A soil real-time sampling detection method for saline-alkali land treatment

Abstract

The present application relates to the technical field of soil monitoring, in particular to a soil real-time sampling detection method for saline-alkali soil treatment, comprising the following steps: obtaining a mechanical compression distribution model by constructing a resistance and crust strength boundary, extracting an impedance coefficient, performing probe penetration matching to quantify deep hydrostatic pressure load, introducing a driving negative pressure and filter membrane hydraulic property differential pressure comparison, positioning a basement expansion origin, setting a soil background interference boundary to extract a charge factor, and obtaining a saline-alkali soil in-situ pH adaptive correction detection value. In the present application, the breaking boundary is extracted by fusing the probe resistance model, the matching accuracy of the in-situ sampling depth is effectively improved, the static water pressure quantitative analysis efficiency is strengthened, the suction flow rate is generated under different pressure difference states, the in-situ pore fluid lossless extraction is optimized, the vertical span is evaluated to enhance the stability of the gel hydration volume determination, the salinity interference boundary is set to extract the migration factor, and the correction ability of the in-situ pH detection value is established.

Description

Technical Field

[0001] This invention relates to the field of soil monitoring technology, and in particular to a real-time soil sampling and testing method for saline-alkali land remediation. Background Technology

[0002] The field of soil monitoring technology mainly involves the continuous observation and quantitative measurement of soil physicochemical properties and environmental parameters. Its core aspects include the dynamic collection and physicochemical analysis of key indicators such as soil moisture content, pH, electrical conductivity and soluble salt composition. This field relies on physical probe penetration, chemical reagent titration and electrochemical sensing to obtain multi-dimensional state characteristics of soil, thereby grasping the trend of soil quality evolution to guide land resource management and ecological restoration projects. The traditional real-time soil sampling and testing method for saline-alkali land management refers to the process of on-site sample collection and index determination for the salt accumulation and abnormal pH characteristics in saline-alkali soil. For obtaining the concentration of soluble salt ions and pH index in soil layers at specific depths in saline-alkali land, a handheld spiral soil drill is used to drill soil core samples at pre-set grid points in the field. Then, soil samples at different depths are layered and placed in polyethylene sealed bags and transferred to an indoor workbench. The soil is placed in a drying tray to air dry naturally. Then, it is crushed with a mortar and pestle and passed through a standard nylon sieve with a 2 mm aperture. Deionized water and sieved soil sample are added to a glass beaker at a water-to-soil mass ratio of 5:1. The sample is placed in a constant temperature shaker for extraction and then allowed to settle. Finally, pH glass electrodes and platinum black conductivity electrodes are directly inserted into the supernatant of the soil suspension to read the potential change and resistance value.

[0003] Traditional soil testing methods for saline-alkali land remediation rely on manual hand-held soil drills to extract soil core samples from the field. The soil samples are then transferred in layers to an indoor workbench for natural air drying, crushing, and sieving. Deionized water and soil samples are mixed in a fixed ratio and placed in a constant-temperature shaker for extraction and settling. Electrodes are then inserted into the supernatant of the soil suspension to read the values. This physical sampling, which is detached from the in-situ environment, and repeated indoor pretreatment processes damage the original pore structure and ion distribution of the soil, causing the measured pH data to deviate significantly from the actual habitat and greatly reducing the accuracy of monitoring. Summary of the Invention

[0004] The purpose of this invention is to overcome the shortcomings of existing technologies and propose a real-time soil sampling and testing method for saline-alkali land remediation.

[0005] To achieve the above objectives, the present invention adopts the following technical solution: a real-time soil sampling and testing method for saline-alkali land remediation, comprising the following steps: S1: Obtain the probe propulsion resistance value and the crust yield strength limit value, integrate the probe propulsion resistance, crust yield strength and drill bit outer cutting stress data to construct a mechanical compressive strength distribution model, extract the fracture peak boundary through stress comparison, and obtain the salt crust fracture resistance coefficient. S2: Call the salt crust breaking resistance coefficient, extract the soil pore layer characteristics, perform the matching judgment between probe penetration and the set target depth, and trigger the tension module to quantify the deep hydrostatic load after the target is met, and obtain the target depth sampling microfluidic driven negative pressure parameters. S3: Call the target depth sampling microfluidic driving negative pressure parameters, introduce the driving negative pressure parameters and the hydraulic conduction properties of the filter membrane, perform fluid dynamics comparison between the instantaneous suction volume of the micro pump and the pore flow velocity benchmark, and dynamically change the pressure difference state through the negative pressure step size step to generate the in-situ leachate suction flow rate; S4: Call the in-situ leachate suction flow rate, combine the suction flow rate with the fluid immersion environment, locate the expansion origin of the liquid collection cavity base and measure the boundary swelling displacement, evaluate the vertical expansion span based on the thickness difference of the gel membrane before and after water absorption, and obtain the hydration volume expansion of the liquid collection cavity gel base. S5: Call the hydration volume expansion of the gel substrate in the liquid collection chamber, extract the dielectric constant calibration value and the in-situ soil conductivity measurement value, collect the solution impedance signal to set the soil background salinity interference limit, extract the hydrogen ion charge transport factor, and obtain the in-situ pH adaptation correction detection value of saline-alkali soil.

[0006] As a further aspect of the present invention, the salt crust breaking resistance coefficient includes shear failure toughness, interfacial friction dissipation energy, and structural deformation internal resistance; the target depth sampling microfluidic driving negative pressure parameters include capillary force critical value, system vacuum stiffness, and pipeline pressure drop; the in-situ leachate suction flow rate includes membrane flux constant, filter cake layer resistance, and fluid kinetic energy flux; the hydration volume expansion of the gel substrate in the collection chamber includes gel swelling equilibrium ratio, cross-linked network elastic modulus, and intermolecular affinity; and the in-situ pH adaptation correction detection value of saline-alkali soil includes response slope factor, zero-point potential shift, and electrode polarization impedance.

[0007] As a further aspect of the present invention, the step of obtaining the salt crust breaking resistance coefficient specifically includes: S111: Obtain the probe propulsion resistance value and the crust yield strength limit value, extract the base stress peak term corresponding to the cutting stress parameter based on the stress sensing element, subtract the probe propulsion resistance value from the base stress peak term to obtain the propulsion offset term, and obtain the crust cutting stress correlation degree. S112: Call the crust cutting stress correlation degree, measure the base pore pressure parameter at the current depth, compare the crust cutting stress correlation degree with the preset salt crust compressive strength boundary term, extract the base pore pressure parameter within the boundary range and sum it with the cutting stress correlation degree to obtain the base combination term, extract the distribution ratio of the combination term in the pore group, and establish the soil breaking node impedance characteristics. S113: Call the impedance characteristics of the soil breaking node, determine the reference item of the detection node state in the soil breaking state, combine the reference item with the impedance characteristics to extract the ratio benchmark item, screen the stress value boundary, and generate the salt crust breaking impedance coefficient.

