater well flushing process is solved.

[0046] Further, the specific steps of S3.3 include: (1) Before starting the large flow well flushing operation, call the flow range constraint determined in S3.1 and the initial adjustment parameter of the water pump rotating speed generated in S3.2 to perform parameter checking, check whether the rotating speed parameter is within the normal operation range of the equipment and whether the flow range constraint matches the upper limit of the pipeline design flow, and at the same time, perform a readiness check on the well flushing equipment, including the opening and closing state of the pipeline valve, the connection stability of the turbidity sensor and the flow sensor, and the preheating state of the water pump motor. After confirming that all parameters are correct and the equipment is in normal state, a start instruction is generated to prepare for entering the well flushing operation stage; (2) Start the well flushing water pump according to the initial adjustment parameter of the water pump rotating speed, set the initial running time of 30 seconds as a stable period, collect the initial flow value through the electromagnetic flow sensor built in the pipeline, record the average flow in the stable period as the initial feedback flow, and at the same time, monitor the auxiliary parameters such as the water pump outlet pressure and motor current. If the initial feedback flow does not reach 50% of the flow range constraint within 30 seconds, the operation is immediately suspended and fault diagnosis is triggered to troubleshoot pipeline blockage or water pump no-load problems; It should be noted that during the stable period of 30 seconds of initial running time, no speed adjustment is performed, and only basic data is collected in real time.

[0047] (3) After the stable period ends, the dynamic pressure regulation algorithm enters the real-time regulation stage, and the flow sensor continuously collects pipeline flow data, generating a flow feedback value every 2 seconds, including: preprocessing the collected pipeline flow data, first eliminating outliers, and then calculating the smoothed flow value using the sliding average method of the adjacent 3 valid data. The smoothed flow value is associated and stored with the corresponding time stamp to form a flow feedback time series; (4) Real-time comparison is made between the preprocessed smoothed flow value and the flow range constraint determined in S3.1, and the flow deviation parameter is calculated, and at the same time, it is judged which interval the smoothed flow value is in, wherein the deviation parameter includes absolute deviation and relative deviation; (5) The deviation proportion level is determined according to the relative deviation value. When the absolute value of the relative deviation is less than or equal to the lower limit of the preset deviation threshold, it is a slight deviation and no speed adjustment is needed. When the absolute value of the relative deviation is greater than the lower limit of the preset deviation threshold and less than the upper limit of the preset deviation threshold, it is a moderate deviation and regular speed adjustment is needed. When the absolute value of the relative deviation is greater than the upper limit of the preset deviation threshold, it is a serious deviation and emergency speed adjustment is needed and a warning is issued; (6) The speed adjustment amount is calculated based on the deviation proportion level, wherein the speed adjustment amount is equal to the product of the current rotating speed adjustment parameter and the relative deviation and the adjustment coefficient, and the adjustment coefficient is set according to the deviation level; (7) the calculated speed adjustment amount is converted into a vector control frequency converter control signal, which is output to the water pump motor through pulse width modulation (PWM) technology, and the response time is controlled within 0.5 seconds. After adjustment, it enters a 10-second observation period, during which the flow feedback value is continuously collected and a new smooth flow value is calculated. The new relative deviation absolute value is compared with the flow range constraint. If the new relative deviation absolute value is reduced to within the preset deviation threshold, it is determined that the adjustment is effective, and the current speed is maintained. If the deviation does not decrease or even increases, the cause of the deviation is analyzed, the adjustment amount is recalculated, and the adjustment coefficient is increased to 1.0 for secondary adjustment. If the adjustment is ineffective for three consecutive times, manual intervention warning is triggered. The PWM technology is a prior art in the field and is not part of the inventive concept of the present application, and will not be described here. (8) After each speed adjustment, record the adjustment time, pre-adjustment speed, adjustment amount, post-adjustment speed, flow deviation change and corresponding pressure change value to form a dynamic adjustment log.

[0048] S3.4: During the start of the large flow well flushing operation, the turbidity sensor and conductivity sensor in the well flushing and sampling integrated device collect water quality data in real time, and transmit the water quality data to the water quality standard judgment model; the water quality data includes turbidity value, sediment content and conductivity fluctuation value; S3.5: The water quality standard judgment model analyzes the received real-time water quality data based on the preset evaluation standard and the target turbidity value of the well flushing flow benchmark parameter. If it does not meet the standard, the flushing path node parameter is dynamically corrected according to the deviation of the current turbidity and the target value. If it meets the standard, the current node is marked as the well flushing end point. Further, the preset evaluation standard of the water quality standard judgment model is that the turbidity is less than or equal to 10 NTU and the conductivity fluctuation is less than or equal to 5% for 5 minutes continuously, wherein NTU represents the turbidity unit, which is a standard unit for measuring the turbidity of water.

[0049] Further, the preset evaluation standard of the water quality standard judgment model is designed for the false clear phenomenon of red layer groundwater. By continuously monitoring the conductivity fluctuation, it avoids water sample pollution caused by premature well flushing and improves the accuracy of subsequent sampling data. S3.6: The pressure adjustment record output by the dynamic pressure adjustment algorithm, the flow stability parameter and the flushing path node parameter correction result determined by the water quality standard judgment model are integrated to form a complete large flow well flushing operation path.

[0050] The specific steps of S4 include: S4.1: Extract the pressure value and flow value at the end point of the large flow well flushing operation path as the sampling start initial conditions, wherein the pressure value is the final outlet pressure during the well flushing stage, and the flow value is the actual flow at the end of the well flushing. S4.2: Call preset small flow sampling pressure reference and pipeline fluid characteristic parameters; the pipeline fluid characteristic parameters include the inner diameter, inner wall roughness of food-grade silicone hose and structural parameters of the gradually expanding interface; S4.3: Input the initial pressure value, flow value, small flow sampling pressure reference and pipeline fluid characteristic parameters into the double-pump linkage control algorithm; the double-pump linkage control algorithm obtains the main pump basic flow parameter and the auxiliary pump bypass compensation value by calculating the difference between the target pressure and the initial pressure and combining the pipeline resistance coefficient along the way; the pipeline resistance coefficient along the way is determined based on the inner wall roughness; Further, the implementation process of S4.3 includes: first, collect and verify the initial pressure, flow, small flow sampling pressure reference and pipeline characteristic parameters; after standardization conversion, calculate the pipeline resistance coefficient along the way based on the inner wall roughness and Reynolds number; calculate the difference between the target and initial pressure and correct the pressure loss influence; determine the main pump basic flow parameter in combination with the flow-pressure relationship model; calculate the auxiliary pump bypass compensation value according to the pressure difference; verify the double-pump parameter matching and adjust to meet the requirements; output the parameters and set a dynamic correction mechanism; realize double-pump collaborative control to achieve the target sampling pressure and flow.

[0051] S4.4: Input the pipeline fluid characteristic parameters and the main pump basic flow parameter into the bubble suppression model to generate the inert gas injection timing; the bubble suppression model calculates the pipeline critical flow velocity by the Hagen-Poiseuille law and generates the inert gas injection timing by introducing the dissolved gas content in the real-time collected water level data, wherein the Hagen-Poiseuille law is prior art content in the field and is not the inventive scheme of the present application, and is not described here; Further, the implementation process of S4.4 includes: first, collect and verify the pipeline fluid characteristic parameters, the main pump basic flow parameter and the dissolved gas content data; after standardization conversion, calculate the pipeline critical flow velocity based on the Hagen-Poiseuille law; analyze the supersaturation degree of dissolved gas and evaluate the bubble risk level; set the inert gas injection basic parameter according to the risk level, generate the initial injection timing, and correct the timing parameter in combination with real-time data; monitor the suppression effect through the bubble sensor, dynamically optimize the injection parameter and model coefficient, finally output the stable inert gas injection timing, and realize effective suppression of bubbles in the pipeline.