[0008] As a further aspect of the present invention, the step of obtaining the target depth sampling microfluidic driving negative pressure parameter specifically includes: S211: Call the salt crust breaking resistance coefficient, perform an equivalence determination between the probe penetration depth value and the target depth setting value, extract the depth overlap term, detect the soil pore structure hierarchical parameters based on the microfluidic capillary channel, analyze the depth overlap term and pore structure parameters, obtain the spatial offset, and superimpose it with the salt crust breaking resistance coefficient to obtain the sampling space excitation threshold. S212: Call the sampling space excitation threshold, identify the hydraulic load workload of the core measurement, perform intersection operation to extract the core activation term, combine the activation term to quantify the hydrostatic pressure parameter, identify the water pressure measurement quantity, extract the variation slope term by differentially differentiating the water pressure measurement quantity, analyze the water pressure measurement quantity and variation slope, and establish the deep pore water pressure calibration value. S213: Call the deep pore water pressure calibration value, obtain the soil water potential setting benchmark, extract the water potential bias term by subtracting the water pressure calibration value from the water potential benchmark, measure the hydraulic impedance loss parameter of the microfluidic channel, and extract the driving benchmark term by superimposing the water potential weighted term and the hydraulic impedance loss, and generate the target depth sampling microfluidic driving negative pressure parameter.

[0009] As a further aspect of the present invention, the specific process of the spatial offset is as follows: extract the pore connectivity value by analyzing the pore structure parameters, multiply the depth overlap term by the pore connectivity value to obtain the effective spatial capacity, and subtract the effective spatial capacity from the preset sampling space reference value to obtain the spatial offset.

[0010] As a further aspect of the present invention, the step of obtaining the in-situ leachate suction flow rate specifically includes: S311: Call the target depth sampling microfluidic drive negative pressure parameters, extract the flow rate deviation term between the instantaneous suction volume of the micropump and the pore flow rate benchmark, measure the effective flow area based on the surface of the dialysis membrane, determine the membrane pore ratio parameter, and sum the drive negative pressure value, flow rate calibration quantity and pore ratio to obtain the initial term of the membrane microflow rate. S312: Call the initial term of the micro-flow of the filter membrane, measure the negative pressure step size, calculate the fluid impedance term based on the hydraulic conductivity of the filter membrane, extract the net driving flow term by subtracting the initial flow term from the fluid impedance, multiply it by the negative pressure step size to obtain the gain bias term, measure the viscosity coefficient of the extract fluid and subtract it from the gain bias term to reduce the dimension, and establish the negative pressure differential iteration command. S313: Call the negative pressure differential change instruction, obtain the initial pressure differential setting parameter on both sides of the filter membrane and superimpose the extracted pressure differential state change amount, measure the micro pump suction action time benchmark and divide it by the change amount to obtain the state decay slope term, measure the flow velocity fluctuation bias term in the pipeline, and generate the in-situ leachate suction flow rate.

[0011] As a further aspect of the present invention, the process of extracting the velocity deviation term between the instantaneous pumping volume of the micropump and the pore velocity reference specifically involves extracting the measured value of the instantaneous pumping volume of the micropump, extracting the set value of the pore velocity reference, and subtracting the set value of the pore velocity reference from the measured value of the instantaneous pumping volume of the micropump to obtain the velocity deviation term.

[0012] As a further aspect of the present invention, the step of obtaining the hydration volume expansion of the gel substrate in the liquid collection cavity specifically includes: S411: Call the in-situ leachate suction flow rate, locate the origin coordinates of the base expansion of the collection chamber, extract the displacement offset term between the suction flow rate and the origin coordinates, and superimpose the displacement offset term with the initial thickness of the gel membrane in the collection chamber to extract the thickness reference term, thereby generating the base swelling coordinate calibration value. S412: Call the substrate swelling coordinate calibration value, measure the swelling displacement parameters of the substrate surface layer boundary under soaking state, compare and extract the over-limit displacement term, measure the gel material hydration tension term, extract the over-limit displacement term and the hydration tension expansion bias term, and establish a gel volume change benchmark. S413: Call the gel volume change benchmark, extract the thickness difference term by subtracting the liquid swelling thickness from the initial thickness, extract the vertical volume change span between the volume change benchmark and the thickness difference, obtain the probe projection direction parameter, and obtain the hydration volume expansion of the gel substrate in the liquid collection cavity.

[0013] As a further aspect of the present invention, the specific process of superimposing the displacement bias term and the initial thickness of the gel membrane in the collecting cavity to extract the thickness reference term is as follows: obtaining the quantitative value of the displacement bias term, extracting the quantitative value of the initial thickness of the gel membrane in the collecting cavity, and performing an addition operation between the quantitative value of the displacement bias term and the quantitative value of the initial thickness of the gel membrane in the collecting cavity to obtain the thickness reference term. The specific process of extracting the over-limit displacement term and the expansion bias term of the hydration tension is as follows: identify the specific value of the over-limit displacement term, extract the specific value of the hydration tension term of the gel material, and perform a multiplication operation between the specific value of the over-limit displacement term and the specific value of the hydration tension term of the gel material to obtain the expansion bias term.

[0014] As a further aspect of the present invention, the step of obtaining the in-situ pH adaptation correction detection value of saline-alkali soil specifically includes: S511: Combine the hydration volume expansion of the collected liquid cavity gel substrate with the dielectric constant calibration to analyze the dielectric variation term, integrate it with the in-situ soil conductivity measurement value, extract the conductivity bias, introduce the pore solution ion distribution concentration detected by the microelectrode, and establish the pore ion distribution calibration by performing coupled calculation with the conductivity bias. S512: Based on the pore ion distribution calibration quantity and the microelectrode solution impedance signal, the impedance slope term is extracted by proportional deduction and differential analysis with the background reference term to delineate the soil background salinity interference boundary. The ion distribution calibration quantity and the interference boundary are analyzed to obtain the interference bias term. The basic charge metric is extracted through secondary feature mapping to generate the effective charge factor span. S513: The target hydrogen ion chemical activity benchmark is introduced to normalize and analyze the effective charge factor span, extract the activity bias term, and combine it with the in-situ soil temperature correction coefficient to perform temperature compensation mapping to obtain the correction charge. The correction charge is logarithmically scaled to extract the concentration ratio measure, and after discretized equidistant sampling, the in-situ pH adaptation correction detection value of saline-alkali soil is obtained.