[0052] S4.5: The bubble suppression model optimizes the gas-liquid equilibrium state in the sampling pipeline based on the pipeline critical flow velocity and the inert gas injection timing, and outputs the pipeline pressure stability threshold and the upper limit of the bubble content control; Further, the implementation process of S4.5 includes: firstly, evaluating the initial gas-liquid equilibrium state, marking the pressure and bubble content abnormality indicators; adjusting the main pump flow rate with the critical flow rate as the constraint, and fine-tuning the inert gas injection timing, increasing the pressure fluctuation triggering mechanism and bubble type targeted measures. A gas-liquid two-phase flow model is constructed to simulate the equilibrium state, calculate the pipeline pressure stability threshold and bubble content control upper limit; real-time monitoring of the optimization effect, feedback correction parameters, finally solidify the threshold and establish a dynamic updating mechanism, realize the continuous optimization of the gas-liquid equilibrium state.

[0053] S4.6: Dual-pump linkage control algorithm combines auxiliary pump bypass compensation value and pipeline pressure stability threshold to generate motor speed regulation parameters through PWM technology; S4.7: Integrating motor speed regulation parameters, pressure maintenance curve, inert gas injection timing and bubble content control upper limit, forming a steady-state sampling execution plan.

[0054] Embodiment 2: Another embodiment provided by the application: an integrated monitoring and sampling system for red-bed groundwater, comprising: Initialization module, water level monitoring module, control module, high-flow well flushing control module, steady-state sampling control module, report generation module; The initialization module is used for monitoring the physical deployment of the monitoring and sampling system, device self-checking and parameter initialization, providing a hardware foundation and environmental verification for the subsequent monitoring and sampling process; The water level monitoring module is used for real-time acquisition of well water level data, and calculates the initial water level reference value based on the preset water level monitoring logic model, providing a dynamic threshold basis for pumping control; The control module generates an intelligent pumping control execution sequence based on the initial water level reference value and device structure parameters, realizes intelligent management of water pump start-stop, power regulation and anti-clogging operation; The high-flow well flushing control module is used for starting the high-flow well flushing process based on the intelligent pumping control execution sequence, realizing well water purification through pressure regulation and water quality monitoring to ensure the accuracy of subsequent sampling data; The steady-state sampling control module is used for switching to a small-flow sampling mode after well flushing is completed, realizing stable pressure sampling through dual-pump linkage and bubble suppression technology to ensure water sample quality; The report generation module is used for summarizing the whole-process monitoring data, generating an integrated monitoring and sampling report through closed-loop logic verification, realizing data traceability and result visualization.

[0055] The water level monitoring module includes: a water level data acquisition unit, a state recording unit, and a water level monitoring logic unit; The water level data acquisition unit acquires the water level data in the well in real time through the ultrasonic water level sensor and the capacitive water level sensor, and records the instantaneous water level, change amplitude and other information according to the time stamp; The state recording unit is used for recording the device state information such as sensor signal stability, water pump running current and pipeline pressure in real time, and providing support for data validity verification.

[0056] The water level monitoring logic unit is used for trend decomposition and noise filtering of the water level data through a built-in time series analysis model, preliminary calculation of an initial water level reference value and a device starting threshold value, generation of a data acquisition interval based on water level fluctuation characteristics, and correction of the initial water level reference value through a sliding average, and finally output of a dynamic water level threshold value to the control module; The control module comprises a data fusion unit, an analysis unit and a control decision unit. The data fusion unit is used for integrating the dynamic water level reference value, the real-time water level deviation value, the change rate parameter and the anti-clogging water pump structure parameter. The analysis unit is used for introducing the Navier-Stokes equation, simulating the water flow state under different water levels, associating and calculating the impeller structure parameter and the critical pumping rate, and outputting the maximum lift and the critical flow rate corresponding to the working condition; The control decision unit is used for generating an intelligent pumping control execution sequence including the water pump start-stop timing, the power adjustment parameter and the anti-clogging instruction based on the preset rule, the water level deviation, the critical pumping rate and the maximum lift data.

[0057] The preset rule is that the water level is triggered to stop the pump when it drops by more than 5 cm in 3 seconds, and the stable state operation is executed when the fluctuation is less than 1 cm in 10 seconds.

[0058] The high-flow well washing control module comprises a well washing parameter configuration unit, a dynamic pressure adjustment unit and a standard judgment unit. The well washing parameter configuration unit is used for calling the preset well washing flow reference parameter, setting the Reynolds number constraint and the initial outlet pressure range, and providing the target threshold value for dynamic adjustment; The dynamic pressure adjustment unit is used for adjusting the speed of the variable speed water pump in real time through the PID control algorithm, maintaining the outlet pressure stable with the well washing flow reference as the constraint, automatically extending the well washing time by 10 minutes and increasing to 60 L / min in stages when the flow decreases by more than 10% within 5 minutes, and ensuring the flushing intensity; The water quality monitoring and standard judgment unit is used for acquiring water quality data in real time through the turbidity sensor and the conductivity sensor; and a built-in water quality standard judgment model is used for determining the completion of well washing and outputting the end point parameter.

[0059] The stable state sampling control module comprises a mode switching unit, a double-pump linkage adjustment unit and a pipeline optimization unit. A mode switching unit is configured to receive a well washing completion signal, switch the variable speed pump system from a large flow well washing mode to a small flow sampling mode, and synchronously adjust a pipeline valve state; A double pump linkage adjusting unit is configured to adjust a main pump basic flow through a vector control frequency converter based on a small flow sampling pressure reference, adjust an auxiliary pump through a bypass compensation pipeline pressure fluctuation, control a motor speed fluctuation through a PWM technology, and ensure a flow stability. A pipeline optimization unit is configured to calculate a pipeline critical flow velocity based on a Hagen-Poiseuille law, control a water flow Reynolds number through a food grade silica gel hose and a gradually expanding interface design, combine a dissolved gas content in water level data, generate an inert gas injection timing, and ensure a bubble content at the interface.

[0060] The embodiments of the present application are described above with reference to the drawings; however, the present application is not limited to the specific embodiments described above, but the specific embodiments described above are merely illustrative and not restrictive. A person of ordinary skill in the art can make changes, modifications, replacements and variations to the above-described embodiments without departing from the spirit and scope of the present application, and these are all within the scope of the present application.

Claims

1. An integrated monitoring and sampling method for addressing red-bed groundwater, characterized in that, The method comprises the following steps: 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 operation 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; S2: based on the initial water level monitoring reference parameter, combined with the real-time collected water level data and the anti-clogging pump structure parameter, through the fuzzy control algorithm and the fluid dynamics model, the intelligent pumping control execution sequence is generated; S3: based on the intelligent pumping control execution sequence, taking the preset well flushing flow reference parameter as the constraint, combined with the water quality sensor real-time monitoring water quality data, through the dynamic pressure regulation algorithm and the water quality standard judgment model, the large flow well flushing operation path is obtained; 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; S5: based on the completion signal of the steady-state sampling execution scheme, the water level final monitoring data and the equipment operation full cycle record are summarized, through the closed-loop control logic and the data archiving rule, the integrated monitoring sampling report is generated.

2. The integrated monitoring and sampling method for dealing with red-bed groundwater of claim 1, wherein, The deployment of the monitoring sampling system and the completion of the initialization configuration comprises: Assembling the intelligent pumping control device and the well flushing and sampling integrated device to form a monitoring sampling system, and lowering it to a preset depth in the target red layer monitoring well; The intelligent pumping control device comprises a water regime sensing start-stop system and an anti-clogging water pump; the water regime sensing start-stop system comprises 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 electrodes of the capacitive water level sensor are made of titanium alloy material, and the electrode spacing is 2 cm; the control module uses 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 provided with a temperature sensor, which combines with the current value for overload protection; The well flushing and sampling integrated device comprises a variable speed water pump system, an intelligent switching and evaluation unit and a sampling pipeline assembly; the variable speed water pump system comprises a vector control frequency converter and a double pump linkage mechanism; the vector control frequency converter establishes a nonlinear mapping model of motor speed and lift; the double pump linkage mechanism comprises a main pump and an auxiliary pump, and the auxiliary pump compensates the pipeline pressure fluctuation through bypass adjustment; the intelligent switching and evaluation unit comprises a turbidity sensor, an electrical conductivity sensor and a control circuit; the control circuit dynamically calculates the well flushing time according to the well depth, the well diameter and the initial turbidity.