[0015] Compared with the prior art, the advantages and positive effects of the present invention are as follows: In this invention, a compressive strength model is constructed by fusing probe resistance and yield strength to extract the breach boundary, effectively improving the matching accuracy of in-situ sampling depth and fully enhancing the quantitative analysis efficiency of hydrostatic load. Driving negative pressure and filter membrane properties are introduced to perform hydrodynamic comparison, and the alternating pressure differential state generates the leachate suction flow rate, greatly optimizing the non-destructive extraction of in-situ pore fluid. The immersion environment is combined to locate the substrate expansion origin and evaluate the vertical span, comprehensively enhancing the stability of gel hydration volume measurement. Impedance signals are collected to set the salt interference limit and extract transport factors, ensuring the correction capability of in-situ pH detection values ​​and significantly achieving highly reliable dynamic tracking and management of habitat evolution trends. Attached Figure Description

[0016] Figure 1 This is a schematic diagram of the main steps of the present invention; Figure 2 This is a flowchart illustrating the process of obtaining the salt crust breaking resistance coefficient in this invention. Figure 3This is a flowchart illustrating the acquisition of target depth sampling microfluidic driven negative pressure parameters in this invention. Figure 4 This is a flowchart illustrating the process of obtaining the in-situ leachate suction flow rate in this invention. Figure 5 This is a flowchart illustrating the process of obtaining the hydration volume expansion of the gel substrate in the liquid collection cavity in this invention. Figure 6 This is a flowchart illustrating the process of obtaining in-situ pH adaptation and correction detection values ​​for saline-alkali soil in this invention. Detailed Implementation Example

[0017] Please see Figure 1 This invention provides a technical solution: a real-time soil sampling and testing method for saline-alkali land remediation, comprising the following steps: S1: Obtain the probe propulsion resistance value and the crust yield strength limit value. Based on the stress sensing element on the outside of the drill bit, obtain the cutting stress parameters of the salt crust layer, construct a mechanical compressive strength distribution model, substitute the probe propulsion resistance value and the crust yield strength limit value into the mechanical compressive strength distribution model to perform stress comparison calculation, extract the stress peak boundary during the demolition process, and obtain the salt crust demolition resistance coefficient. S2: Call the salt crust breaking resistance coefficient, extract the probe penetration depth value and the sampling target depth setting value, quantify the soil pore structure level based on the microfluidic depth sensing component, perform depth coincidence judgment for probe penetration depth and target setting value, activate the tension measurement core to quantify deep hydrostatic load parameters, and obtain the target depth sampling microfluidic driven negative pressure parameters. S3: Call the target depth sampling microfluidic drive negative pressure parameters, extract the instantaneous suction volume of the micropump and the pore velocity benchmark of the ceramic head, measure the negative pressure step size of the air pump, read the hydraulic conductivity properties of the microporous dialysis membrane, perform fluid dynamics comparison calculation between the instantaneous suction volume and the pore velocity benchmark, trigger the step size step accumulation and iterative pressure difference state, and generate the in-situ leachate suction flow rate; S4: Call the in-situ leachate suction flow rate, extract the initial thickness and swelling thickness of the gel membrane in the collection chamber, locate the origin coordinates of the swelling of the base of the collection chamber, measure the swelling displacement parameters of the base surface layer boundary under fluid immersion, calculate and evaluate the volume change span of the vertical water absorption and swelling of the gel based on the thickness difference, and obtain the hydration volume expansion of the gel base in the collection chamber. S5: Call the volume expansion of the hydration of the gel substrate in the collection chamber, extract the dielectric constant calibration value and the in-situ soil conductivity measurement value, collect the solution impedance signal to set the soil background salinity interference limit, extract the hydrogen ion charge transport factor, and obtain the in-situ pH adaptation correction detection value of saline-alkali soil.

[0018] The resistance coefficient for salt crust breaking includes shear failure toughness, interfacial frictional dissipation energy, and structural deformation internal resistance. The microfluidic-driven negative pressure parameters for target depth sampling include capillary force critical value, system vacuum stiffness, and pressure drop along the pipeline. The in-situ leachate suction velocity includes membrane flux constant, filter cake layer resistance, and fluid kinetic energy flux. The hydration volume expansion of the gel substrate in the collection chamber includes gel swelling equilibrium ratio, cross-linked network elastic modulus, and intermolecular affinity. The in-situ pH adaptation correction detection values ​​for saline-alkali soil include response slope factor, zero-point potential shift, and electrode polarization impedance.

[0019] Please see Figure 2 The specific steps for obtaining the salt crust breaking resistance coefficient are as follows: S111: Obtain the probe propulsion resistance value and the crust yield strength limit value, extract the base stress peak term corresponding to the cutting stress parameter based on the stress sensing element, subtract the probe propulsion resistance value from the base stress peak term to obtain the propulsion offset term, and obtain the crust cutting stress correlation degree. The probe propulsion resistance and crust yield strength threshold values ​​are obtained by acquiring continuous resistance signals during penetration through a pressure sensor array deployed at the front end of the penetration device. Low-pass filtering is applied to the acquired continuous resistance signals to remove high-frequency mechanical noise, and the arithmetic mean of the smoothed resistance sequence within a fixed time window is extracted, outputting the probe propulsion resistance value, which is 158 kPa. The geomechanical exploration database is accessed to match the crust yield strength threshold value associated with the current probe node coordinates, obtaining a value of 125 kPa. Based on the stress sensing element, the basement stress peak term corresponding to the cutting stress parameters is extracted. The time-domain electrical signal output from the probe sidewall shear sensor is received and converted into a cutting stress sequence. The set of transient stress parameters during the probe's contact with the crust is identified, and the transient parameters within the set are sorted in descending order. The largest quantifiable term is extracted as the basement stress peak term, measured at 95 kPa. The difference between the probe propulsion resistance value and the basement stress peak term is used to obtain the propulsion bias term. Subtracting 95 kPa from 158 kPa yields the difference, outputting the propulsion bias term as 63 kPa. A mapping mechanism between the propulsion bias term and the crust yield strength limit value is established. The dimensionless ratio is extracted by dividing the propulsion bias term by the crust yield strength limit value, generating the crust cutting stress correlation degree. Obtaining the cutting stress correlation degree eliminates the dimensional error introduced by the penetration rate and reflects the crust micro-fracture resistance.

[0020] S112: Call the crust cutting stress correlation degree, measure the base pore pressure parameter at the current depth, compare the crust cutting stress correlation degree with the preset salt crust compressive strength boundary term, extract the base pore pressure parameter within the boundary range and sum it with the cutting stress correlation degree to obtain the base combination term, extract the distribution ratio of the combination term in the pore group, and establish the soil breaking node impedance characteristics. The crust cutting stress correlation is invoked, and the pore water pressure sensing probe is controlled to read the real-time pore water pressure sequence at the current penetration elevation at a fixed sampling frequency. The median value of the continuous sampling period is set as the basement pore pressure parameter, and the measured result is 42 kPa. The salt crust compressive strength boundary term preset in the internal storage unit is retrieved, with a preset limit range of 0.3 to 0.6. The crust cutting stress correlation is compared with the preset salt crust compressive strength boundary term, and it is determined that 0.5 falls completely within the preset limit range. The basement pore pressure parameter within the boundary range is extracted and summed with the cutting stress correlation to obtain the basement combination term. The 42 kPa and 0.5 are algebraically added, and the basement combination term is output as 42.5 kPa. The distribution ratio of the basement combination term in the pore group is extracted, and a preset pore pressure distribution evaluation benchmark is introduced. The basement combination term is matched with the middle layer pressure range within the benchmark, and the associated distribution ratio benchmark constant of 0.55 is extracted. The distribution ratio benchmark constant is multiplied with the basement combination term to establish the impedance characteristics of the breakthrough node. Multiplying 0.55 by 42.5 kPa yields a ground-breaking node impedance characteristic of 23 kPa. By introducing a weighted product constant based on the distribution weight, the pore response at a single depth is integrated with the spatial distribution characteristics of the pore group.

[0021] S113: Call the impedance characteristics of the soil breaking node, determine the reference item of the detection node state in the soil breaking state, combine the reference item with the impedance characteristics to extract the ratio benchmark item, screen the stress numerical boundary, and generate the salt crust breaking impedance coefficient. The impedance characteristics of the soil-breaking node are used to collect the peak acceleration and vertical displacement difference at the moment the probe penetrates the soil layer. The peak value of the output signal from the micro accelerometer is extracted, and the initial penetration stroke calculated by the displacement sensor is read. The peak acceleration of 1.2 m / s² is divided by the vertical displacement difference of 0.05 m, and the output probe node status reference item is 24. The ratio benchmark item is extracted by mapping the reference item with the impedance characteristics. The probe node status reference item 24 is divided by the soil-breaking node impedance characteristic of 23 kPa, and the ratio benchmark item is 1.04. The stress value boundary is screened. The preset stress value limit range of 0.8 to 1.5 is retrieved, and the value range inclusion judgment is performed to confirm that the ratio benchmark item falls within the stress value boundary range. The ratio benchmark item 1.04 is subtracted from the lower limit of the stress value boundary range of 0.8, and the difference operation is performed to output the salt crust breaking impedance coefficient. The dynamic soil breaking parameters and static impedance characteristics are subjected to ratio standardization calculation, and invalid mechanical disturbance data is filtered out in conjunction with the boundary difference screening mechanism.

[0022] Please see Figure 3 The specific steps for obtaining the target depth sampling microfluidic-driven negative pressure parameters are as follows: S211: Call the salt crust breaking resistance coefficient, perform an equivalence judgment between the probe penetration depth value and the target depth set value, extract the depth overlap term, detect the soil pore structure hierarchical parameters based on the microfluidic capillary channel, analyze the depth overlap term and pore structure parameters, obtain the spatial offset, and superimpose it with the salt crust breaking resistance coefficient to obtain the sampling space excitation threshold. The specific process of spatial offset is as follows: extract the pore connectivity value by analyzing the pore structure parameters, multiply the depth coincidence term by the pore connectivity value to obtain the effective space capacity, and subtract the effective space capacity from the preset sampling space reference value to obtain the spatial offset. The impedance coefficient for salt crust breaking is used, and the current probe penetration depth is obtained as 120 mm using a laser ranging component. A preset target depth setting of 120 mm is also retrieved from memory. The absolute difference between the two is compared; if the difference is zero, they are considered equal, and the value is extracted as the depth overlap term of 120 mm. Soil pore structure layer parameters are then detected using microfluidic capillary channels. A water pressure pulse of a specified frequency is released into the soil layer, and the attenuation time constant of the echo signal is extracted and quantified into pore structure layer parameters, measured at 3 milliseconds. The spatial offset is obtained by analyzing the depth overlap term and pore structure parameters. The pore connectivity value is extracted by parsing the pore structure parameters. The pore structure layer parameters are divided by the reference constant of 10 milliseconds, outputting a pore connectivity value of 0.3. Multiplying the depth overlap term 120 mm by the pore connectivity value 0.3 yields an effective spatial capacity of 36 cubic millimeters. A preset sampling space reference value of 50 cubic millimeters is retrieved, and this effective spatial capacity of 36 cubic millimeters is subtracted to obtain a spatial offset of 14 cubic millimeters. The sampling space excitation threshold is obtained by superimposing the spatial bias and the salt crust breaking impedance coefficient. Adding 14 to 0.24 outputs the sampling space excitation threshold. The composite derivation of the product and difference between the depth positioning parameter and the micropore connectivity parameter achieves accurate quantification of the practically usable sampling space.

[0023] S212: Call the sampling space excitation threshold, identify the hydraulic load workload of the core measurement, perform intersection operation to extract the core activation term, combine the activation term to quantify the hydrostatic pressure parameter, identify the water pressure measurement quantity, extract the variation slope term by differential differentiation of the water pressure measurement quantity, analyze the water pressure measurement quantity and variation slope, and establish the deep pore water pressure calibration value. The sampling space excitation threshold is invoked, and the microfluidic pump operating resistance sequence is read through the built-in torque sensing element. Integration is performed within a fixed time window, outputting a hydraulic load workload of 15 mJ. An intersection operation is performed to extract the core activation term. The hydraulic load workload is divided by the sampling space excitation threshold, and the ratio between the two is calculated. Dividing 15 by 14.24 yields a core activation term of 1.05. The hydrostatic pressure parameter is quantified using the activation term, obtaining a base hydraulic pressure value of 20 kPa from the microfluidic channel sidewall feedback. Multiplying this base hydraulic value by the core activation term outputs a hydrostatic pressure parameter of 21 kPa. The water pressure measurement is identified, and pressure sampling is performed at a preset interval frequency at the end of the sampling pipeline. The arithmetic mean of the continuous sampling sequence is extracted as the water pressure measurement, yielding a measurement of 38 kPa. The differential of the water pressure measurement is used to extract the slope term, and the difference between the current water pressure measurement and the previous cycle's water pressure measurement is extracted, yielding a slope term of 1.5 kPa / s. By analyzing the measured water pressure and the slope of the fluctuation, a calibration value for deep pore water pressure is established. The slope of the fluctuation is multiplied by the response time constant of 2 seconds to obtain a compensation term of 3 kPa. The measured water pressure of 38 kPa is added to the compensation term. The first-order difference slope is used to perform feedforward compensation calculation on dynamic pressure fluctuations to avoid pressure misjudgment caused by fluid hysteresis.