3. The integrated monitoring and sampling method for dealing with red-bed groundwater of claim 2, wherein, The calculation process of the initial water level monitoring reference parameter in S1 comprises: S1.1: start the deployed monitoring sampling system, and start real-time acquisition of well water level data through the ultrasonic water level sensor and the capacitive water level sensor of the intelligent pumping control device, and record the equipment operation state information at the same time; the equipment operation state information includes sensor signal transmission delay, water pump initial current and pipeline sealing parameter; S1.2: input the real-time collected well water level data into a 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 a time series analysis method, and preliminarily calculates an initial water level reference value and a device starting threshold value; the device starting threshold value is a water level critical value for triggering the start and stop of the water pump; S1.3: the water level monitoring logic model automatically generates a data collection interval based on the fluctuation characteristics of the initial water level reference value; S1.4: the water level monitoring logic model performs a moving average correction on the initial water level reference value at a determined data collection interval as a period, and generates a corrected water level reference value; S1.5: integrate the corrected water level reference value and the device starting threshold value into a dynamic water level threshold value, and output to the fluid dynamics model.

4. The integrated monitoring and sampling method for dealing with red-bed groundwater of claim 3, wherein, The specific steps of S2 include: S2.1: extract the corrected initial water level reference value and the device starting threshold value in the initial water level monitoring reference parameter, compare the corrected initial water level reference value with the real-time collected well water level data, and obtain a water level deviation value and a change rate parameter; S2.2: call the anti-clogging water pump structure parameters and input the anti-clogging water pump structure parameters into the fluid dynamics model, the fluid dynamics model introduces the Navier-Stokes equation, associates and calculates the impeller diameter in the anti-clogging water pump structure parameters with the critical pumping rate to obtain the critical pumping rate under different water levels, and calculates the maximum lift under the corresponding working condition in combination with the impeller diameter parameter; the anti-clogging water pump structure parameters include impeller diameter, curvature radius, installation angle, spiral guide groove size and surface coating characteristics; S2.3: input the water level deviation value, the change rate parameter and the device starting threshold value in the initial water level monitoring reference parameter, and the critical pumping rate and the maximum lift under different water levels into a fuzzy control algorithm, the fuzzy control algorithm makes decisions based on preset rules to generate an intelligent pumping control execution sequence including a water pump start-stop time sequence, a running power adjustment parameter and an anti-clogging control instruction.

5. The integrated monitoring and sampling method for dealing with red-bed groundwater of claim 4, wherein, The generation process of the intelligent pumping control execution sequence in S2.3 includes: determine the starting time and the stopping time based on the device starting threshold value and the water level deviation value to obtain the water pump start-stop time sequence; based on the matching relationship between the maximum lift and the critical pumping rate, calculate the power distribution ratio to obtain the running power adjustment parameter; in combination with the characteristics of the spiral guide groove, output an impeller speed correction value according to the critical pumping rate to obtain the intelligent pumping control execution sequence.

6. The integrated monitoring and sampling method for dealing with red-bed groundwater of claim 5, wherein, The specific steps of S3 include: S3.1: extract the final flow value in the intelligent pumping control execution sequence as a well flushing starting flow reference, call preset well flushing flow reference parameters to determine the flow range constraint and the corresponding pipe Reynolds number threshold value in the large flow well flushing stage; S3.2: input the final flow value and the well-flushing flow benchmark parameter into the dynamic pressure regulation algorithm, which is constrained by a Reynolds number threshold, calculate the current Reynolds number from the final flow value and match the corresponding turbulent flow state, construct a flow-pressure mapping relationship model based on historical data, calculate the maximum allowable flushing pressure based on the turbulent flow state, and finally generate the initial adjustment parameter of the water 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: start the high-flow well-flushing operation based on the generated initial adjustment parameter of the water pump speed, the dynamic pressure regulation 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 by the deviation proportion; S3.4: during the started high-flow well-flushing operation, the turbidity sensor and the conductivity sensor in the well-flushing and sampling integrated device collect water quality data in real time, and transmit the water quality data to the water quality standard judgment model; the water quality data includes turbidity value, sediment content and conductivity fluctuation value; S3.5: the water quality standard judgment model analyzes the received real-time water quality data based on the preset evaluation standard and the target turbidity value of the well-flushing flow benchmark parameter, if it does not meet the standard, the flushing path node parameter is dynamically corrected according to the deviation of the current turbidity and the target value, if it meets the standard, the current node is marked as the well-flushing end point; S3.6: integrate the pressure regulation record output by the dynamic pressure regulation algorithm, the flow stability parameter and the flushing path node parameter correction result determined by the water quality standard judgment model to form a complete high-flow well-flushing operation path.

7. The integrated monitoring and sampling method for dealing with red-bed groundwater of claim 6, wherein, The specific steps of S4 include: S4.1: extract the pressure value and flow value at the end point of the high-flow well-flushing operation path as the initial conditions for sampling start, wherein the pressure value is the final outlet pressure in the well-flushing stage, and the flow value is the actual flow at the end of well-flushing; S4.2: call the preset small-flow sampling pressure benchmark and pipeline fluid characteristic parameters; the pipeline fluid characteristic parameters include the inner diameter, inner wall roughness of food-grade silicone hose and the structural parameters of the gradually expanding interface; S4.3: input the initial pressure value, flow value, small-flow sampling pressure benchmark and pipeline fluid characteristic parameters into the double-pump linkage control algorithm, the double-pump linkage control algorithm calculates the difference between the target pressure and the initial pressure, combines the pipeline resistance coefficient along the way to obtain the main pump basic flow parameter and the auxiliary pump bypass compensation value; the pipeline resistance coefficient along the way is determined based on the inner wall roughness; S4.4: input the pipeline fluid characteristic parameters and the main pump basic flow parameter into the bubble suppression model, the bubble suppression model calculates the pipeline critical flow rate by the Hagen-Poiseuille law, and introduces the dissolved gas content in the real-time collected water level data to generate the inert gas injection timing; S4.5: the bubble suppression model optimizes the gas-liquid equilibrium state in the sampling pipeline based on the pipeline critical flow rate and the inert gas injection timing, and outputs the pipeline pressure stability threshold and the upper limit of bubble content control; S4.6: The double-pump linkage control algorithm combines the auxiliary pump bypass compensation value and the pipeline pressure stability threshold to generate motor speed regulation parameters through PWM technology; S4.7: The motor speed regulation parameters, pressure maintenance curve, inert gas injection timing, and bubble content control upper limit are integrated to form a steady-state sampling execution plan.

8. The integrated monitoring and sampling method for dealing with red-bed groundwater of claim 7, wherein, The double-pump linkage control algorithm in S4 maintains the basic pressure of the main pump according to the small-flow sampling pressure reference and calculates the pulse frequency compensation for pressure fluctuations based on the bubble suppression model.

9. The integrated monitoring and sampling method for red-bed groundwater of claim 8, wherein, The integrated monitoring and sampling report in S5 includes water level change curve, well flushing duration, water quality index change trend, sampling pressure stability analysis, equipment operation status record table, and abnormal situation handling log.

10. An integrated monitoring and sampling system for addressing red-bed groundwater for implementing the method of any one of claims 1-9, characterized in that, Comprise: Initialization module, water level monitoring module, control module, large-flow well flushing control module, steady-state sampling control module, report generation module; The initialization module is used for monitoring the physical deployment of the sampling system, equipment self-checking and parameter initialization; The water level monitoring module is used for real-time acquisition of well water level data, and the initial water level reference value is calculated by combining the preset water level monitoring logic model; The control module generates intelligent pumping control execution sequence based on the initial water level reference value and equipment structure parameters, realizes intelligent management of water pump start-stop, power regulation and anti-clogging operation; The large-flow well flushing control module is used for starting the large-flow well flushing process based on the intelligent pumping control execution sequence, and realizes well water purification through pressure regulation and water quality monitoring; The steady-state sampling control module is used for switching to small-flow sampling mode after well flushing is completed, and realizes stable pressure sampling through double-pump linkage and bubble suppression technology; The report generation module is used for summarizing the whole-process monitoring data, and generating an integrated monitoring and sampling report after closed-loop logic verification.

Images