[0024] S213: Call the deep pore water pressure calibration value, obtain the soil water potential setting benchmark, extract the water potential bias term by subtracting the water pressure calibration value from the water potential benchmark, measure the hydraulic impedance loss parameter of the microfluidic channel, superimpose the water potential weighted term and the hydraulic impedance loss to extract the driving benchmark term, and generate the target depth sampling microfluidic driving negative pressure parameter. The deep pore water pressure calibration value of 41 kPa is used. The soil water potential setting reference term matching the current environment, 18 kPa, is read from the control database. The difference between the water pressure calibration value and the water potential reference term is used to extract the water potential bias term. Subtracting the soil water potential setting reference term of 18 kPa from the deep pore water pressure calibration value of 41 kPa yields a water potential bias term of 23 kPa. The hydraulic impedance loss parameter of the microfluidic channel is then measured. The pressure drop measured by the differential pressure sensors at both ends of the microchannel is read as the hydraulic impedance loss parameter output, measuring 5 kPa, and the driving reference term is extracted. A preset water potential weighting constant of 0.8 is introduced. The water potential bias term of 23 kPa is multiplied by the water potential weighting constant to obtain a water potential weighting term of 18.4 kPa. The water potential weighting term and the hydraulic impedance loss parameter are algebraically summed. Adding 18.4 kPa to 5 kPa yields the driving reference term of 23.4 kPa, generating the target depth sampling microfluidic driving negative pressure parameter. Multiplying the driving baseline term by the micropump negative pressure conversion constant of 1.2, the calculated microfluidic driving negative pressure parameter is 28 kPa. A weighted sum of the environmental water potential baseline and the pipeline dynamic impedance loss is constructed to build a numerical calculation model reflecting the suction resistance. By using the microfluidic driving negative pressure parameter to regulate the pump body, the success rate of fluid extraction within the microstructured pores is improved.

[0025] Please see Figure 4 The specific steps for obtaining the in-situ leachate suction flow rate are as follows: S311: Call the target depth sampling microfluidic drive negative pressure parameters, extract the flow rate deviation term between the instantaneous suction volume of the micropump and the pore flow rate benchmark, measure the effective flow area based on the dialysis membrane surface, determine the membrane pore ratio parameter, and sum the drive negative pressure value, flow rate calibration quantity and pore ratio to obtain the initial term of the membrane microflow rate. The process of extracting the velocity deviation term between the instantaneous pumping volume of the micropump and the pore velocity reference is as follows: extract the measured value of the instantaneous pumping volume of the micropump, extract the set value of the pore velocity reference, and subtract the set value of the pore velocity reference from the measured value of the instantaneous pumping volume of the micropump to obtain the velocity deviation term. The target depth sampling microfluidic drive negative pressure parameter is 28 kPa. The real-time suction volume fed back by the flow meter is read as 15 μL / s. The set value of the pore flow velocity benchmark is extracted and read from the configuration file as 12 μL / s. The measured value of the instantaneous suction volume of the micropump is subtracted from the set value of the pore flow velocity benchmark, i.e., 15 μL / s minus 12 μL / s, resulting in a flow velocity deviation term of 3 μL / s. The effective flow area is measured based on the dialysis membrane surface. An image recognition algorithm is used to count the number of pixels in the clear area of ​​the membrane surface, and the effective flow area is calculated to be 85 square millimeters. The membrane porosity parameter is measured. The effective flow area is divided by the total membrane area of ​​100 square millimeters to obtain a porosity parameter of 0.85. The drive negative pressure value, the flow rate calibration value, and the porosity are summed, and the flow rate calibration value of 10 μL / s is read. The drive negative pressure value of 28 kPa, the flow rate calibration value of 10 μL / s, and the porosity parameter of 0.85 are directly added together to output the initial term of the membrane micro-flow rate. The multidimensional summation of dynamic negative pressure, actual flow rate, and microscopic channel pore parameters smooths out the fluctuations of a single variable.

[0026] S312: Call the initial term of the micro-flow rate of the filter membrane, measure the negative pressure step size, calculate the fluid impedance term based on the hydraulic conductivity of the filter membrane, extract the net driving flow rate term by subtracting the initial flow rate term from the fluid impedance, multiply it by the negative pressure step size to obtain the gain bias term, measure the viscosity coefficient of the extract fluid and subtract it from the gain bias term to reduce the dimension, and establish the negative pressure differential iteration command. The initial flow rate of the filter membrane is retrieved, and the power adjustment level difference set by the main control chip is read. The operating negative pressure step size is set to 2 kPa. The fluid impedance term is calculated based on the hydraulic conductivity of the filter membrane. The liquid permeation time under known pressure is measured, and a division calculation yields a fluid impedance term of 6 units. The net driving flow rate term is extracted by subtracting the initial flow rate term from the fluid impedance term. The initial flow rate term of the filter membrane (38.85) is subtracted from the fluid impedance term (6), resulting in a net driving flow rate term of 32.85. This is multiplied by the negative pressure step size to obtain the gain bias term. The net driving flow rate term of 32.85 is multiplied by the operating negative pressure step size of 2 kPa, resulting in an output gain bias term of 65.7. The viscosity coefficient of the extract is measured. The flow velocity parameter output by the hot-wire sensing component within the microchannel is read and converted into the fluid viscosity coefficient based on the temperature drop attenuation law. The measured value is 1.2 mPa·s. The negative pressure differential change command is established by subtracting the gain bias term from the dimensionality reduction of the gain bias term 65.7 and subtracting the fluid viscosity coefficient 1.2. The energy loss parameter caused by fluid viscosity damping is deducted from the product of net driving flow rate and step size, thereby realizing the damping control of the negative pressure change amplitude.

[0027] S313: Call the negative pressure differential change command, obtain the initial pressure differential setting parameter on both sides of the filter membrane and superimpose the extracted pressure differential state change amount, measure the micro pump suction action time benchmark and divide it by the change amount to obtain the state decay slope term, measure the flow velocity fluctuation bias term in the pipeline, and generate the in-situ leachate suction flow rate. The negative pressure differential iteration command is invoked to read the preset differential pressure data under the sampling start state, obtaining an initial differential pressure setting parameter of 15 kPa. The differential pressure state variation is extracted by multiplying the negative pressure differential iteration command parameter by a conversion ratio constant of 0.1 to extract a differential pressure state variation of 6.45 kPa. The micro-pump suction action duration baseline is measured and divided by the variation to obtain the state decay slope term. The operating time parameter of the timer interface is read, and the suction action duration baseline is output as 5 seconds. The differential pressure state variation of 6.45 kPa is divided by the suction action duration baseline of 5 seconds to obtain the state decay slope term of 1.29 kPa / s. The flow velocity fluctuation bias term within the pipeline is measured. The range calculation result of the flow velocity measurement in the continuous time domain is obtained, and the flow velocity fluctuation bias term is measured as 1.2 μL / s, generating the in-situ leachate suction flow rate.

[0028] Please see Figure 5 The specific steps for obtaining the hydration volume expansion of the gel substrate in the collection cavity are as follows: S411: Call the in-situ leachate suction flow rate, locate the origin coordinates of the base expansion of the collection chamber, extract the displacement offset term between the suction flow rate and the origin coordinates, superimpose the displacement offset term with the initial thickness of the gel membrane in the collection chamber to extract the thickness reference term, and generate the base swelling coordinate calibration value. The specific process of superimposing the displacement bias term with the initial thickness of the gel membrane in the collecting cavity to extract the thickness reference term is as follows: obtain the quantized value of the displacement bias term, extract the quantized value of the initial thickness of the gel membrane in the collecting cavity, and perform an addition operation on the quantized value of the displacement bias term and the quantized value of the initial thickness of the gel membrane in the collecting cavity to obtain the thickness reference term. The in-situ leachate suction flow rate is used to map the initial contact point at the center of the image to a two-dimensional coordinate system, extracting the vertical coordinate value and setting it as the origin, with a vertical coordinate measurement of 0 micrometers. The displacement offset term between the suction flow rate and the origin coordinates is extracted. The suction flow rate parameter is multiplied by the flow rate-time integration constant 0.5 to obtain an initial displacement of 10.77 micrometers. The displacement offset term of 10.77 micrometers is extracted by superimposing the displacement and origin coordinates, obtaining the quantized value of the displacement offset term. The quantized value of the initial thickness of the gel membrane in the collection chamber is extracted, and the factory calibration parameter solidified in the memory chip is read as 50 micrometers. The quantized value of the displacement offset term is added to the quantized value of the initial thickness of the gel membrane in the collection chamber to obtain the thickness reference term. The 10.77 micrometers and 50 micrometers are algebraically added, outputting the thickness reference term as 60.77 micrometers, generating the substrate swelling coordinate calibration value. The obtained thickness reference value was mapped to a spatial longitudinal axis coordinate, and the superposition calculation of dynamic displacement by flow velocity time integral and static thickness established the gel interface tracking coordinate.

[0029] S412: Call the substrate swelling coordinate calibration value, measure the swelling displacement parameters of the substrate surface layer boundary under soaking state, compare and extract the over-limit displacement term, measure the hydration tension term of the gel material, extract the over-limit displacement term and the expansion bias term of the hydration tension, and establish the gel volume change benchmark. The specific process of extracting the over-limit displacement term and the expansion bias term of the hydration tension is as follows: identify the specific value of the over-limit displacement term, extract the specific value of the hydration tension term of the gel material, and perform a multiplication operation between the specific value of the over-limit displacement term and the specific value of the hydration tension term of the gel material to obtain the expansion bias term. The substrate swelling coordinate calibration value was used to read the actual longitudinal displacement measurement of 65 micrometers obtained by scanning the upper surface of the gel using a laser interferometer. The excess displacement term was extracted by comparing the actual longitudinal displacement measurement of 65 micrometers with the substrate swelling coordinate calibration value of 60.77 micrometers, resulting in an excess displacement term of 4.23 micrometers. The hydration tension term of the gel material was then measured. The electrical signal output from the micro-strain element inside the gel was read and converted using a stress conversion factor to obtain a hydration tension term of 12 kPa. The expansion bias term of the excess displacement term and the hydration tension term was extracted. The specific value of the excess displacement term (4.23) and the specific value of the hydration tension term (12) were extracted. These two values ​​were multiplied to obtain an expansion bias term of 50.76, establishing a baseline for gel volume change. The area conversion constant of 2 square micrometers was extracted, and the expansion bias term was multiplied by the area conversion constant to output the volume dimension parameter. The product of the over-limit displacement and the internal hydration tension was fused to quantify the three-dimensional swelling characteristics of the gel. The expansion bias term, which includes the hydration tension calculation, improved the deformation prediction and calculation rate.

[0030] S413: Call the gel volume change benchmark, extract the thickness difference term by subtracting the thickness upon liquid swelling from the initial thickness, extract the vertical volume change span between the volume change benchmark and the thickness difference, obtain the probe projection direction parameter, and obtain the hydration volume swelling of the gel substrate in the liquid collection cavity. Using the gel volume change benchmark, the real-time liquid-induced swelling thickness measured by ultrasonic echo was obtained as 68 micrometers, and the initial thickness of 50 micrometers was extracted. Subtracting 50 micrometers from 68 micrometers, a difference operation was performed to extract the thickness difference term as 18 micrometers. The vertical volume change span between the volume change benchmark and the thickness difference was then extracted. Dividing the gel volume change benchmark of 101.52 cubic micrometers by the thickness difference term of 18 micrometers yielded a vertical volume change span of 5.64 square micrometers, from which the probe projection direction parameter was obtained. The relative angle parameter output from the tilt meter was read, and the cosine scalar of the angle was extracted to calculate the probe projection direction parameter, obtaining a value of 0.98, thus obtaining the hydration volume expansion of the gel substrate in the collection cavity. Multiplying the vertical volume change span of 5.64 by the probe projection direction parameter of 0.98, and then by the thickness restoration constant of 10 micrometers, the spatial volume data was extracted. The introduction of the cosine parameter of the probe attitude angle corrected the geometric error of the vertical projection, and the accuracy of the volume expansion calculation error control was enhanced after attitude projection compensation.

[0031] Please see Figure 6 The specific steps for obtaining in-situ pH adaptation correction values ​​for saline-alkali soil are as follows: S511: Combine the hydration volume expansion of the gel substrate in the liquid collection chamber with the dielectric constant calibration to analyze the dielectric variation term, integrate it with the in-situ soil conductivity measurement value, extract the conductivity bias, introduce the pore solution ion distribution concentration detected by the microelectrode, and establish the pore ion distribution calibration by performing coupled calculation with the conductivity bias. By combining the hydration volume expansion of the gel substrate in the collection chamber with the dielectric constant calibration, the dielectric constant calibration value output by high-frequency electromagnetic detection was determined to be 15. The hydration volume expansion of 55.27 was divided by the dielectric constant calibration value of 15, and the quotient was analyzed to extract the dielectric variation term of 3.68. This value was then fused with the in-situ soil conductivity measurement to extract the conductivity bias. The in-situ soil conductivity measurement obtained by the quadrupole conductivity probe was read as 4.3 mSiemens per centimeter. The dielectric variation term of 3.68 was algebraically added to the in-situ soil conductivity measurement value of 4.3, resulting in a conductivity bias value of 7.98. The pore solution ion distribution concentration detected by the microelectrode was introduced, and the limiting diffusion current was converted to obtain a pore solution ion distribution concentration of 12 mmol / L. A pore ion distribution calibration value was established by coupling the ion distribution with the conductivity bias value. By introducing a coupling adjustment coefficient of 1.5 related to concentration levels, the conductivity bias of 7.98 is multiplied by the ion distribution concentration of 12, and then multiplied by the coupling adjustment coefficient of 1.5 to perform a continuous product operation. The ternary coupling calculation of dielectric variation and in-situ conductivity eliminates the nonlinear distortion of complex solutions.

[0032] S512: Based on the pore ion distribution calibration quantity and the microelectrode solution impedance signal, the impedance slope term is extracted by proportional deduction and differential analysis with the background reference term. The soil background salinity interference boundary is delineated. The ion distribution calibration quantity and the interference boundary are analyzed to obtain the interference bias term. The basic charge metric is extracted through secondary feature mapping to generate the effective charge factor span. Based on the pore ion distribution calibration value, the microelectrode solution impedance signal from the AC perturbation test feedback was read as 45 kΩ. The pore ion distribution calibration value (143.64) was divided by the microelectrode solution impedance signal (45 kΩ) to extract the impedance slope term, which was 3.19. This was then analyzed by differential analysis with the background reference term. The background reference term measurement under calibration conditions was retrieved as 1.0. The differential analysis value was extracted by subtracting the background reference term 1.0 from the impedance slope term (3.19), resulting in 2.19. The soil background salinity interference boundary was defined, and the background salinity interference boundary measurement from the geological classification database was read as 2.5. Deviation analysis was performed between the ion distribution calibration value and the interference boundary to obtain the interference bias term. The ion distribution calibration value was divided by a constant 100, and the interference boundary was subtracted to obtain the difference. This difference (-1.06) was multiplied by the differential analysis value (2.19) to obtain the interference bias term, which was -2.32. The fundamental charge metric is extracted through secondary feature mapping. Adding an offset constant of 10 to the interference bias term yields a fundamental charge metric of 7.68, generating the effective charge factor span. Multiplying the fundamental charge metric by a magnification of 1.5 and performing differential accumulation between the background reference and the interference boundary filters out environmental ion masking interference.

[0033] S513: The target hydrogen ion chemical activity benchmark is introduced to normalize the effective charge factor span, extract the activity bias term, combine it with the in-situ soil temperature correction coefficient to perform temperature compensation mapping to obtain the correction charge, perform logarithmic scale transformation on the correction charge to extract the concentration ratio measure, and obtain the in-situ pH adaptation correction detection value of saline-alkali soil through discretized equidistant sampling. A target hydrogen ion chemical activity benchmark was introduced to normalize the effective charge factor span, and a pre-stored reference calibration constant was extracted as the target hydrogen ion chemical activity benchmark of 5.0. The effective charge factor span of 11.52 was divided by the target hydrogen ion chemical activity benchmark of 5.0 to perform division normalization, yielding an activity bias term of 2.3. A temperature compensation mapping was performed on the effective charge factor using the in-situ soil temperature correction coefficient to obtain the correction charge. The environmental parameters output by the temperature probe were read to match and find the in-situ soil temperature correction coefficient, which was 1.1. The activity bias term 2.3 was multiplied by the in-situ soil temperature correction coefficient 1.1 to obtain the correction charge of 2.53. A logarithmic scaling transformation was performed on the correction charge to extract the concentration proportion metric. The logarithmic parameter of the correction charge of 2.53 (base 10) was calculated to be 0.4. This logarithmic parameter was multiplied by a negative factor of -1 and a neutral compensation value of 7.0 was added to obtain a concentration proportion metric of 6.6. Discretized equidistant sampling was used to obtain the in-situ pH adaptation correction detection value for saline-alkali soil. The concentration ratio measurement is truncated to two decimal places, and the thermodynamic coefficient and logarithmic scale compensation operation realize the conversion of the detection signal into acidity and alkalinity data. The temperature compensation mechanism eliminates the measurement deviation under extreme conditions.

Claims

1. A method for real-time soil sampling and testing for saline-alkali land remediation, characterized in that, Includes the following steps: S1: Obtain the probe propulsion resistance value and the crust yield strength limit value, integrate the probe propulsion resistance, crust yield strength and drill bit outer cutting stress data to construct a mechanical compressive strength distribution model, extract the fracture peak boundary through stress comparison, and obtain the salt crust fracture resistance coefficient. S2: Call the salt crust breaking resistance coefficient, extract the soil pore layer characteristics, perform the matching judgment between probe penetration and the set target depth, and trigger the tension module to quantify the deep hydrostatic load after the target is met, and obtain the target depth sampling microfluidic driven negative pressure parameters. S3: Call the target depth sampling microfluidic driving negative pressure parameters, introduce the driving negative pressure parameters and the hydraulic conduction properties of the filter membrane, perform fluid dynamics comparison between the instantaneous suction volume of the micro pump and the pore flow velocity benchmark, and dynamically change the pressure difference state through the negative pressure step size step to generate the in-situ leachate suction flow rate; S4: Call the in-situ leachate suction flow rate, combine the suction flow rate with the fluid immersion environment, locate the expansion origin of the liquid collection cavity base and measure the boundary swelling displacement, evaluate the vertical expansion span based on the thickness difference of the gel membrane before and after water absorption, and obtain the hydration volume expansion of the liquid collection cavity gel base. S5: Call the hydration volume expansion of the gel substrate in the liquid collection chamber, extract the dielectric constant calibration value and the in-situ soil conductivity measurement value, collect the solution impedance signal to set the soil background salinity interference limit, extract the hydrogen ion charge transport factor, and obtain the in-situ pH adaptation correction detection value of saline-alkali soil.

2. The method for real-time soil sampling and testing for saline-alkali land remediation according to claim 1, characterized in that: The salt crust breaking resistance coefficient includes shear failure toughness, interfacial friction dissipation energy, and structural deformation internal resistance. The target depth sampling microfluidic driven negative pressure parameters include capillary force critical value, system vacuum stiffness, and pipeline pressure drop. The in-situ leachate suction flow rate includes membrane flux constant, filter cake layer resistance, and fluid kinetic energy flux. The hydration volume expansion of the gel substrate in the collection chamber includes gel swelling equilibrium ratio, cross-linked network elastic modulus, and intermolecular affinity. The in-situ pH adaptation correction detection value of saline-alkali soil includes response slope factor, zero-point potential shift, and electrode polarization impedance.

3. The method for real-time soil sampling and testing for saline-alkali land remediation according to claim 1, characterized in that: The specific steps for obtaining the salt crust breaking resistance coefficient are as follows: S111: Obtain the probe propulsion resistance value and the crust yield strength limit value, extract the base stress peak term corresponding to the cutting stress parameter based on the stress sensing element, subtract the probe propulsion resistance value from the base stress peak term to obtain the propulsion offset term, and obtain the crust cutting stress correlation degree. S112: Call the crust cutting stress correlation degree, measure the base pore pressure parameter at the current depth, compare the crust cutting stress correlation degree with the preset salt crust compressive strength boundary term, extract the base pore pressure parameter within the boundary range and sum it with the cutting stress correlation degree to obtain the base combination term, extract the distribution ratio of the combination term in the pore group, and establish the soil breaking node impedance characteristics. S113: Call the impedance characteristics of the soil breaking node, determine the reference item of the detection node state in the soil breaking state, combine the reference item with the impedance characteristics to extract the ratio benchmark item, screen the stress value boundary, and generate the salt crust breaking impedance coefficient.

4. The method for real-time soil sampling and testing for saline-alkali land remediation according to claim 3, characterized in that: The specific steps for obtaining the target depth sampling microfluidic driven negative pressure parameters are as follows: S211: Call the salt crust breaking resistance coefficient, perform an equivalence determination between the probe penetration depth value and the target depth setting value, extract the depth overlap term, detect the soil pore structure hierarchical parameters based on the microfluidic capillary channel, analyze the depth overlap term and pore structure parameters, obtain the spatial offset, and superimpose it with the salt crust breaking resistance coefficient to obtain the sampling space excitation threshold. S212: Call the sampling space excitation threshold, identify the hydraulic load workload of the core measurement, perform intersection operation to extract the core activation term, combine the activation term to quantify the hydrostatic pressure parameter, identify the water pressure measurement quantity, extract the variation slope term by differentially differentiating the water pressure measurement quantity, analyze the water pressure measurement quantity and variation slope, and establish the deep pore water pressure calibration value. S213: Call the deep pore water pressure calibration value, obtain the soil water potential setting benchmark, extract the water potential bias term by subtracting the water pressure calibration value from the water potential benchmark, measure the hydraulic impedance loss parameter of the microfluidic channel, and extract the driving benchmark term by superimposing the water potential weighted term and the hydraulic impedance loss, and generate the target depth sampling microfluidic driving negative pressure parameter.

5. The method for real-time soil sampling and testing for saline-alkali land remediation according to claim 4, characterized in that: The specific process of the spatial offset is as follows: extract the pore connectivity value by analyzing the pore structure parameters, multiply the depth overlap term by the pore connectivity value to obtain the effective spatial capacity, and subtract the effective spatial capacity from the preset sampling spatial reference value to obtain the spatial offset.

6. The method for real-time soil sampling and testing for saline-alkali land remediation according to claim 4, characterized in that: The specific steps for obtaining the in-situ leachate suction flow rate are as follows: S311: Call the target depth sampling microfluidic drive negative pressure parameters, extract the flow rate deviation term between the instantaneous suction volume of the micropump and the pore flow rate benchmark, measure the effective flow area based on the surface of the dialysis membrane, determine the membrane pore ratio parameter, and sum the drive negative pressure value, flow rate calibration quantity and pore ratio to obtain the initial term of the membrane microflow rate. S312: Call the initial term of the micro-flow of the filter membrane, measure the negative pressure step size, calculate the fluid impedance term based on the hydraulic conductivity of the filter membrane, extract the net driving flow term by subtracting the initial flow term from the fluid impedance, multiply it by the negative pressure step size to obtain the gain bias term, measure the viscosity coefficient of the extract fluid and subtract it from the gain bias term to reduce the dimension, and establish the negative pressure differential iteration command. S313: Call the negative pressure differential change instruction, obtain the initial pressure differential setting parameter on both sides of the filter membrane and superimpose the extracted pressure differential state change amount, measure the micro pump suction action time benchmark and divide it by the change amount to obtain the state decay slope term, measure the flow velocity fluctuation bias term in the pipeline, and generate the in-situ leachate suction flow rate.

7. The method for real-time soil sampling and testing for saline-alkali land remediation according to claim 6, characterized in that: The process of extracting the velocity deviation term between the instantaneous pumping volume of the micropump and the pore velocity reference specifically involves extracting the measured value of the instantaneous pumping volume of the micropump, extracting the set value of the pore velocity reference, and subtracting the set value of the pore velocity reference from the measured value of the instantaneous pumping volume of the micropump to obtain the velocity deviation term.

8. The method for real-time soil sampling and testing for saline-alkali land remediation according to claim 6, characterized in that: The specific steps for obtaining the hydration volume expansion of the gel substrate in the liquid collection chamber are as follows: S411: Call the in-situ leachate suction flow rate, locate the origin coordinates of the base expansion of the collection chamber, extract the displacement offset term between the suction flow rate and the origin coordinates, and superimpose the displacement offset term with the initial thickness of the gel membrane in the collection chamber to extract the thickness reference term, thereby generating the base swelling coordinate calibration value. S412: Call the substrate swelling coordinate calibration value, measure the swelling displacement parameters of the substrate surface layer boundary under soaking state, compare and extract the over-limit displacement term, measure the gel material hydration tension term, extract the over-limit displacement term and the hydration tension expansion bias term, and establish a gel volume change benchmark. S413: Call the gel volume change benchmark, extract the thickness difference term by subtracting the liquid swelling thickness from the initial thickness, extract the vertical volume change span between the volume change benchmark and the thickness difference, obtain the probe projection direction parameter, and obtain the hydration volume expansion of the gel substrate in the liquid collection cavity.

9. The method for real-time soil sampling and testing for saline-alkali land remediation according to claim 8, characterized in that: The specific process of superimposing the displacement bias term with the initial thickness of the gel membrane in the collecting cavity to extract the thickness reference term is as follows: obtain the quantized value of the displacement bias term, extract the quantized value of the initial thickness of the gel membrane in the collecting cavity, and perform an addition operation between the quantized value of the displacement bias term and the quantized value of the initial thickness of the gel membrane in the collecting cavity to obtain the thickness reference term. The specific process of extracting the over-limit displacement term and the expansion bias term of the hydration tension is as follows: identify the specific value of the over-limit displacement term, extract the specific value of the hydration tension term of the gel material, and perform a multiplication operation between the specific value of the over-limit displacement term and the specific value of the hydration tension term of the gel material to obtain the expansion bias term.

10. The method for real-time soil sampling and testing for saline-alkali land remediation according to claim 8, characterized in that: The specific steps for obtaining the in-situ pH adaptation correction detection value of saline-alkali soil are as follows: S511: Combine the hydration volume expansion of the collected liquid cavity gel substrate with the dielectric constant calibration to analyze the dielectric variation term, integrate it with the in-situ soil conductivity measurement value, extract the conductivity bias, introduce the pore solution ion distribution concentration detected by the microelectrode, and establish the pore ion distribution calibration by performing coupled calculation with the conductivity bias. S512: Based on the pore ion distribution calibration quantity and the microelectrode solution impedance signal, the impedance slope term is extracted by proportional deduction and differential analysis with the background reference term to delineate the soil background salinity interference boundary. The ion distribution calibration quantity and the interference boundary are analyzed to obtain the interference bias term. The basic charge metric is extracted through secondary feature mapping to generate the effective charge factor span. S513: The target hydrogen ion chemical activity benchmark is introduced to normalize and analyze the effective charge factor span, extract the activity bias term, and combine it with the in-situ soil temperature correction coefficient to perform temperature compensation mapping to obtain the correction charge. The correction charge is logarithmically scaled to extract the concentration ratio measure, and after discretized equidistant sampling, the in-situ pH adaptation correction detection value of saline-alkali soil is obtained.

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